Optimal information disclosure strategies?
I recently wanted to look up stuff on optimal information disclosure strategies in social settings – i.e. stuff on how to make the implicit information sharing strategies people use more explicit in order to better optimize them. The goal would be to better understand which (classes of personal-) information to share with whom, at which point in time, etc. This stuff is hard – inappropriate information sharing, both in the form of oversharing and undersharing, as well as related issues such as those of (lack of) reciprocity, are common pitfalls in social settings, and given how social feedback systems tend to work people are often not informed when they make errors in judgment in this area. I haven’t really found the sort of stuff I’ve been looking for, and I think it’s probably because I’m not looking the right places (use the right search terms). If readers know where to find such material I’d be interested to learn more – I have a comment section for a reason..
Here are some examples of what may happen when people don’t optimize:
The sketches are sufficiently exaggerated and sufficiently specific to not feel like personal attacks on people who engage in not-too-dissimilar strategies, which is why (/some people think) they’re funny. Flawed information sharing strategies are not the only things which make these sketches funny, nor are information sharing strategies the only applied strategies which are suboptimal here; but they are an important part of the problem in quite a few of the sketches (do note that non-verbal information shared is relevant as well..). Do note that the examples here are for one domain-specific application only; this stuff also applies to friends, coworkers, acquaintances, and people you’ve never met before. I’m well aware that different strategies are optimal in different domains, even though different domains likely share many similar features at the (optimal) strategy level.
Anyway, coming up with good strategies seems to me to be really hard. I assume the fact that most online dating sites don’t seem to use user-uploaded videos even now in the youtube age is probably a clue that using this medium is highly likely to lead to oversharing. Maybe there’s a cost component as well (it’s easier to just write a bit of stuff about yourself), but I’m not convinced this explanation is satisfactory without adding coordination problems and similar stuff as well (you don’t want to be the only one making a video because that presumably makes you look desperate compared to the people who do not?). I’m still a bit confused as to why videos aren’t more common in this area; they somehow seem efficient. Do privacy concerns drive this as well? I don’t know.
I tend to rely on ‘personal judgment’ regarding when to share what and in which manner; but as mentioned I’ll often find it hard to tell if my ‘personal judgment’ is off because I don’t really know very much about this stuff, and I rarely make an effort of ‘inviting new people into my life’ so I don’t have a lot of experience either. Learning these skills requires a certain amount of trial and error, sure, but it should be possible to study this stuff as well. To some extent I rely on implicit models of my own (‘personal judgment’ does include variables such as ‘time we’ve known each other’, ‘estimated degree of intimacy’, ‘information shared by the other party in the past’, etc.), but these models are likely flawed and incomplete and they don’t contain much information about dynamic elements in the equation because that’s the stuff I find particularly hard to figure out; stuff like who is supposed to ‘escalate’ – and how and ‘how far’ to ‘escalate’ – when a desire to move the social relationship from one point to another on the implicit intimacy-scale exists. Where to find better models, or at least a conceptual treatment of this kind of stuff?
Or am I overthinking all of this and the implementation of near-optimal information sharing strategies is basically considered irrelevant by most people because only severe deviations from the norm are ever (surreptitiously) punished anyway? Social interaction stuff is very complex so this would make sense; if it’s easy to get things not-quite-right it’ll often be optimal for the other party to allow for a wide margin of error.
A problem I have with the explanation in the above paragraph is that even if the level of model complexity involved here is staggering, most people do seem to engage to some extent in such optimization processes anyway – using whichever sources of information they consider to be reliable and informative (for example I’m aware that some subreddits are filled with this kind of stuff). They wouldn’t do this if ‘semi-normal’ deviations from ‘acceptable behaviour’ didn’t matter, so on some ‘relevant’ margins they clearly do.
What people will (and will not) say to others..
Here are 5 statements:
“You have a nice place.”
“You’re a bit lazy, and I’m sure you’d have gotten more out of the latest lecture if you’d read the material more carefully beforehand.”
“you have a fantastic episodic memory.”
“I love that you actually read these kinds of things…”
“The place would have looked less messy if you’d dusted a bit before we arrived.”
Yesterday I was told 3 of those things. One is a direct quote, the other two are English translations of what was said in Danish. I don’t think it takes a lot of work for you to realize which of the above statements I ‘made up’.
There’s a lot of stuff you can’t say. And a lot of stuff you’re expected to say. And there’s a lot of stuff that doesn’t go into either of those categories.
I assume that saying nice things to others will most often make others think you’re more likely to be a nice person, because saying nice things is certainly something most people would assume that nice people are more likely to do (doing nice things is a stronger signal than saying nice things, but saying nice things provides many psychological benefits as well).
Providing constructive criticism will often be a much more risky thing to do than to say something nice, even if that criticism includes potentially much more useful information. This is, among other things, because the more potentially useful the criticism provided is, the more likely the other party is to respond emotionally, rather than rationally, to the criticism in question. So people are unlikely to run the risk of providing useful constructive criticism to another individual before they know the other party well (…and presumably have said a lot of nice things to them). Granted, someone who knows the other individual well is also more likely to be able to provide constructive criticism so this dynamic is not without benefits (lower signal to noise ratio), but the total amount of constructive criticism supplied would surely be much higher if it was costless to provide it to strangers. One big problem is that it’s hard to credibly commit to not taking constructive criticism personally and responding emotionally.
At this point it seems to me that most people who interact with me regularly are being nice to me and mostly say nice things to me. I find it interesting that I rarely explicitly acknowledge that this fact may not necessarily have anything to do with me and my attributes, and that people may say nice things simply because of how they believe such statements reflect on themselves (‘I’m the kind of person who says that he has a nice place. That’s what nice people say – so I must be a nice person.’). Also, communication strategies may be implicit and not subject to close scrutiny by the people employing them – indeed it may be optimal not to subject your communication strategies to close scrutiny, as an implicit approach to these matters makes it harder to evaluate e.g. the level of sincerity displayed (and thus makes you more likely to successfully claim at the very least plausible deniability when you’re not being perfectly honest). Different perceptions of an individual’s status, attributes, etc., may make some sincere nice statements from one individual to the other seem insincere to the receiver (making a (negative) emotional response more likely).
Maybe a good way of thinking about this stuff is in terms of a binary social (verbal) feedback varible, which may be either ‘nice’ or ‘critical’, and then making an analogy to consumption vs investment. Nice things being said have consumption value; we like when others say nice things about us, and we derive pleasure from that. Criticism has investment characteristics; it’s initially costly (it hurts to be told you’re lazy), but it may have large positive effects in the long run if potential improvement strategies are addressed. Most of income is consumption – we’re mostly told nice things. If consumption is very low (not enough social validation from peers), it may be better for an individual to lower income than to invest the marginal unit of income; even potentially very useful criticism may not be very welcome when you feel socially rejected by others. Actually you’ll only be willing to undertake an investment (accept critical remarks) once your consumption is higher than some specific baseline level (people are required to say a lot of nice things to others before they’re allowed to say less nice things to them without repercussions).
I don’t know. I like when people say nice things to me, so I’m certainly not telling anybody to stop doing that. But social stuff is confusing when you start to think about it.
On a related note – yesterday three people said something nice to me. Yesterday was a good day.
Stuff
i. Econometric methods for causal evaluation of education policies and practices: a non-technical guide. This one is ‘work-related’; in one of my courses I’m writing a paper and this working paper is one (of many) of the sources I’m planning on using. Most of the papers I work with are unfortunately not freely available online, which is part of why I haven’t linked to them here on the blog.
I should note that there are no equations in this paper, so you should focus on the words ‘a non-technical guide’ rather than the words ‘econometric methods’ in the title – I think this is a very readable paper for the non-expert as well. I should of course also note that I have worked with most of these methods in a lot more detail, and that without the math it’s very hard to understand the details and really know what’s going on e.g. when applying such methods – or related methods such as IV methods on panel data, a topic which was covered in another class just a few weeks ago but which is not covered in this paper.
This is a place to start if you want to know something about applied econometric methods, particularly if you want to know how they’re used in the field of educational economics, and especially if you don’t have a strong background in stats or math. It should be noted that some of the methods covered see wide-spread use in other areas of economics as well; IV is widely used, and the difference-in-differences estimator have seen a lot of applications in health economics.
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ii. Regulating the Way to Obesity: Unintended Consequences of Limiting Sugary Drink Sizes. The law of unintended consequences strikes again.
You could argue with some of the assumptions made here (e.g. that prices (/oz) remain constant) but I’m not sure the findings are that sensitive to that assumption, and without an explicit model of the pricing mechanism at work it’s mostly guesswork anyway.
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iii. A discussion about the neurobiology of memory. Razib Khan posted a short part of the video recently, so I decided to watch it today. A few relevant wikipedia links: Memory, Dead reckoning, Hebbian theory, Caenorhabditis elegans. I’m skeptical, but I agree with one commenter who put it this way: “I know darn well I’m too ignorant to decide whether Randy is possibly right, or almost certainly wrong — yet I found this interesting all the way through.” I also agree with another commenter who mentioned that it’d have been useful for Gallistel to go into details about the differences between short term and long term memory and how these differences relate to the problem at hand.
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iv. Plos-One: Low Levels of Empathic Concern Predict Utilitarian Moral Judgment.
“An extensive body of prior research indicates an association between emotion and moral judgment. In the present study, we characterized the predictive power of specific aspects of emotional processing (e.g., empathic concern versus personal distress) for different kinds of moral responders (e.g., utilitarian versus non-utilitarian). Across three large independent participant samples, using three distinct pairs of moral scenarios, we observed a highly specific and consistent pattern of effects. First, moral judgment was uniquely associated with a measure of empathy but unrelated to any of the demographic or cultural variables tested, including age, gender, education, as well as differences in “moral knowledge” and religiosity. Second, within the complex domain of empathy, utilitarian judgment was consistently predicted only by empathic concern, an emotional component of empathic responding. In particular, participants who consistently delivered utilitarian responses for both personal and impersonal dilemmas showed significantly reduced empathic concern, relative to participants who delivered non-utilitarian responses for one or both dilemmas. By contrast, participants who consistently delivered non-utilitarian responses on both dilemmas did not score especially high on empathic concern or any other aspect of empathic responding.”
In case you were wondering, the difference hasn’t got anything to do with a difference in the ability to ‘see things from the other guy’s point of view’: “the current study demonstrates that utilitarian responders may be as capable at perspective taking as non-utilitarian responders. As such, utilitarian moral judgment appears to be specifically associated with a diminished affective reactivity to the emotions of others (empathic concern) that is independent of one’s ability for perspective taking”.
On a small sidenote, I’m not really sure I get the authors at all – one of the questions they ask in the paper’s last part is whether ‘utilitarians are simply antisocial?’ This is such a stupid way to frame this I don’t even know how to begin to respond; I mean, utilitarians make better decisions that save more lives, and that’s consistent with them being antisocial? I should think the ‘social’ thing to do would be to save as many lives as possible. Dead people aren’t very social, and when your actions cause more people to die they also decrease the scope for future social interaction.
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v. Lastly, some Khan Academy videos:
(Relevant links: Compliance, Preload).
(This one may be very hard to understand if you haven’t covered this stuff before, but I figured I might as well post it here. If you don’t know e.g. what myosin and actin is you probably won’t get much out of this video. If you don’t watch it, this part of what’s covered is probably the most important part to take away from it.)
It’s been a long time since I checked out the Brit Cruise information theory playlist, and I was happy to learn that he’s updated it and added some more stuff. I like the way he combines historical stuff with a ‘how does it actually work, and how did people realize that’s how it works’ approach – learning how people figured out stuff is to me sometimes just as fascinating as learning what they figured out:
(Relevant wikipedia links: Leyden jar, Electrostatic generator, Semaphore line. Cruise’ play with the cat and the amber may look funny, but there’s a point to it: “The Greek word for amber is ηλεκτρον (“elektron”) and is the origin of the word “electricity”.” – from the first link).
(Relevant wikipedia links: Galvanometer, Morse code)
Be amazed!
The universe, our lives, all this stuff – it’s just so incredible it sometimes boggles my mind how we can just walk around, doing whatever it is that we’re doing, just taking all this stuff for granted, overlooking everything. There’s so much to see, to appreciate!
I’ll start here – with a picture of a rock:
It’s not just any rock though – it’s been through a lot. Almost too much to imagine. Allow me to demonstrate what I mean by that…
Now, I should note that I think that timescales are funny things. I sometimes sort of feel like I don’t really understand them, how they work. I have similar problems with distances now and then, but we’ll get to that later. Of course it’s not that hard to imagine an hour passing by, or a day, or perhaps even a year. But a millenium? I don’t really have a good idea how much time a millenium is – it’s such a long time it boggles the mind. A million years? That’s just crazy. I have no way to conceptualize that kind of time-scale, my mind is much too small for that. So recently I tried to come up with a way to imagine how much time these big multiples of the numbers we usually use to denote time passing by actually represent. I decided to engage in a thought experiment where I’d be counting the years that have gone by and see where I’d end up, starting out where we are now. I pretend I’m able to count one year each second. That way 60 years will go by in just a minute – an entire life of a human being in just a minute. After an hour of counting I’d be close to the starting point of written history; we’d now be 3600 years into the past. We all sort of tell ourselves that we know roughly how long that is; the Jesus stuff is supposed to have happened 2000 years ago, and 3600 years isn’t that different from 2000 after all. But here’s a picture:
This is the Sicilian Temple of Juno Lacinia, and this is what 2400 years – just 2 thirds of the amount of time we’ve counted so far – looks like from a certain point of view.
Let’s count on: After a day of counting we’d be 86.000 years into the past – so what happened 86.000 years ago? We have little idea, it’s so very long ago. After a year of counting without rest, we’d be 31 million and 536 thousand years into the past – you can count one year each second every second without pause for an entire year of your life and you’re not even half-way to the dinosaurs!
If we assume you count every second of your entire life and you can expect to live 75 years, then the last number you’ll get to is the year that happened 2 billion 365 million and 200 thousand years ago.
Here’s the kicker: The rock in the image above is much, much older than that.
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I’ve been to Copenhagen a few times this year. My parents also went there not too long ago – they came to the city and went back home the same day, for reasons which are not important here. I’ll pretend the trip was 220 kilometres each way; it’s close enough.
200 km is actually a really big distance, once you start thinking about it. We usually don’t, because we have means of transportation that will bring us very fast from A to B. So I decided to think about what would happen if we didn’t have those things; what if they had had to walk to Copenhagen instead of going by car? Well, walking takes more time, but it’s also a lot harder. So I decided to say that it probably wasn’t realistic that they walked more than 12 hours per day, at 5 km/hour. Or 5000 meter per 60 minutes, if you’re of a different persuasion. How long would it have taken them to get back and forth? Well, 5 km/hour and 12 hours per day gives 60 km per day, or 420 km per week. 220 km each way adds up to 440 km in total. So they’d have had to walk for more than a week to get to Copenhagen and back. It would have taken them more than a month to walk to Paris (~900 km) and back.
The closest big thingy you can see at night when you look up into the sky is basically a big rock which reflects the light of a huge ball of fire which luckily is quite a bit farther away from us than the big rock is; a ball of fire which has been burning without stop for a much longer amount of time than you can count years during your life. We like to think the big light-reflecting rock up there is quite near us; some humans have even been up there, so it can’t be that far away, right? Actually it isn’t – from a certain point of view. It’s average distance to Earth is around 385.000 km. If you could ‘fly-walk’ at a very human speed of 5 km/hour, you’d be there in just 17,5 years or so. You could leave at the age of 15 and be back here again at the age of 50. If these kinds of things were possible, which of course they’re not.
Here’s a different way to conceptualize that distance: Let’s think in terms of human-scale magnitudes (one human = ~1,5-2 metres), so that the distance is now 385.000.000 metres, instead of all that cheating with metrics like kilometres. Let’s say an average human is close to 2 metres tall and let’s say we wanted to get up to the moon by standing on top of each other; in order to reach them moon, you’d need something like 200 million people. Let’s do the counting thing again: Count one person per second. It’d take you close to 7 years to count the people you’d need to make that happen. (Of course there are various reasons why that kind of thing wouldn’t work.)
I mentioned that ‘the average distance’ was 385.000. It’s an average because the Moon is moving very fast, just like the Earth, and it doesn’t move around in a perfectly spherical manner. But the Earth and Moon is – as people know these days, although it took a very long time to convince all those well-dressed monkeys that that was how it worked – moving around the Sun as well, and this is where it gets a bit more interesting. The movement around the Sun is, well, fast. The Sun is approximately (such a wonderful word, considering which kinds of distances and differences are actually hidden here) 150 million km away from us. We don’t have enough humans to do the same trick we did with the Moon, not even close. But let’s look a little closer at the speeds involved. The average distance to the Sun is of course not the distance that the Earth travels during a year – the latter number is quite a bit larger, and like many other things it involves the number pi. The Earth goes roughly one billion km/year (940 million km/year, assuming the orbit is circular), which is 108.000 km/hour! Or 30 km each second. It’s almost unbelievable that we don’t notice, that we don’t fall off – that everything just happen the way things do, without anyone sparing much thought as to how utterly insane this is. We don’t even notice.
There’s a lot more on stuff like distances and time frames at Khan Academy.
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Now, a different thing you could wonder about is how you can even think the thoughts you’re thinking now. It’s incredibly hard to understand what’s going on there, and we don’t have a very detailed model of the brain as it is. So let’s be less ambitious – let’s just have a look at some of the cells you have hanging around in ‘your body’. Here are a few juxtaglomerular cells, the likes of which are now hanging out in your kidneys (doing useful stuff):
There are a lot of different types of cells in the human body, and the total number of cells in your body is much higher than the number of humans on Earth. So you probably shouldn’t try to count them, like we tried counting other stuff before – you won’t get very far. Obviously they’re probably not very big, given that we don’t seem to notice them in our day-to-day lives even though there are so many of them. Until a few hundred years ago we didn’t even know such things existed. Now we do, and each day we as a species learn more about the almost infinite number of awesome small living things hanging around everywhere here on Earth. There are so incredibly many of them that cooperate with each other to keep you alive, and even though some of the types of cells in your body live only for a few hours, the combined work of all of them keep ‘you’ going for years, decades. So many things could in theory go wrong – after all even a single cell messing up and dividing the wrong way can end up killing you. Yet somehow things very rarely go wrong, you stay alive, year after year, until one day the little ones have done all they can for ‘you’ and so start worrying more about themselves than about what made you you.
These small things have been around about as long as the rock in the picture above has.
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On top of all that… If you look even closer at the cells we talked about, you’ll see that they’re made up of tiny little atoms which are jiggling around all the time, everywhere, at insane speeds and in complex patterns we don’t always understand very well at all. Even though cells are really small, it takes a lot of atoms to make a cell – a lot of atoms which need to constantly ‘cooperate with-’ and interact with each other to maintain the structure of the cell. We talked about how there were more cells in your body than there are humans on Earth; it turns out that the number of atoms in a cell is roughly the same as the number of cells in a human body - 1014, or 100 trillion. The little atoms get broken down and reassembled in all kinds of ways, all over the place, all the time. I sometimes find it very confusing how all these interactions, all these things can happen everywhere and all the time, right under our noses (and over it, and in it, and…) without us being any the wiser. We look at the world and our eyes interpret the light which is available to us in a manner which the organisms which came before us benefited from. The way our eyes work is part of why we’re alive, why we’re here today – they enabled our ancestors to spot other huge collections of atoms and cells in order to facilitate the most optimal types of interaction with all those other collections of cells and atoms. Oh yes, our eyes are immensely useful things, and if you go into a bit more detail about how they work they’re fascinating things in and of themselves – yes, but even so: Such a profoundly limited, such a coarse-grained view of the world they have given us, compared to what actually is going on!
Or you could talk about the waves, all the different kinds of waves moving around in our environment – sound, heat, light, … Many of them humans can’t even see or feel, and many humans have lived their entire lives without ever knowing they even existed. Just as many people don’t know what that rock at the beginning really looks like when you start to zoom in, and which factors have caused it to look the way it does now, so relatively unharmed by time. I’ve read some stuff about rocks, but I also don’t know that in any amount of detail. And that’s okay – there’s so much stuff to learn you can’t possibly ever get to the bottom of it all.
We’re just smart (yet also incredibly stupid), well-dressed apes – but if you were thinking that this sentence would lead to reduced complexity, a ‘and it’s all really very simple…’-point, well, then you’d be wrong. We’re just smart apes, but we’re apes which interact with each other and with our environment. And if you have a closer look at that stuff, it turns out that the interaction patterns that form our lives and our behaviours are so complex that they almost defy belief.
And it gets worse, or better, depending on whom you ask – because there are trillions of other places out there without smart well-dressed apes; places so remote we can’t even imagine the distances involved, but at the same time also places where we don’t even need to go near to understand a lot of what is happening there. Because we through a combined effort as a species have gotten wonderfully good at understanding what’s going on in this remarkable universe we’re a part of. Ignorance is the default state. But it should not be a desired end state. The world gets so much bigger, so much more interesting, once you start to look closer at what’s going on.
So much stuff to learn, to understand, to take with you! The world is an amazing place – allow yourself to be amazed!
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I’m sad Feynman died before I ever got to at least have a chance to meet him. He set a good example:
(The last one is a repost, but I love that one.)
Stuff
i. Better Colleges Failing to Lure Talented Poor, by David Leonhardt.
“Only 34 percent of high-achieving high school seniors in the bottom fourth of income distribution attended any one of the country’s 238 most selective colleges [...] Among top students in the highest income quartile, that figure was 78 percent. [...]
Among high-achieving, low-income students, 6 percent were black, 8 percent Latino, 15 percent Asian-American and 69 percent white [...]
The researchers defined high-achieving students as those very likely to gain admission to a selective college, which translated into roughly the top 4 percent nationwide. Students needed to have at least an A-minus average and a score in the top 10 percent among students who took the SAT or the ACT.
Of these high achievers, 34 percent came from families in the top fourth of earners, 27 percent from the second fourth, 22 percent from the third fourth and 17 percent from the bottom fourth. (The researchers based the income cutoffs on the population of families with a high school senior living at home, with $41,472 being the dividing line for the bottom quartile and $120,776 for the top.) [...]
If they make it to top colleges, high-achieving, low-income students tend to thrive there, the paper found. Based on the most recent data, 89 percent of such students at selective colleges had graduated or were on pace to do so, compared with only 50 percent of top low-income students at nonselective colleges.”
For people with access to nber papers, here’s the direct link to the study.
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ii. What effect size would you expect?
The p-value isn’t the only thing you should care about when evaluating small-N studies and larger N replication attempts. It shouldn’t be news, but lots of people get this stuff wrong. Do remember that even in the replication studies, N may be quite small.
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iii. Will we ever regenerate limbs?
“Seifert doubts we will ever have an injectable cocktail of molecules that triggers regeneration. There’s too much complexity in the transition from wound to blastema to new limb, he says. It will also be a lengthy process. [...] “Even if a human could grow a limb back, it might take 15-20 years,” says Seifert. A finger might be more realistic.”
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iv. New insights into differences in brain organization between Neanderthals and anatomically modern humans. Razib Khan’s blog has some comments in case you’re curious.
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iv. ‘The 99% percent’ weren’t really all that representative, it seems: The Geospatial Characteristics of a Social Movement Communication Network:
“Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.”
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v. Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level.
“we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. [...] results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.”
N = 28, so it’s a small sample size. But at least the results “seem to support the idea that aerobic training produces selective benefits in cognitive performance.”
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vi. How you behave online can tell (a lot? something? a bit? – people seem to disagree about how ‘impressive’ the findings are…) about who you are: Private traits and attributes are predictable from digital records of human behavior, by Kosinski, Stillwell & Graepel.
Figure 2 is probably the main figure from this paper – it “shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female)”:
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vii. Farm Use of Antibiotics Defies Scrutiny.
“Eighty percent of the antibiotics sold in the United States goes to chicken, pigs, cows and other animals that people eat, yet producers of meat and poultry are not required to report how they use the drugs — which ones, on what types of animal, and in what quantities. This dearth of information makes it difficult to document the precise relationship between routine antibiotic use in animals and antibiotic-resistant infections in people”
This is insane. I had no idea the problem in the US was this big.
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viii. One of my guilty pleasures:
(If you just want to watch the chess, you can skip the first 3 minutes or so.)
Stuff
i. Remember ‘the good old days’ of film-making? Here’s a reminder: The Hays Code.
“1. No picture shall be produced that will lower the moral standards of those who see it. Hence the sympathy of the audience should never be thrown to the side of crime, wrongdoing, evil or sin.
2. Correct standards of life, subject only to the requirements of drama and entertainment, shall be presented.
3. Law, natural or human, shall not be ridiculed, nor shall sympathy be created for its violation. [...]
The sanctity of the institution of marriage and the home shall be upheld. Pictures shall not infer that low forms of sex relationship are the accepted or common thing.
1. Adultery, sometimes necessary plot material, must not be explicitly treated, or justified, or presented attractively.
2. Scenes of Passion
a. They should not be introduced when not essential to the plot.
b. Excessive and lustful kissing, lustful embraces, suggestive postures and gestures, are not to be shown.
c. In general passion should so be treated that these scenes do not stimulate the lower and baser element. [...]
1. No film or episode may throw ridicule on any religious faith.
2. Ministers of religion in their character as ministers of religion should not be used as comic characters or as villains. [...]
The reason why ministers of religion may not be comic characters or villains is simply because the attitude taken toward them may easily become the attitude taken toward religion in general. Religion is lowered in the minds of the audience because of the lowering of the audience’s respect for a minister.”
Background etc. here and here.
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ii. I’d love to see some corresponding Danish numbers:
“Italians born in 1970, who are about 43 now, will pay 50% more in taxes as a percentage of their lifetime income than those born in 1952, according to research from the Bank of Italy and the University of Verona. The research also found they will receive half the pension benefits that Italy’s 60-somethings are getting or are poised to get.” (link, via MR)
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iii. Longevity AmongHunter-Gatherers: A Cross-Cultural Examination. Some main findings and conclusions from the paper:
“Post-reproductive longevity is a robust feature of hunter-gatherers and of the life cycle of Homo sapiens. Survivorship to grandparental age is achieved by over two-thirds of people who reach sexual maturity and can last an average of 20 years.
Adult mortality appears to be characterized by two stages. Mortality rates remain stable and fairly low at around 1 percent per year from the age of maturity until around age 40. After age 40, the rate of mortality increase is exponential (Gompertz) with a mortality rate doubling time of about 6–9 years. The two decades without detectable senescence in early and mid-adulthood appear to be an important component of human life span extension.
The average modal age of adult death for hunter-gatherers is 72 with a range of 68–78 years. This range appears to be the closest functional equivalent of an “adaptive” human life span.
Departures from this general pattern in published estimates of life expectancy in past populations (e.g., low child and high adult mortality) are most likely due to a combination of high levels of contact-related infectiousdisease, excessive violence or homicide, and methodological problems that lead to poor age estimates of older individuals and inappropriate use of model life tables for deriving demographic estimates.
Illnesses account for 70 percent, violence and accidents for 20 percent, and degenerative diseases for 9 percent of all deaths in our sample. Illnesses largely include infectious and gastrointestinal disease, although less than half of all deaths in our sample are from contact-related disease.
Comparisons among hunter-gatherers, acculturated hunter-gatherers, wild chimpanzees, and captive chimpanzees illustrate the interaction of improved conditions and species differences. Within species, improved conditions tend to decrease mortality rates at all ages, with a diminishing effect at older ages. Human and chimpanzee mortality diverge dramatically at older ages, revealing selection for a longer adult period in humans. [...]
Our results contradict Vallois’s (1961: 222) claim that among early humans, “few individuals passed forty years, and it is only quite exceptionally that any passed fifty,” and the more traditional Hobbesian view of a nasty, brutish, and short human life (see also King and Jukes 1969; Weiss 1981). The data show that modal adult lifespan is 68–78 years, and that it was not uncommon for individuals to reach these ages”
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iv. What is it like when one of your parents gets Alzheimer’s? It’s not fun.
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In people with impaired glucose tolerance interventions are clinically and cost effective
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Screening for type 2 diabetes to allow early detection might be cost effective in certain groups
What this study adds
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Modelling the whole screening and intervention pathway from screening to death shows that screening for type 2 diabetes and impaired glucose tolerance, followed by interventions, seems to be cost effective compared with no screening
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Uncertainty still exists concerning the cost effectiveness of screening for type 2 diabetes alone
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Screening populations with a higher prevalence of glucose intolerance might result in better clinical outcomes, although cost effectiveness seems unaffected”
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N is small but even so this is an interesting finding.
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vii. “Commercial fishing operations in the past 40 years have precipitated a dramatic change in ocean fish stocks, with tuna and other big predators declining and small fish like anchovies and sardines surging. That’s the conclusion of the most ambitious study ever completed of fish populations in the Earth’s oceans, conducted by Villy Christensen of the University of British Columbia’s Fisheries Centre.In the past 100 years, 80% of the biomass of fish in the world’s oceans has been lost, Christensen says in a AAAS video that coincided with a symposium at the Annual Meeting. “Just in the last 40 years, we have lost 60% of the biomass,” he explained. “So we’ve seen some very serious declines, and there’s no doubt about what the cause is: We’re talking about overfishing—overfishing at the global scale.” [...] Christensen’s team of scientists based their conclusions on more than 200 marine ecosystem models and more than 68,000 estimates of fish biomass from 1880 to 2007, the Vancouver Sun reported, citing a University of British Columbia news release.”
Stuff
i. Sample Size in Psychological Research Over the Past 30 Years.
“Summary. —The American Psychological Association (APA) Task Force on Statistical Inference was formed in 1996 in response to a growing body of research demonstrating methodological issues that threatened the credibility of psychological research, and made recommendations to address them. One issue was the small, even dramatically inadequate, size of samples used in studies published by leading journals. The present study assessed the progress made since the Task Force’s final report in 1999. Sample sizes reported in four leading APA journals in 1955, 1977, 1995, and 2006 were compared using nonparametric statistics, while data from the last two waves were fit to a hierarchical generalized linear growth model for more in-depth analysis. Overall, results indicate that the recommendations for increasing sample sizes have not been integrated in core psychological research, although results slightly vary by field. This and other implications are discussed in the context of current methodological critique and practice.”
I unfortunately can’t find an ungated copy of this paper online, but here’s a little more stuff from the paper:
“Cohen (1962) concluded, “Increased sample size is likely to prove the most effective general prescription for improving power” (p. 153), but there is little evidence that the field has taken note. After reviewing the literature, Holmes (1979) reported finding only two studies that examined sample sizes directly. One study reported the number of articles published about single-subject samples (Dukes, 1965), and the other examined sample sizes reported in two British journals, finding that every reported study had N ≤ 25 (Cochrane & Duffy, 1974).
Holmes (1979, 1983) himself examined sample sizes in four APA journals in 1955 and 1977, and reported median sample sizes for the total study and each of the comparison groups. His general conclusions were that sample size had not changed significantly between 1955 and 1977, and that the typical sample size in psychology did not seem large [...] the purpose of the present study was to examine sample sizes reported in the same four journals examined by Holmes (1979, 1983), but in more recent volumes. Two additional data collections were undertaken, one in 1995 (about the time the Task Force was formed), and the other in 2006 [...]
(click to view in a higher resolution)
So yeah, the median sample size was 32 in 1995 and 40 in 2006. 25% of published studies had n=14 or less in 1995, and n=18 or less in 2006. The sample size that occured most often in the 1995 sample was n=8; in 2006 it was 16.
“Our modeling showed that sample size depends on the field. Smaller samples are needed in experimental settings, presumably because sufficient control of extraneous variation is in place, and standard errors tend to be smaller. (Higher cost per participant may also be a factor, due to sophisticated measurement equipment or laboratory controls.) However some fields, such as applied and developmental psychology, depend much more on quasi-experimental research because of their greater emphasis on comparisons of naturally occurring groups and ecological validity. Such research designs result in more variation in the data, and larger samples are necessary to gain feasible standard errors. (Lower cost per participant may also be a factor, because of the availability of institutional archival data.) [...]
We found that overall, the relatively small sample sizes found by Holmes did not increase significantly over the next 29 years. However, there was significant variability in the change in sample size over time by field, with increases from 1977 to 2006 appearing in the Journal of Abnormal Psychology and Developmental Psychology, and no change in Experimental Psychology or Applied Psychology (which actually showed a slight decrease for individual sample size).
The third hypothesis was that sample sizes remained unchanged after the Task Force report in 1999. A change would have been reflected in a significant difference in sample size between 1995 and 2006, but none was found. This result is not surprising, given previous research on power (e.g., Cohen, 1962; Sedlmeier & Gigerenzer, 1989; Rossi, 1990; Maddock & Rossi, 2001; Maxwell, 2004) and Holmes’ own studies on sample size (Holmes, 1979, 1983; Holmes, et al., 1981). However, it is troubling, especially when one considers the increased use of sophisticated multivariate analyses and statistical modeling techniques during this time that would require the employment of larger sample sizes (Merenda, 2007; Rodgers, 2010).”
Here’s a link to one of the ungated power studies mentioned in the paper.
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ii. Old pictures. Lots of old pictures.
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iii. BookOs.
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iv. “What [would happen] if I took a swim in a typical spent nuclear fuel pool? Would I need to dive to actually experience a fatal amount of radiation? How long could I stay safely at the surface?”
There’s a little background stuff on the subject here.
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v. For some reason this picture touched me deeply (click to view full size):
Via Wikipedia.
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vi. “Facebook killed TV.” – from this Paul Graham essay on Why TV Lost.
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vii. The End of History Illusion.
“We measured the personalities, values, and preferences of more than 19,000 people who ranged in age from 18 to 68 and asked them to report how much they had changed in the past decade and/or to predict how much they would change in the next decade. Young people, middle-aged people, and older people all believed they had changed a lot in the past but would change relatively little in the future. People, it seems, regard the present as a watershed moment at which they have finally become the person they will be for the rest of their lives. This “end of history illusion” had practical consequences, leading people to overpay for future opportunities to indulge their current preferences.”
Unfortunately I’ve not been able to find an ungated link, but here’s a bit more from the concluding remarks of the paper:
“Across six studies of more than 19,000 participants, we found consistent evidence to indicate that people underestimate how much they will change in the future, and that doing so can lead to suboptimal decisions. Although these data cannot tell us what causes the end of history illusion, two possibilities seem likely. First, most people believe that their personalities are attractive, their values admirable, and their preferences wise (10); and having reached that exalted state, they may be reluctant to entertain the possibility of change. People also like to believe that they know themselves well (11), and the possibility of future change may threaten that belief. In short, people are motivated to think well of themselves and to feel secure in that understanding, and the end of history illusion may help them accomplish these goals.
Second, there is at least one important difference between the cognitive processes that allow people to look forward and backward in time (12). Prospection is a constructive process, retrospection is a reconstructive process, and constructing new things is typically more difficult than reconstructing old ones (13, 14). The reason this matters is that people often draw inferences from the ease with which they can remember or imagine (15, 16). If people find it difficult to imagine the ways in which their traits, values, or preferences will change in the future, they may assume that such changes are unlikely. In short, people may confuse the difficulty of imagining personal change with the unlikelihood of change itself.
Although the magnitude of this end of history illusion in some of our studies was greater for younger people than for older people, it was nonetheless evident at every stage of adult life that we could analyze. Both teenagers and grandparents seem to believe that the pace of personal change has slowed to a crawl and that they have recently become the people they will remain. History, it seems, is always ending today.”
Stuff
i. PLOS ONE: Till Death (Or an Intruder) Do Us Part: Intrasexual-Competition in a Monogamous Primate.
“Polygynous animals are often highly dimorphic, and show large sex-differences in the degree of intra-sexual competition and aggression, which is associated with biased operational sex ratios (OSR). For socially monogamous, sexually monomorphic species, this relationship is less clear. Among mammals, pair-living has sometimes been assumed to imply equal OSR and low frequency, low intensity intra-sexual competition; even when high rates of intra-sexual competition and selection, in both sexes, have been theoretically predicted and described for various taxa. Owl monkeys are one of a few socially monogamous primates. Using long-term demographic and morphological data from 18 groups, we show that male and female owl monkeys experience intense intra-sexual competition and aggression from solitary floaters. Pair-mates are regularly replaced by intruding floaters (27 female and 23 male replacements in 149 group-years), with negative effects on the reproductive success of both partners. Individuals with only one partner during their life produced 25% more offspring per decade of tenure than those with two or more partners. The termination of the pair-bond is initiated by the floater, and sometimes has fatal consequences for the expelled adult. The existence of floaters and the sporadic, but intense aggression between them and residents suggest that it can be misleading to assume an equal OSR in socially monogamous species based solely on group composition. Instead, we suggest that sexual selection models must assume not equal, but flexible, context-specific, OSR in monogamous species.”
You sort of want to extrapolate out of sample (/…out of species?) here, but be careful:
“Our findings differ from those reported for some monogamous birds, where remaining life-time reproductive success (i.e., the expected future gains) of the individual that initiates or tolerates a ‘divorce’ was higher than if it remained with its initial partner. For example, in kittiwakes (Rissa tridactyla) and many other pair-living birds, but also in some human societies, it is sometimes advantageous to ‘divorce’, if partners prove incompatible [25], [27], [35]. In contrast, our data strongly indicate that break-ups were associated with factors extrinsic to the pair, and that partners did not voluntarily leave or “divorce” as it has been reported for birds, gibbons, and (in at least one case) brown titi monkeys (Callicebus brunneus) [25]–[27], [36], [37]. On the other hand, in some species (oystercatchers, Haematopus ostralegus), the reproductive success of stable pairs is not only higher, but there are also accrued benefits with increased duration of the pair-bond, independent of effects of age or experience [38]. This was not the case for owl monkeys, since the number of offspring produced did not change with increased duration of the pair-bond (Fig. 2).”
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ii. Smbc (click to watch in a higher resolution):
Just to remind you that SMBC is still awesome. Here are a couple of related comics from the site.
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“The ability to control fire was a crucial turning point in human evolution, but the question when hominins first developed this ability still remains. Here we show that micromorphological and Fourier transform infrared microspectroscopy (mFTIR) analyses of intact sediments at the site of Wonderwerk Cave, Northern Cape province, South Africa, provide unambiguous evidence—in the form of burned bone and ashed plant remains—that burning took place in the cave during the early Acheulean occupation, approximately 1.0 Ma. To the best of our knowledge, this is the earliest secure evidence for burning in an archaeological context.”
[Another reminder that SMBC is awesome: Here's a recent comic which is very handy here - it explains what a Fourier transform is, in case you don't know... (If you actually want to know there's always wikipedia...)]
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iv. I never covered this here and though some of you may already have read it I thought I might as well link to Ed Yong’s write-up on replication studies in Nature published last year. A few quotes from the article:
“Positive results in psychology can behave like rumours: easy to release but hard to dispel. They dominate most journals, which strive to present new, exciting research. Meanwhile, attempts to replicate those studies, especially when the findings are negative, go unpublished, languishing in personal file drawers or circulating in conversations around the water cooler. “There are some experiments that everyone knows don’t replicate, but this knowledge doesn’t get into the literature,” says Wagenmakers. The publication barrier can be chilling, he adds. “I’ve seen students spending their entire PhD period trying to replicate a phenomenon, failing, and quitting academia because they had nothing to show for their time.
These problems occur throughout the sciences, but psychology has a number of deeply entrenched cultural norms that exacerbate them. It has become common practice, for example, to tweak experimental designs in ways that practically guarantee positive results. And once positive results are published, few researchers replicate the experiment exactly, instead carrying out ‘conceptual replications’ that test similar hypotheses using different methods. This practice, say critics, builds a house of cards on potentially shaky foundations.
These problems have been brought into sharp focus by some high-profile fraud cases, which many believe were able to flourish undetected because of the challenges of replication. Now psychologists are trying to fix their field.”
Good luck with that. I don’t see a fix happening anytime soon. A few numbers:
“In a survey of 4,600 studies from across the sciences, Daniele Fanelli, a social scientist at the University of Edinburgh, UK, found that the proportion of positive results rose by more than 22% between 1990 and 2007 (ref. 3). Psychology and psychiatry, according to other work by Fanelli4, are the worst offenders: they are five times more likely to report a positive result than are the space sciences, which are at the other end of the spectrum [...]. The situation is not improving. In 1959, statistician Theodore Sterling found that 97% of the studies in four major psychology journals had reported statistically significant positive results5. When he repeated the analysis in 1995, nothing had changed6.”
But maybe other fields are just as bad? Well, as already mentioned the space sciences do better – and that goes for other fields too (though I’d say there seems to be major problems in many areas besides psychology and psychiatry):
A major problem here is that unless you’re actually a researcher in the field or know whom to ask, the file drawer effect can be completely invisible to you.
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v. Globalization of Diabetes – The role of diet, lifestyle, and genes. A new publication in Diabetes Care. As usual when they say ‘diabetes’ they mean ‘type 2 diabetes’. Some numbers from the article:
“According to the International Diabetes Federation (1), diabetes affects at least 285 million people worldwide, and that number is expected to reach 438 million by the year 2030, with two-thirds of all diabetes cases occurring in low- to middle-income countries. The number of adults with impaired glucose tolerance will rise from 344 million in 2010 to an estimated 472 million by 2030.
Globally, it was estimated that diabetes accounted for 12% of health expenditures in 2010, or at least $376 billion—a figure expected to hit $490 billion in 2030 (2). [...] Asia accounts for 60% of the world’s diabetic population. [Do note that this does not mean that Asian countries are on average overrepresented in the diabetes statistics. Asia also has roughly 60% of the World's population. - US] [...] In 1980, less than 1% of Chinese adults had the disease. By 2008, the prevalence had reached nearly 10% [...] in urban areas of south India, the prevalence of diabetes has reached nearly 20% [...] Compared with Western populations, Asians develop diabetes at younger ages, at lower degrees of obesity, and at much higher rates given the same amount of weight gain [...]
If current worldwide trends continue, the number of overweight people (BMI >25 kg/m^2) is projected to increase from 1.3 billion in 2005 to nearly 2.0 billion by 2030 (6). [...] the prevalence of overweight and obesity in Chinese adults increased from 20% in 1992 to 29.9% in 2002 (8) [...]
In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk. [...] Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk [...] Cigarette smoking is an independent risk factor for type 2 diabetes. A meta-analysis found that current smokers had a 45% increased risk of developing diabetes compared with nonsmokers (29). Moreover, there was a dose-response relationship between the number of cigarettes smoked and diabetes risk. [That one I did not know about!] [...] Light-to-moderate alcohol consumption is associated with reduced risk of diabetes. A meta-analysis of 370,000 individuals with 12 years of follow-up showed a U-shaped relationship, with a 30–40% reduced risk of the disease among those consuming 1–2 drinks/day compared with heavy drinkers or abstainers (37). [...]
common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20–30% of Caucasian populations but only 3–5% of Asians [...] Conversely, a variant in the KCNQ1 gene associated with a 20–30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians [...]
Several randomized clinical trials have demonstrated that diabetes is preventable. One of the first diabetes prevention trials was conducted in Daqing, China (58). After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group. In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59). Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.”
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vi. Why chess sucks.
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A trip to the movies
After completing 3 books in four days, I’ve decided that this evening I’m going to watch The Hobbit in a local movie theater.
When I ordered the ticket online I started thinking back, about when I’ve last been to one of those things. My impression was that it was probably a few years ago, perhaps 3. I’m pretty sure by now that the answer to the question is 2009 – I’m almost certain the last movie I watched in such a venue was Australia, which I watched with my parents not too long after it came out in Denmark. It’s the last movie I remember watching such a place anyway. (Which is not to say that the movie was memorable – it wasn’t).*
I believe people generally have some not-insignificant degree of influence over how unpleasant it is for them to forgo pleasurable activities the costs of which might not be justified given the budget constraint. I’ve basically not given the idea of going to a movie theater and pay quite a bit of money simply to watch a movie a thought for a few years, and when you don’t think about what you’re missing out on it doesn’t hurt nearly as much as if what you’re missing out on is an activity you engage in on a regular basis. Actually it doesn’t really hurt at all.
So I’ve employed a ‘don’t think about it, just forget that this type of thing is even an option’-strategy for some years now regarding these matters. Over the last couple of years I’ve started employing similar strategies when it comes to my eating habits. There are types of food I just don’t consume, and so never really think about anymore. Naturally most of the things I’ve deliberately cut out like that are unhealthy types of food.
Habits are very important both when it comes to which types of behaviour we engage in, and how we feel about how we spend our time and what we do. I believe that engaging in a bit of systematic thinking about optimal ways to impact habit formation every now and then may be time and effort well spent. Implicitly people do engage in this type of ‘selective blindness’ behaviour, where they overlook ways their lives are worse than others, without thinking about it all the time; you can’t really function well if you don’t. Most normal people don’t get annoyed every day by the fact that they don’t have a maid or a cook employed in their private residence, even though some other people do. The point is that if you mentally make an effort of systematically adding more variables to this ‘this is really quite irrelevant stuff I shouldn’t worry about anyway..’-set, you may be less likely to feel deprived (even though maybe you are).
Incidentally I don’t think I’m particularly likely to go to the movies again this year after today – it’s a one-time thing, which is a big part of why it’ll probably (hopefully?) be an enjoyable experience. Also, I’m finding it quite unlikely that I’ll be thinking in the months to come that I’m missing out after having had this experience and being reminded of what it’s like; another established habit of mine is to avoid new movies and books as much as possible, unless there are some quite exceptional circumstances at play which make me believe that I’m highly likely to enjoy the work in question.
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*On second thought I now recall that I have watched Watchmen with my big brother, and I’m reasonably certain we went to a movie theater to see it. It also came out in 2009, but later during the year than Australia.
Independence?
Right now I’m listening to Beethoven’s second symphony on my computer. There are probably about 100 people who contributed to that recording, thinking just in terms of the people sitting there with their instruments. Then there’s the person who recorded the piece. Of course there’s also the composer, but he died a long time ago. And there are the people who made the code that enables my computer to translate the zeros and ones into music. Then there are the people who made my computer, and my earphones, and the people who keep my internet connection up and running. The people who manage the power grid and make sure I have electricity so that I can play the piece without my computer shutting down because of power failure (in case the battery in it, which someone also made, needed to be recharged). I’m sitting in a room that is brightly lit. I don’t know who made the two lamps I have turned on right now, nor do I know who made sure the lamps were transported to a place close to me so that I could buy them, but I’m grateful that they did all that work.
I’m not in need of food at the moment – I had dinner a few hours ago, and I’m sure more people were involved in getting the stuff I ate from a) to b) than I’d like to think about; for one thing, people don’t grow bananas in Denmark in January. I would have gotten sick from eating the banana if I hadn’t been able to take medicine to help my body process the carbohydrates; I’m sure a lot of people were involved both in developing the medicine, producing it, and transporting it to a pharmacy close to where I live, so that I have access to it and am able to use it to keep my illness from killing me. If they didn’t do that work I’d die, so of course I’m grateful.
It’s not cold in my appartment – it’s a pleasant temperature, and it’s not really because of anything I did. Oh yes, there’s a thermostat I can adjust, true, but that wouldn’t really be worth a lot if the people who currently work on making sure that heat is produced and moved around stopped doing what they do. Part of why I’m not cold is that I’m wearing clothes. My t-shirt was made on a different continent thousands of kilometres away. The place where I currently live is not exactly new, and I didn’t build it. Other people did, in the past. I think there were a lot of people involved, because it’s a big building and they’ve used a lot of different materials once you start to have a closer look. Someone made a big window so that even though I’m inside, I can look out and see what’s going on outside. Or I can decide not to, by closing the curtains someone made at some point and had other people transport to a shop near where I live, where I was able to buy them. I say ‘near’ where I live, but it’s not really – if I had to walk on my own two feet, it would have taken me more than an hour to get to the shop. I don’t recall which mode of transportation I used instead, which is in itself rather interesting – there are so many different ones (parents’ car, bicycle, bus?) that it’s hard to keep track of how you’ve gotten around.
I’m sitting down on a chair, which is nice. It’s much better than standing up or sitting down on the floor. I wonder how many people were involved in the process of producing the chair, transporting it, selling it… The same questions could be asked about the table the computer is standing on.
The hot food I got before I ate the banana wasn’t always hot. Some of it used to be very cold, it was taken from the freezer. You know, the thing most people own and that they use to put stuff in to keep it cold, even though it’s actually quite cold outside, so that the food can stay fresh much longer and won’t spoil – the thing that was made on some factory far away, much farther away than I could have travelled in a week if I had to walk there. Of course I didn’t heat up the food by starting a fire in my kitchen – for one thing, someone has made a smoke alarm which is set up quite close to my kitchen so that would not have worked very well. No, I used a stove which someone somewhere has made so that people like me can heat up the food I take out of my other machine keeping it cold until it needs to be heated up. And I didn’t eat the food with my hands – well, in a way I did, but I also used a knife, which is probably made in some other country, and a fork – which is also likely made in some other country.
I’m not worried about being unable to locate a source of potable water in case I get thirsty soon. This is because from where I sit I have less than 10 metres to a sink and a faucet, out of which I can make water come out – almost as if by magic. I don’t know the people who make sure that the pipes are clean and that the water is not contaminated with bugs I wouldn’t even be able to see if they were in the water, or perhaps toxins and/or heavy metals which could also easily make me sick; I sort of take it for granted that water comes out of the faucet when I want it to and that I’ll not get sick from drinking it. Not only that – I take it for granted that I have control over the temperature of the water.
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I think people radically underestimate how much their continued existence, to speak nothing of ‘the kind of life they lead at this point’, depends upon people whom they have never even met.
On a related note I generally feel like crap this time of year – the exam periods really have included many of the worst times of my life. But there’s stuff to be grateful for. Stuff to keep remembering. Thoughts like these make it a bit harder for me to feel sorry for myself. I don’t want to feel sorry for myself – in my experience it only if anything tends to make things worse.
Poor modelling
A few recent examples:
i. I played Citadels with my little brother this Christmas. I spotted two obvious instances of poor modelling which happened during the game.
The game is complex and I won’t go over all the rules here – it should be noted that the game complexity is probably part of why these errors to be described below were made in the first place. But anyway, we were in a situation where my brother had picked a specific card. Having picked that specific card he had to try to guess which card I had on my hand – if he guessed correctly, I’d lose my turn and the income that turn would generate (which would benefit him and harm me, making him more likely to win the game). There were two obvious candidates; one card generating a potential income of 2 and another other card generating a potential income of 5. He knew I’d taken one of these cards but not which of them I’d picked – if I randomized my draw completely there’d thus be a 50% chance for him to pick the right card. The situation took place during one ’round’ (subgame) of the game, and both of us knew that this would not be the last round in the game. But we did not know how many more rounds were to be played – a conservative estimate would be at least 4 or 5. Whether it would make sense to consider the round to be one round of several in a semi-’pure’ repeated game or not, and which type of repeated game we’re talking about, depended to some extent on which cards would be picked in future rounds (as I mentioned, the game is complicated – the fundamentals of the stage game can change during gameplay, e.g. I might end up in my brother’s position, i.e. as the player who should guess which card the other player had taken, in a future round); but it would make little sense to consider it a single-shot game.
Now the first thing to note here is that if you consider it a repeated game, it probably doesn’t make a lot of sense not to at least consider to mix strategies. You could probably make an even stronger argument: Consider that if I play ’2′ (the card giving me an income of 2) with a probability of 100 % my brother would probably pick up on that relatively fast and pick that card every round, and I’d end up with an income of zero – and if I always played ’5′, he’d always pick 5. So the second person, the one picking the card to be guessed, has to consider adding some uncertainty to the table or he’s probably going to be in trouble. Now let’s think about how one might best mix strategies in this situation. An important theoretical aspect here is that while it’s certainly a finite game, the lenght of the game is still unknown, or at least uncertain, to the players (they do have some idea how long it’ll take to finish the game). This uncertainty adds complexity, and even though only relatively few rounds of the game is left, the game is still much too complex to be solvable by backwards induction by the players while they play the game even if such a solution might exist. Incidentally in the specific game in question when playing that specific subgame I evaluated the costs of reversing the roles of the players (so that I’d get to be the one guessing, which would be a permissible change to the stage game given a specific subgame strategy constellation) to be too high to implement – but my brother didn’t know that.
The first modelling error here was done by me when I was deciding which card to pick. I did pure randomization when I picked my card – basically I shuffled the cards and picked one of the two cards at random. Basically this was just me being stupid, because this is obviously not the best mixed strategy (it’s only optimal in the case where the expected income derived from the two cards are equal). One way to think about this is that a 50% likelihood of picking either card gives you an expected income of 0,5 x 2 + 0,5 x 5 = 3,5 if your opponent also mixes 50/50 – and foolishly I’d considered only that strategic response to my mixing strategy. The problem is that of course the opponent needn’t mix at all! A mixing strategy on his part is obviously dominated by the pure strategy of always picking ’5′ – if he always picks ’5′, I end up with an average income of only 1 (I get an income of 2 every second round). I realized this 5 seconds after I’d picked my card..
This is where we get to the second modelling error. My little brother said after that specific round had been played – where he’d picked 5 and I’d gotten lucky and randomly picked 2 (so the inferior strategy did not cost me anything in this specific case) that ‘of course he’d picked 5, it was the dominant strategy’. I thought that this was obviously true in the specific case of a mixing strategy on my part with 50/50 mixing, but that it would not be an optimal response to other mixing strategies with a low probability of playing ’5′ (nor would it be an optimal response to the pure strategy of 2). I assumed we’d play at least four more rounds, and in that case it would probably be optimal to go with a mixing strategy with a ~30/70% likelihood or something along those lines (i.e. one ’5′ and 3 ’2′s in the rounds to come) – I figured that 5 is 2,5 as much as 2, so I should play ’2′ 2,5 times as often as ’5′ in equilibrium; i.e. 2,5 ’2′s for every 1 ’5′, meaning I should play ’2′ in 2,5 out of 3,5 rounds, which would be about 70% of the time. I assumed my little brother would mix as well in the rounds to come when I would no longer obviously mix 50/50 and that he’d reach a similar conclusion – that he should pick 5 more often than 2 to minimize my potential income and end up near the (assumed) long-run equilibrium. After the game my little brother made it clear to me that he had not mixed but had played 5 every time, and he stated that he’d picked that strategy because it was ‘the dominant strategy’ and because it would be his best response to any strategy I could come up with. Which it clearly wasn’t.
ii. I went shopping yesterday. I got to the store and it was full of people. I generally dislike shopping when there’s a lot of people around, and I generally avoid this by strategically shopping at times during the day where I know not very many people go shopping. I have previously arrived to a store, decided it was too full of people and postponed my shopping to a later point in time because of that, but yesterday I decided instead to just get it over with fast. When I came back home I remembered that it’s been mentioned in the papers that a lot of people are sick with influenza in Aarhus, and so I realized that I’d just exposed myself to a huge health risk considering how many people were in the store. If asked about this type of stuff before I left my home, I’d have said that such a risk would be completely unacceptable to me, because I have exams before long and thus it would be very inconvenient for me to get sick at this point. If I’d included that health risk in my model, I would not have gone shopping yesterday.
I will often avoid taking public transportation when it’s possible for me to do so due to similar health-related reasons – diseases are easily transmitted in such environments. People often do not remember to include risks like these in their mental models. That’s poor modelling.
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Even (reasonably) simple card games and everyday decisions about stuff like when and where to go grocery shopping can include models too complex for humans to handle well; our cognitive limitations are easy to ignore if we don’t think about them, but they’re there just the same. Social dynamics are usually a lot more complex to model than the stuff in the post. Sometimes it seems almost unbelievable to me that people somehow make all this stuff work – taking all those decisions they do on an average day, interacting with all those other people along the way… Given how complex the world is and how even very simple things like a card game can cause us all kinds of problems when we start thinking about them, I find this pretty amazing to think about.
“A long-held myth regarding development is that as people age, they all become alike. This view is refuted by the third principle of adult development and aging, which asserts that as people age, they become more different from each other rather than more alike. With increasing age, older adults become a more diverse segment of the population in terms of their physical functioning, psychological performance, and conditions of living. In one often-cited study, researchers examined a large number of studies of aging to compare the amount of variability in older versus younger adults (Nelson & Dannefer, 1992). This research established that the variability, or how differently people responded to the measures, was far greater among older adults. Research continues to underscore the notion that individuals continue to become less alike with age. Such findings suggest that diversity becomes an increasingly prominent theme during the adult years, a point we will continue to focus on throughout this book.
The fact that there are increasing differences among adults as they grow older also ties into the importance of experiences in shaping development. As people go through life, their experiences cause them to diverge from others of the same age in more and more ways. You have made the decision to go to college, while others in your age group may have enlisted for military service. You may meet your future spouse in college, while your best friend remains on the dating scene for years. Upon graduation, some may choose to pursue graduate studies as others enter into the workforce. You may or may not choose to start a family, or have already begun the process. With the passage of time, your differing experiences build upon each other to helpmold the person you become. The many possibilities that can stem from the choices you make help to illustrate that the permutations of events in people’s lives are virtually endless. Personal historiesmove in increasingly idiosyncratic directions with each passing day, year, and decade of life.”
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I didn’t post this quote when I first blogged Adult Development and Aging mainly because I figured the insight was probably important enough to merit a post of its own, but also because I figured that if they dealt with this aspect in more details later on I’d rather wait until then to handle the specifics. Anyway it’ll be a while until I get to that stuff and I find myself thinking about these things now and then these days. I’m mostly thinking about how this stuff relates to how we form friendships and establish romantic partnerships. As people age it seems to me that they become less likely to meet that ‘someone who’s just right for me’; and not just because of the work of their romantic rivals. Because of the increasing variation in the behaviours, preferences and outcomes perhaps people who are aging gradually realize that it is strategically optimal for them to become more tolerant, more permissive, and so they implicitly gradually implement such strategies to increase their chances – but that’s hardly always the case and to the extent that it is, the process likely involves them making compromises that perhaps would have been unnecessary if the partners in question had met a decade earlier in their lives. (Though I may here underestimate how much work is required to make a relationship last that long.) Path dependence matters a lot when it comes to both friends and relationships. As I’ve underscored before here on the blog a ‘new’ friend is most often introduced by an ‘old’ friend or acquaintance, and most people rely to a very great extent on their existing social network when they want to make adjustments to it. Over time people’s social networks become entrenched; it gets harder to find and keep new friends not only because every potential new friend is competing for your attention with the whole set of friends you already have, but also because the potential new friend becomes increasingly less likely to share your interests or preferences over time, at the very least when compared with the people with whom you frequently interact. Interaction affects preferences and behaviours, for friends, family and partners alike.
Though people in general tend to become more different from each other as they age, I tend to believe that cohabitating partners do not and that they on the other hand tend to become more alike over time. This is of course because they tend to form similar habits, do similar stuff. Another noteworthy dynamic is the ‘I’ve known you a long time and I’ve invested a lot in this relationship at this point, so it doesn’t matter as much to me that you’re not as compatible as I’d like you to be as it would if we’d only just met’. Of course there’s also (hopefully) the frequent feedback from the partner, making you less likely to stray far away from the partner ideal of the other party – such feedback is harder to obtain for people not in a relationship. There’s also the ‘my previous partner/parents/whatever behaved this way (/cheered for the Green team) and so if you don’t behave this way we won’t be compatible’. Politics, religion and similar stuff’s really important, and often people’s opinions about these matters crystallize over time. If crystallization of this kind of stuff takes place over time, it will generally harm outsiders (singles) and benefit insiders (couples); the people in romantic relationships become more alike over time and so they’ll feel a closer bond to each other as time goes by, and the aging single will in the absence of a romantic partner often obtain much of the relevant social feedback from other singles who may not be able to give useful feedback regarding this aspect of life. For example a single aging man may start to think that his religious- or political views cannot possibly matter a great deal to a potential partner because such things do not matter a great deal to the people with whom he usually interacts. It should perhaps also be noted that the potential decreased compatibility of the remaining outsiders with the insiders makes the outside option become less attractive to the insiders (making them less likely to break up with their partners).
About a decade ago I had relatively few problems talking to and interacting with my extended family (cousins, uncles). These days it’s a pain for me to do it for any period of time, and I found myself actively avoiding the presence of some of these people this Christmas. To the extent that I did interact with them I was polite and helpful, but I did avoid them and I did not want to spend time with them. I find myself worried about where I’ll end up in another decade if things do not go well. Or is it ‘if things do go well?’
Stuff
I thought I should update the blog even though these days I don’t do a lot of blogging-worthy stuff.
i. A blog I recently discovered: Empirical Zeal. There’s some interesting posts there, for example I liked this one on the state of Indian rural education (though the findings reported are not exactly worthy of celebration).
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ii. The acquisition of language by children. From the introduction:
“Imagine that you are faced with the following challenge. You must discover the internal structure of a system that contains tens of thousands of units, all generated from a small set of materials. These units, in turn, can be assembled into an infinite number of combinations. Although only a subset of those combinations is correct, the subset itself is for all practical purposes infinite. Somehow you must converge on the structure of this system to use it to communicate. And you are a very young child.
This system is human language. The units are words, the materials are the small set of sounds from which they are constructed, and the combinations are the sentences into which they can be assembled. Given the complexity of this system, it seems improbable that mere children could discover its underlying structure and use it to communicate. Yet most do so with eagerness and ease, all within the first few years of life.”
It’s actually pretty wild, once you start thinking about it.
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iii. The Null Ritual – What You Always Wanted to Know About Significance Testing but Were Afraid to Ask (via Gwern? I no longer remember how I found this.). An excerpt from the article:
“Question 1: What Does a Significant Result Mean?
What a simple question! Who would not know the answer? After all, psychology students spend months sitting through statistics courses, learning about null hypothesis tests (significance tests) and their featured product, the p-value. Just to be sure, consider the following problem (Haller & Krauss, 2002; Oakes, 1986):
Suppose you have a treatment that you suspect may alter performance on a certain task. You compare the means of your control and experimental groups (say, 20 subjects in each sample). Furthermore, suppose you use a simple independent means t-test and your result is signifi cant (t = 2.7, df = 18, p = .01). Please mark each of the statements below as “true” or “false.” False means that the statement does not follow logically from the above premises. Also note that several or none of the statements may be correct.
(1) You have absolutely disproved the null hypothesis (i.e., there is no difference between the population means). ® True False ®
(2) You have found the probability of the null hypothesis being true. ® True False ®
(3) You have absolutely proved your experimental hypothesis (that there is a difference between the population means). ® True False ®
(4) You can deduce the probability of the experimental hypothesis being true. ® True False ®
(5) You know, if you decide to reject the null hypothesis, the probability that you are making the wrong decision. ® True False ®
(6) You have a reliable experimental finding in the sense that if, hypothetically, the experiment were repeated a great number of
times, you would obtain a significant result on 99% of occasions. ® True False ®
Which statements are true? If you want to avoid the I-knew-it-all-along feeling, please answer the six questions yourself before continuing to read. When you are done, consider what a p-value actually is: A p-value is the probability of the observed data (or of more extreme data points), given that the null hypothesis H0 is true, defined in symbols as p(D |H0).Th is defi nition can be rephrased in a more technical form by introducing the statistical model underlying the analysis (Gigerenzer et al., 1989, chap. 3). Let us now see which of the six answers are correct:
Statements 1 and 3: Statement 1 is easily detected as being false. A significance test can never disprove the null hypothesis. Significance tests provide probabilities, not definite proofs. For the same reason, Statement 3, which implies that a significant result could prove the experimental hypothesis, is false. Statements 1 and 3 are instances of the illusion of certainty (Gigerenzer, 2002).
Statements 2 and 4: Recall that a p-value is a probability of data, not of a hypothesis. Despite wishful thinking, p(D |H0) is not the same as p(H0 |D), and a significance test does not and cannot provide a probability for a hypothesis. One cannot conclude from a p-value that a hypothesis has a probability of 1 (Statements 1 and 3) or that it has any other probability (Statements 2 and 4). Therefore, Statements 2 and 4 are false. The statistical toolbox, of course, contains tools that allow estimating probabilities of hypotheses, such as Bayesian statistics (see below). However, null hypothesis testing does not.
Statement 5: The “probability that you are making the wrong decision” is again a probability of a hypothesis. This is because if one rejects the null hypothesis, the only possibility of making a wrong decision is if the null hypothesis is true. In other words, a closer look at Statement 5 reveals that it is about the probability that you will make the wrong decision, that is, that H0 is true. Thus, it makes essentially the same claim as Statement 2 does, and both are incorrect.
Statement 6: Statement 6 amounts to the replication fallacy. Recall that a p-value is the probability of the observed data (or of more extreme data points), given that the null hypothesis is true. Statement 6, however, is about the probability of “significant” data per se, not about the probability of data if the null hypothesis were true. The error in Statement 6 is that p = 1% is taken to imply that such significant data would reappear in 99% of the repetitions. Statement 6 could be made only if one knew that the null hypothesis was true. In formal terms, p(D |H0) is confused with 1 – p(D). The replication fallacy is shared by many, including the editors of top journals. [...] To sum up, all six statements are incorrect. Note that all six err in the same direction of wishful thinking: They overestimate what one can conclude from a p-value. [...]
We posed the question with the six multiple-choice answers to 44 students of psychology, 39 lecturers and professors of psychology, and 30 statistics teachers [...] How many students and teachers noticed that all of the statements were wrong? As Figure 1 shows, none of the students did. [...] Ninety percent of the professors and lecturers also had illusions, a proportion almost as high as among their students. Most surprisingly, 80% of the statistics teachers shared illusions with their students.”
The article has much more.
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“More than 25% of the U.S. population aged [>65] years has diabetes (1), and the aging of the overall population is a significant driver of the diabetes epidemic. [...] The incidence of diabetes increases with age until about age 65 years, after which both incidence and prevalence seem to level off”. I should have known the first number was in that neighbourhood, but somehow I had failed to realize that it was that high; most often prevalence estimates are calculated/reported using the entire population in the denominator, but of course such estimates can be deceiving if you do not think about how they are calculated and I clearly hadn’t. At least 1 in 4 in the above-65 age bracket. That’s a lot of people. The article doesn’t have a lot of data, it’s a ‘consensus report’ handling mostly various treatment guideline suggestions and similar stuff.
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v. What is the most uncomfortable situation have you ever been put in- by a guy? Any kind of unwanted flirtation- or something of that nature (Reddit). Lots of really horrible stuff; reading stuff like this makes what might be perceived of as some females’ ‘somewhat overcautious’ behaviour towards members of the opposite sex easier to understand. An example from the link:
“The last stranger-danger moment I will share tonight was at an end-of-midterms party sponsored by the student union at a local bar. I was there with my best friend, and she’s very pretty and very friendly, so we’d very quickly attracted a group of four or five men who were hanging around with us for most of the night. I hadn’t seen any of them before, so I assumed they were students from a different department, and we end up getting a table together and talking for a while. Once my friend mentions that she has a boyfriend, most of them shift their attention to me, though there’s one who still seems interested in her. As I’m talking to them, I find that they’re not students at our university, but that they’re a group of friends visiting from the a couple towns over. Nothing too creepy, so far.
My friend finishes her drink, so the guy she’s talking to goes to buy her another. She’s a little suspicious, so she starts drinking it VERY slowly. Meanwhile, I’m getting distracted talking to one of the guys who works in the same field I’ll be entering soon, and we end up talking for a while about that. He keeps telling me that I’m very beautiful, which I keep brushing off because I knew he was interested in my friend initially, and I was interested in someone else at the time, anyway. Somewhere in the middle of all this, my friend has stopped drinking the drink that was bought for her, and someone asks if she’s going to finish it. She says no.
Eventually, the guy I’m talking to apologizes for his “bad” English, saying that he hasn’t really had to use it since he was in school, which was OVER TEN YEARS AGO. At about the same time, my friend is telling the guy she’s talking to that it’s funny that they decided to visit our city on that particular weekend, because this is a student end-of-midterm party, and he answers, “I know. That’s kind of why we came here.” Someone else asks my friend if she’s going to finish her drink, and she says no, but he can have it if he wants. The drink ‘accidentally’ gets spilled in the process, and she’s signalling me to get the fuck out of there, so I take the opportunity to drag her to the bathroom. I start to notice that she’s acting really fucked up – she can usually drink a ton more than I can, and she’d only had one drink of her own and maybe a third (probably less than that, actually) of the one that guy bought for her. She says she thinks the drink they gave her was drugged, and then she gets sick. I ended up staying the night at her place to keep an eye on her, but I didn’t think to take her to the hospital or anything, so I guess we’ll never know what exactly happened…”
Of course if you’re like me you don’t engage in risky behaviours like drinking with strangers and in that case it doesn’t really matter much if you’re male or female, but then again I’m not like normal people. Most males probably significantly underestimate how risky some of their behaviours – behaviours they would not ever even think of as ‘particularly risky’ – are when a female engages in them. Note that even males that fall into the “I can’t imagine you raising your voice”-category (a female friend said this about me in a conversation I had with her earlier today) are likely to be affected by the behaviours of the (type of) males described in the link; once a female has been through situations like the ones described at the link, she’s less likely to give males the benefit of the doubt and more likely to misinterpret behaviour and the motivations driving behaviour. Reading this stuff has made me believe that the behaviour of ‘overcautious’ females may be better justified and less ‘irrational’ than males tend to think it is.
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vi. I haven’t commented on the new DSM-5 – let’s just say I’ve had better things to do. Here’s one take on it (“It’s arcane, contradictory and talks about invisible entities which no-one can really prove. Yes folks, the new psychiatric bible has been finalised.”). The most ‘relevant’ change to me is the fact that they’ll remove the Asperger Syndrome diagnosis, and instead merge it with other autism spectrum disorders. If you’re asking me what I think about that, the answer is that I don’t really care.
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vii. Cheetahs on the Edge (via Ed Yong). A must-see:
“Using a Phantom camera filming at 1200 frames per second while zooming beside a sprinting cheetah, the team captured every nuance of the cat’s movement as it reached top speeds of 60+ miles per hour.
The extraordinary footage that follows is a compilation of multiple runs by five cheetahs during three days of filming.”
Merry Xmas
What (part of) Denmark looks like now:
I’m at my parents’ and will be for the next few days as well.
Stuff
I don’t like when the blog isn’t updated for several days, so here are some links to stuff I’ve encountered on the internet in the recent past:
i. Diabetic Autonomic Neuropathy. An overview article which covers a lot of ground; it has approximately 1000 citations and I believe it’s one of the most read articles published in Diabetes Care, a journal you incidentally should know about if you’re diabetic or are interested in diabetes.
ii. Also diabetes-related and closely related to the above paper: The EKG in Diabetes Mellitus. This article is particularly relevant to me because I had an EKG last week and will be told the results of it tomorrow where I have a doctor’s appointment – reading stuff like this first makes it easier to ask the right questions. I jokingly explained to a friend yesterday that if the results of that test come out a specific way, it will be much easier for me to make pension plans (meaning I’d most likely be dead long before the official retirement age – naturally I do not hope for that outcome to happen). I’ll also learn the results of the standard Hba-1c blood test – which is measured 3-4 times a year – as well as the annual urin-sample analysis to check for microalbuminuria (kidney damage). Also, cholesterol levels and triglycerides. So I’ll learn more from this check-up than I usually do. I hope everything is fine but there’s a reason why they perform tests like these; I have no way of knowing myself if there’s a problem here.
Anyway, a few quotes from the paper:
“Fibrotic changes, especially in the basal area of the left ventricle, have frequently been observed in diabetic patients, even when cardiac involvement is clinically not yet evident. [...] The EURODIAB Insulin-Dependent Diabetes Mellitus Complications Study (EURODIAB IDDM)9 investigated 3250 type 1 diabetes patients with an average diabetes duration of >30 years; the prevalence of left ventricular hypertrophy was found to be 3 times greater than that reported in the general population of similar age. [...] Baroreflex dysfunction and disturbed heart rate variability are the most commonly used methods to assess CAN [Cardiovascular autonomic neuropathy, US]. [...]
Ong et al14 found the QTc to be shorter if patients had signs of neuropathy, although these patients’ heart rate was higher and their circadian patterns seemed to be preserved. Valensi et al15 found an unchanged QTc in mild neuropathy, although the circadian day/night QTc pattern was reversed. Pappachan et al16 expressed the view that the QTc interval can be used to diagnose CAN with reasonable sensitivity, specificity, and positive predictive value. Grossmann et al17 observed a prolonged QTc only in diabetic patients with CAN; late potentials were not recorded in any of these patients with CAN. CAN patients with prolonged variability in QTc, QT, or both had high incidence of sudden death.18 [...]
Myocardial ischemia is more often painless in patients with diabetes mellitus.19 Resting ECG abnormalities20 as well as cardiac autonomic dysfunction21 were found to be predictors of silent ischemia in asymptomatic persons with T1D.
In otherwise healthy diabetic men during an average follow-up of 16 years, an abnormal and even an equivocal exercise ECG response was associated with a statistically significant high risk for all-cause and cardiac mortality and morbidity, independently of physical fitness and other traditional risk factors; fit men had a higher survival rate than did unfit men.22 [One more reason why I shouldn't have that much trouble motivating myself to stay in shape.] [...]
The early stage of diabetic cardiomyopathy may already be associated with a range of metabolic abnormalities and even with abnormalities in diastolic function. Frequently, no structural cardiac abnormalities can be identified at this stage; the often subtle ECG alterations may be our only way to diagnose early diabetic cardiomyopathy. [...]
Conclusions
Even early in the course of diabetes mellitus, ECG alterations such as sinus tachycardia, long QTc, QT dispersion, changes in heart rate variability, ST-T changes, and left ventricular hypertrophy may be observed. ECG alterations help evaluate cardiac autonomic neuropathy and detect signs of myocardial ischemia even in asymptomatic patients. Prolonged myocardial fibrosis leads to diabetic cardiomyopathy, with peculiar ECG presentation. Electrocardiographic changes are already present in fetuses, children, and adolescents. The resting ECG, frequently complemented by exercise ECG, assists in cardiac screening of diabetic individuals and helps detect silent ischemia, assess prognosis, and predict mortality”
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iii. Boredom Proneness: Its Relationship to Psychological- and Physical-Health Symptoms, by Sommers and Vodanovich.
“The relationship between boredom proneness and health-symptom reporting was examined. Undergraduate students (N 5 200) completed the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance indicated that individuals with high boredomproneness total scores reported significantly higher ratings on all five subscales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression). The results suggest that boredom proneness may be an important element to consider when assessing symptom reporting. Implications for determining the effects of boredom proneness on psychological- and physical-health symptoms, as well as the application in clinical settings, are discussed.”
I had no idea there was such a thing as a ‘Boredom Proneness Scale’! I found the literature overview in the beginning of the paper much more interesting than the study itself (one word: WEIRD). Judging from the reported results there, if you’re bored a lot and/or have a really boring job you may be well advised to do something about that – because being bored is associated with a lot of bad stuff:
“To date, the work on boredom proneness has focused on its association with negative affect, as well as problems in academic and work settings. For instance, significant positive relationships have been found between the tendency to experience boredom and depression, anxiety, hostility, anger, loneliness, and hopelessness (e.g., Ahmed, 1990; Farmer & Sundberg, 1986; Rupp & Vodanovich, 1997; Vodanovich, Verner, & Gilbride,
1991; Watt & Davis, 1991). Other researchers have reported boredom proneness to be related significantly to lower educational achievement, truancy rate, and poor work performance (e.g., Branton, 1970; Drory, 1982; Gardell, 1971; Maroldo, 1986; O’Hanlon, 1981; Robinson, 1975; Smith, 1981).
Limited work, however, has been devoted to investigating the association between boredom and psychological- and physical-health symptoms. Evidence for such a relationship can be inferred from studies reporting significant, positive correlations between boredom and substance abuse and eating disorders (e.g., Abramson & Stinson, 1977; Ganley, 1989; Johnston & O’Malley, 1986; Martin, 1989; Pascale & Sylvester, 1988).
Other researchers have established a connection between boredom and detrimental health effects in organizational settings. For instance, Smith, Cohen, and Stammerjohn (1981) found that workers in monotonous jobs reported more visual, musculoskeletal, and emotional-health complaints than those performing non-monotonous work. Samilova (1971) found that female Russian workers employed in repetitive tasks experienced higher incidence of health problems, including gastritis, peripheral neurological disorders, and joint, tendon, muscle, and cardiovascular disease, than workers in less-repetitive jobs. Ferguson (1973) found that telegraphists who complained of task monotony indicated a greater occurrence of physical-health problems, such as asthma, bronchitis, trunk myalgia, and hand tremors, as compared to other workers in less-monotonous positions.”
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iv. Ideology, Motivated Reasoning, and Cognitive Reflection: An Experimental Study. I haven’t actually gotten around to reading this yet, but I bookmarked it for a reason; I probably will later during the week.
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v. Media Use Among White, Black, Hispanic, and Asian American Children, by Rideout, Lauricella and Wartella. I’ve written about that stuff before but I haven’t written about this data. It’s survey data so it should be taken with a grain of salt. Even if it is, however, I think there’s some interesting information here. Some stuff from the report:
“Historically, scholars have been aware of differences in the amount of time that White and minority children spend with media, especially TV. But last year’s Generation M2 study indicated a large increase in the amount of time both Black and Hispanic youth are spending with media, to the point where they are consuming an average of 13 hours worth of media content a day (12:59 for Blacks and 13:00 for Hispanics), compared with about eight and a half hours (8:36) for White youth, a difference of about four and a half hours a day.” [my emphasis] [...]
The biggest differences are in the amount of time spent with a TV (a difference of about one to two hours of TV a day between White and minority youth), music (a difference of about an hour a day), computers (up to an hour and a half difference), and video games (from 30 to 40 minutes difference).”
Here’s the ‘big picture’, click to view full size:
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vi. I really, truly dislike (and that’s putting it mildly) the new format for the discover magazine blogs, but I really liked this post by Razib Khan. Then again it was posted before the switch. I like a lot of his stuff so I tend not to link to individual posts (I’d have to link to a lot of stuff…) but I figure I should remind you now and then that you should be reading his blog. Even if the new format sucks.
Stuff
i. American nuclear guinea pigs : three decades of radiation experiments on U.S. citizens.
“SUMMARY AND CONCLUSIONS
Documents provided by the Department of Energy reveal the frequent and systematic use of human subjects as guinea pigs for radiation experiments. Some experiments were conducted in the 1940s at the dawn of the nuclear age, and might be attributed to an ignorance of the long term effects of radiation exposure, or to the atomic hubris that accompanied the making of the first nuclear bombs. But other experiments were conducted during the supposedly more enlightened 1960s and 1970s. In either event, such experiments cannot be excused.
These experiments were conducted under the sponsorship of the Manhattan Project, the Atomic Energy Commission, or the Energy Research and Development Administration, all predecessor agencies of the Department of Energy. These experiments spanned roughly thirty years. This report presents the findings of the Subcommittee staff on this project.
Literally hundreds of individuals were exposed to radiation in experiments which provided little or no medical benefit to the subjects. The chief objectives of these experiments were to directly measure the biological effects of redioactive material; to measure doses from injected, ingested, or inhaled redioactive substances; or to measure the time it took radioactive substances to pass through the human body. American citizens thus became nuclear calibration devices.
In many cases, subjects willingly participated in experiments, but they became willing guinea pigs nonetheless. In some cases, the human subjects were captive audiences or populations that experimenters might frighteningly have considered “expendable”: the elderly, prisoners, hospital patients suffering from terminal diseases or who might not have retained their full faculties for informed consent. For some human subjects, informed consent was not obtained or there is no evidence that informed consent was granted. For a number of these same subjects, the government covered up the nature of the experiments and deceived the families of deceased victims as to what had transpired. In many experiments, subjects received doses that approached or even exceeded presently recognized limits for occupational radiation exposure. Doses were as great as 98 times the body burden recognized at the time the experiments were conducted.”
It seems that the Tuskegee syphilis experiment wasn’t quite as unique as I’d thought.
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ii. Diuretic Treatment of Hypertension. Interesting, lots of stuff there I didn’t know.
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iii. Income disparity and Risk of death: The Importance of Health Behaviors and Other Mediating Factors
“After adjusting for age, sex, education, and race/ethnicity, risk of death was higher in low-income than high-income group for both all-cause mortality (Hazard ratio [HR], 1.98; 95% confidence interval [CI]: 1.37, 2.85) and cardiovascular disease (CVD)/diabetes mortality (HR, 3.68; 95% CI: 1.64, 8.27). The combination of the four pathways attenuated 58% of the association between income and all-cause mortality and 35% of that of CVD/diabetes mortality. Health behaviors attenuated the risk of all-cause and CVD/diabetes mortality by 30% and 21%, respectively, in the low-income group. Health status attenuated 39% of all-cause mortality and 18% of CVD/diabetes mortality, whereas, health insurance and inflammation accounted for only a small portion of the income-associated mortality (≤6%).
Conclusion
Excess mortality associated with lower income can be largely accounted for by poor health status and unhealthy behaviors. Future studies should address behavioral modification, as well as possible strategies to improve health status in low-income people.”
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iv. Influence of Opinion Dynamics on the Evolution of Games. I’ve only just skimmed this, but it looks interesting. Here’s the abstract:
“Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.”
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v. This is awesome.
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“Determining the fitness consequences of sibling interactions is pivotal for understanding the evolution of family living, but studies investigating them across lifetime are lacking. We used a large demographic dataset on preindustrial humans from Finland to study the effect of elder siblings on key life-history traits. The presence of elder siblings improved the chances of younger siblings surviving to sexual maturity, suggesting that despite a competition for parental resources, they may help rearing their younger siblings. After reaching sexual maturity however, same-sex elder siblings’ presence was associated with reduced reproductive success in the focal individual, indicating the existence of competition among same-sex siblings. Overall, lifetime fitness was reduced by same-sex elder siblings’ presence and increased by opposite-sex elder siblings’ presence. Our study shows opposite effects of sibling interactions depending on the life-history stage, and highlights the need for using long-term fitness measures to understand the selection pressures acting on sibling interactions.”
Where did they get their data? Well, it was hard for people living in the 17th and 18th century to avoid death or taxes too:
“The demographic dataset from historical Finnish populations was compiled from records of the Lutheran church, which was obliged by law to document all dates of births, marriages and deaths in the population for tax purposes [25–29]. As migration events were relatively rare and the migration records maintained by the church allowed us to follow dispersers in the majority of the cases, these records provide us with relatively accurate information on individual survival and reproductive histories [28] (e.g. 91% of individuals with known birth date were followed to sexual maturity at age 15 years). Our study period is limited to the eighteenth and nineteenth centuries, before the transition to reduced birth and mortality rates [30].”
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vii. I’ve posted about this topic before, here’s a new study on cancer screening procedures: Effect of Three Decades of Screening Mammography on Breast-Cancer Incidence. I think the results are depressing:
“The introduction of screening mammography in the United States has been associated with a doubling in the number of cases of early-stage breast cancer that are detected each year, from 112 to 234 cases per 100,000 women — an absolute increase of 122 cases per 100,000 women. Concomitantly, the rate at which women present with late-stage cancer has decreased by 8%, from 102 to 94 cases per 100,000 women — an absolute decrease of 8 cases per 100,000 women. With the assumption of a constant underlying disease burden, only 8 of the 122 additional early-stage cancers diagnosed were expected to progress to advanced disease. After excluding the transient excess incidence associated with hormone-replacement therapy and adjusting for trends in the incidence of breast cancer among women younger than 40 years of age, we estimated that breast cancer was overdiagnosed (i.e., tumors were detected on screening that would never have led to clinical symptoms) in 1.3 million U.S. women in the past 30 years. We estimated that in 2008, breast cancer was overdiagnosed in more than 70,000 women; this accounted for 31% of all breast cancers diagnosed.
Conclusions
Despite substantial increases in the number of cases of early-stage breast cancer detected, screening mammography has only marginally reduced the rate at which women present with advanced cancer. Although it is not certain which women have been affected, the imbalance suggests that there is substantial overdiagnosis, accounting for nearly a third of all newly diagnosed breast cancers, and that screening is having, at best, only a small effect on the rate of death from breast cancer.”
Stuff
i. Temporal view of the costs and benefits of self-deception, by Chance, Nortona, Ginob, and Ariely. The abstract:
“Researchers have documented many cases in which individuals rationalize their regrettable actions. Four experiments examine situations in which people go beyond merely explaining away their misconduct to actively deceiving themselves. We find that those who exploit opportunities to cheat on tests are likely to engage in self-deception, inferring that their elevated performance is a sign of intelligence. This short-term psychological benefit of self-deception, however, can come with longer-term costs: when predicting future performance, participants expect to perform equally well—a lack of awareness that persists even when these inflated expectations prove costly. We show that although people expect to cheat, they do not foresee self-deception, and that factors that reinforce the benefits of cheating enhance self-deception. More broadly, the findings of these experiments offer evidence that debates about the relative costs and benefits of self-deception are informed by adopting a temporal view that assesses the cumulative impact of self-deception over time.”
A bit more from the paper:
“People often rationalize their questionable behavior in an effort to maintain a positive view of themselves. We show that, beyond merely sweeping transgressions under the psychological rug, people can use the positive outcomes resulting from negative behavior to enhance their opinions of themselves—a mistake that can prove costly in the long run. We capture this form of self-deception in a series of laboratory experiments in which we give some people the opportunity to perform well on an initial test by allowing them access to the answers. We then examine whether the participants accurately attribute their inflated scores to having seen the answers, or whether they deceive themselves into believing that their high scores reflect new-found intelligence, and therefore expect to perform similarly well on future tests without the answer key.
Previous theorists have modeled self-deception after interpersonal deception, proposing that self-deception—one part of the self deceiving another part of the self—evolved in the service of deceiving others, since a lie can be harder to detect if the liar believes it to be true (1, 2). This interpersonal account reflects the calculated nature of lying; the liar is assumed to balance the immediate advantages of deceit against the risk of subsequent exposure. For example, people frequently lie in matchmaking contexts by exaggerating their own physical attributes, and though such deception might initially prove beneficial in convincing an attractive prospect to meet for coffee, the ensuing disenchantment during that rendezvous demonstrates the risks (3, 4). Thus, the benefits of deceiving others (e.g., getting a date, getting a job) often accrue in the short term, and the costs of deception (e.g., rejection, punishment) accrue over time.
The relative costs and benefits of self-deception, however, are less clear, and have spurred a theoretical debate across disciplines (5–10). [...]
As we had expected, social recognition exacerbated self-deception: those who were commended for their answers-aided performance were even more likely to inflate their beliefs about their subsequent performance. The fact that social recognition, which so often accompanies self-deception in the real world, enhances self-deception has troubling implications for the prevalence and magnitude of self-deception in everyday life.”
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ii. Nonverbal Communication, by Albert Mehrabian. Some time ago I decided that I wanted to know more about this stuff, but I haven’t really gotten around to it until now. It’s old stuff, but it’s quite interesting. Some quotes:
“The work of Condon and Ogston (1966, 1967) has dealt with the synchronous relations of a speaker’s verbal cues to his own and his addressee’s nonverbal behaviors. One implication of their work is the existence of a kind of coactive regulation of communicator-addressee behaviors which is an intrinsic part of social interaction and which is certainly not exhausted through a consideration of speech alone. Kendon (1967a) recognized these and other functions that are also served by implicit behaviors, particularly eye contact. He noted that looking at another person helps in getting information about how that person is behaving (that is, to monitor), in regulating the initiation and termination of speech, and in conveying emotionality or intimacy. With regard to the regulatory function, Kendon’s (1967a) findings showed that when the speaker and his listener are baout to change roles, the speaker looks in the direction of his listener as he stops talking, and his listener in turn looks away as he starts speaking. Further, when speech is fluent, the speaker looks more in the direction of his listener than when his speech is disrupted with errors and hesitations. Looking away during these awkward moments implies recognition by the speaker that he has less to say, and is demanding less attention from his listener. It also provides the speaker with some relief to organize his thoughts.
The concept of regulation has also been studied by Scheflen (1964, 1965). According to him, a communicator may use changes in posture, eye contact, or position to indicate that (1) he is about to make a new point, (2) he is assuming an attitude relative to several points being made by himself or his addresse, or (3) he wishes to temporarily remove himself from the communication situation, as would be the case if he were to select a great distance from the addressee or begin to turn his back on him. There are many interesting aspects of this regulative function of nonverbal cues that have been dealt with only informally. [...]
One of the first attempts for a more general characterization of the referents of implicit behavior and, therefore, possibly of the behaviors themselves, was made by Schlosberg (1954). He suggested a three-dimensional framework involving pleasantness-unpleasantness, sleep-tension, and attention-rejection. Any feeling could be assigned a value on each of these three dimensions, and different feelings would correspond to different points in this three-dimensional space. This shift away from the study of isolated feelings and their corresponding nonverbal cues and toward a characterization of the general referents of nonverbal behavior on a limited set of dimensions was seen as beneficial. It was hoped that it could aid in the identification of large classes of interrelated nonverbal behaviors.
Recent factor-analytic work by Williams and Sundene (1965) and Osgood (1966) provided further impetus for characterizing the referents of implicit behavior in terms of a limited set of dimensions. Williams and Sundene (1965) found that facial, vocal, or facial-vocal cues can be categorized primarily in terms of three orthogonal factors: general evalution, social control, and activity.
For facial expression of emotion, Osgood (1966) suggested the following dimensions as primary referents: pleasantness (joy and glee versus dread and anxiety), control (annoyance, disgust, contempt, scorn, and loathing versus dismay, bewilderment, surprise, amazement, and excitement), and activation (sullen anger, rage, disgust, scorn, and loathing versus despair, pity, dreamy sadness, boredom, quiet pleasure, complacency, and adoration). [...]
Scheflen (1964, 1965, 1966) provided detailed observations of an informal quality on the significance of postures and positions in interpersonal situations. Along similar lines, Kendon (1967a) and Exline and his colleagues explored the many-faceted significance of eye contact with, or observation of, another [...] These investigations consistently found, among same-sexed pairs of communicators, that females generally had more eye contact with each other than did males; also, members of both sexes had less eye contact with one another when the interaction between them was aversive [...] In generally positive exchanges, males had a tendency to decrease their eye contact over a period of time, whereas females tended to increase it (Exline and Winters, 1965). [...]
extensive data provided by Kendon (1967a) showed that observation of another person duing a social exchange varied from about 30 per cent of 70 per cent, and that corresponding figures for eye contact ranged from 10 per cent to 40 per cent. [...]
Physical proximity, touching, eye contact, a forward lean rather than a reclining position, and an orientation of the torso toward rather than away from an addressee have all been found to communicate a more positive attitude toward him. A second set of cues that indicates postural relaxation includes asymmetrical placement of the limbs, a sideways lean and/or reclining position by the seated communicator, and specific relaxation measures of the hands or neck. This second set of cues relates primarily to status differences between the communicator and his addressee: there is more relaxation with an addressee of lower status, and less relaxation with one of higher status. [...]
In sum, the findings from studies of posture and position and subtle variations in verbal statements [...] show that immediacy cues primarily denote evaluation, and postural relaxation ues denote status or potency in a relationship. It is interesting to note a weaker effect: less relaxation of one’s posture also conveys a more positive attitude toward another. One way to interpret this overlap of the referential significance of less relaxation and more immediacy in communicating a more positive feeling is in terms of the implied positive connotations of higher status in our culture. A respectful attitude (that is, when one conveys that the other is of higher status) does indeed have implied positive connotations. Therefore it is not surprising that the communication of respect and of positive attitude exhibits some similarity in the nonverbal cues that they require. However, whereas the communication of liking is more heavily weighted by variations in immediacy, that of respect is weighted more by variations in relaxation.”
I should probably note here that whereas it makes a lot of sense to be skeptical of some of the reported findings in the book, simply to get an awareness of some of the key variables and some proposed dynamics may actually be helpful. I don’t know how deficient I am in these areas because I haven’t really given body language and similar stuff much thought; I assume most people haven’t/don’t, but I may be mistaken.
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iii. A friend let me know about this ressource and I thought I should share it here. It’s a collection of free online courses/lectures provided by Yale University.
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iv. Prevalence, Heritability, and Prospective Risk Factors for Anorexia Nervosa. It’s a pretty neat setup: “During a 4-year period ending in 2002, all living, contactable, interviewable, and consenting twins in the Swedish Twin Registry (N = 31 406) born between January 1, 1935, and December 31, 1958, underwent screening for a range of disorders, including AN. Information collected systematically in 1972 to 1973, before the onset of AN, was used to examine prospective risk factors for AN.”
“Results The overall prevalence of AN was 1.20% and 0.29% for female and male participants, respectively. The prevalence of AN in both sexes was greater among those born after 1945. Individuals with lifetime AN reported lower body mass index, greater physical activity, and better health satisfaction than those without lifetime AN. [...]
[...]
This study represents, to our knowledge, the largest twin study conducted to date of individuals with rigorously diagnosed AN. Our results confirm and extend the findings of previous studies on prevalence, risk factors, and heritability.
Consistent with several studies, the lifetime prevalence of AN identified by all sources was 1.20% in female participants and 0.29% in male participants, reflecting the typically observed disproportionate sex ratio. Similarly, our data show a clear increase in prevalence of DSM-IV AN (broadly and narrowly defined) with historical time in Swedish twins. The increase was apparent for both sexes. Hoek and van Hoeken3 also reported a consistent increase in prevalence, with a leveling out of the trajectory around the 1970s. Future studies in younger STR participants will allow verification of this observation.
Several observed differences between individuals with and without AN were expected, ie, more frequent endorsement of symptoms of eating disorders. Other differences are noteworthy. Consistent with previous observations, individuals with lifetime AN reported lower BMIs at the time of interview than did individuals with no history of AN. Although this could be partially accounted for by the presence of currently symptomatic individuals in the sample, our results remained unchanged when we excluded individuals likely to have current AN (ie, current BMI, ≤17.5). Previous studies have shown that, even after recovery, individuals with a history of AN have a low BMI.59 Although perhaps obvious, a history of AN appears to offer protection against becoming overweight. The protective effect also holds for obesity (BMI, ≥30), although there were too few individuals in the sample with histories of AN who had become obese for meaningful analyses. Despite the obvious nature of this observation, the mechanism whereby protection against overweight is afforded is not immediately clear. Those with a history of AN reported greater current exercise and a perception of being in better physical health. One possible interpretation of this pattern of findings is that individuals with a history of AN continue to display subthreshold symptoms of AN (ie, excessive exercise and caloric restriction) that contribute to their low BMIs. Alternatively, symptoms that were pathologic during acute phases of AN, such as excessive exercise and decreased caloric intake, may resolve over time into healthy behaviors, such as consistent exercise patterns and a healthful diet, that result in better weight control and self-rated health.
Regardless of which of these hypotheses is true, another intriguing difference is that individuals with lifetime AN report a lower age at highest BMI, although the magnitude of the highest lifetime BMI does not differ in those with and without a history of AN. Those with AN report their highest lifetime BMIs early in their fourth decade of life on average, whereas those without AN report their highest BMIs in the middle of their fifth decade of life (close to the age at interview). On a population level, adults tend to gain on average 2.25 kg (5 lb) per decade until reaching their eighth decade of life.60 Although more detailed data are necessary to make definitive statements about different weight trajectories, our results suggest not only that individuals with AN may maintain low BMIs but also that they may not follow the typical adult weight gain trajectories. These data are particularly intriguing in light of recent reports of AN being associated with reduced risk of certain cancers61 - 62 and protective against mortality due to diseases of the circulatory system.63 - 64 Energy intake is closely related to fat intake and obesity, both of which have also been related to cancer development65 - 66 and both of which are reduced in AN. Further detailed studies of the weight trajectories and health of individuals with histories of AN are required to explicate the nature and magnitude of these intriguing findings.
Of the variables assessed in 1972 to 1973, neuroticism emerged as the only significant prospective predictor of AN. This is notable because there have been few truly prospective risk factor studies of AN.”
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v. The music is a bit much for me towards the end, but this is just an awesome video. I think I’d really have liked to know that guy:
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vi. Political Sorting in Social Relationships: Evidence from an Online Dating Community, by Huber and Malhotra.
I found these data surprising (and I’m skeptical about the latter finding):
“Among paid content, online dating is the third largest driver of Internet traffic behind music and games (Jupiter Research 2011).A substantial number of marriages also result from interactions started online. For instance, a Harris Interactive study conducted in 2007 found that 2% of U.S. marriages could be traced back to relationships formed on eHarmony.com, a single online dating site (Bialik 2009).”
Anyway I’ll just post some data/results below and leave out the discussion (click to view tables in full size). Note that there are a lot of significant results here:
The last few figures are also interesting (people really care about that black/white thing when they date (online)…). but you can go have a look for yourself. As I’ve already mentioned there are a lot of significant results – they had a huge number of data to work with (170,413 men and 132,081 women).
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Richard Feynman – The Pleasure Of Finding Things Out
I recently found this gem on youtube and I thought I should share it:
Stuff
File under: Stuff you probably didn’t know about that actually matters a great deal.
“Generation of electricity using coal started at the end of the 19th century. The first power stations had an efficiency of around 1%, and needed 12.3 kg of coal for the generation of 1 kWh. [...] With increasing experience, in combination with research and development, these low efficiency levels improved rapidly. Increased technical experience with coal processing and combustion technology enabled a steady increase in the steam parameters ‘pressure’ and ‘temperature’, resulting in higher efficiency. In the years 1910, efficiency had already increased to 5%, reaching 20% by 1920. In the fifty’s, power plants achieved 30% efficiency, but the average efficiency of all operating power plants was still a modest 17%. [...] continuous development resulted around the mid 80′s in an average efficiency of 38% for all power stations, and best values of 43%. In the second half of the nineties, a Danish power plant set a world record at 47%. [...] The average efficiency of all coal power stations in the world is around 31%. [...] In the next 10 years [the paper is from 2005, US], efficiencies up to 55% can be expected.” [...]
Often, the question is asked why the ‘other 45%’ cannot be converted into electricity. This relates to the laws of physics: the absolute maximum efficiency is the so-called ‘Carnot efficiency‘. For a turbine operating with gasses of 600°C, it is 67%. Then we need to take into account the exergy content of steam (around 94%). Also combustion has an efficiency less than 100% (around 95%). The transfer of combustion heat to steam in the boiler is for example 96% efficient. Losses due to friction can be around 5% (efficiency 95%). The efficiency of a generator is about 98% on average . . . .
To obtain the combined efficiency, one needs to multiply the efficiency of each process. Taking the above mentioned components, one obtains 0.67 x 0.94 x 0.95 x 0.96 x 0.95 x 0.98 = 0.535 or 53.5%.
This does not yet take into account the efficiency of all components. The power station’s own power use for motors to grind coal, pumps, ventilators, . . . further reduces efficiency. In practice, net efficiency will be around 40 and 45%. Continuous load changes, i.e. following the load, and start-up/shutdown procedures further lower efficiency. The increasing variability of the load, through increased use of intermittent sources such as wind, will lead to increased swings in the load of the power station, reducing efficiency.”
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ii. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. From the abstract:
“Allostatic load (AL) has been proposed as a new conceptualization of cumulative biological burden exacted on the body through attempts to adapt to life’s demands. Using a multisystem summary measure of AL, we evaluated its capacity to predict four categories of health outcomes, 7 years after a baseline survey of 1,189 men and women age 70–79. Higher baseline AL scores were associated with significantly increased risk for 7-year mortality as well as declines in cognitive and physical functioning and were marginally associated with incident cardiovascular disease events, independent of standard socio-demographic characteristics and baseline health status. The summary AL measure was based on 10 parameters of biological functioning, four of which are primary mediators in the cascade from perceived challenges to downstream health outcomes. Six of the components are secondary mediators reflecting primarily components of the metabolic syndrome (syndrome X). AL was a better predictor of mortality and decline in physical functioning than either the syndrome X or primary mediator components alone. The findings support the concept of AL as a measure of cumulative biological burden.
In elderly populations, comorbidity in the form of multiple co-occurring chronic conditions is the norm rather than the exception. For example, in the U.S. 61% of women and 47% of men age 70–79 report two or more chronic conditions. These figures rise to 70% of women and 53% of men age 80–89 with 2+ chronic conditions (1). No single form of comorbidity occurs with high frequency, but rather a multiplicity of diverse combinations are observed (e.g., osteoarthritis and diabetes, colon cancer, coronary heart disease, depression, and hypertension). This diversity underscores the need for an early warning system of biomarkers that can signal early signs of dysregulation across multiple physiological systems.
One response to this challenge was the introduction of the concept of allostatic load (AL) (2–4) as a measure of the cumulative physiological burden exacted on the body through attempts to adapt to life’s demands. The ability to successfully adapt to challenges has been referred to by Sterling and Eyer (5) as allostasis. This notion emphasizes the physiological imperative that, to survive, “an organism must vary parameters of its internal milieu and match them appropriately to environmental demands” (5). When the adaptive responses to challenge lie chronically outside of normal operating ranges, wear and tear on regulatory systems occurs and AL accumulates.”
They conclude that: “The analyses completed to date suggest that the concept of AL offers considerable insight into the cumulative risks to health from biological dysregulation across multiple regulatory systems.” I haven’t come across the concept before but I’ll try to keep it in mind. There’s a lot of stuff on this.
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iii. Surviving a plane crash is surprisingly common.
“a few years ago, I learned that it’s actually pretty common to survive a plane crash. Like most people, I’d assumed that the safety in flying came from how seldom accidents happened. Once you were in a crash situation, though, I figured you were probably screwed. But that’s not the case.
Looking at all the commercial airline accidents between 1983 and 2000, the National Transportation Safety Board found that 95.7% of the people involved survived. Even when they narrowed down to look at only the worst accidents, the overall survival rate was 76.6%. Yes, some plane crashes kill everyone on board. But those aren’t the norm. So you’re even safer than you think. Not only are crashes incredibly rare, you’re more likely to survive a crash than not. In fact, out of 568 accidents during those 17 years, only 71 resulted in any fatalities at all.”
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iv. Now that we’re talking about planes: What does an airplane actually cost? Here’s one article on the subject:
“As for actual prices, airlines occasionally let numbers slip, either because of disclosure requirements or loose tongues.
Southwest Airlines Co., LUV +0.11% for example, recently published numbers related to its new order for Boeing 737 Max jetliners in a government filing. Mr. Liebowitz of Wells Fargo crunched the data and estimated an actual base price of roughly $35 million per plane, or a discount of around 64%. He noted that Southwest is one of Boeing’s best customers and that early buyers of new models get preferential pricing. A Southwest spokeswoman declined to comment.
Air India, in seeking funding last year for seven Boeing 787 Dreamliners it expects to receive this year, cited an average “net cost” of about $110 million per plane. The current list price is roughly $194 million, suggesting a 43% discount. Air India didn’t respond to a request for comment for this article.
In March 2011, Russian flag carrier Aeroflot mentioned in a securities filing that it would pay at most $1.16 billion for eight Boeing 777s…”
100+ million dollars for a plane. I had not seen that one coming. File under: Questions people don’t seem to be asking, which I think is sort of weird. Now that we’re at it, what about trains? Here’s a Danish article about our new IC4-trains. A conservative estimate is at $1,09 billion (6,4 billion kroner) for 83 trains, which is ~$13,2 million/train (or rather per trainset (US terminology) or ~77 million Danish kroner. That’s much cheaper than the big airplanes, but it sure is a lot of money. What about busses? I’ve often thought about this one, perhaps because it’s a mode of transportation I use far more frequently than the others. Here’s one bit of information about the situation in the US, which is surely different from the Danish one but not that different:
“Diesel buses are the most common type of bus in the United States, and they cost around $300,000 per vehicle, although a recent purchase by the Chicago Transit Authority found them paying almost $600,000 per diesel bus. Buses powered by natural gas are becoming more popular, and they cost about $30,000 more per bus than diesels do. Los Angeles Metro recently spent $400,000 per standard size bus and $670,000 per 45 foot bus that run on natural gas.
Hybrid buses, which combine a gasoline or diesel engine with an electric motor much like a Toyota Prius, are much more expensive than either natural gas or diesel buses. Typically, they cost around $500,000 per bus with Greensboro, NC’s transit system spending $714,000 per vehicle.”
So of course you can’t actually compare these things this way because of the different way costs are calculated, but let’s just for fun assume you can: When you use the average price of a standard US diesel bus and compare it to the price of the recently bought Danish trains, the conclusion is that you could buy 44 busses for the price of one train. And you could buy 367 busses for the price of one of the Dreamliners.
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v. A new blog you might like: Collectively Unconscious. A sort of ‘The Onion’ type science-blog.
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vi. I was considering including this stuff in a wikipedia-post, but I thought I’d include it here instead because what’s interesting is not the articles themselves but rather their differences: Try to compare this english language article, about a flame tank designed in the United States, with this article about the same tank but written in Russian. I thought ‘this is weird’ – anybody have a good explanation for this state of affairs?
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vii. The Emergence and Representation of Knowledge about Social and Nonsocial Hierarchies. I haven’t found an ungated version of the paper, but here’s the summary:
“Primates are remarkably adept at ranking each other within social hierarchies, a capacity that is critical to successful group living. Surprisingly little, however, is understood about the neurobiology underlying this quintessential aspect of primate cognition. In our experiment, participants first acquired knowledge about a social and a nonsocial hierarchy and then used this information to guide investment decisions. We found that neural activity in the amygdala tracked the development of knowledge about a social, but not a nonsocial, hierarchy. Further, structural variations in amygdala gray matter volume accounted for interindividual differences in social transitivity performance. Finally, the amygdala expressed a neural signal selectively coding for social rank, whose robustness predicted the influence of rank on participants’ investment decisions. In contrast, we observed that the linear structure of both social and nonsocial hierarchies was represented at a neural level in the hippocampus. Our study implicates the amygdala in the emergence and representation of knowledge about social hierarchies and distinguishes the domain-general contribution of the hippocampus.”
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viii.
I’ve only actually watched the first 15 minutes (and I’m not sure I’ll watch the rest), but I assume some of you will find this interesting.
Job interviews, a few thoughts…
So I thought about this stuff a while ago while I was out for a walk, and I decided back then that I should blog it when I got home. When I did get home I’d forgotten all about it (it was a long walk). Today I was out walking again, and well…
Okay, so let’s assume a job interviewer asks you how you’d feel about working with X, X being the kind of stuff you could be expected to work with in the job function in question. The obvious answer to many people would be ‘I’d feel great about working with X, I’d be very excited to have that opportunity’ or something along those lines. Though ‘it’s what I’ve dreamt of my entire life’ is probably an unwise reply in some situations (desk clerk, bouncer, renovation worker..), in general it seems obvious that it makes a lot of sense to fake interest and excitement in such a situation; this is because such an approach is usually perceived to make you more likely to land the job.
But why is that again? Let’s think a little bit about the signalling aspects here. People who are intrinsically motivated need lower monetary compensation rates to motivate them to do their jobs than do people who are not; they’ll be happy with a lower wage because they like what they do, and if they really like what they do they’re less likely to complain about stuff like e.g. a poor work environment. So if you signal that you’re eager to work with this stuff, you signal that you have a lower reservation wage. This makes you more likely to land the job if you’re perceived to meet the task requirements, but the deceit should in equilibrium affect the employer’s expectations about your productivity – people who have lower reservation wages are all else equal less productive. On the other hand perhaps the reason why you’re eager is that you know a lot about the subject, which means that all else isn’t equal and that your interest might lead to higher productivity on the job or lower training costs. Depending on the specifics there are likely multiple optimal strategies here; and it’s worth having in mind that individual characteristics are highly likely to impact which strategy is optimal for a given individual in a given setting.
Now consider another variable that’s likely to come up in a job interview setting: Ambition. Again people are often implicitly encouraged to fake ambition because it’s perceived in some areas (though far from all) to increase their employment opportunities. If you’re ambitious you’re willing to work harder than the other guy. If you’re ambitious this means you care about the social hierarchy in the organisation, and if you care about that stuff you’ll be more likely to follow the instructions you’re given which is often a useful ability for an employee to possess. If you’re ambitious you’re probably likely to be willing to do a lot of extra stuff to impress the people above you so that you can rise in the social hierarchy, which corresponds to working harder for a lower level of monetary compensation. On the other hand some employers prefer to limit the competition for the management spots by selecting people who are not ‘too ambitious’ for a given job function. And if a vacancy is created for a job function where it’s unlikely that a satisfactory performance will lead to further advancement in the organisational hierarchy, an employer may prefer an unambitious applicant, as he or she is less likely to become disgruntled by the absence of career advancement opportunities. Ambitious people are incidentally quite likely to be perceived of as more aggressive than their unambitious counterparts, which also translates to higher expected wage demands (for the same amount of work).
If you’re perceived to be dishonest about your goals or attributes to a greater extent than is tolerated in such situations this will most likely harm your opportunities greatly, but it’s worth noting that the tolerated level of dishonesty may vary a lot across organisations. Note that organisations always have an incentive to create the illusion that honesty is your best bet at a job interview; that’s because it’s the best bet for the organisation, i.e. the strategy which, if applied by all applicants, would give the organisation the highest potential payoff. This is because if all applicants supply all the decision-relevant information to the organisation, this will make the organisation most likely to be able to pick the best applicant for the job. But here’s the thing; the organisational payoff should at the point where you’re not yet hired by the organisation be irrelevant to you. You don’t care about the organisational payoff at the job interview stage, at this stage you only care about your likelihood of landing the job and the expected pay; withholding information will most frequently be optimal if that information might make you less likely to land the job or likely to earn less. Please do not assume that just because firms implicitly punish deceit, complete honesty is the best strategy for you – in most settings, it’ll likely be a stochastically dominated strategy. On the other hand if you have to grossly misrepresent who you are in order to land the job, the expected derived utility from landing the job probably isn’t as high as you think it is; the employer is not the only one who should care about whether you’re a good match for the job. The optimal amount of deceit is non-zero, but the risk of getting the wrong job should be weighed against the risk of not getting the job. When deciding on the optimal level of deceit do recall that the firm may have an incentive to withhold information from you as well, either by lying to you about which types of information that are important to them when it comes to whom to hire (in order to stop people from trying to game the system and weed out dishonest candidates), by misrepresenting the career opportunities associated with the job (if applicants think the job is high-profile and is likely to increase their future job market opportunities, they’ll likely decrease their wage demands because of the human capital investment value of the job), or perhaps by misrepresenting to some extent what you’ll actually be doing when you get the job (bait-and-switch type strategies are likely sometimes optimal, because it can lead to lower wage demands).
Like in romantic settings, displaying a low level of self-confidence is likely sub-optimal here. If you can’t convince yourself you’re the applicant they should pick, this is a great example of the kind of information you should be trying to hide from them. Don’t give the people involved the impression that you’re doing them a favour by showing up to the interview. Most of the people who go to an interview don’t get the job, and from a certain point of view the firm you’re interviewing with is quite likely to simply be wasting your time.
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