Two short courses
September 8, 2008 11:11 AM Subscribe
A Short Course In Behavioral Economics, an "Edge Master Class" from Richard Thaler and Nobel Laureate Daniel Kahneman.
Also from Kahneman (who is considered by many to be the world's greatest living psychologist) and Edge: A Short Course in Thinking About Thinking.
previously: (1, 2)
Also from Kahneman (who is considered by many to be the world's greatest living psychologist) and Edge: A Short Course in Thinking About Thinking.
previously: (1, 2)
Related, from Prospect Magazine: Pete Lunn and Tim Harford on Behavioural economics: is it such a big deal?
posted by daniel_charms at 12:05 PM on September 8, 2008
posted by daniel_charms at 12:05 PM on September 8, 2008
During the mid-morning break (with cookies), Richard Thaler shows videos from a 40-year-old study (Walter Mischel, 1973) of children offered one cookie now or two if they wait. The observed behavior correlates strongly, by almost any measure, with both the economic success of the parents and the child's future success. Hypothesis: small behavioral shifts might produce (or "nudge") large economic results.
This paragraph alone is worth the post.
posted by ewkpates at 12:11 PM on September 8, 2008
This paragraph alone is worth the post.
posted by ewkpates at 12:11 PM on September 8, 2008
Very interesting stuff. A couple of excerpts to whet the appetite:
Daniel Kahneman, a Nobel laureate for his work in behavioral economics told us about priming—how a subtle influence radically shifts how people act. So, in one experiment people are asked to fill out a survey. In the corner of the room is a computer, with a screen saver running. That's it—nothing overt, just a background image in the room. If the screen saver shows pictures of money, the survey answers are radically different. Danny went through example after example like this where occurred. The first impulse one has in hearing this is no, this can't be the case. People can't be that easily and subconsciously influenced. You don't want to believe it. But Danny in his professorial way says, "Look, this is science. Belief isn't an option". Repeated randomized trials confirm the results. Get over it.And:
The second impression is perhaps even more surprising—the influences are quite predictable. Show people images of money, and they tend to be more selfish and less willing to help others. Make people plot points on graph paper that are far apart, and they act more distant in lots of way. Make them plot points that are close together, and damned if they don't act closer. Again, it seems absurd, but cheap metaphors capture our minds. Humans, it seems, are like drunken poets, who can't glimpse a screen saver in the corner, or plot some points on graph paper without swooning under the metaphorical load and going off on tangents these stray images inspire. ...
Sendhil Mullainathan gave a fascinating talk about applying behavior economics to understand poverty. If this succeeds (it is a work in progress) it would be extremely important.posted by languagehat at 12:13 PM on September 8, 2008 [1 favorite]
He showed a bunch of data on itinerant fruit vendors (all women) in India. 69% of them are constantly in debt to moneylenders who charge 5% per day interest. The fruit ladies make 10% per day profit, so half their income goes to the moneylender. They also typically buy a couple cups of tea per day. Sendhil shows that 1-cup of tea per day less would let them be debt free in 30 days, doubling their income. 31% of these women have figured that out, so it is not impossible. Why don't the rest get there? ...
Sendhil is studying 1000 of these fruit vendors (all women). Their total debt is typically $25 each, so he is just stepping in and paying off the debt for 500 of them! The question is then to see how many of them revert to being in debt over time, versus the 500 who are studied, but do not have their debt paid off. The experiment is underway and he has no idea what the result will be.
The interesting thing here is that, for these people, one can do a meaningful experiment (N = 500 gives good statistics) without much money in absolute. It would be hard to do this experiment with debt relief for poor nations, or even the US poor, but in India you can do serious field experiments for little money. ...
Sendhil Mullainathan opened the first hour, on the subject of scarcity, by repeating the first day's question: what is it that prevents the fruit vendors (who borrow their working capital daily at high interest) from saving their way out of recurring debt? According to Sendhil, many vendors do manage to escape, but a core-group remains trapped.
Sendhil shows a graph with $$ on the X-axis and Temptation on the Y-axis. The curve starts out flat and then ascends steeply upward before leveling off. The dangerous area is the steep slope when a person begins to acquire disposable income and meets rapidly increasing temptations. "To understand the behavior you have to understand the scale." Thaler interjects: "It's a mental accounting problem—but I think everything is a mental accounting problem." All human beings are subject to temptation, but the consequences are higher for the poor. Conclusion: temptation is a regressive tax.
Daniel Kahneman has an amazing body of work I got to study as a psych student -- he notices the subtles things other researches miss. Thanks for the awesome link.
posted by Alexandra Kitty at 12:59 PM on September 8, 2008
posted by Alexandra Kitty at 12:59 PM on September 8, 2008
Ah - wonderful post!! I hadn't seen these links before. Behavioural Economics is a totally fascinating part of current research activity. I've been considering this work (as my limited time permits) from a Capital Markets perspective.
We (as a discipline) have put a lot of effort into quantitative modeling of the stochastic processes underlying markets, and while we've made much progress from "the old days" of hand waving, we still can't explain some phenomenon routinely observed. Why do participants depart from rational behavior, so often and to such extremes? After all, we see asset bubbles roughly every decade, and going back several hundred years regardless of what government is in charge or what monetary system is in use? Even post credit crunch it probably won't be much different, efforts of the regulators aside.
It seems that many of us are now beginning to conclude that no matter how parsimonious or accurate our models, how clean the data used to drive the models, and regardless of the calibration process undertaken, until we understand the human element the predictive power of our models, our abstractions of the markets will, under some circumstances, always lag. And sometimes markedly. Nobody likes to employ an inefficient model, but they're all we've got at present, and in some markets under some conditions (note I didn't say all) there's much room for improvement.
Take todays upward spike in the equity markets; a relief rally by definition, it wasn't really warranted by the markets fundamentals, in fact most models would have had traders sitting on the sidelines, or perhaps still selling in the Monday AM session (the "Weekend Effect" aside). But during our Fannie and Freddie discussion I mentioned that a strong enough statement by Treasury would incite a rally; this was just a hunch, based upon watching the equity markets for so so long, but no model could have captured it that feeling. Folks were looking for an excuse to draw a line under the mess, and with Treasury's statement they got it. Nobody's even worried about the bill - let's buy us some shares boys and make us some money!! Clearly, as a discipline, we really are lacking from multiple perspectives.
That being said, behavioural is itself a huge field, inside a huge field; I tend to look at this problem solely from a Capital Markets perspective (as that's my academic & professional background), but there is a lot of good research being done to link behavioural to a wide range of applications in finance and economics; for example and especially timely - Monetary Policy (see Cuthbertson, K., Hyde, S., Nitzsche, D., 2006, "Monetary Policy and Behavioral Finance", Cass Business School, Working Paper), Corporate Governance (see Morck, R., 2007, "Behavioral Finance in Corporate Governance - Independent Directors and Non-Executive Chairs") to name but two areas we're seeing lots of interesting work emerge from (my research cluster at University is very active, and we tend to bounce interesting papers off each other).
Another interesting and again timely area that I've been considering lately is the work being done regarding expectations of inflation and inflation itself, as observed in subsequent periods. Many Central Banks are now starting to gauge the expectation of inflation in addition to market observations (spot, forwards, prices) when setting policy. And many of the funds are active in this area as well. Just recently I've heard some tantalizing rumours about a data mining project some of my ex-colleagues are working on, linking high frequency trading models to chatter. I've got one of their dictionaries around, and the concept - categorise words for emotive impact, monitor chatter and use this information to refine the predictive power of your models - is both intellectually appealing and easy to understand. It will be interesting to see if they actually start to make money, but that's a completely different topic - the merging of approaches is something that interests me.
This is FASCINATING area and one I suspect LOTS of work will be done in going forward. Because while what we've got now in terms of quantitative modeling is at times pretty damn good, there are significant omissions. Thanks again for the FPP.
posted by Mutant at 1:26 PM on September 8, 2008 [2 favorites]
We (as a discipline) have put a lot of effort into quantitative modeling of the stochastic processes underlying markets, and while we've made much progress from "the old days" of hand waving, we still can't explain some phenomenon routinely observed. Why do participants depart from rational behavior, so often and to such extremes? After all, we see asset bubbles roughly every decade, and going back several hundred years regardless of what government is in charge or what monetary system is in use? Even post credit crunch it probably won't be much different, efforts of the regulators aside.
It seems that many of us are now beginning to conclude that no matter how parsimonious or accurate our models, how clean the data used to drive the models, and regardless of the calibration process undertaken, until we understand the human element the predictive power of our models, our abstractions of the markets will, under some circumstances, always lag. And sometimes markedly. Nobody likes to employ an inefficient model, but they're all we've got at present, and in some markets under some conditions (note I didn't say all) there's much room for improvement.
Take todays upward spike in the equity markets; a relief rally by definition, it wasn't really warranted by the markets fundamentals, in fact most models would have had traders sitting on the sidelines, or perhaps still selling in the Monday AM session (the "Weekend Effect" aside). But during our Fannie and Freddie discussion I mentioned that a strong enough statement by Treasury would incite a rally; this was just a hunch, based upon watching the equity markets for so so long, but no model could have captured it that feeling. Folks were looking for an excuse to draw a line under the mess, and with Treasury's statement they got it. Nobody's even worried about the bill - let's buy us some shares boys and make us some money!! Clearly, as a discipline, we really are lacking from multiple perspectives.
That being said, behavioural is itself a huge field, inside a huge field; I tend to look at this problem solely from a Capital Markets perspective (as that's my academic & professional background), but there is a lot of good research being done to link behavioural to a wide range of applications in finance and economics; for example and especially timely - Monetary Policy (see Cuthbertson, K., Hyde, S., Nitzsche, D., 2006, "Monetary Policy and Behavioral Finance", Cass Business School, Working Paper), Corporate Governance (see Morck, R., 2007, "Behavioral Finance in Corporate Governance - Independent Directors and Non-Executive Chairs") to name but two areas we're seeing lots of interesting work emerge from (my research cluster at University is very active, and we tend to bounce interesting papers off each other).
Another interesting and again timely area that I've been considering lately is the work being done regarding expectations of inflation and inflation itself, as observed in subsequent periods. Many Central Banks are now starting to gauge the expectation of inflation in addition to market observations (spot, forwards, prices) when setting policy. And many of the funds are active in this area as well. Just recently I've heard some tantalizing rumours about a data mining project some of my ex-colleagues are working on, linking high frequency trading models to chatter. I've got one of their dictionaries around, and the concept - categorise words for emotive impact, monitor chatter and use this information to refine the predictive power of your models - is both intellectually appealing and easy to understand. It will be interesting to see if they actually start to make money, but that's a completely different topic - the merging of approaches is something that interests me.
This is FASCINATING area and one I suspect LOTS of work will be done in going forward. Because while what we've got now in terms of quantitative modeling is at times pretty damn good, there are significant omissions. Thanks again for the FPP.
posted by Mutant at 1:26 PM on September 8, 2008 [2 favorites]
behavioral economics is well and good, but it definitely ignores the context in which its science is deemed valuable. i tend to defer to SSK stuff on these kids of economistic observations about human behavior and ask instead, anthropologically speaking, are there other things at work other than plottable data about mankind?
for instance, given the information above about the indian women in debt, a behavioral economist asks: why do they remain in debt to these moneylenders? an anthropologist, however, asks: how cannot it be that people believe they are NOT indebted to anyone? that is, we are all embedded in economic systems that rely on gift exchange (that appears free) but indebts us to those give gifts. i would argue, instead, that these women are indebted to moneylenders because of large, thick cultural contexts that ensure that people are connected in ways the capitalist market completely fetishizes.
(this is not to say i endorse poverty, indebtedness, etc as a "cultural relativist", which many people hearitly call anthropologists. rather, that we must see debt and gifts and non simple purchases as more than just economics. they are now, and have been for thousands of years, the fabric of human relationships.)
posted by yonation at 1:57 PM on September 8, 2008
for instance, given the information above about the indian women in debt, a behavioral economist asks: why do they remain in debt to these moneylenders? an anthropologist, however, asks: how cannot it be that people believe they are NOT indebted to anyone? that is, we are all embedded in economic systems that rely on gift exchange (that appears free) but indebts us to those give gifts. i would argue, instead, that these women are indebted to moneylenders because of large, thick cultural contexts that ensure that people are connected in ways the capitalist market completely fetishizes.
(this is not to say i endorse poverty, indebtedness, etc as a "cultural relativist", which many people hearitly call anthropologists. rather, that we must see debt and gifts and non simple purchases as more than just economics. they are now, and have been for thousands of years, the fabric of human relationships.)
posted by yonation at 1:57 PM on September 8, 2008
yonation, you lost me at "anthropologically speaking"
posted by AceRock at 2:06 PM on September 8, 2008
posted by AceRock at 2:06 PM on September 8, 2008
uh, from the perspective of one who studies anthropology, the study of human cultural interaction?
posted by yonation at 2:07 PM on September 8, 2008
posted by yonation at 2:07 PM on September 8, 2008
Humans, it seems, are like drunken poets, who can't glimpse a screen saver in the corner, or plot some points on graph paper without swooning under the metaphorical load and going off on tangents these stray images inspire. ...
Nice way of thinking about it.
posted by ersatz at 2:55 PM on September 8, 2008
Nice way of thinking about it.
posted by ersatz at 2:55 PM on September 8, 2008
Humans, it seems, are like drunken poets, who can't glimpse a screen saver in the corner, or plot some points on graph paper without swooning under the metaphorical load and going off on tangents these stray images inspire. ...
That one about the screen saver triggers my b.s. detector.
How many of these behavioral experiments were double-blind? What was the sample size and what was the criteria used to assess the responses of the test subjects?
posted by storybored at 6:59 PM on September 8, 2008
That one about the screen saver triggers my b.s. detector.
How many of these behavioral experiments were double-blind? What was the sample size and what was the criteria used to assess the responses of the test subjects?
posted by storybored at 6:59 PM on September 8, 2008
More from Kahneman on Edge.org, this time about happiness:
The sad tale of the aspiration treadmillposted by AceRock at 7:31 PM on September 8, 2008
The central question for students of well-being is the extent to which people adapt to circumstances. Ten years ago the generally accepted position was that there is considerable hedonic adaptation to life conditions. The effects of circumstances on life satisfaction appeared surprisingly small: the rich were only slightly more satisfied with their lives than the poor, the married were happier than the unmarried but not by much, and neither age nor moderately poor health diminished life satisfaction. Evidence that people adapt — though not completely — to becoming paraplegic or winning the lottery supported the idea of a "hedonic treadmill": we move but we remain in place. The famous "Easterlin paradox" seemed to nail it down: Self-reported life satisfaction has changed very little in prosperous countries over the last fifty years, in spite of large increases in the standard of living.
Hedonic adaptation is a troubling concept, regardless of where you stand on the political spectrum. If you believe that economic growth is the key to increased well-being, the Easterlin paradox is bad news. If you are a compassionate liberal, the finding that the sick and the poor are not very miserable takes wind from your sails. And if you hope to use a measure of well-being to guide social policy you need an index that will pick up permanent effects of good policies on the happiness of the population.
About ten years ago I had an idea that seemed to solve these difficulties: perhaps people's satisfaction with their life is not the right measure of well-being. The idea took shape in discussions with my wife Anne Treisman, who was (and remains) convinced that people are happier in California (or at least Northern California) than in most other places. The evidence showed that Californians are not particularly satisfied with their life, but Anne was unimpressed. She argued that Californians are accustomed to a pleasant life and come to expect more pleasure than the unfortunate residents of other states. Because they have a high standard for what life should be, Californians are not more satisfied than others, although they are actually happier. This idea included a treadmill, but it was not hedonic – it was an aspiration treadmill: happy people have high aspirations.
The aspiration treadmill offered an appealing solution to the puzzles of adaptation: it suggested that measure of life satisfaction underestimate the well-being benefits of life circumstances such as income, marital status or living in California. The hope was that measures of experienced happiness would be more sensitive. I eventually assembled an interdisciplinary team to develop a measure of experienced happiness (Kahneman, Krueger, Schkade, Stone and Schwarz, 2004) and we set out to demonstrate the aspiration treadmill. Over several years we asked substantial samples of women to reconstruct a day of their life in detail. They indicated the feelings they had experienced during each episode, and we computed a measure of experienced happiness: the average quality of affective experience during the day. Our hypothesis was that differences in life circumstances would have more impact on this measure than on life satisfaction. We were so convinced that when we got our first batch of data, comparing teachers in top-rated schools to teachers in inferior schools, we actually misread the results as confirming our hypothesis. In fact, they showed the opposite: the groups of teachers differed more in their work satisfaction than in their affective experience at work. This was the first of many such findings: income, marital status and education all influence experienced happiness less than satisfaction, and we could show that the difference is not a statistical artifact. Measuring experienced happiness turned out to be interesting and useful, but not in the way we had expected. We had simply been wrong.
Experienced happiness, we learned, depends mainly on personality and on the hedonic value of the activities to which people allocate their time. Life circumstances influence the allocation of time, and the hedonic outcome is often mixed: high-income women have more enjoyable activities than the poor, but they also spend more time engaged in work that they do not enjoy; married women spend less time alone, but more time doing tedious chores. Conditions that make people satisfied with their life do not necessarily make them happy.
Social scientists rarely change their minds, although they often adjust their position to accommodate inconvenient facts. But it is rare for a hypothesis to be so thoroughly falsified. Merely adjusting my position would not do; although I still find the idea of an aspiration treadmill attractive, I had to give it up.
To compound the irony, recent findings from the Gallup World Poll raise doubts about the puzzle itself. The most dramatic result is that when the entire range of human living standards is considered, the effects of income on a measure of life satisfaction (the "ladder of life") are not small at all. We had thought income effects are small because we were looking within countries. The GDP differences between countries are enormous, and highly predictive of differences in life satisfaction. In a sample of over 130,000 people from 126 countries, the correlation between the life satisfaction of individuals and the GDP of the country in which they live was over .40 – an exceptionally high value in social science. Humans everywhere, from Norway to Sierra Leone, apparently evaluate their life by a common standard of material prosperity, which changes as GDP increases. The implied conclusion, that citizens of different countries do not adapt to their level of prosperity, flies against everything we thought we knew ten years ago. We have been wrong and now we know it. I suppose this means that there is a science of well-being, even if we are not doing it very well.
Mutant: Just recently I've heard some tantalizing rumours about a data mining project some of my ex-colleagues are working on, linking high frequency trading models to chatter. I've got one of their dictionaries around, and the concept - categorise words for emotive impact, monitor chatter and use this information to refine the predictive power of your models - is both intellectually appealing and easy to understand.
A lot of people already do this, I can tell you that it's an area that's definitely not fully arbed out. If it's done well enough, by doing training on the "chatter" (say.. naive bayes :-) for about a year, you can model the S&P 500 to a high degree of accuracy without ANY input on price action at all. Tell them not to use a pre-set dictionary, though -- that just fucks things up.
posted by amuseDetachment at 7:51 PM on September 8, 2008
A lot of people already do this, I can tell you that it's an area that's definitely not fully arbed out. If it's done well enough, by doing training on the "chatter" (say.. naive bayes :-) for about a year, you can model the S&P 500 to a high degree of accuracy without ANY input on price action at all. Tell them not to use a pre-set dictionary, though -- that just fucks things up.
posted by amuseDetachment at 7:51 PM on September 8, 2008
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posted by Xoebe at 11:42 AM on September 8, 2008