Econstudentlog

Improving your vocabulary…

Haughty, persiflage, assignation, curdle, tousle, gabble, decamp, varmint, trumpery, efflorescence, brim, bedizen, rostrum, peroration, farrago, vernal, expiate, astringent, prepossessing, dowdy, nigglevainglorious, veneer, abnegation, horology, ignoble, fulcrum, skein, acidulous, syncretism, exultant, peremptory, cognomendebonair, lachrymose, subservience, commiseration, equipoise, animadversiondiffidence, reprobate, martinet, garret, superannuate, asseverate, gravamen, saunterlassitude, verisimilar, appurtenance, oenophile, lambent, welt, churlish, ingenue, plait, inundate, scamper, incontrovertible, abscond, requite, milliner, caboose, …

The words above are all words I’ve encountered over the last few days, either in novels or during my vocabulary-building exercises on vocabulary.com. The site is a really nice learning tool, though I prefer Webster’s dictionary to theirs (which should explain the links above). I know I’ve mentioned the site before and there’s also a link to it in the sidebar, but as I recently ‘revived’ my account after some period of relative inactivity I assumed there was some positive probability that others reading along here may also have been using the site in the past and then forgot about it/given up on it.

I was wondering about whether or not I should make word-posts like these, with lists of some of the words I’ve been ‘working on’, a regular feature of the blog; it seems nice to get words you’ve come across and would like to remember, and/or words you’re actively learning/reviewing, refreshed here on the blog, and posts like this one might also provide motivation for others to have a go at this kind of stuff. The only real downside I can think of at the moment is that if I start posting stuff like this, there’s a small risk people who come across my blog might get confused and start thinking I’m an intellectual or something along those lines.

March 4, 2015 Posted by | random stuff | 2 Comments

Personal Relationships (6)

Here’s a link to a previous post in the series about the book. Before proceeding to the coverage of the textbook, I thought I should mention that I yesterday read another novel by Wodehouse, and started on a third one. His stuff is awesome – if you’re having trouble finding fiction which is fun and enjoyable to read, you should definitely check out Wodehouse if you haven’t already.

Okay, back to the textbook. When I started out writing this post I thought it would be my last post about the book, but in the end I decided that the post would get too long if I covered all the remaining chapters in this post. So I may or may not cover the rest of the book later. The first topic I’ll cover in this post is intimacy. Some observations from the chapter on that topic:

“Individuals can influence the evolution of an emerging relationship by adjusting the breadth (the number of topics disclosed) and the depth of their self-disclosure (the degree of personal relevance). In addition, nonverbal behaviors (e.g., gaze, touch, body orientation) are expressions that can augment and interact with verbal self-disclosures to influence intimacy in a relationship […] Self-disclosure has been found to account for just below half of the variance in ratings of couples’ level of intimacy […] To contribute to the development of intimacy in a relationship, an individual’s responses have to demonstrate concern for the discloser. A response must be sincere and immediate, capture the content of the original communication, and meet the need of the discloser […]. Responsiveness has been found to play an important role in disclosure reciprocity, liking, and closeness in relationships […]. Recently, researchers have conceptualized responsiveness as a process whereby a person communicates understanding, validation, and caring in response to a partner’s self-disclosure […] In personal relationships, receiving validation and acceptance can often take on a self-esteem maintaining or protective function, in that individuals often seek to confirm their self-concept through the responses of others […]. Reis and Shaver (1988) argued that the speaker’s perception and judgment of the listener’s response as understanding, validating, and caring are important factors in the experience of intimacy, above and beyond the listener’s actual responsiveness. […] According to Reis and Shaver (1988), intimacy is an interpersonal, transactional process with two principal components: self-disclosure and partner responsiveness. Intimacy can be initiated when one person communicates personally relevant and revealing information to another person. […] For the intimacy process to continue, the listener must emit emotions, expressions, and behaviors that are both responsive to the specific content of the disclosure and convey acceptance, validation, and caring toward the individual disclosing. For the interaction to be experienced as intimate by the discloser, he or she must perceive both the descriptive qualities (understanding of content) and evaluative qualities (validation and caring) of the response. […] a consistent finding is that individuals with an insecure attachment style are less responsive than more securely attached individuals according to both objective third-party ratings and subjective reports”.

“A notable tenet of existing models of intimacy […] is that intimacy is achieved when Partner A self-discloses and feels validated, cared for, and understood by Partner B’s attempts at responsiveness. Although we agree that this model describes the intimacy process, we believe that in many ways it is decidedly one-sided. Is the experience of intimacy only achieved when one feels that a relationship partner is responding to one’s needs? We argue that an individual may experience intimacy while providing understanding, care, and validation, as well as while receiving it. In other words, Partner B’s feelings of intimacy may match Partner A’s, even though A is the one being validated.” (I should note that I have made a similar argument during conversations with a good friend, and that I share the opinion of the authors that this aspect is important as well).

“Nonverbal cues have been thought to contribute to intimacy in two ways. First, they communicate specific emotional messages, which may stand alone or be considered along with concurrent verbal messages. Second, nonverbal cues may intensify emotions that are experienced during intimate interactions […] nonverbal cues can increase the likelihood of an intimate outcome, whereas others may decrease the possibility. Specifically, smiling, eye contact, and physical proximity tend to engross the listener, especially if the behaviors amplify the speaker’s words […] Observational studies have shown that husbands and wives use different nonverbal behaviors when delivering positive and negative messages” [I’ll again remind people reading along here that ‘observational studies’ in this context means studies where they’ve actually observed people interacting with each other, instead of e.g. relying on self-reports].

“Self-disclosures have been classified into two types: factual–descriptive (e.g., personal information, such as the number of one’s siblings) versus emotional–evaluative (e.g., feelings about those siblings […]). Emotional disclosures have been shown to be more important to intimate interactions […] Research has shown that more emotional information is transmitted nonverbally than verbally […] Nonverbal cues are often better indicators of feelings, emotions, and attitudes than are words […] when there is a discrepancy between verbal and nonverbal messages, people tend to believe the nonverbal ones […] There is evidence that nonverbal communication affects the outcomes of a wide variety of relationships. In married couples nonverbal behavior is more likely than verbal behavior to distinguish between distressed and undistressed pairs […]. Poor nonverbal skills have been shown to be associated with less satisfying relationships for married couples […], romantic partners […], roommates […], children’s peer relationships […], and adults in general […] To create intimacy in an interaction, several nonverbal processes must occur. First, the discloser must display appropriate emotional nonverbal cues. Second, the listener must be able to decode them accurately. Third, the listener must then respond with appropriate nonverbal expressiveness. Finally, the original discloser must perceive these expressive cues accurately. In any interaction, this process is repeated continuously, and thus there is substantial room for error. […] A wealth of literature supports the conclusion that nonverbal skills are essential to relationship outcomes. Few studies, however, have focused on issues related to mechanism: How do nonverbal behaviors and skills affect relationship outcomes and processes?”

The next chapter is about ‘Social Networks and Personal Communities’. A few observations from the chapter:

“Generally, there is a shortage of longitudinal material on what might be termed the routine natural history of personal communities – the ways in which different relationships unremarkably alter over time, some becoming more central in people’s lives and others becoming of lesser consequence. Importantly, too, the studies there have been have tended to be short rather than long term. […] [A few exceptions exist, and what] these studies indicate, not surprisingly, is that social change routinely occurs across the life course, affecting people’s social location and in turn the sets of relationships they sustain. Although based on a shorter term study, Morgan and his colleagues […] made the important point that although the personnel making up an individual’s network may alter over time, the properties of the network itself can be more stable. In this study of widows, a core segment of key relationships remained relatively constant over the course of the research, whereas relationships that were more peripheral waxed and waned. Thus, as Morgan, Neal, et al. (1997) expressed it, “the stability of the aggregate properties in personal networks is much greater than the stability of the membership in these networks” (p. 22).”

The next chapter, on ‘Relationships in home and community environments’, is terrible, so I won’t talk about that here. Instead I’ll end the coverage here with some observations from the chapter about ‘Relationships, Culture, and Social Change':

“There are several theoretical reasons for studying relationships across cultures. First, there may be variation in the relative magnitude of different relationship phenomena. […] Second, culture can have a moderating impact on the association between individual-level factors (such as personality) and various relationship phenomena. […] Finally, even when there are strong universal relational phenomena consistent across cultures, the ways in which these influence actual behaviors may differ. As we note later, individuals may feel passionately for each other in some cultures, but their passion may have relatively little impact on who they end up with as partners. Instead, pragmatic considerations (family pressures, but also basic economic realities) may have a far more significant role in partner choice.”

“In the last 2 decades a number of major international studies have sought to differentiate cultures empirically on the basis of their scores on key values. The most influential of these has been the dimensions that arose from Hofstede’s (1980) seminal study of IBM employees of 50 nations and more than one hundred thousand respondents. In this study, Hofstede (1980) concluded that cultures vary along four dimensions: power distance (deference to authority), masculinity–femininity (relative emphasis on achievement or interpersonal harmony), uncertainty avoidance (stability and “planning ahead”) and individualism–collectivism (which concerns the relationship between the individual and the group). Individualism–collectivism has been the most widely researched of these dimensions”

“Most research into PR assumes that close relationships partners are chosen rather in the manner of an individual shopping in a supermarket, with individuals free to choose from a wide variety of products, in a multiplicity of shapes and sizes, from a range of different origins. […] In reality, this image is unlikely to be accurate even in the most individualistic of societies. Personal reputation, availability of social networks, and even opportunities to travel and shop around are basic limiters of choice in most cultures. However, in some cultures there is little opportunity to form any kind of romantic relationship outside of the most tightly restricted range. Indeed, we can plot a continuum ranging from those cultures in which partner choice is rarely restricted (usually those cultures where mate selection studies are conducted) to those cultures where partner choice might be prescribed as early as birth […]. Across the world, the majority of marriages are by arrangement, usually with the aid of matchmakers or relatives (Ingoldsby, 1995). [I dislike having to rely on a 20-year old study here, but I would caution people who think that just because it’s 20 years old, it’s probably obsolete and the results worthless. For example the relationship between ‘modernization’ and marriage is, complicated – see e.g. this paper (“No empirical support was found for any of our hypotheses which link the level of modernization to the risk of divorce”).] Marriage in such cultures is not regarded as a union of two individuals but of two families, with the families likely to be similar in terms of values, customs, and norms. […] Arranged relationships can be seen as invaluable in cementing family liaisons, helping build new economic ties, and maintaining the influence of the extended network on the new couple. Because such arrangements are of such significance to the wider family, opportunities for Western-style dating and partner choice outside of those approved as eligible is likely to be highly restricted”.

“Because partner choice is restricted among some cultures and cultural groups, the role of love in the choice of marital partner is also likely to vary across the world. There is strong evidence that Western beliefs in the significance of love for marriage may not be universal […]. In cultures where marriages are arranged, love is often assumed to grow out of marriage, rather than to be a motivator for the formation of a particular relationship […] because of the importance to family honor and economic success of an “appropriate” relationship match, in societies where marriage is arranged love is most likely to be sanctioned between only certain partners.”

“In those societies in which arranged marriages dominate, divorce or even separation are often difficult or impossible […]. Although marital dissatisfaction undoubtedly exists here as elsewhere, it is important not to exaggerate the unhappiness felt in many more traditional cultures. Instead, in such societies, different expectations about marriage may lead to different kinds of expectations as to what is – and is not – to be obtained from a marital relationship. […] One enduring debate has been the extent to which free-choice matches are happier than arranged marriages. This is difficult to assess because expectations for marriage differ, and in those societies in which arranged marriages predominate divorce is often difficult. To address this issue Xiaohe and Whyte (1990) tested a representative probability sample of 586 ever-married women in the Sichuan Province of mainland China. Their data suggested that women in arranged marriages were consistently less satisfied than those that had chosen their own partners. Controlling for a large number of measures (including age at marriage and family income), their study did suggest that freedom of mate choice was the strongest predictor of marital quality.”

“There are significant culture differences not only in network size and sources of support but also in network utilization. In the West, individuals are expected to solicit help from others actively […], whereas in Eastern cultures a greater sensitivity to others’ needs and feelings may make help seeking less necessary […]. In collectivists cultures where social connectedness is high, help is expected to be voluntarily provided, and asking for help may be regarded as socially demeaning”.

“Buss, Shackleford, Kirkpatrick, and Larsen (2001) reviewed partner preferences over a more than 50-year period using the same instrument (1939, 1956, 1967, 1977, 1984, and 1996). Over this time period, they found important generational shifts in mate preferences. Both men and women increasingly valued mutual attraction and love, education and intelligence, sociability and good looks, and decreased their stress on refinement, neatness, and chastity. Men increasingly valued similar educational background and good financial prospects and decreasingly valued a woman being a good cook and housekeeper, whereas women placed less value on ambition and industriousness. Partner preferences across genders became generally similar over this time period, with men’s preferences moving toward those of women.”

March 2, 2015 Posted by | books, marriage, Psychology | Leave a comment

Something Fresh

This is a wonderful little book by P. G. Wodehouse, the first of his books I’ve read (but most likely very far from the last one). I don’t usually blog fiction, but this book was great enough for me to want to share a few quotes from the book here. If Wodehouse’ other books are as funny and entertaining as this one was, I know which fiction author I’ll be reading in the weeks to come. I am very partial in particular to the character Lord Emsworth.

Sample quotes below:

“owing to the pressure of other engagements, he unfortunately omitted to do any work, and, when the hour of parting arrived, he was peculiarly unfitted for any of the learned professions. Having, however, managed to obtain a sort of degree, enough to enable him to call himself a Bachelor of Arts, and realizing that you can fool some of the people some of the time, he applied for and secured a series of private tutorships.”

“Tell me, Adams, have I eaten my cheese?’
‘Not yet, your lordship. I was about to send the waiter for it.’
‘Never mind. Tell him to bring the bill instead. I remember that I have an appointment. I must not be late.’
‘Shall I take the fork, your lordship?’
‘The fork?’
‘Your lordship has inadvertently put a fork in your coat pocket.’
Lord Emsworth felt in the pocket indicated, and with the air of an inexpert conjurer whose trick has succeeded contrary to his expectations produced a silver-plated fork. He regarded it with surprise; then he looked wonderingly at Adams.
‘Adams, I’m getting absent-minded. Have you ever noticed any traces of absent-mindedness in me before?’
‘Oh, no, your lordship.'”

“The cab drew up before a house gay with flowered window-boxes. Lord Emsworth paid the driver, and stood on the side-walk looking up at this cheerful house, trying to remember why on earth he had told the man to drive there.”

“There is every kind of restaurant in London, from the restaurant which makes you fancy you are in Paris to the restaurant which makes you wish you were.”

“Blandings Castle was one of the more important of England’s show-places, and Beach, accordingly, had acquired a dignified inertia which almost qualified him for inclusion in the vegetable kingdom.”

“One of Mr. Peters’ most painful memories was of a two weeks’ visit which he had once paid to Mr William Muldoon at his celebrated health-restoring establishment at White Plains in the State of New York. He had been persuaded to go there by a brother-millionaire whom till then he had always regarded as a friend.”

February 28, 2015 Posted by | books | Leave a comment

Belief-Based Stability in Coalition Formation with Uncertainty…

“In this book we present several novel concepts in cooperative game theory, but from a computer scientist’s point of view. Especially, we will look at a type of games called non-transferable utility games. […] In this book, we extend the classic stability concept of the non-transferable utility core by proposing new belief-based stability criteria under uncertainty, and illustrate how the new concept can be used to analyse the stability of a new type of belief-based coalition formation game. Mechanisms for reaching solutions of the new stable criteria are proposed and some real life application examples are studied. […] In Chapter 1, we first provide an introduction of topics in game theory that are relevant to the concepts discussed in this book. In Chapter 2, we review some relevant works from the literature, especially in cooperative game theory and multi-agent coalition formation problems. In Chapter 3, we discuss the effect of uncertainty in the agent’s beliefs on the stability of the games. A rule-based approach is adopted and the concepts of strong core and weak core are introduced. We also discuss the effect of precision of the beliefs on the stability of the coalitions. In Chapter 4, we introduce private beliefs in non-transferable utility (NTU) games, so that the preferences of the agents are no longer common knowledge. The impact of belief accuracy on stability is also examined. In Chapter 5, we study an application of the proposed belief-based stability concept, namely the buyer coalition problem, and we see how the proposed concept can be used in the evaluation of this multi-agent coalition formation problem. In Chapter 6, we combine the works of earlier chapters and produce a complete picture of the introduced concepts: non-transferable utility games with private beliefs and uncertainty. We conclude this book in Chapter 7.”

The above quote is from the preface of the book, which I finished yesterday. It deals with some issues I was slightly annoyed about not being covered in a previous micro course; my main problem being that it seemed to me back then that the question of belief accuracy and the role of this variable was not properly addressed in the models we looked at (‘people can have mistaken beliefs, and it seems obvious that the ways in which they’re wrong can affect which solutions are eventually reached’). The book makes the point that if you look at coalition formation in a context where it is not reasonable to assume that information is shared among coalition partners (because it is in the interest of the participants to keep their information/preferences/willingness to pay private), then the beliefs of the potential coalition partners may play a major role in determining which coalitions are feasible and which are ruled out. A key point is that in the model context explored by the authors, inaccurate beliefs of agents will expand the number of potential coalitions which are available, although coalition options ruled out by accurate beliefs are less stable than ones which are not. They do not discuss the fact that this feature is unquestionably a result of implicit assumptions made along the way which may not be true, and that inaccurate beliefs may also in some contexts conceivably lead to lower solution support in general (e.g. through variables such as disagreement, or, to think more in terms of concepts specifically included in their model framework, higher general instability of solutions which can feasibly be reached, making agents less likely to explore the option of participating in coalitions in the first place due to the lower payoffs associated with the available coalitions likely to be reached – dynamics such as these are not included in the coverage). I decided early on to not blog the stuff in this book in major detail because it’s not the kind of book where this makes sense to do (in my opinion), but if you’re curious about how they proceed, they talk quite a bit about the (classical) Core and discuss why this is not an appropriate solution concept to apply in the contexts they explore, and they then proceed to come up with new and better solution criteria, developed with the aid of some new variables and definitions along the way, in order to end up with some better solution concepts, their so-called ‘belief-based cores’, which are perhaps best thought of as extensions of the classical core concept. I should perhaps point out, as this may not be completely clear, that the beliefs they talk about deal both with the ‘state of nature’ (which in part of the coverage is assumed to be basically unobservable) and the preferences of agents involved.

If you want a sort of bigger picture idea of what this book is about, I should point out that in general you have two major sub-fields of game theory, dealing with cooperative and non-cooperative games respectively. Within the sub-field of cooperative games, a distinction is made between games and settings where utilities are transferable, and games/settings where they are not. This book belongs in the latter category; it deals with cooperative games in which utilities are non-transferable. The authors in the beginning make a big deal out of the distinction between whether or not utilities are transferable, and claim that the assumption that they’re not is the more plausible one; whereas they do have a point, I however also actually think the non-transferability assumption might in some of the specific examples included in the book be a borderline questionable assumption. To give an example, the non-transferability assumption seems in one context to imply that all potential coalition partners have the same amount of bargaining power. This assumption is plausible in some contexts, but wildly implausible in others (and I’m not sure the authors would agree with me about which contexts would belong to which category).

The professor teaching the most recent course in micro I took had a background in computer science, rather than economics – he was also Asian, but this perhaps goes without saying. This book is supposedly a computer science book, and they argue in the introduction that: “instead of looking at human beings, we study the problem from an intelligent software agent’s perspective.” However I don’t think a single one of the examples included in the book would be an example you could not also have found in a classic micro text, and it’s really hard to tell in many parts of the coverage that the authors aren’t economists with a background in micro – there seems to be quite a bit of field overlap here (this field overlap incidentally extends to areas of economics besides micro, is my impression; one econometrics TA I had, teaching the programming part of the course, was also a CS major). In the book they talk a bit about coalition formation mechanisms and approaches, such as propose-and-evaluate mechanisms and auction approaches, and they also touch briefly upon stuff like mechanism design. They state in the description that: “The book is intended for graduate students, engineers, and researchers in the field of artificial intelligence and computer science.” I think it’s really weird that they don’t include (micro-)economists as well, because this stuff is obviously quite close to/potentially relevant to the kind of work some of these people are working on.

There are a lot of definitions, theorems, and proofs in this book, and as usual when doing work on game theory you need to think very carefully about the stuff they cover to be able to follow it, but I actually found it reasonably accessible – the book is not terribly difficult to read. Though I would probably advise you against reading the book if you have not at least read an intro text on game theory. Although as already mentioned the book deals with an analytical context in which utilities are non-transferable, it should be pointed out that this assumption is sort of implicit in the coverage, in the sense that the authors don’t really deal with utility functions at all; the book only deals with preference relations, not utility functions, so it probably helps to be familiar with this type of analysis (e.g. by having studied (solved some problems) dealing with the kind of stuff included in the coverage in chapter 1 of Mas-Colell).

Part of the reason why I gave the book only two stars is that the authors are Chinese and their English is terrible. Another reason is that as is usually the case in game theory, these guys spend a lot of time and effort being very careful to define their terms and make correct inferences from the assumptions they make – but they don’t really end up saying very much.

February 28, 2015 Posted by | books, Computer science, economics | Leave a comment

Wikipedia articles of interest

i. Invasion of Poland. I recently realized I had no idea e.g. how long it took for the Germans and Soviets to defeat Poland during WW2 (the answer is 1 month and five days). The Germans attacked more than two weeks before the Soviets did. The article has lots of links, like most articles about such topics on wikipedia. Incidentally the question of why France and Britain applied a double standard and only declared war on Germany, and not the Soviet Union, is discussed in much detail in the links provided by u/OldWorldGlory here.

ii. Huaynaputina. From the article:

“A few days before the eruption, someone reported booming noise from the volcano and fog-like gas being emitted from its crater. The locals scrambled to appease the volcano, preparing girls, pets, and flowers for sacrifice.”

This makes sense – what else would one do in a situation like that? Finding a few virgins, dogs and flowers seems like the sensible approach – yes, you have to love humans and how they always react in sensible ways to such crises.

I’m not really sure the rest of the article is really all that interesting, but I found the above sentence both amusing and depressing enough to link to it here.

iii. Albert Pierrepoint. This guy killed hundreds of people.

On the other hand people were fine with it – it was his job. Well, sort of, this is actually slightly complicated. (“Pierrepoint was often dubbed the Official Executioner, despite there being no such job or title”).

Anyway this article is clearly the story of a guy who achieved his childhood dream – though unlike other children, he did not dream of becoming a fireman or a pilot, but rather of becoming the Official Executioner of the country. I’m currently thinking of using Pierrepoint as the main character in the motivational story I plan to tell my nephew when he’s a bit older.

iv. Second Crusade (featured). Considering how many different ‘states’ and ‘kingdoms’ were involved, a surprisingly small amount of people were actually fighting; the article notes that “[t]here were perhaps 50,000 troops in total” on the Christian side when the attack on Damascus was initiated. It wasn’t enough, as the outcome of the crusade was a decisive Muslim victory in the ‘Holy Land’ (Middle East).

v. 0.999… (featured). This thing is equal to one, but it can sometimes be really hard to get even very smart people to accept this fact. Lots of details and some proofs presented in the article.

vi. Shapley–Folkman lemma (‘good article’ – but also a somewhat technical article).

vii. Multituberculata. This article is not that special, but I add it here also because I think it ought to be and I’m actually sort of angry that it’s not; sometimes the coverage provided on wikipedia simply strikes me as grossly unfair, even if this is perhaps a slightly odd way to think about stuff. As pointed out in the article (Agustí points this out in his book as well), “The multituberculates existed for about 120 million years, and are often considered the most successful, diversified, and long-lasting mammals in natural history.” Yet notice how much (/little) coverage the article provides. Now compare the article with this article, or this.

February 25, 2015 Posted by | biology, economics, history, mathematics, Paleontology, wikipedia, Zoology | 2 Comments

Mammoths, Sabertooths, and Hominids: 65 Million Years of Mammalian Evolution in Europe (2)

Here’s my first post about the book.

I wasn’t quite sure how to rate the book, but I ended up at four stars on goodreads. The main thing holding me back from giving it a higher rating is that the book is actually quite hard to read and there’s a lot of talk about teeth; one general point I learned from this book is that the teeth animals who lived in the past have left behind for us to find are sometimes really useful, because they can help us to make/support various inferences about other things, from animal behaviours to climatic developments. As for the ‘hard to read’-part, I (mostly) don’t blame the author for this because a book like this would have to be a bit hard to read to provide the level of coverage that is provided; that’s part of why I give it four stars in spite of this. If you have a look at the links in the first post, you’ll notice the many Latin names. You’ll find a lot of those in the text as well. This is perfectly natural as there were a lot of e.g. horse-like and rhino-like species living in the past and you need to be clear about which one of them you’re talking about now because they were all different, lived in different time periods, etc. For obvious reasons the book has a lot of talk about species/genera with no corresponding ‘familiar/popular’ names (like ‘cat’ or ‘dog’), and you need to keep track of the Latin names to make sense of the stuff; as well as keeping track of the various other Latin terms used e.g. in osteometry. So you’ll encounter some passages where there’s some talk about the differences between two groups whose names look pretty similar, and you’re told about how one group had two teeth which were a bit longer than they were in the other group and the teeth also looked slightly different (and you’ll be told exactly which teeth we’re talking about, described in a language you’d probably have to be a dentist to understand without looking up a lot of stuff along the way). Problems keeping track of the animals/groups encountered also stem from the fact that whereas some species encountered in the book do have modern counterparts, others don’t. The coverage helps you to figure out which ecological niche which group may have inhabited, but if you’re completely unfamiliar with the field of ecology I’m not sure how easy it is to get into this mindset. The text does provide some help navigating this weird landscape of the past, and the many fascinating illustrations in the book make it easier to visualize what the animals encountered along the way might have looked like, but reading the book takes some work.

That said, it’s totally worth it because this stuff’s just plain fascinating! The book isn’t quite ‘up there’ with Herrera et al. (it reminded me a bit more of van der Geer et al., not only because of the slight coverage overlap), but some of the stuff in there’s pretty damn awesome – and it’s stuff you ought to know, because it’ll probably change how you think about the world. The really neat thing about reading a book like this is that it exposes a lot of unwarranted assumptions you’ve been making without knowing it, about what the past used to be like. I’m almost certain anyone reading a book like this will encounter ideas which are very surprising to them. We look at the world through the eyes of the present, and it can be difficult to imagine just how many things used to be different. Vague and tentative ideas you might have had about how the world used to look like and how it used to work can through reading books like this one be replaced with a much more clear, and much better supported, picture of the past. Even though there’s still a lot of stuff we don’t know, and will never know. I could mention almost countless examples of things I was very surprised to learn while reading this book, and I’m sure many people reading the book would encounter even more of these, as I actually was somewhat familiar with parts of the related literature already before reading the book.

I’ve added a few sample quotes and observations from the book below.

“Europe, although just an appendage of the Eurasian supercontinent, acted during most of its history as a crossroad where Asian, African, and American faunas passed one another, throughout successive dispersal and extinction events. But these events did not happen in an isolated context, since they were the response to climatic and environmental events of a higher order. Thus this book pays special attention to the abundant literature that for the past few decades has dedicated itself to the climatic evolution of our planet.”

“A common scenario tends to posit the early evolutionary radiation of placental mammals as occurring only after the extinction of the dinosaurs at the end of the Cretaceous period. The same scenario assumes a sudden explosion of forms immediately after the End Cretaceous Mass Extinction, filling the vacancies left by the vanished reptilian faunas. But a close inspection of the first epoch of the Cenozoic provides quite a different picture: the “explosion” began well before the end of the Cretaceous period and was not sudden, but lasted millions of years throughout the first division of the Cenozoic era, the Paleocene epoch. […] our knowledge of this remote time of mammalian evolution is much more obscure and incomplete than our understanding of the other periods of the Cenozoic. […] compared with our present world, and in contrast to the succeeding epochs, the Paleocene appears to us as a strange time, in which the present orders of mammals were absent or can hardly be distinguished: no rodents, no perissodactyls, no artiodactyls, bizarre noncarnivorous carnivorans. […] although the Paleocene was mammalian in character, we do not recognize it as a clear part of our own world; it looks more like an impoverished extension of the late Cretaceous world than the seed of the present Age of Mammals.”

“The diatrymas were human-size — up to 2 m tall — ground-running birds that inhabited the terrestrial ecosystems of Europe and North America in the Paleocene and the early to middle Eocene […] Besides the large diatrymas, a large variety of crocodiles — mainly terrestrial and amphibious eusuchian crocodiles — populated the marshes of the Paleocene rainforests. […] The high diversification of the crocodile fauna throughout the Paleocene and Eocene represents a significant ecological datum, since crocodiles do not tolerate temperatures below 10 to 15°C (exceptionally, they could survive in temperatures of about 5 or 6°C). Their existence in Europe indicates that during the first part of the Cenozoic the average temperature of the coldest month never fell below these values and that these mild conditions persisted at least until the middle Miocene.”

“At the end of the Paleocene, approximately 55.5 million years ago, there was a sudden, short-term warming known as the Latest Paleocene Thermal Maximum. Over a period of tens of thousands of years or less, the temperature of all the oceans increased by around 4°C. This was the highest warming during the entire Cenozoic, reaching global mean temperatures of around 20°C. There is some evidence that the Latest Paleocene Thermal Maximum resulted from a sudden increase in atmospheric CO2. Intense volcanic activity developed at the Paleocene–Eocene boundary, associated with the rifting process in the North Atlantic and the opening of the Norwegian-Greenland Sea. […] According to some analyses, atmospheric CO2 during the early Eocene may have been eight times its present concentration. […] The high temperatures and increasing humidity favored the extension of tropical rainforests over the middle and higher latitudes, as far north as Ellesmere Island, now in the Canadian arctic north. There, an abundant fauna — including crocodiles, monitor lizards, primates, rodents, multituberculates, early perissodactyls, and the pantodont Coryphodon — and a flora composed of tropical elements indicates the extension of the forests as far north as 78 degrees north latitude. […] The global oceanic level at the beginning of the Eocene was high, and extensive areas of Eurasia were still under the sea. In this context, Europe consisted of a number of emerged islands forming a kind of archipelago. A central European island consisted of parts of present-day England, France, and Germany, although it was placed in a much more southerly position, approximately at the present latitude of Naples. […] To the east, the growing Mediterranean opened into a wide sea, since the landmasses of Turkey, Iraq, and Iran were still below sea level. To the east of the Urals, the Turgai Strait still connected the warm waters of the Tethys Sea with the Polar Sea. […] Despite the opening of the Greenland-Norwegian Sea, Europe and North America were still connected during most of the early and middle Eocene across two main land bridges […] the De Geer Corridor [and] the Thule Bridge […] these corridors must have been effective, since the European fossil record shows a massive entry of American elements […] The ischyromyid and ailuravid rodents, as well as the miacid carnivores, were among the oldest representatives of the modern orders of mammals to appear in Europe during the early Eocene. However, they were not the only ones, since the “modernization” of the mammalian communities at this time went even further, and groups such as the first true primates, bats (Chiroptera), flying lemurs (Dermoptera), and oddtoed (Perissodactyla) and even-toed (Artiodactyla) ungulates entered onto the scene, in both Europe and North America.”

“Although it was the first member of the horse lineage, Pliolophus certainly did not look like a horse. As classically stated, it had the dimensions of a medium dog (“a fox-terrier”), bearing four hooves on the front legs and three on the hind legs. […] the first rhino-related forms included Hyrachius, a small rhino about the size of a wolf that during the Eocene inhabited a wide geographic range, from North America to Europe and Asia.” (Yep, in case you didn’t know Europe had rhinos for millions and millions of years…) “The artiodactyls are among the most successful orders of mammals, having diversified in the past 10 million years into a wide array of families, subfamilies, tribes, and genera all around the world, including pigs, peccaries, hippos, chevrotains, camels, giraffes, deer, antelopes, gazelles, goats, and cattle. They are easily distinguished from the perissodactyls because each extremity is supported on the two central toes, instead of on the middle strengthened toe. […] The oldest member of the order is Diacodexis, […] a rabbit-size ungulate”

“Although the number of middle Eocene localities in Europe is quite restricted, we have excellent knowledge of the terrestrial communities of this time thanks to the extraordinary fossiliferous site of Messel, Germany. […] several specimens from Messel retain in their gut their last meal, providing a rare opportunity for testing the teeth-inferred dietary requirements of a number of extinct mammalian groups. […] A dense canopy forest surrounded Messel lake, formed of several tropical and paratropical taxa that today live in Southeast Asia”.

“At the end of the middle Eocene, things began to change in the European archipelago. Several late Paleocene and early Eocene survivors had become extinct […] The last part of the middle Eocene saw a clear change in the structure of the herbivore community as specialized browsing herbivores […] replaced the small to medium-size omnivorous/ frugivorous archaic ungulates of the early Eocene and became the dominant species. […] These changes among the mammalian faunas were most probably a response to the major tectonic transformations occurring at that time and the associated environmental changes. During the middle Eocene, the Indian plate collided with Asia, closing the Tethys Sea north of India. The collision of India and the compression between Africa and Europe formed an active alpine mountain belt along the southern border of Eurasia. In the western Mediterranean, strong compression occurred during the late Eocene, […] leading to the final emergence of the Pyrenees. To the south of the Pyrenees, the sea branch between the Iberian plate and Europe retreated”

“The European terrestrial ecosystems at the end of the Eocene were quite different from those inherited from the Paleocene, which were dominated by archaic, unspecialized groups. In contrast, a diversified fauna of specialized small and large browsing herbivores […] characterized the late Eocene. From our perspective, they looked much more “modern” than those of the early and early-middle Eocene and perfectly adapted to the new late Eocene environmental conditions characterized by the spread of more open habitats.”

“during the Eocene […] Australia and South America were still attached to Antarctica, as the last remnants of the ancient Gondwanan supercontinent. Today’s circumpolar current did not yet exist, and the equatorial South Atlantic and South Pacific waters went closer to the Antarctic coasts, thus transporting heat from the low latitudes to the high southern latitudes. However, this changed during the late Eocene, when a rifting process began to separate Australia from Antarctica. At the beginning of the Oligocene, between 34 and 33 million years ago, the spread between the two continents was large enough to allow a first phase of circumpolar circulation, which restricted the thermal exchange between the low-latitude equatorial waters and the Antarctic waters. A sudden and massive cooling took place, and mean global temperatures fell by about 5°C. […] During a few hundred thousand years (the estimated duration of this early Oligocene glacial episode), the ice sheets expanded and covered extensive areas of Antarctica, particularly in its western regions. […] The onset of Antarctic glaciation and the growing of the ice sheets in western Antarctica provoked an important global sea-level lowering of about 30 m. Several shallow epicontinental seas became continental areas, including those that surrounded the European Archipelago. The Turgai Strait, which during millions of years had isolated the European lands from Asia, vanished and opened a migration pathway for Asian and American mammals to the west. […] The tectonic movements led to the final split of the Tethys Sea into two main seas, the Mediterranean Sea to the south and the Paratethys Sea, the latter covering the formerly open ocean areas of central and eastern Europe. […] After the retreat of the Turgai Strait and the emergence of the Paratethys province, the European Archipelago ceased to exist, and Europe approached its present configuration. The ancient barriers that had prevented Asian faunas from settling in this continental area no longer existed, and a wave of new immigrants entered from the east. This coincided with the trend toward more temperate conditions and the spread of open environments initiated during the late Eocene. Consequently, most of the species that had characterized the middle and late Eocene declined or became completely extinct, replaced by herds of Asian newcomers.”

 

February 23, 2015 Posted by | biology, books, Geology, Paleontology, Zoology | Leave a comment

Personal Relationships (5)

Here’s a link to a previous post in the series, with links to other posts about the book as well.

In the post I’ll cover a few more chapters from the book. Let’s start with some observations from the chapter about relationship satisfaction. When you compare distressed couples with satisfied couples, distressed couples tend to show a range of dysfunctional communicative behaviours which include higher levels of criticism and complaining, hostility, defensiveness and disengagement, and not responding to the partner. “With regard to sequences of behavior, the “signature” of dissatisfied couples is the existence of reciprocated negative behavior that tends to escalate in intensity.” Attempts to repair the relationship usually employ meta-communication – e.g. “You’re not listening to me” – and these are typically delivered with negative affect (e.g. anger). The other party responds to the negative affect and reciprocates; on the other hand in satisfied couples the parties are usually more responsive to the repair attempts. The demand-withdrawal interaction pattern is another commonly observed interaction pattern in distressed couples which I’ve talked about before in my coverage of this book; this pattern involves one party pressuring the other with demands, complaints and criticism, and the other party withdrawing and reacting with defensiveness and passive inaction. An argument can be made that conflict interaction patterns may be relatively stable over time; for example researchers have looked at variables such as active listening, anger, and negative affect reciprocity in newly-weds and used these variables to successfully predict marital satisfaction and stability (presumably relationship dissolution risk, but this is not explicit in the text) 6 years later.

The chapter notes that research on cognitions in the relationship context has looked at the presence of unrealistic relationship beliefs early on in the relationship and used these unrealistic beliefs to predict relationship outcomes/dynamics later; it turns out that unrealistic relationship beliefs predict relationship dissatisfaction and observed couple behaviours. Other studies have instead looked at what they in the chapter term ‘functional’ unrealistic beliefs. I can’t recall if I’ve talked about this stuff before here in my coverage, but I haven’t talked about this chapter before anyway and the chapter notes that such studies have found e.g. that happy couples view their partners in a more positive light than the partners view themselves, and that “egocentrically assuming similarities between partner and self that do not exist is characteristic of being in a satisfying relationship.” The chapter notes that it has been known for a long time that happy couples tend to overestimate the positive qualities and underestimate the negative qualities of their partners, whereas unhappy couples tend to do the opposite. As mentioned earlier in the coverage, “happy spouses [tend to] make egocentric attributions for negative relationships events (e.g., arguments) but partner-centric attributions for positive relationships events”. They observe in the chapter that “[m]ore work has been conducted on attributions in close relationships than on any other cognitive variable. Evidence for an association between attribution and relationship satisfaction is overwhelming, making it possibly the most robust, replicable phenomenon in the study of close relationships”. Attributions affect many dimensions and one perhaps surprising variable involved is our memories. They mention a 5-year longitudinal study of dating couples in the chapter, which found that even though the participants’ self-reports of love of their partner declined during the year every year in the study, participants at the end of the year still consistently reported that they loved their partner more than they had the year before. People are funny sometimes.

The next chapter in the book deals with the topic of ‘romantic love’. I found this part quite interesting:

“Data from animal studies […] support the hypothesis that elevated activities of central dopamine play a primary role in attraction in mammalian species. In rats, blocking the activities of dopamine diminishes specific proceptive behaviors, including hopping and darting […]. Further, when a female lab-raised prairie vole is mated with a male, she forms a distinct preference for this partner. This preference is associated with a 50% increase of dopamine in the nucleus accumbens […] when a dopamine antagonist is injected directly into the nucleus accumbens, females no longer prefer [the] partner and when a female is injected with a dopamine agonist, she begins to prefer a conspecific who is present at the time of infusion, even if the female has not mated with this male […] In sum, the considerable data on mate preference in mammalian (and avian) species, and the association of this mate preference with subcortical dopaminergic pathways in human and animal studies suggest that attraction in mammals (and its human counterpart, romantic love) is a specific biobehavioral brain system; that it is associated with at least one specific neurotransmitter, dopamine; and that this brain system evolved to facilitate a specific reproductive function: mate preference and pursuit of this preferred mating partner.”

When looking at what makes a person likeable, people are usually found to like people who are similar to themselves – “perceived shared attitudes plays a highly consistent role across many experiments” – “but when other variables are also free to vary, the effect sizes are often relatively small”, and the authors note that reduced attraction to perceived dissimilars may play a big role here; maybe it doesn’t matter so much whether or not someone is particularly similar to yourself, and what really matters is that the individual is not ‘too different’. They note that “perceived similarity is much more important than actual similarity”. As for the mere-exposure effect, the authors note that the main effect of the variable is through providing an opportunity for interaction/relationship formation and that there is “little direct evidence for it playing much of a direct role in falling in love”. I found the research included on the ‘arousal at time of meeting the partner’-variable interesting. A psychological experiment from the 70es involving males meeting up with a female confederate on a bridge indicated that when interactions took place on a shaky suspension bridge, the males were more attracted to good-looking female confederates than they were when the two met up on a solid, low bridge. Later studies have since then demonstrated similar effects in a variety of contexts involving positive and negative sources of arousal. I guess if you don’t know the details of the followup-studies and you’re a woman who’ve found a potential partner you’d like to ask out, you might consider suggesting the first date take place on a (poorly constructed?) suspension bridge (or near one)..

The next chapter deals with the topic of commitment. Various conceptual models which provide different ways to think about commitment are presented early in the chapter, but I won’t really talk about that part of the coverage. The first observation I thought worth including here is that if you want to understand topics like abusive relationships, you need to understand stuff like commitment and related topics; it’s not very helpful to explain abuse as the result of the irrationality and stupidity of the victims, and the authors argue that the early literature on such topics were too focused on e.g. relationship satisfaction, which made it hard to understand what was going on. When people started including variables such as the investments (time, money, etc.) people had put into the relationship and available alternatives, some other ways to think about these things presented themselves – as they put it in the chapter, “Once researchers recognized the importance of commitment, it became evident that abuse victims may remain in their relationships because they are trapped – because they have poor alternatives (especially economic alternatives; e.g., limited financial resources, poor employment options) or because important investments bind them to their partners (e.g., young children, joint home ownership). […] recent empirical work supports the claim that persistence in abusive relationships is at least partially attributable to poor alternatives and high investments”.

Researchers in the field have argued that the reason why high commitment levels tend to keep relationships together is because they promote adaptive relationship-relevant acts, termed relationship maintenance phenomena. The important point is of course that you need a mechanism to explain why high commitment leads to different outcomes (it does), and that you need to look at behaviours. Although behaviours are important, so are cognitions (once again); to give a few examples, it’s been shown that people strongly committed to a relationship tend to shield themselves from attractive alternative partners by cognitively derogating tempting alternatives, and that people with strong commitment to a relationship react to periods of doubt or uncertainty by cognitively enhancing their partners and relationships. One behavioural mechanism supporting relationship persistence is that people with strong commitment are inclined to accommodate rather than retaliate when a partner engages in potentially destructive behaviours (instead of yelling when the partner is rude, the other party disengages and asks if s/he had a bad day at work (I’m not sure I’d have termed this type of behaviour ‘accommodation’, but that’s how they frame it in the chapter – as people might notice, this type of behaviour parallels the ‘bad behaviour is (explained away as) situational, good behaviour is personality’-cognitive angle mentioned multiple times during my coverage of the book already)). Research has found that committed people are more likely to sacrifice their personal interests to promote the interests of the partner and relationship (I’m sure some would argue this is/ought to be part of the construct), and that they’re more likely to forgive if confronted with acts of betrayal.

A few quotes related to the above: “Maintenance acts such as accommodation and sacrifice are beneficial not only because they prevent the escalation of conflict and yield better immediate outcomes, but also because they help each partner recognize the extent of the other’s commitment. For this reason, the situations that call forth maintenance acts […] have been termed diagnostic situations […] Such situations are “diagnostic” in that it is possible to discern the strength of another’s commitment only in situations wherein the behavior that benefits a relationship is at odds with the behavior that would benefit the individual […] Why are diagnostic situations important? Confidence in a partner’s commitment is reflected in trust, defined as the strength of one’s conviction that the partner will be responsive to one’s needs […] As such, one person’s trust in the other is a rough gauge of the strength of the other’s commitment […] As people become increasingly trusting, they become more willing to place themselves in vulnerable positions relative to the partner by becoming increasingly dependent – that is, they not only become more satisfied with the relationship, but are also more willing to drive away or derogate alternative partners (i.e., burn their bridges) and invest in the relationship in material and non-material ways […] increasing dependence yields strengthened commitment, which in turn causes […] a variety of prosocial maintenance acts”. Of course for a variety of reasons a desirable cycle like that may be interrupted or fail to materialize, and when things are not going well the cognitive mechanisms which usually help support relationship maintenance will instead support relationship dissolution – an unrealistically favourable view of the partner will e.g. be replaced by an unrealistically favourable view of the available alternatives. Relationship satisfaction is closely linked to commitment, and one thing to note here is that fluctuations in this variable have been shown to predict breakups independent of the level of relationship satisfaction. The authors note in the last part of the chapter that it’s likely that not all types of commitment are equal (e.g. ‘enthusiastic vs. moral’) and that different types of commitment may have different effects on e.g. the risk of relationship dissolution, but it doesn’t seem like a lot of research had been done on this stuff when the book was published – they don’t really go into the details.

February 19, 2015 Posted by | books, Psychology | Leave a comment

A brief note on the Copenhagen shootings

I don’t generally comment on current affairs and I was debating for a long time whether or not to post anything about this. I had actually decided not to, but then I changed my mind this afternoon.

I haven’t spent much time on political stuff for a few years, and I think this is a very good decision; so it’s not like I’m suddenly starting out as a political blogger now. I won’t discuss the details of the event, but back when I was interested in political stuff I did have a look at some data which I thought might be worth briefly revisiting now. Here’s exhibit a), a quote from a Danish newspaper article, summing up the main results of a major Danish opinion poll conducted a few years ago:

“Angreb på religion bør være strafbart i Danmark. Det svarer halvdelen af indvandrere og efterkommere fra muslimske lande i en meningsmåling, som Danmarks Statistik har gennemført for den liberale tænketank Cepos.

2.792 personer har svaret på, om loven bør forbyde film og bøger, der angriber religion. Ja, mener 50 pct. af både indvandrere og deres efterkommere. Nej, siger 35 pct. af indvandrerne og 40 pct. af deres efterkommere.”

[I found it hard to translate this ‘directly’ because it’s ‘newspaper language’, but here’s my attempt at a translation (you can always use google translate to get a second opinion)]:

“Attacks on religion ought to be a crime in Denmark. This was the response of half of immigrants and descendants from muslim countries who participated in a recent opinion poll conducted by Statistics Denmark for the liberal think tank Cepos.

2792 persons responded to the question of whether or not they think films and books that attack religion ought to be outlawed. 50 % of both immigrants and descendants answered yes to this question, whereas 35 % of the immigrants and 40 % of the descendants answered no.”

I should probably make clear that a rule of thumb I’ve seen a few times is that you’re usually doing okay in terms of representativeness/external validity if you’ve asked 1000 people in a Danish poll. 2792 respondents from a subgroup is way more than is technically required for the results of such a poll to be reliable in terms of making out-of-sample inferences.

Everybody with half a brain cell will condemn the terrorist attack over the next days, if they haven’t already. Yet half of muslims in Denmark probably would be in favour of putting people like Lars Vilks in jail, or give people like him fines so that they stop talking.

Exhibit b) – who are the Danish anti-semites these days? It’s hard to tell, but here are some presumably relevant international data from this Pew report. The included results from muslim countries roughly mirror what I’d imagine you’d have got from a survey answered by members of the Waffen-SS in the early 1940es, allowing for a few drunk respondents etc.

If you want more data on what muslims around the world think about various things, have a look at these numbers. Did you know that roughly two out of three South-Asian muslims according to these numbers are in favour of killing apostates, or that more than half of South-Asian, Iraqi, Egyptian, and Palestinian muslims refused to say that honour killings are never justified?

I’ll allow comments here, but don’t expect me to engage.

Update: Some of you will already know this, but I thought I should point this out to people who don’t: A few years back I translated and blogged a substantial proportion of a statistical publication in Danish about immigrants living in Denmark. You can read those posts – which have quite a bit of data about which types of immigrants live in Denmark and how well they do here – here, here, here, and here.

February 15, 2015 Posted by | current affairs | Leave a comment

Promoting the unknown, a continuing series

I more or less discontinued these types of posts during the last year, but I figured that given how infrequently I post these days I might as well revive these. I’m sure I’ve included some of the pieces below in previous posts, but I don’t really care – if a couple of people who read along when I first posted them still remember those pieces from my previous coverage, they probably liked them anyway.

 

February 15, 2015 Posted by | music | Leave a comment

Mammoths, Sabertooths, and Hominids: 65 Million Years of Mammalian Evolution in Europe

I’m currently reading this book. It’s quite nice so far, though the title is slightly misleading (I’ve read 82 pages so far and I’ve yet to come across any mammoths, sabertooths or hominids…). I mentioned yesterday that I wanted to cover the systems analysis text in more detail today, but that turned out to be really difficult to do without actually rewriting the book (or at the very least quoting very extensively), something I really don’t want to do. I decided to cover this book instead, though it’s admittedly slightly ‘lazy coverage’. Below I have added some links to stuff he talks about in the book. It’s the sort of book which is reasonably easy to blog, so I’m quite sure I’ll add more detail and context later, especially considering how most people presumably know far more (…okay, well, more) about the lives of the dinosaurs than they do about the lives of their much more recent ancestors, which lived during the Cenozoic.

The book frequently has more information about a given species/genus than does wikipedia’s corresponding article (and there’s stuff in here which wikipedia does not have articles about at all…), and/but I’ve tried to avoid linking to stubs below. Some articles below have decent coverage, but these are in general topics not well covered on wikipedia – I don’t think there’s a single featured article among the articles included. Even so, it’s probably worth having a look at some of the articles below if you’re curious to know which kind of stuff’s covered in this book. Aside from the links, I decided to also include a few pictures from the articles.

Paleocene.
Eocene.
Late Paleocene Thermal Maximum.
Turgai Strait.
Multituberculata.
Leptictidium.
Messel site.
Hyaenodon.

Hyaenodon_Heinrich_Harder
Pantolestidae.
Mixodectidae.
Condylarth.
Arctocyonidae.
Purgatorius.
Dyrosauridae.
Hypsodont.
Gastornis.

Gastornis,_a_large_flightless_bird_from_the_Eocene_of_Wyoming
Plesiadapis.
Pristichampsus.
Pantodonta.
Barylambda_BWMiacids.
Carnassial.
Coryphodon.
Alpine orogeny.
Phenacondus.
Perissodactyla.
Icaronycteris.
Palaeochiropteryx.

800px-Palaeochiropteryx_Paleoart
Adapidae.
Omomyidae.
Artiodactyla.
Palaeotherium.
Chalicotheres.
Eurotamandua.
Strigogyps.

February 13, 2015 Posted by | biology, books, evolution, Paleontology, Zoology | Leave a comment

Introduction to Systems Analysis: Mathematically Modeling Natural Systems (I)

“This book was originally developed alongside the lecture Systems Analysis at the Swiss Federal Institute of Technology (ETH) Zürich, on the basis of lecture notes developed over 12 years. The lecture, together with others on analysis, differential equations and linear algebra, belongs to the basic mathematical knowledge imparted on students of environmental sciences and other related areas at ETH Zürich. […] The book aims to be more than a mathematical treatise on the analysis and modeling of natural systems, yet a certain set of basic mathematical skills are still necessary. We will use linear differential equations, vector and matrix calculus, linear algebra, and even take a glimpse at nonlinear and partial differential equations. Most of the mathematical methods used are covered in the appendices. Their treatment there is brief however, and without proofs. Therefore it will not replace a good mathematics textbook for someone who has not encountered this level of math before. […] The book is firmly rooted in the algebraic formulation of mathematical models, their analytical solution, or — if solutions are too complex or do not exist — in a thorough discussion of the anticipated model properties.”

I finished the book yesterday – here’s my goodreads review (note that the first link in this post was not to the goodreads profile of the book for the reason that goodreads has listed the book under the wrong title). I’ve never read a book about ‘systems analysis’ before, but as I also mention in the goodreads review it turned out that much of this stuff was stuff I’d seen before. There are 8 chapters in the book. Chapter one is a brief introductory chapter, the second chapter contains a short overview of mathematical models (static models, dynamic models, discrete and continuous time models, stochastic models…), the third chapter is a brief chapter about static models (the rest of the book is about dynamic models, but they want you to at least know the difference), the fourth chapter deals with linear (differential equation) models with one variable, chapter 5 extends the analysis to linear models with several variables, chapter 6 is about non-linear models (covers e.g. the Lotka-Volterra model (of course) and the Holling-Tanner model (both were covered in Ecological Dynamics, in much more detail)), chapter 7 deals briefly with time-discrete models and how they are different from continuous-time models (I liked Gurney and Nisbet’s coverage of this stuff a lot better, as that book had a lot more details about these things) and chapter 8 concludes with models including both a time- and a space-dimension, which leads to coverage of concepts such as mixing and transformation, advection, diffusion and exchange in a model context.

How to derive solutions to various types of differential equations, how to calculate eigenvalues and what these tell you about the model dynamics (and how to deal with them when they’re imaginary), phase diagrams/phase planes and topographical maps of system dynamics, fixed points/steady states and their properties, what’s an attractor?, what’s hysteresis and in which model contexts might this phenomenon be present?, the difference between homogeneous and non-homogeneous differential equations and between first order- and higher-order differential equations, which role do the initial conditions play in various contexts?, etc. – it’s this kind of book. Applications included in the book are varied; some of the examples are (as already mentioned) derived from the field of ecology/mathematical biology (there are also e.g. models of phosphate distribution/dynamics in lakes and models of fish population dynamics), others are from chemistry (e.g. models dealing with gas exchange – Fick’s laws of diffusion are e.g. covered in the book, and they also talk about e.g. Henry’s law), physics (e.g. the harmonic oscillator, the Lorenz model) – there are even a few examples from economics (e.g. dealing with interest rates). As they put it in the introduction, “Although most of the examples used here are drawn from the environmental sciences, this book is not an introduction to the theory of aquatic or terrestrial environmental systems. Rather, a key goal of the book is to demonstrate the virtually limitless practical potential of the methods presented.” I’m not sure if they succeeded, but it’s certainly clear from the coverage that you can use the tools they cover in a lot of different contexts.

I’m not quite sure how much mathematics you’ll need to know in order to read and understand this book on your own. In the coverage they seem to me to assume some familiarity with linear algebra, multi-variable calculus, complex analysis (/related trigonometry) (perhaps also basic combinatorics – for example factorials are included without comments about how they work). You should probably take the authors at their words when they say above that the book “will not replace a good mathematics textbook for someone who has not encountered this level of math before”. A related observation is also that regardless of whether you’ve seen this sort of stuff before or not, this is probably not the sort of book you’ll be able to read in a day or two.

I think I’ll try to cover the book in more detail (with much more specific coverage of some main points) tomorrow.

February 11, 2015 Posted by | books, mathematics, science | Leave a comment

Some links (Open Thread?)

It’s been quite a while since the last time I posted a ‘here’s some interesting stuff I’ve found online’-post, so I’ll do that now even though I actually don’t spend much time randomly looking around for interesting stuff online these days. I added some wikipedia links I’d saved for a ‘wikipedia articles of interest’-post because it usually takes quite a bit of time to write a standard wikipedia post (as it takes time to figure out what to include and what not to include in the coverage) and I figured that if I didn’t add those links here I’d never get around to blogging them.

i. Battle of Dyrrhachium. Found via this link, which has a lot of stuff.

ii. An AMA by someone who claims to have succeeded in faking his own death.

iii. I found this article about the so-called “Einstellung” effect in chess interesting. I’m however not sure how important this stuff really is. I don’t think it’s sub-optimal for a player to spend a significant amount of time in positions like the ones they analyzed on ideas that don’t work, because usually you’ll only have to spot one idea that does to win the game. It’s obvious that one can argue people spend ‘too much’ time looking for a winning combination in positions where by design no winning combinations exist, but the fact of the matter is that in positions where ‘familiar patterns’ pop up winning resources often do exist, and you don’t win games by overlooking those or by failing to spend time looking for them; occasional suboptimal moves in some contexts may be a reasonable price to pay for increasing your likelihood of finding/playing the best/winning moves when those do exist. Here’s a slightly related link dealing with the question of the potential number of games/moves in chess. Here’s a good wiki article about pawn structures, and here’s one about swindles in chess. I incidentally very recently became a member of the ICC, and I’m frankly impressed with the player pool – which is huge and includes some really strong players (players like Morozevich and Tomashevsky seem to play there regularly). Since I started out on the site I’ve already beaten 3 IMs in bullet and lost a game against Islandic GM Henrik Danielsen. The IMs I’ve beaten were far from the strongest players in the player pool, but in my experience you don’t get to play titled players nearly as often as that on other sites if you’re at my level.

iv. A picture of the Andromeda galaxy. A really big picture. Related link here.

v. You may already have seen this one, but in case you have not: A Philosopher Walks Into A Coffee Shop. More than one of these made me laugh out loud. If you like the post you should take a look at the comments as well, there are some brilliant ones there as well.

vi. Amdahl’s law.

vii. Eigendecomposition of a matrix. On a related note I’m currently reading Imboden and Pfenninger’s Introduction to Systems Analysis (which goodreads for some reason has listed under a wrong title, as the goodreads book title is really the subtitle of the book), and today I had a look at the wiki article on Jacobian matrices and determinants for that reason (the book is about as technical as you’d expect from a book with a title like that).

viii. If you’ve been wondering how I’ve found the quotes I’ve posted here on this blog (I’ve posted roughly 150 posts with quotes so far), links like these are very useful.

ix. Geology of the Yosemite area.

February 7, 2015 Posted by | astronomy, Chess, Geology, history, mathematics, Open Thread, random stuff, wikipedia | Leave a comment

An Introduction to Medical Diagnosis (II)

Here’s my first post about the book. In this post I’ll cover two more of the individual systems chapters – the first of the chapters I’ll talk about is the one about the renal system (kidneys). Some key symptoms which may suggest renal pathology are disorders of micturition (urination), disorders of urine volume, changes in urine composition, loin pain, oedema, and hypertension. Disorders of micturition can relate to frequency, poor urinary stream (typically caused by outflow obstructions) and dysuria (pain on micturition). There are 19 different causes of frequency mentioned in a table in the chapter, so there are a lot of possible causes. Volume changes may be termed polyuria (increase in volume), oliguria (decrease-), or anuria (total loss of urine output – this is bad); it’s important to note that frequency does not necessarily imply polyuria. Blood in the urine is called haematuria, a symptom which will often cause people to seek medical attention – for good reason: “Any patient above the age of 40 years with haematuria (visible or invisible) requires urgent evaluation by a urologist to look for malignant disease of the urinary tract.” It should however be noted that red/brown urine doesn’t necessarily indicate haematuria – other common causes are drugs and vegetable dyes – and relatedly it should be mentioned that blood in the urine may not be visible (haematuria is sometimes caught as an incidental finding by dip-stick analysis of the urine). When blood is present in the urine at the start of micturition only it usually indicates urethral bleeding, whereas bleeding towards the end of micturition is indicative of bladder/prostate bleeding. In the context of kidney disease pain patterns are inconsistent, but when there’s pain it’s usually due to renal tract inflammation or obstruction (e.g. due to a kidney stone). Cancer need not cause pain: “The cardinal feature of transitional cell carcinoma of the urinary tract is painless haematuria”, which may or may not be visible to the naked eye. In bladder cancer ‘local’ symptoms such as frequency and nocturia present before systemic symptoms such as weight loss, and the latter symptoms usually present late. Risk factors include smoking, occupational exposure to hydrocarbons, ionizing radiation (e.g. previous cancer treatment), prolonged immunosuppression, and bladder stones.

There are a number of inherited renal diseases, as well as a huge number of medical conditions associated with renal disease (18 of them are listed in the chapter). Aside from specific medical conditions a large number of drugs may also impact kidney function and the risk of developing renal disease. Pregnancy is a risk factor. Dietary factors may be important in some cases; for example excessive salt intake may lead to hypertension, as may alcohol, and hypertension is bad for the kidneys. Another example would be inadequate fluid intake or high intake of animal protein, both of which may promote lithiasis (stone formation). Tobacco is a significant risk factor for the development and progression of kidney disease. Of the many causes of kidney failure, diabetic renal disease is the most common cause of end-stage renal disease (-ESRD) in the Western world, according to the book accounting for 20-50% of new patients with end-stage renal disease (presumably the estimate is so broad-banded due to major cross-country differences). Another important cause is autosomal dominant polycystic kidney disease (ADPKD), which make up 10 per cent of patients with ESRD. Women have urinary tract infections (-UTI) much more often than men, and 50-60 per cent of women have at least one UTI during their lives. In males the risk has been estimated to be 5/10.000/year. It’s noted in the next chapter of the book that “Urinary tract infections (UTIs) are common in women, but uncommon in men under 50 years old”, but that “[o]lder men may get UTIs secondary to bladder outflow obstruction from prostatic hypertrophy”. I won’t talk much about that chapter, about the genitourinary system, as I’ve talked quite a bit about these sorts of things before when covering Holmes et al., e.g. here and here, but one other important quote is probably worth including here as well: “Seventy-five per cent of people infected with HSV [herpes simplex virus] are not aware that they have genital herpes either because their symptoms are very mild/absent or because the symptoms have been assumed to be due to something else (most commonly thrush).”

The other main chapter I’ll cover here is the chapter about the nervous system. I liked the way the author starts out the chapter – here’s a quote from the beginning of the chapter: “Inexperienced clinicians often order sophisticated (and expensive!) investigations hoping that the diagnosis may be revealed, but sadly this rarely happens. Many investigations are relatively sensitive but not necessarily disease specific”. Later on he also notes that it is his opinion that: “Electroencephalograms (EEGs) are grossly overordered. They should not be used as a diagnostic tool in epilepsy as they are relatively non-specific and non-sensitive.” I liked this stuff in part because I’m the sort of person who cares about cost-effectiveness, but also because Eysenck and Keane’s Cognitive Psychology text, part of which I read last December, contained some reasonably detailed coverage of various imaging methods used in these contexts and what you can and cannot use them for; and I think it’s highly likely that the author of the chapter is right. I may go into much more details about this kind of stuff later if I decide to cover E&K’s book, but I won’t talk about it here. One related observation worth including here is however that in the context of a seizure, something as ‘low-tech’ as an available eye-witness is often crucial (was there jerking? pallor? gaze aversion?) to make a diagnosis and distinguish between an epileptic seizure and a cardiovascular syncope (the most common diagnostic dilemma here).

Headaches are common. It’s useful to know that whereas an acute headache may be a sign of sinister pathology, chronic headaches rarely are. Acute headaches may be almost instantaneous (hyperacute), or they may develop over hours to days. Instantaneous headache may be (but of course needn’t be) due to life-threatening conditions such as subarachnoid haemorrhage, venous sinus thrombosis, cerebral haemorrhage, and phaeochromocytoma, all of which may present that way. The combination of neck stiffness and photophobia (together with headache) is called meningism and this is something that requires urgent investigation, as it may be due to meningitis or encephalitis. Muscle weakness is a common neurological symptom, and here it’s important to note that hyperacute limb weakness is usually caused by a stroke, and is most commonly unilateral (i.e. affecting e.g. only one arm or leg, rather than both), whereas bilateral weakness is a marker of spinal cord disease. Sensory symptoms may be either ‘positive’ (e.g. tingling, dysaesthesia) or negative (numbness); stroke usually causes negative symptoms, whereas various genetic or acquired disorders may also present with ‘positive’ symptoms as well. “Neuropathic pain (cf diabetes) is often lower limb predominant and described as burning, stinging or throbbing.” Relatedly: “Diabetes is the commonest cause of neuropathy in the UK; distal predominantly sensory neuropathy, diabetic amyotrophy (pain and wasting in femoral distribution), nerve entrapments (carpal tunnel syndrome), cranial neuropathy and autonomic neuropathy are relatively common complications.” As for the aforementioned strokes, they’re sometimes (in 15 per cent of cases, according to the book) preceded by a TIA (a transient ischaemic attack), a sort of ‘mini-stroke’ which causes a reversible neurological deficit lasting less than 24 hours (‘in practice much shorter duration’). A recent TIA puts you in high immediate risk of stroke, which is probably useful to know – for more details, see this link.

The nervous system deals with a lot of stuff, so a lot of things can go wrong. Autonomic nervous system disorders may cause symptoms/problems such as: sphincter disturbances (e.g. incontinence), change in sweating patterns, photophobia (when the pupil is affected), night blindness, orthostatic hypotension, dry mouth, dry eyes, erectile/ejaculatory failure, and vomiting. Specific nerves doing specific things can cause specific symptoms when they stop working the way they’re supposed to, and these sorts of symptoms are very far from limited to ‘people being unable to move their arms or legs'; neurological problems can also cause you to e.g. go blind or deaf. The distinction between monolateral (‘vascular’) and bilateral (‘neurological’) symptoms and how this distinction relate to the underlying medical cause seems to apply not only to major limbs, but also to other areas of the body – for example if you’re experiencing vision loss in both eyes it’s more often a neurological problem, whereas problems caused by retinal pathology tend to cause unilateral symptoms. On a related note, in elderly people monocular loss of vision can be a harbinger of stroke. They mention in this chapter that neurological dysphagia (difficulty swallowing) may affect liquids first, whereas a mechanical obstruction (e.g. due to a tumour) will preferentially affect solids (I mentioned this in my last post about the book). “The duration of anterograde amnesia is an extremely useful indicator of the severity of head injury.” In the last part of the chapter the author talks about various specific conditions causing neurological problems, such as Parkinson’s disease, Motor Neuron Disease, Multiple Sclerosis, Myasthenia gravis, and Guillain–Barré syndrome – I won’t cover these in any detail as the book only covers them very briefly (you can google them if you’re curious).

February 6, 2015 Posted by | books, medicine | Leave a comment

Personal Relationships (4)

Here’s a previous post about the book, with links to other posts in the series.

I decided in this post to have a look at a few of the chapters in the first part(s) of the book. As earlier mentioned I lost my notes and highlights to these parts of the book due to computer trouble, making it much more difficult and time consuming to blog this stuff, but I wanted to cover some of that stuff even so because if I don’t I’ll forget the details (to the extent that I have not already – I should caution that this post provides relatively ‘lazy coverage’ as I felt it to be completely out of the question to select material from the book to talk about here using the same criteria I normally employ).

An obvious but important conclusion from the chapter on The Affective Structure of Marriage, which is a chapter that among other things covers multiple conceptual models dealing with relationship change, is that: “various models of marital change are useful because no single pathway describes changes in all, or even most, marriages. Even among couples sharing a similar outcome (e.g., divorce), there is considerable variation in the course toward that outcome. This implies that attempts to develop a single explanation or description of divorce are likely to be, at best, incomplete. Concluding that multiple models are useful is merely recognizing that there are multiple developmental processes in marriage.” Relationships may change for all sorts of reasons, and there is no full model out there which explains everything. This stuff is complicated. It’s noted in the chapter that some of the attempts people have made at trying to e.g. predict which couples divorced over a given time period turned out to perform really quite well on one sample (using longitudinal data – this is not just unsophisticated cross section analyses we’re dealing with), but then it turned out later that they perform really quite horribly on validation samples using the same type of data to predict outcomes in different couples. Similar stimuli may have different effects depending on how long people have been together. Different models deal with time frame aspects in different ways.

I’ll mention a few results from the literature covered in that chapter here. One is that couples who were initially more affectionate and less antagonistic were happier 13 years later than were other couples who were still together at that point in time but had lower initial levels of affection/higher antagonism. It’s also been found that couples which are high in antagonism early on in the relationship (‘lots of drama’) are more likely to divorce early on; disillusionment after a few years of marriage seems to be a better predictor of divorce years later, with initial affection being an important moderating variable in the sense that couples who were initially higher in affection were together for a longer period of time before they eventually divorced. Shorter relationship duration at the time of marriage seems to predict divorce. Personality characteristics such as (trait) (presumably also state-, US) anxiety and neuroticism are associated with relationship dissatisfaction and divorce risk. I should probably once again emphasize that the only reason why I’m not providing effect sizes here is that the authors do not, and so I’m not able to. Some conclusions from the chapter:

“one of the most exciting nascent trends in the marital literature involves the recognition that there is not a single unitary process leading to marital distress and divorce […]. Some couples begin marriage with lower marital satisfaction than most other couples but remain married indefinitely, whereas other couples begin marriage very satisfied but end up divorcing. Moreover, the predictors of dissatisfaction and divorce are not always the same; for instance, stable characteristics such as trait anxiety appear to be more strongly related to satisfaction than they are to divorce […]. Even the processes leading to divorce are not uniform, with some couples who eventually divorce beginning marriage with high levels of hostility and divorcing quickly, others beginning marriage with moderate amounts of both positive and negative elements before becoming quite low in affection, and still others beginning marriage with exceedingly high levels of affection that are not sustained over the early years of marriage. Also, the predictors of divorce are different for divorces that occur earlier in marriage compared with those that happen later in marriage. […] Being high in conscientiousness [for example] appears to diminish the chances that one will divorce early in marriage but does not appear to prevent eventual divorce”.

It should be noted, as they also do, that much of this research is based on what they in the book call ‘observational data’, which in this context means data obtained by actually observing the individuals, usually in a lab, and then coding specific behaviours in specific ways. They didn’t just ask people if they were affectionate towards each other; they tried to estimate whether or not they were, based on behaviours they could observe. There are problems with this sort of data and they talk about that in the chapter; for example it has been argued (I think I may have talked about this before in my coverage) that the most effective kind of support may well be invisible support (“actions that take place outside the recipients’ awareness”, or supportive actions which are provided “in such a skillful way that, although the information about the transaction is available to the recipient, the transaction is not coded as enacted support”) – and this sort of support is difficult to observe in a lab; whereas on the other hand the most visible sort of support, which is the easiest type to code by observers, may be counterproductive (such actions may provide a signal to the partner that the other party considers him/her too incompetent to handle the task on his/her own, which may lead to self-doubt etc. in the recipient), perhaps making interpretations slightly more difficult than one might think they are. A related problem seems to me to be that not providing support may in some situations be the optimal approach to take by the partner (‘my partner obviously doesn’t need my help right now, and if I were to provide support in this situation this would not be helpful’), and so such behaviours may be indicative of a strong relationship – yet that’s not how such behaviours will be coded in the studies. There are some problems here.

Next, a few observations dealing with divorce and postdivorce relationships. This data is old, but better than nothing: “Most divorced adults find another romantic partner. In the United States, the probability of cohabiting after the dissolution of first marriage is 70% after 10 years […] Census estimates project that in the United States nearly 85% of divorced people remarry […]. Although the remarriage rate is lower in other Western societies, most divorced people eventually cohabit or remarry […] It is an almost universal finding that children have more difficulty adapting to parental remarriage than do the adults.” I thought I should mention in a slightly unrelated context that I recently came across a Danish article about how children are dealt with here in the divorce context; I was not surprised to learn that women get custody in 90% of the cases – the politicians are thinking about changing this (this did surprise me), which has caused some organizations to argue that it’s a bad idea to change this state of affairs (again, not surprising). I’ve blogged US data on this stuff before – go have a look at the archives/use the search function if you’re curious, I’m too lazy to provide a link. I believe the US numbers are reasonably similar. An important observation made in the chapter is that parenting roles have evolved over time, and that the institutional setup had not really evolved with them at the time this book was written: “Child support policies have been predicated on the notion of fathers having only one set of children to support. In fact, increases in multiple marital and cohabiting relationships means that nearly 75% of remarried men have multiple sets of children to support (emotionally and financially) both inside and outside their current relationship.” It’s important to observe in this context that the proportion of all marriages which were remarriages was really high in the US, and that the remarried couples made up a big proportion of the total: “About half of all U.S. marriages are remarriages for one or both partners” (data from the U.S. Census Bureau, 2000). Things may or may not be different today.

Some observations from the chapter about personal relationships in adolescence and early adulthood: “Friendships and romantic relationships are tightly interwoven in adolescence and early adulthood. Unsupervised mixed-gender peer groups during adolescence provide opportunities and supportive environments for “pairing off” between group members. By mid-adolescence, most individuals have been involved in at least one romantic relationship; by the early years of early adulthood, most are currently participating in an ongoing romantic relationship (Collins, 2003). […] Existing findings point to a shift in the qualitative characteristics of dating relationships between the ages of 15 and 17 years, and dating among early adults seems similar in key ways to dating among late adolescents. After age 17, the likelihood of being involved in a romantic relationship changes little […] Having a romantic relationship and the quality of that relationship are associated positively with romantic self-concept and, in turn, with feelings of self-worth […], and longitudinal evidence indicates that by late adolescence, self-perceived competence in romantic relationships emerges as a reliable component of general competence […]. Whether adolescent romantic relationships play a distinctive role in identity formation during adolescence is not known, although considerable speculation and some theoretical contentions imply a link […] The most widely studied patterns have to do with variations in the timing of involvement in both romantic relationships and sexual activity, typically showing that early dating and sexual activity are risk factors for current and later problem behaviors and social and emotional difficulties […] The social worlds of those involved in romantic relationships differ from those who are not because romantic partners quickly become dominant in the relationship hierarchy […]. Although romantic interconnections initially are predicated on principles of social exchange, commitment drives participants to transform this voluntary relationship into one that is more obligatory and permanent […]. Eventually, most early adults marry and reproduce, further transforming the relationship and marginalizing remaining friendships, thus effectively ending the peer group’s dominance of relationship experiences”.

And finally some data and observations from the chapter about close relationships in middle and late adulthood: “The majority of adults in the United States are married, but the proportion is smaller in old age than earlier in adulthood (ages 35 to 54 years = 71.3%, 55 to 64 years = 74.2%, and 65 or older = 56.7%), and a notable sex difference in the proportion married exists between men and women aged 65 or older (75.7% versus 42.9%, respectively). The majority of households comprise family households (68%), usually of married couples (52%), but 32% of adults live in non-family households, including the 26% who live alone [do keep in mind that many of those 26% are involved in romantic relationships as well, though the characteristics of the relationships they have are different]. Among persons aged 75 years or older, however, the proportion living alone is much higher (39.6%) because of the greater likelihood of being widowed (ages 35 to 54 years = 1.6%, 55 to 64 years = 6.7%, 65 to 74 = 19.6%, and 75 or older = 41%, U.S. Census Bureau, 2003). […] the proportion of householders with children of any age at home remains above 50% even in the 45- to 54-year-old age group (Russell, 2001). […] One of the key findings of research on the causes and consequences of relational difficulties in adulthood is that negative dimensions of interactions have stronger effects than positive ones on relationship quality and satisfaction.”

February 3, 2015 Posted by | books, demographics, Psychology | Leave a comment

Life is beautiful and wonderful…

(A slightly unusual post, but I hope you’ll bear with me…)

*Someone’s sitting on death row, condemned to death for a crime he did not commit.

*A young woman just got raped.

*A childless widow near retirement age just lost most of her life savings by participating in a financial scheme she did not understand.

*A child died of AIDS.

*A man got diagnosed with ALS.

*A traffic accident killed the parents of two children, who are now orphans.

*A married woman just realized her husband of two decades has been cheating on her for a long time. She does not know what to do.

*An old man decides to end his pathetic existence and shoots himself.

*A dog starved to death because the owner neglected to take care of it.

*Some people are employed to try to minimize the number of bullets which leave the barrel of a machine gun and do not proceed to later hit a human being.

*A young man lost most of his teeth in a bad mugging.

*A guy working in a saw-mill lost his right hand due to a work accident with a chain saw.

*A child forgot to look both ways before crossing the street, and was killed by a woman whose life will never be the same again.

*A decision is reached at the family council. The father summarizes and tells his two sons that they are to kill their sister next week in order to protect the honor of the family. They all feel that this is the best option.

*Yesterday people from the government came by and took her two children away from her.

*An old man with dementia who for the last few years has had no visitors dies at the local retirement home.

*A man who’d been married to his wife for fifteen years was yesterday told that she no longer loves him, and that she wants a divorce.

*A woman is unable to sleep due to pain from a broken arm. She broke her arm in a recent violent argument with her abusive husband, who refuses to let her see a doctor. She lives in a part of the world where there are no women’s shelters, and she has no money or friends who might be able to help her.

*A long-time smoker has been feeling scared for weeks because he’s started to cough up blood and is worried that this might be something serious. He’s too afraid to see a doctor.

*A couple was just told that their new-born child has Down’s Syndrome. Before the child was born they had no idea anything might be wrong with the child.

*A Chinese man in his forties has lost weight over the last months and some of his teeth have fallen out. He’s also had stomach pain and memory problems. He’s worried that this might have something to do with his long-time work at the local battery manufacturing plant, but he can’t afford to see a doctor about it.

*A poor alcoholic went blind from drinking methanol.

*While they were on vacation abroad, their house burned down.

*A sixty-five year old woman suddenly feels the onset of the worst headache she’s ever had while out shopping with her grandchild. She’s dead before she reaches the hospital.

*Three children are praying with their mother. A major storm hit the coast last evening, and their father is a fisherman who did not return from his fishing trip yesterday.

*A young man was recently in a bar fight. The other guy had a knife. The young man is now awaiting a kidney transplant.

*Yesterday a homeless man died from hypothermia.

*The morphine can no longer block out the pain. The woman starts to scream.

*A mother is told by her adult son that after thinking things over carefully, he’s decided that he’ll never speak to her again, because of what she did.

*A taxi-driver was involved in an accident and has just learned that he’ll need a wheelchair for the rest of his life.

*A couple is told that if they don’t pay the rent they owe next week, they’ll get kicked out of their flat. They already know they’ll never be able to raise the money in time, but they don’t know what to do.

*A young man learns that one of his former classmates, whom he used to bully in middle school, has recently taken his life. He feels partly responsible.

*A prostitute was beaten up by one of her clients.

(I’ve been thinking about writing/publishing ‘inspirational short stories based on real-life events’ for a while…) (No, not really…)

February 2, 2015 Posted by | random stuff | Leave a comment

An Introduction to Medical Diagnosis (1)

“The student of medicine has to learn both the ‘bottom up’ approach of constructing a differential diagnosis from individual clinical findings, and the ‘top down’ approach of learning the key features pertaining to a particular diagnosis. In this textbook we have integrated both approaches into a coherent working framework that will assist the reader in preparing for academic and professional examinations, and in every day practice. […] We have split this textbook into three sections. The first section introduces the basic skills underpinning much of what follows – how to take a history and perform an examination, how to devise a differential diagnosis and select appropriate investigations, and how to record your findings in the case notes and present cases on ward rounds. The second section takes a systems-based approach to history taking and examining patients, and also includes information on relevant diagnostic tests and common diagnoses for each system. Each chapter begins with the individual ‘building blocks’ of the history and examination, and ends by drawing these elements together into relevant diagnoses. […] The third and final section of the book covers ‘special situations’, including the assessment of the newborn, infants and children, the acutely ill patient, the patient with impaired consciousness, the older patient and death and the dying patient.”

The above quote is from the preface of the book. This is a medical textbook with 500 pages and 26 chapters written by 27 contributors, so it has a lot of stuff; I’ve been conflicted about how to blog it for this reason. It has as lot of stuff which is useful to know but which most people don’t, and I think it’s the sort of book I might be tempted to ‘consult’ later on; the various 100 Cases… books I’ve read include some similar useful observations, but I think it’d be more natural to consult this book first because it’s much more likely that this book will at least have something about the medical condition you’re curious/can’t remember the details about. I think it was somewhat easier to read than was McPhee et al., and I’m not sure this is only because I read the former first (while I was reading McPhee et al. I was learning part of the vocabulary which is needed to read this book).

In the coverage below I have not talked about the stuff included in the first part; I don’t need to e.g. be able to take a medical history and navigate medical records, and if some of my readers do I’ll assume they have the necessary skills already, or know where/how to obtain such skills. In this post I’ll focus on the coverage of major systems in part two, with my coverage focused on ‘key variables’, and, well, ‘stuff I found interesting’ – which also means that I won’t talk about stuff like ‘this is how you palpate a liver’ and ‘this is how you grade heart murmurs’ (the book also covers that kind of stuff in some detail). Nor will I tell you what Buerger’s test or Trendelenburg’s test are used for, or give you a full account of the many, many different types of ‘named medical signs’ included and described in the book (Charcot’s triad, Cullen’s sign, Grey Turner’s sign, Murphy’s sign, Courvoisier’s sign, Kussmaul’s sign, Levine’s sign, etc. …).

I may in my coverage of this book tend to focus more on acute conditions than on chronic conditions, in part because it seems more useful to me to know/remember whether or not someone is, say, having a heart attack than whether or not someone with chronic kidney failure will be bothered by pitting edema. I think this approach makes sense.

The book has split the systems coverage in part 2 up into 15 chapters – there are specific chapters about: *The cardiovascular system, *the respiratory system, *the gastrointestinal system, *the renal system, *the genitourinary system, *the nervous system, *psychiatric assessment, *the musculoskeletal system, *the endocrine system, *the breast, *the haematological system, *skin, nails and hair, *the eye, *ear, nose and throat, and *infectious and tropical diseases. Most of the book coverage is devoted to this treatment of individual systems, as these 15 chapters make up roughly 350 pages of the total. I found it, interesting, that there was close to zero overlap between the coverage in this book and Newman and Kohn’s text; I’m not quite sure what to think about that.

In this post I’ll mostly talk about the first three ‘systems’ chapters. When dealing with cardiovascular disease, the major symptoms are chest discomfort, breathlessness, palpitation (an awareness of the heartbeat), dizziness and syncope (‘transient loss of consciousness resulting from transient global cerebral hypoperfusion’), and peripheral oedema (usually ankle swelling, most often associated with heart failure, often worse in the evening). An important observation is that myocardial ischemia (‘the heart muscle doesn’t get enough blood/oxygen’) can cause breathlessness and chest discomfort, and “in many cases breathlessness is the predominant symptom (particularly in women).” Deep vein thrombosis can be asymptomatic, but it commonly causes pain and swelling in the affected leg – the main acute risk factor associated with the condition (which is not particularly rare among elderly people) is that the blood clot travels to the lungs and causes a pulmonary embolism.

Next, the respiratory system: “respiratory conditions are common – accounting for more than 13 per cent of all emergency admissions and more than 20 per cent of general practitioner consultations”. I was very surprised the number was that high! I can’t provide a source as the authors did not provide a source; there are no inline citations in this book, which is part of the reason why the book did not get five stars on goodreads. Six key symptoms of respiratory diseases are chest pain (that may be extended to chest sensations), dyspnoea (shortness of breath/breathlessness), cough (“the commonest symptom that is associated with pure respiratory disease”), wheeze, sputum production, and haemoptysis (coughing up blood/blood in the sputum – this is, perhaps unsurprisingly, often, but not always, a ‘red flag symptom': “Current recommendations indicate that urgent referral to a hospital clinic should be made when patients have haemoptysis, are over the age of 40, and are current or ex-smokers. However, a young patient who has a small amount of streak (lines in sputum) haemoptysis in the context of an upper respiratory tract infection usually will not require referral”).

In respiratory medicine, cough duration is an important variable in the diagnostic context; I was surprised that even simple respiratory tract infections may cause cough for up to three weeks, and that this is not necessarily something to worry about. Longer than that and it’s however less likely to be due to a self-limiting condition, and is more likely to be due to either lung cancer or one of the many causes of chronic cough (cough is not chronic until it’s lasted longer than 6 months) – these causes include, but are not limited to, astma, COPD, and GERD. As should be clear from the above, both heart and lung conditions may cause shortness of breath, so you can’t always conclude that shortness of breath is a lung issue. This is of course far from the only symptom which may present in different disease contexts, and the heart and lungs are connected in other ways as well; for example problems in both systems may cause clubbing. When dealing with a case of pneumonia it’s useful to be familiar with the CURB 65 score to assess risk/severity. Lung cancer can be either ‘non-small cell’ or ‘small cell’ lung cancer – in terms of presenting symptoms they’re reasonably similar, but the latter is more often associated with paraneoplastic syndromes (though these are still rare in an absolute sense, presenting in 5% of small cell lung cancers and 1% of non-small cell lung cancers, according to the book). The most common symptom is a cough, followed by persistent ‘chest infections’ (which are of course not infections) and bloody sputum/coughing up blood – but “some patients have remarkably few signs.” In the context of acute conditions affecting the lungs, pleuritic chest pain is an important symptom; this means pain which is made worse by breathing and which often has a sharp and stabbing quality to it – acute onset pleuritic chest pain can be due to a pulmonary embolism (60% of patients with PE have acute onset pleuritic chest pain; in another 25% there is a sudden onset of acute breathlessness) or a pneumothorax (‘collapsed lung’ – may also cause acute breathlessness). Although the two conditions are different, if you have either of them you want to get to a hospital, fast – sudden onset pleuritic chest pain seems to me a very good reason to call for an ambulance/visit the local emergency department.

“The gastrointestinal system includes the alimentary tract from mouth to anus, the liver, hepatobiliary structures including the gallbladder, pancreas and the biliary and pancreatic ductal systems.” This is a big system. And it’s often hard to get a good look at what’s the problem: “Almost half of gastrointestinal problems are not associated with physical signs or positive test results. Hence, the diagnosis and management is often based entirely on the inferences drawn from a patient’s symptoms.” Difficulty swallowing is a ‘red flag’ symptom, because “many patients with this symptom will have clinically significant pathology.” Weight loss combined with worsening difficulty swallowing (solids first, liquids later) means that oesophageal cancer is likely to be the cause (this one has a really bad prognosis). A useful observation when it comes to distinguishing between angina (‘heart issue’) and heartburn (‘gastrointestinal issue’), which may cause somewhat similar symptoms, is that whereas angina is often worsened by physical exertion, heartburn is not and often occurs at rest. It’s worth noting that when dealing with gastrointestinal disorders, you can learn a lot by figuring out where exactly the pain is coming from - stomach pain isn’t just stomach pain. Pain localized to one specific section of the stomach is much more likely to be due to condition X than condition Y (e.g., pain in the right upper quadrant = maybe biliary obstruction or hepatomegaly; pain in the left lower quadrant = maybe diverticulitis or infectious colitis). This may not be particularly useful for people in general to know, but I thought it was interesting. Duration of pain is a key variable: “Sudden onset of well-localized severe pain is likely to be due to catastrophic events [and] [p]ain present for weeks to months is often less life-threatening than pain presenting within hours of symptom onset.” The authors point out that the severity of abdominal pain can be underestimated in elderly people, very young patients, people who are immunosuppressed and diabetics (the latter presumably due to autonomous-/diabetes-associated enteric neuropathy). “Presence of blood in the stool points towards either inflammatory bowel disease or malignancy, but in those with infective diarrhoea it is highly specific for infections with a invasive organism.” The authors mention a few pointers to specific nutritional deficiencies which are probably useful to know about – iron deficiency may cause a flat angle or ‘spooning‘ of the nails, and it may also (together with vitamin B12-deficiency) cause soreness/redness of the tongue. Redness and cracks at the angles of the mouth are also associated with deficiencies of iron and vitamin-B12, as well as deficiencies of riboflavin, and folate.

January 31, 2015 Posted by | books, medicine | Leave a comment

Recountings: Conversations with MIT Mathematicians

This post will be brief but I thought that since it’s been a while since I last posted anything and since I just finished reading this book, I wanted to add a few remarks about it here while it was still ‘fresh in my mind’. I’m gradually coming to the conclusion that if I’m to blog all the books I’m reading in the amount of detail I’d ideally like to, I’ll have to read a lot less. This option does not appeal to me; I’d rather provide limited coverage of a book I’ve actually read than not read a book in order to provide more extensive coverage of another book.

Anyway, the book is a rather nice collection of interviews with mathematicians from MIT’s ‘early days’ (in some sense at least – MIT is a rather old institution, but at least some of the people interviewed in this book came along during the days before MIT was what it is today), who talk about the history of the mathematics department of MIT, and other stuff – the people interviewed include an Abel Prize winner and a few people who’ve been members of the Institute for Advanced Study, a former MacArthur Fellow, as well as a guy who used to be on the selection committee for the MacArthur Foundation. All of them are really, really smart, and some of them have lived quite interesting lives. To the extent that these guys aren’t impressive enough on their own, some of them also knew some people most non-mathematicians have probably heard about – this book includes contributions from people who were friends of people like John Nash, Grothendieck, Shannon, Minsky, and Chomsky, and they are people who’ve met and talked to people like John von Neumann, Oppenheimer, Weyl, Heisenberg, and Albert Einstein. They talk a little bit about their work and the history of the mathematics department, but they also talk about other stuff as well; there are various amusing anecdotes along the way (for example one interviewee tells the story about the time he lectured in a gorilla suit at MIT), there are stories about the private parties and social lives of the MIT staff during the fifties (and later), we get some personal stories about mathematicians who fled Europe when the Nazis started to cause trouble, and there are stories about student protests in the late sixties and how they were dealt with – the books spans widely. There was some repetition across the interviews (various people answering similar questions in similar ways), and there was more talk about ‘administrative matters’ than I’d have liked – probably a natural consequence of the fact that a few of them (3? At least three of the contributors..) were former department heads – which is part of why I didn’t give it five stars, but it’s really a quite nice book. I may or may not blog it later in more detail.

January 30, 2015 Posted by | books, mathematics | Leave a comment

Diabetes: The Biography

“When I retired from clinical practice in 1998, my intention was (and still is) to write a definitive, exhaustively referenced, history of diabetes, which would be of interest primarily to doctors. However, I jumped at the suggestion of the editors of this series at Oxford University Press that I should write a biography of diabetes that would be about a tenth of the length of a full history with a minimum of references, for a wide general readership.”

This book is the result. As I pointed out on goodreads, this book is really great. The book is not particularly technical compared to other books about diabetes which I’ve read in the past, however this semi-critical review does make the point that the coverage is occasionally implicitly ‘asking too much’ even from diabetic readers (“There were parts of all this that lost my interest or that I lacked the background to appreciate”). Whereas the reviewer was apparently to some extent getting lost in the details, so was I – but in a completely different way; I was simply amazed at the amount of small details and interesting observations included in the book that I did not know, and I loved every single chapter of the book. The author of the other review incidentally also states that: “I don’t recommend that anyone read this who is not already familiar with diabetes, either by having it or knowing someone with it.” I’d note that I’m not sure I agree with this recommendation, to the extent that it’s even ‘relevant’ – these days such people who don’t even know anyone with diabetes might well be a bit hard to find, on account of the fact that diabetes has become a quite common illness. Presumably a significant proportion of the people who assume they don’t know anyone with the disease might well do so anyway, because a very large number of people have type 2 diabetes without knowing it. I think a reader would get more out of the book if he or she has diabetes or knows someone with diabetes, but a lot of people who do not would also benefit from knowing the stuff in this book. Not only in a ‘and now you know how bad type 2 is and why you should get checked out if you think you’re at risk’-sense (there’s incidentally also a lot of stuff about type 1), but also in the ‘the history of diabetes is really quite fascinating’-sense. I do think it is.

Have a look at this image. The book included a similar picture (not exactly the same one, but it’s of the same patient and the ‘before’ picture is obviously taken at the same time this one was), which is of Billy Leroy, a type 1 diabetic, before and after he started insulin. He was one of the first patients treated with insulin (the first human treated with insulin was Leonard Thompson, in 1922). Billy Leroy’s weight in the first picture, where he was 3 years old, was 6.8 kg (the 5 % (underweight) CDC body weight cutoff at the age of 3 is 12 kg) – during the three months after he started on insulin, his weight doubled. The author argues in the beginning of the book that: “When people are asked to rank diseases in order of seriousness, diabetes is usually at the mild end of the spectrum.” This may or may not be true, but the picture to which I link above certainly adds a detail which is important to keep in mind but easy to forget when evaluating ‘the severity’ of the disease today – type 1 diabetes in particular is not much fun if you don’t have access to insulin, and until the early 1920s people with this disease simply died, most of them quite fast. (They all still do – like all other humans – but they live a lot longer before they die…)

The author knows his stuff and the book has a lot of content, making it hard to know what to pick out and mention in particular in a review like this – however below I have added a few quotes from the book and some observations made along the way. The content covering the late nineteenth century and the first couple of decades of the twentieth century, before it was discovered that insulin could save the lives of a lot of sick children, would in my opinion on its own be a strong reason for reading the book; but the chapters covering the periods that came after are wonderful as well. When insulin was discovered a religiously inclined mind might well be tempted to think of the effects on young type 1 diabetic children as almost miraculous; but gradually doctors treating diabetics came to realize (the patients never knew, because they were not told – it is pointed out in the book that the fact that it might make a lot of sense to give patients with a disease like diabetes some discretion in terms of how to treat their illness is a in a historical context very new idea; active patient involvement in medical decision-making is one of the cornerstones of current treatment regimes, for good reason, and I found it really surprising and frustrating to learn how this disease was treated in the past) that things might be more complicated than they had initially been assumed to be. Type 2 diabetics had suffered from late stage complications like blindness and kidney failure for centuries, but such complications had never before been observed in type type 1 diabetics before insulin, because diabetes presenting in children were pre-insulin universally fatal. It turned out that many of the children who were initially ‘saved’ by insulin in the early 1920s ended up suffering from severe complications just a couple of decades later, and many of them died early from these complications:

“After the Second World War it became clear that [diabetic] kidney disease could also affect the young, and there were increasingly frequent reports of diabetics who had been saved by insulin as children only to succumb to kidney failure in their 20s and 30s. Fifty of Joslin’s child patients who had started insulin before 1929 were followed up in 1949, when a third had died at an average age of 25, after having had diabetes for an average of 17.6 years. One half had died of kidney failure and the other half of tuberculosis and other infections. […] In the experience of the Joslin group, only 2 per cent of deaths of young diabetic patients before 1937 were due to kidney disease, but, of those who died between 1944 and 1950, more than half had advanced kidney disease. Results in Europe were equally bad. In 1955 all of eighty-seven Swiss children had signs of kidney disease after sixteen years of diabetes, and after twenty-one years all had died. Most young people with diabetic kidney disease also had severe retinopathy and many became blind—by the mid 1950s diabetes was the commonest cause of new blindness in people under the age of 50. […] Such devastating cases were being increasingly reported in the medical literature in the late 1940s and early 1950s, but they were not publicized in the lay press, presumably to avoid spreading despair and despondency and puncturing the myth that insulin had solved the problem of diabetes […] The British Diabetic Association (founded in 1935) produced a quarterly Diabetic Journal for its lay members, but no issue from 1940 to 1960 mentions complications”.

The book makes it clear that patients were for many years basically to some extent kept in the dark about the severity of their condition, but in all fairness for a long time the doctors treating them frankly didn’t know enough to give them good information on a lot of topics anyway. The book has some really interesting observations included about how medical men of the times thought about various aspects of the illness and treatment, and how many of the things we know today, some of which ‘seem obvious’, really were not to people at the time. Many attempts have been made over time to explain why people got diabetes, and especially type 1 was really quite hard to pin down – type 2 was somewhat easier because the lifestyle component was hard to miss; however it was natural to explain the disease in terms of the symptoms it caused, and some of those symptoms in type 2 diabetics were complications which are best considered secondary to the ‘true’ disease process. For example because many type 2 diabetics suffered from disorders of the nervous system, neuropathy, the nervous system was for a while assumed to be implicated in causing diabetes – but although disorders of the nervous system can and often do present in long-standing diabetes, they are not why type 2 diabetics get sick. Kidney problems were thought to be “part and parcel of diabetes in the 19th century.” Oskar Minkowsky made it clear in 1889 that removal of the pancreas caused severe (‘type 1-like’) diabetes in dogs – but despite this discovery it still took a long time for people to figure out how it worked. This wasn’t because people at the time were stupid. One problem faced at the time was that the pancreas actually looked quite normal in people who died from diabetes – the islet cells which are implicated in the disease weigh around 1-1.5 grams altogether, and make up only a very small proportion of the pancreas (1% or so). Many doctors found it hard to imagine that the islets cells could be reponsible for controlling carbohydrate metabolism (and other aspects of metabolism as well – “It is important to realize that diabetes is not just a glucose disease. There are also abnormalities of fat metabolism”). The pancreas wasn’t the only organ that looked normal – despite the excessive urination the kidneys did as well, and so did other organs, to the naked eye. All major features of diabetic retinopathy (diabetic eye disease) had been described by the year 1890 with the aid of the ophthalmoscope, so people knew the eyes of people with long-standing diabetes looked different; how to interpret these findings was however not clear at the time – some argued the eye damage found in diabetics was not different from eye damage caused by hypertension, and treatment options were non-existent anyway.

Many of the treatment options discussed among medical men before insulin were diets, and although dietary considerations are important in the treatment context today, it’s probably fair to say that not all of the supposed dietary remedies of the past were equally sensible: “One diet that had a short vogue in the 1850s was sugar feeding, brainchild of the well-known but eccentric French physician Pierre Piorry (1794–1879). He thought that diabetics lost weight and felt so weak because of the amount of sugar they lost in the urine and that replacing it should restore their strength”. (Aargh!). For the curious (or desperate) man, though, there were alternatives to diets: “A US government publication in 1894 listed no less than forty-two anti-diabetic remedies including bromides, uranium nitrate, and arsenic.” Relatedly, “in England until 1925, any drug could be advertised and marketed as a cure for any disease, even if it was completely ineffective”. Whether or not diets ‘worked’ depended in part on what those proposed diets included (see above..), whether people followed them, and whether people who presented were thin or fat. In the book Tattersall mentions that already from the middle of the nineteenth century many physicians thought that there were two different types of diabetes (there are more than two, but…). The thin young people presenting with symptoms were by many for decades considered hopeless cases (that they were hopeless cases was even noted in medical textbooks at the time), because they had this annoying habit of dying no matter what you did.

It should be noted that the book indirectly provides some insights into the general state of medical research and medical treatment options over time; for an example of the former it is mentioned that the first clinical trial (with really poor randomization/selection mechanisms, it seems from the description in the book) dealing with diabetes was undertaken in the 1960es: “the FDA demanded randomized controlled trials for the first time in 1962, and [the University Group Diabetes Program (UGDP)] was the first in diabetes. Before 1962 the evidence in support of therapeutic efficacy put to the FDA was often just ‘testimonials’ from physicians who casually tested experimental drugs on their patients and were paid for doing so.” See also this link. An example of the latter would be the observation made in the book that: “until the 1970s treatment for a heart attack was bed rest for five or six weeks, while nature took its course.” Diabetics were not the only sick people who had a tough time in the past.

One interesting question related to what people didn’t know in the beginning after the introduction of insulin was how the treatment might work long-term. The author notes that newspapers in the early years made people believe that insulin would be a cure; it was thought that insulin might nurse the islet cells back to health, so that they’d start producing insulin on their own again – which was actually not a completely stupid idea, as e.g. kidneys had the ability to recover after acute glomerulonephritis. The fact that diabetics often started on high doses which could then be lowered a month or two later even lent support to this idea; however it was discovered quite fast that regeneration was not taking place. Remarkably, insulin was explored as a treatment option for other diseases in the 1920s, and was actually used to stimulate appetite in tuberculosis patients and ‘in the insane refusing food’, an idea which came about because one of its most obvious effects was weight gain. This effect was also part of the reason why insulin was for a long time not considered an attractive option for type 2 diabetics, who instead were treated only with diet unless this treatment failed to reduce blood sugar levels sufficiently (these were the only two treatment options until the 1950s); most of them were already overweight and insulin caused weight gain, and besides insulin didn’t work nearly as well in them as it did in young and lean people with type 1 because of insulin resistance, which lead to the requirement of high doses of the drug.

Throughout much of the history of diabetes, diabetics did not measure their blood glucose regularly – what they did instead was measuring their urine, figuring out if it contained glucose or not (glucose in the urine indicates that the blood glucose is quite high). This meant that the only metric they had available to them to monitor their disease on a day to day basis was one which was unable to measure low blood glucose, and which could only (badly) distinguish between much too high blood glucose values and not-much-too-high values. Any type of treatment regime like the one I’m currently on would be completely impossible without regular blood tests on a daily basis, and I was very surprised about how late the idea of self-monitoring of blood glucose appeared; like the measurement of Hba1c, this innovation did not appear until the late 1970s. Few years after that, the first insulin pen revolutionized treatment regimes and made treatment regimes using multiple rejections each day much more common than they had been in the past, facilitating much better metabolic control.

The book has a lot of stuff about specific complications and the history of treatment advances – both the ones that worked and some of the ones that didn’t. If you’re a diabetic today, you tend to take a lot of stuff for granted – and reading a book like this will really make you appreciate how many ideas had to be explored, how many false starts there were, how much work by so many different people actually went into giving you the options you have today, keeping you alive, and perhaps even relatively well. One example of the type of treatment options which were considered in the past but turned out not to work was curative pancreas transplants, which were explored in the 60es and 70es: “Pancreas transplantation offered a potential cure of type 1 diabetes. The first was done in 1966 […] Worldwide in the next eleven years, fifty-seven transplants were done, but only two worked for more than a year”. Recent attempts to stop people at risk of developing type 1 diabetes from becoming sick are also discussed in the last part of the book, and in this context he makes a point I was familiar with: “[Repeated] failures [in this area] are particularly frustrating, because, in the best animal model of type 1 diabetes, the NOD mouse, over 100 different interventions can prevent diabetes.” This is one of the reasons why I tend to be skeptical about results from animal studies. Although he spends many pages on complications – which in a book like this makes a lot of sense given how common these complications were (and to some extent still are), and how important a role they have played in the lives of people suffering from diabetes throughout the ages – I have talked about many of these things before, just as I have talked about the results of various large-scale trials like the DCCT trial and the UKPDS (see e.g. this and this), so I will not discuss such topics in detail here. I do however want to briefly remind people of what kind of a disease badly managed type 2 diabetes (the by far most common of the two) is, especially if it is true as the author argues in the introduction that many people perceive of it as a relatively mild disease – so I’ll end the post with a few quotes from the book:

“I took over the diabetic clinic in Nottingham in 1975 and three years later met Lilian, an overweight 60-year-old woman who was on tablets for diabetes. She had had sugar in her urine during her last pregnancy in 1957 but was well until 1963, when genital itching (pruritus vulvae) led to a diagnosis of diabetes. She attended the clinic for two years but was then sent back to her GP with a letter that read: ‘I am discharging this lady with mild maturity onset diabetes back to your care.’ She continued to collect her tablets but had no other supervision. When I met her after she had had diabetes for eighteen years she was blind, had had a heart attack, and had had one leg amputated below the knee. The reason for the referral to me was an ulcer on her remaining foot, which would not heal. […] Someone whose course is not dissimilar to that of Lilian is Sue Townsend (b. 1946), author of the Adrian Mole books. She developed diabetes at the age of 38 and after only fifteen years was blind from retinopathy and wheelchair bound because of a Charcot foot, a condition in which the ankle disintegrates as a result of nerve damage. Neuropathy has also destroyed the nerve endings in her fingers, so that, like most other blind diabetics, she cannot read Braille. She blames her complications on the fact that she cavalierly disregarded the disease and kept her blood sugars high to avoid the inconvenience of hypoglycaemic (low-blood-sugar) attacks.”

January 25, 2015 Posted by | books, diabetes, medicine | Leave a comment

Books 2015

This is a list of books I’ve read to completion this year so far. I’ll try to update the list regularly throughout the year. See my 2014 post for details on how these lists work.

1. The Last Dragonslayer (f). Jasper Fforde. Short goodreads review here.

2. The Song of the Quarkbeast (f). Jasper Fforde. Short goodreads review here.

3. The Eye of Zoltar (f). Jasper Fforde.

4. Statistical Models for Proportions and Probabilities (nf. Springer). Blog coverage here.

5. Negotiation Theory and Research (2, nf. Psychology Press). Goodreads review here. Blog coverage here, here, and here.

6. The Cyberiad (3, f). Stanislaw Lem. Goodreads review here.

7. Chamberlain’s Symptoms and Signs in Clinical Medicine: An Introduction to Medical Diagnosis (4, nf. CRC Press). Blog coverage here and here.

8. Diabetes: The Biography (5, nf. Oxford University Press). My goodreads review is worth reposting here: “This book is awesome. This is simply a wonderful account of the history of diabetes. Highly recommended.” Blog coverage here.

9. Lord of the Flies (2, f). William Golding. Short goodreads review here.

10. Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (5, nf. Springer). Goodreads review here. Blog coverage here.

11. Recountings: Conversations with MIT Mathematicians (4, nf. AK Peters). Blog coverage here.

12. Whose Body? (2, f). Dorothy Sayers.

13. Clouds of Witness (3, f). Dorothy Sayers.

14. Introduction to Systems Analysis: Mathematically Modeling Natural Systems (3, nf. Springer). Note that goodreads has listed this book under the wrong title, which is the reason why the title in this post deviates from the title on goodreads. Goodreads review here. Blog coverage here.

15. Unnatural Death (2, f). Dorothy Sayers.

16. Mammoths, Sabertooths, and Hominids: 65 Million Years of Mammalian Evolution in Europe (4, nf. Columbia University Press). Blog coverage here and here.

17. Belief-Based Stability in Coalition Formation with Uncertainty: An Intelligent Agents’ Perspective (2, nf. Springer). Blog coverage here.

18. Lord Peter Views the Body (2, f). Dorothy Sayers.

19. Something Fresh (5, f). P. G. Wodehouse. Blog coverage here.

20. Leave It to Psmith (5, f). P. G. Wodehouse.

21. Summer Lightning (5, f). P. G. Wodehouse.

January 23, 2015 Posted by | books | Leave a comment

Model Selection and Multi-Model Inference (I)

“We wrote this book to introduce graduate students and research workers in various scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a “best” model and a ranking and weighting of the remaining models in a pre-defined set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (multimodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. […] Information theory includes the celebrated Kullback–Leibler “distance” between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) expectation of Kullback–Leibler distance based on Fisher’s maximized log-likelihood. His measure, now called Akaike’s information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a fundamental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. […] We do not claim that the information-theoretic methods are always the very best for a particular situation. They do represent a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and they are objective and practical to employ across a very wide class of empirical problems. Inference from multiple models, or the selection of a single “best” model, by methods based on the Kullback–Leibler distance are almost certainly better than other methods commonly in use now (e.g., null hypothesis testing of various sorts, the use of R2, or merely the use of just one available model).

This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. […] This book might be useful as a text for a course for students with substantial experience and education in statistics and applied data analysis. A second primary audience includes honors or graduate students in the biological, medical, or statistical sciences […] Readers should ideally have some maturity in the quantitative sciences and experience in data analysis. Several courses in contemporary statistical theory and methods as well as some philosophy of science would be particularly useful in understanding the material. Some exposure to likelihood theory is nearly essential”.

The above quotes are from the preface of the book, which I have so far only briefly talked about here; this post will provide a lot more details. Aside from writing the post in order to mentally process the material and obtain a greater appreciation of the points made in the book, I have also as a secondary goal tried to write the post in a manner so that people who are not necessarily experienced model-builders might also derive some benefit from the coverage. Whether or not I was successful in that respect I do not know – given the outline above, it should be obvious that there are limits as to how ‘readable’ you can make stuff like this to people without a background in a semi-relevant field. I don’t think I have written specifically about the application of information criteria in the model selection context before here on the blog, at least not in any amount of detail, but I have written about ‘model-stuff’ before, also in ‘meta-contexts’ not necessarily related to the application of models in economics; so if you’re interested in ‘this kind of stuff’ but you don’t feel like having a go at a post dealing with a book which includes word combinations like ‘the (relative) expectation of Kullback–Leibler distance based on Fisher’s maximized log-likelihood’ in the preface, you can for example have a look at posts like this, this, this and this. I have also discussed here on the blog some stuff somewhat related to the multi-model inference part, how you can combine the results of various models to get a bigger picture of what’s going on, in these posts – they approach ‘the topic’ (these are in fact separate topics…) in a very different manner than does this book, but some key ideas should presumably transfer. Having said all this, I should also point out that many of the basic points made in the coverage below should be relatively easy to understand, and I should perhaps repeat that I’ve tried to make this post readable to people who’re not too familiar with this kind of stuff. I have deliberately chosen to include no mathematical formulas in my coverage in this post. Please do not assume this is because the book does not contain mathematical formulas.

Before moving on to the main coverage I thought I’d add a note about the remark above that stuff like AIC is “little taught in statistics classes and far less understood in the applied sciences than should be the case”. The book was written a while back, and some things may have changed a bit since then. I have done coursework on the application of information criteria in model selection as it was a topic (briefly) covered in regression analysis(? …or an earlier course), so at least this kind of stuff is now being taught to students of economics where I study and has been for a while as far as I’m aware – meaning that coverage of such topics is probably reasonably widespread at least in this field. However I can hardly claim that I obtained a ‘great’ or ‘full’ understanding of the issues at hand from the work on these topics I did back then – and so I have only gradually, while reading this book, come to appreciate some of the deeper issues and tradeoffs involved in model selection. This could probably be taken as an argument that these topics are still ‘far less understood … than should be the case’ – and another, perhaps stronger, argument would be Seber’s comments in the last part of his book; if a statistician today may still ‘overlook’ information criteria when discussing model selection in a Springer text, it’s not hard to argue that the methods are perhaps not as well known as should ‘ideally’ be the case. It’s obvious from the coverage that a lot of people were not using the methods when the book was written, and I’m not sure things have changed as much as would be preferable since then.

What is the book about? A starting point for understanding the sort of questions the book deals with might be to consider the simple question: When we set out to model stuff empirically and we have different candidate models to choose from, how do we decide which of the models is ‘best’? There are a lot of other questions dealt with in the coverage as well. What does the word ‘best’ mean? We might worry over both the functional form of the model and which variables should be included in ‘the best’ model – do we need separate mechanisms for dealing with concerns about the functional form and concerns about variable selection, or can we deal with such things at the same time? How do we best measure the effect of a variable which we have access to and consider including in our model(s) – is it preferable to interpret the effect of a variable on an outcome based on the results you obtain from a ‘best model’ in the set of candidate models, or is it perhaps sometimes better to combine the results of multiple models (and for example take an average of the effects of the variable across multiple proposed models to be the best possible estimate) in the choice set (as should by now be obvious for people who’ve read along here, there are some sometimes quite close parallels between stuff covered in this book and stuff covered in Borenstein & Hedges)? If we’re not sure which model is ‘right’, how might we quantify our uncertainty about these matters – and what happens if we don’t try to quantify our uncertainty about which model is correct? What is bootstrapping, and how can we use Monte Carlo methods to help us with model selection? If we apply information criteria to choose among models, what do these criteria tell us, and which sort of issues are they silent about? Are some methods for deciding between models better than others in specific contexts – might it for example be a good idea to make criteria adjustments when faced with small sample sizes which makes it harder for us to rely on asymptotic properties of the criteria we apply? How might the sample size more generally relate to our decision criterion deciding which model might be considered ‘best’ – do we think that what might be considered to be ‘the best model’ might depend upon (‘should depend upon’?) how much data we have access to or not, and if how much data we have access to and the ‘optimal size of a model’ are related, how are the two related, and why? The questions included in the previous sentence relate to some fundamental differences between AIC (and similar measures) and BIC – but let’s not get ahead of ourselves. I may or may not go into details like these in my coverage of the book, but I certainly won’t cover stuff like that in this post. Some of the content is really technical: “Chapters 5 and 6 present more difficult material [than chapters 1-4] and some new research results. Few readers will be able to absorb the concepts presented here after just one reading of the material […] Underlying theory is presented in Chapter 7, and this material is much deeper and more mathematical.” – from the preface. The sample size considerations mentioned above relate to stuff covered in chapter 6. As you might already have realized, this book has a lot of stuff.

When dealing with models, one way to think about these things is to consider two in some sense separate issues: On the one hand we might think about which model is most appropriate (model selection), and on the other hand we might think about how best to estimate parameter values and variance-covariance matrices given a specific model. As the book points out early on, “if one assumes or somehow chooses a particular model, methods exist that are objective and asymptotically optimal for estimating model parameters and the sampling covariance structure, conditional on that model. […] The sampling distributions of ML [maximum likelihood] estimators are often skewed with small samples, but profile likelihood intervals or log-based intervals or bootstrap procedures can be used to achieve asymmetric confidence intervals with good coverage properties. In general, the maximum likelihood method provides an objective, omnibus theory for estimation of model parameters and the sampling covariance matrix, given an appropriate model.” The problem is that it’s not ‘a given’ that the model we’re working on is actually appropriate. That’s where model selection mechanisms enters the picture. Such methods can help us realize which of the models we’re considering might be the most appropriate one(s) to apply in the specific context (there are other things they can’t tell us, however – see below).

Below I have added some quotes from the book and some further comments:

“Generally, alternative models will involve differing numbers of parameters; the number of parameters will often differ by at least an order of magnitude across the set of candidate models. […] The more parameters used, the better the fit of the model to the data that is achieved. Large and extensive data sets are likely to support more complexity, and this should be considered in the development of the set of candidate models. If a particular model (parametrization) does not make biological [/’scientific’] sense, this is reason to exclude it from the set of candidate models, particularly in the case where causation is of interest. In developing the set of candidate models, one must recognize a certain balance between keeping the set small and focused on plausible hypotheses, while making it big enough to guard against omitting a very good a priori model. While this balance should be considered, we advise the inclusion of all models that seem to have a reasonable justification, prior to data analysis. While one must worry about errors due to both underfitting and overfitting, it seems that modest overfitting is less damaging than underfitting (Shibata 1989).” (The key word here is ‘modest’ – and please don’t take these authors to be in favour of obviously overfitted models and data dredging strategies; they spend quite a few pages criticizing such models/approaches!).

“It is not uncommon to see biologists collect data on 50–130 “ecological” variables in the blind hope that some analysis method and computer system will “find the variables that are significant” and sort out the “interesting” results […]. This shotgun strategy will likely uncover mainly spurious correlations […], and it is prevalent in the naive use of many of the traditional multivariate analysis methods (e.g., principal components, stepwise discriminant function analysis, canonical correlation methods, and factor analysis) found in the biological literature [and elsewhere, US]. We believe that mostly spurious results will be found using this unthinking approach […], and we encourage investigators to give very serious consideration to a well-founded set of candidate models and predictor variables (as a reduced set of possible prediction) as a means of minimizing the inclusion of spurious variables and relationships. […] Using AIC and other similar methods one can only hope to select the best model from this set; if good models are not in the set of candidates, they cannot be discovered by model selection (i.e., data analysis) algorithms. […] statistically we can infer only that a best model (by some criterion) has been selected, never that it is the true model. […] Truth and true models are not statistically identifiable from data.”

“It is generally a mistake to believe that there is a simple “true model” in the biological sciences and that during data analysis this model can be uncovered and its parameters estimated. Instead, biological systems [and other systems! – US] are complex, with many small effects, interactions, individual heterogeneity, and individual and environmental covariates (most being unknown to us); we can only hope to identify a model that provides a good approximation to the data available. The words “true model” represent an oxymoron, except in the case of Monte Carlo studies, whereby a model is used to generate “data” using pseudorandom numbers […] A model is a simplification or approximation of reality and hence will not reflect all of reality. […] While a model can never be “truth,” a model might be ranked from very useful, to useful, to somewhat useful to, finally, essentially useless. Model selection methods try to rank models in the candidate set relative to each other; whether any of the models is actually “good” depends primarily on the quality of the data and the science and a priori thinking that went into the modeling. […] Proper modeling and data analysis tell what inferences the data support, not what full reality might be […] Even if a “true model” did exist and if it could be found using some method, it would not be good as a fitted model for general inference (i.e., understanding or prediction) about some biological system, because its numerous parameters would have to be estimated from the finite data, and the precision of these estimated parameters would be quite low.”

A key concept in the context of model selection is the tradeoff between bias and variance in a model framework:

“If the fit is improved by a model with more parameters, then where should one stop? Box and Jenkins […] suggested that the principle of parsimony should lead to a model with “. . . the smallest possible number of parameters for adequate representation of the data.” Statisticians view the principle of parsimony as a bias versus variance tradeoff. In general, bias decreases and variance increases as the dimension of the model (K) increases […] The fit of any model can be improved by increasing the number of parameters […]; however, a tradeoff with the increasing variance must be considered in selecting a model for inference. Parsimonious models achieve a proper tradeoff between bias and variance. All model selection methods are based to some extent on the principle of parsimony […] The concept of parsimony and a bias versus variance tradeoff is very important.”

“we reserve the terms underfitted and overfitted for use in relation to a “best approximating model” […] Here, an underfitted model would ignore some important replicable (i.e., conceptually replicable in most other samples) structure in the data and thus fail to identify effects that were actually supported by the data. In this case, bias in the parameter estimators is often substantial, and the sampling variance is underestimated, both factors resulting in poor confidence interval coverage. Underfitted models tend to miss important treatment effects in experimental settings. Overfitted models, as judged against a best approximating model, are often free of bias in the parameter estimators, but have estimated (and actual) sampling variances that are needlessly large (the precision of the estimators is poor, relative to what could have been accomplished with a more parsimonious model). Spurious treatment effects tend to be identified, and spurious variables are included with overfitted models. […] The goal of data collection and analysis is to make inferences from the sample that properly apply to the population […] A paramount consideration is the repeatability, with good precision, of any inference reached. When we imagine many replicate samples, there will be some recognizable features common to almost all of the samples. Such features are the sort of inference about which we seek to make strong inferences (from our single sample). Other features might appear in, say, 60% of the samples yet still reflect something real about the population or process under study, and we would hope to make weaker inferences concerning these. Yet additional features appear in only a few samples, and these might be best included in the error term (σ2) in modeling. If one were to make an inference about these features quite unique to just the single data set at hand, as if they applied to all (or most all) samples (hence to the population), then we would say that the sample is overfitted by the model (we have overfitted the data). Conversely, failure to identify the features present that are strongly replicable over samples is underfitting. […] A best approximating model is achieved by properly balancing the errors of underfitting and overfitting.”

Model selection bias is a key concept in the model selection context, and I think this problem is quite similar/closely related to problems encountered in a meta-analytical context which I believe I’ve discussed before here on the blog (see links above to the posts on meta-analysis) – if I’ve understood these authors correctly, one might choose to think of publication bias issues as partly the result of model selection bias issues. Let’s for a moment pretend you have a ‘true model’ which includes three variables (in the book example there are four, but I don’t think you need four…); one is very important, one is a sort of ‘60% of the samples variable’ mentioned above, and the last one would be a variable we might prefer to just include in the error term. Now the problem is this: When people look at samples where the last one of these variables is ‘seen to matter’, the effect size of this variable will be biased away from zero (they don’t explain where this bias comes from in the book, but I’m reasonably sure this is a result of the probability of identification/inclusion of the variable in the model depending on the (‘local’/’sample’) effect size; the bigger the effect size of a specific variable in a specific sample, the more likely the variable is to be identified as important enough to be included in the model – Bohrenstein and Hedges talked about similar dynamics, for obvious reasons, and I think their reasoning ‘transfers’ to this situation and is applicable here as well). When models include variables such as the last one, you’ll have model selection bias: “When predictor variables [like these] are included in models, the associated estimator for a σ2 is negatively biased and precision is exaggerated. These two types of bias are called model selection bias”. Much later in the book they incidentally conclude that: “The best way to minimize model selection bias is to reduce the number of models fit to the data by thoughtful a priori model formulation.”

“Model selection has most often been viewed, and hence taught, in a context of null hypothesis testing. Sequential testing has most often been employed, either stepup (forward) or stepdown (backward) methods. Stepwise procedures allow for variables to be added or deleted at each step. These testing-based methods remain popular in many computer software packages in spite of their poor operating characteristics. […] Generally, hypothesis testing is a very poor basis for model selection […] There is no statistical theory that supports the notion that hypothesis testing with a fixed α level is a basis for model selection. […] Tests of hypotheses within a data set are not independent, making inferences difficult. The order of testing is arbitrary, and differing test order will often lead to different final models. [This is incidentally one, of several, key differences between hypothesis testing approaches and information theoretic approaches: “The order in which the information criterion is computed over the set of models is not relevant.”] […] Model selection is dependent on the arbitrary choice of α, but α should depend on both n and K to be useful in model selection”.

 

January 22, 2015 Posted by | books, statistics | Leave a comment

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