Whither experimental economics?

When I was applying to graduate school, I looked at three options: neuroscience, computer science, and economics. I had, effectively, done an economics major as an undergrad and had worked at an economic consulting firm. But the lack of controlled experimentation in economics kept me from applying and I ended up as a neuroscientist. (There is, of course, a very experimental non-human economics which goes by the name of ecology, though I did not recognize it at the time.)

I profess to being confused as to the lack of experimentation in economics, especially for a field that constantly tries to defend its status as a science. (Well, I understand: incentives, existing capabilities, and all that.)

A recent dissertation on the history of experimental economics – as opposed to behavioral economics – is enlightening:

“We were describing this mechanism and Vernon says, “You know, I can test this whether it works or not.“ I said, “What do you mean?“ And he says, “I’ll run an experiment.“ I said, “What the heck are you talking about? What do you do?“ And so he hauls seven or eight graduate students into a classroom. He ran the mechanism and it didn’t work. It didn’t converge to the equilibrium. It didn’t produce the outcomes the theory said it would produce. And I thought, okay. So back to [doing] theory. I don’t care; this doesn’t bother me.

It bothered Vernon a lot because we sat around that evening talking and he says, “Oh, I know what I did wrong.“ And he went back the next day and he hauled the students back in the room, changed the rules just a little bit in ways that the theory wouldn’t notice the difference. From our theory point of view, it wouldn’t have mattered. But he changed the rules a little bit and bang that thing zapped in and converged.“

The difference between the two experiments was the information shared with the test subjects. The first time around, the subjects wrote down their number on a piece of paper and then Smith wrote them up on the board. Then he asked the subjects to send another message and if the messages were the same twice in a row he would stop, since that stability would be interpreted as having reached equilibrium. But the messages did not stop the first time Smith had run the experiment a day earlier…

The fact that the experiment did not converge at the first attempt, but did at the second with a change of only one rule (the information structure available to the participants) not required by theory to make its prediction made a lasting impact on Ledyard.

And this is exactly why we do experiments:

[T]he theory didn’t distinguish between those two rules, but Vernon knew how to find a rule that would lead to an equilibrium. It meant he knew something that I didn’t know and he had a way of demonstrating it that was really neat.

In psychology and neuroscience, there are many laboratories doing animal experiments testing some sort of economic decision-making hypothesis, though it is debatable how much of that work has filtered into the economic profession. What the two fields could really use, though, are economic ideas about more than just basic decision-making. Much of economics is about markets and mass decisions; there is very animal experimentation of these questions.

(via Marginal Revolution)


Richard Thaler on behavioral economics and nudges

Since the Nobel Prize committee decided to honor the rationality of the markets (or lack thereof), here’s a well-timed interview with behavioral economist RIchard Thaler:

Region: It’s hard to summarize the field, but you’ve written that there are three characteristics that differentiate Homo economicus from Homo sapiens: bounded rationality, bounded self-interest and bounded self-control.

Thaler: Those are the three things that—in the terminology Cass Sunstein and I use in our book Nudge.—distinguish humans from “econs,” short for Homo economicus. But I’ve now added a fourth “bound” that we also need in order to have behavioral economics: bounded markets.

If you had asked me in 1980 to say which field do you think you have your best shot at affecting, finance would have been the least likely, essentially because of the arguments that Becker’s making: The stakes are really high, and you don’t survive very long if you’re a trader who loses money.

Region: And you found that investors overreacted to both good and bad news; also, they were overconfident in their investing ability. The implication was that market prices weren’t always right. In other words, markets weren’t necessarily efficient, in contradiction to the efficient market hypothesis (EMH). Then in 2001, with Owen Lamont, you studied equity carve-outs and found more evidence that markets aren’t good at estimating fundamental value.

Thaler: Yes. Those papers highlight the two aspects of the efficient market hypothesis that I sometimes call the “no free lunch” part and the “price is right” part.
De Bondt and Thaler, “Does the Stock Market Overreact?” was about the no- free-lunch argument. When we were writing that paper in the early ’80s, it was generally thought by economists that the one thing we knew for sure is that you can’t predict future stock prices from past stock prices.

He goes on and talks about his work with the British government putting in successful ‘nudges’ and his relationship with Fama (they sit in mirror opposite offices at Chicago). He points out that when behavioral economics started with ‘bounded rationality’, a lot of the criticism was that it didn’t appear consistently or at the macro level. If you can’t aggregate the behavior, who cares? Well the more we investigate, the more important it turns out to be. I think neuroeconomics is in a similar stage – I’m not sure many economists really care, yet, because it will take time to figure out how to aggregate it. I wish I knew what Thaler thought about neuroeconomics. Anyone have a link to remarks of his on the topic?

Here’s an interview with Shiller who is teaming up with Akerlof to write a book about manipulation and deception in markets.

How trade develops: thinking in terms of “we”

This is an absolutely fantastic classroom experiment by Bart Wilson:

In the traditional market experiment, the experimenters explain to the participants how to trade. For this experiment that seemed more than a little heavy handed if the question is, what is the process by which exchange “gives occasion,” as Adam Smith says, to discovering the “division of labour”? …Thus the first requirement in building the design was that participants would have to discover specialization and exchange…

The participants choose how much of their daily production time they would like to allocate to producing red and blue items in their field. They are then told, deliberately in the passive voice, that “you earn cash based upon the number of red and blue items that have been moved to your house.” What they have to discover is that not only can they move items to their own house, but that they can move items to other people’s houses…

At one extreme, the economy achieves 88% of the possible wealth above self-sufficiency by the last day[.] And at the other extreme, only 6% of the possible wealth above autarky is realized[…] Why the disparity? These students are immediately engaging their counterparts as part of an inclusive “we”. The same is not true in group 4 [which achieved less wealth].

He then goes into detail on the words and mode of thinking that different groups used to develop the idea of trade and markets. The conclusion is that the development of trade and specialization arises from considering the group and not the individual. And this is in a capitalist society! It is not to say that the only way for trade and specialization to develop is a kind of group-consciousness, and it is not to say that it wouldn’t have developed anyway. But it’s a bit of evidence that it can foster the conditions that make mutually beneficial trade networks increasingly likely.

As a second experiment, I would be interested in how quickly students familiar with the idea and the mathematics would find the optimal solution, and how it would evolve in a ‘noisy’ environment. I’d really like to see more advanced analyses of the text as well, the communication networks that evolve, and how they coordinate the development of the intellectual idea. Is there a tipping point? Is it a steady accumulation towards the optimum? Are there ‘laggards’ that are unconvinced?

But this is a great experiment and a great teacher.

Decision Theory Journal Club: The failure of rationality when foraging

When an animal forages for food, it leaves it current location for what it hopes is a better locale.  We like to believe that this foraging decision is made when the animal expects to get more food if it leaves than if it stays.  Simple and obvious, right?  Unfortunately for our intuition, this doesn’t seem to be the case.  When I was at the Foraging workshop at Cosyne, one of the main themes of the day seemed to be that animals aren’t optimal foragers and they don’t look like our idealized rational economic agents, either.

Let’s consider an animal that is forced to choose between different sources of food.  Life isn’t easy, so it takes some time to gather the food from each source and after gathering it must leave the area before its allowed to gather food there again.  The way this particular experiment happens to be set up, if our animal was an Optimal Forager that was trying to get as much food as possible, it would apportion its time solely on the easiest food source, ignoring the rest.  Anything else is a waste of time and energy.  And yet – and yet! – the animal doesn’t do this; it still will go gather food at one location, move to the next and gather food there, and move on again.  Look at how often (pWait) an animal will wait at a food source versus how often they should wait.  It doesn’t exactly look rational to me.

Rats aren't rational economic actors

Okay, so maybe Optimal Foraging isn’t the way to go; maybe an animal doesn’t apportion its time based on maximizing food rate but uses some other economic criterion.  Maybe it matches its investment and reward so that the more reward it gets from a food source, the more time it spends there?  Data says nope.  Maybe it is a temporal discounter and prefers rewards that happen sooner over rewards that happen later?  Nope again.

Let’s consider another option: maybe the animals get attached to the different options, or just like sampling different opportunities.  What if we introduce a parameter into these different theories that represents an unwillingness to reject potential food options?  This helps a little with the temporal discounting model but where it really shines is when added to the Optimal Foraging model.   But foragers aren’t just always opposed to rejecting a food option.  Rather, as the environment becomes more resource-rich, they are more willing to reject a food option when the environment is resource poor (low opportunity cost of time) than when it is resource rich (high opportunity cost of time).

Rats don't like to give up an opportunity

But does this tell us what we think it does?  It seems like much of the effect is explained by the length of time the animal must wait for the longest food source; the length of the other options don’t seem to affect whether an animal will reject those options.  This gives us another possible option: the task was just too easy for the animals.  I would like to see whether the usefulness of the discounting model changes as everything gets harder and the environment becomes sufficiently ‘resource poor’, and also how animals behave when the basic optimal foraging model predicts something other than “always go to the easiest option”.


Wikenheiser, A., Stephens, D., & Redish, A. (2013). Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task Proceedings of the National Academy of Sciences, 110 (20), 8308-8313 DOI: 10.1073/pnas.1220738110

Genes for savings behavior

The genoeconomics revolution is on!  Kind of.  Via Evolving Economics, a recent paper described how savings across life are explainable genetically.  Using twins, roughly one-third of the variance is explained by shared genes, in line with the genetic heritability of other behavioral traits..  This shouldn’t be remotely surprising.  Not only do they have another similar paper, but so do other people.  Evolving Economics points out one interesting (though again unsurprising) part of the study, though:

The evidence that parental influence fades out for older subjects and disappears by age 45, compared to the relatively constant genetic effects, is interesting. The break down of effects by age is not a regular feature of studies such as these (it comes at the cost of sample size). The authors write:

Our interpretation of this evidence is that social transmission from parents to their children affects children’s savings behavior early on in life, but unlike genetic effects, parenting does not have a lifelong impact on an individual’s savings behavior. These results are broadly consistent with research in behavioral genetics which has found a significant effect of the common family environment in early ages on, e.g., personality, but also shown that such effects approach zero in adulthood

The really interesting thing, though, should be: what genes are responsible for these behaviors?  Are risk-taking and overall savings rate related to the same genes?  How do these genes interact with the environment?  A quick search reveals a relationship between 5-HTTLPR (serotonin transporter; how much serotonin is in the body) as well as DRD4 (dopamine D4 receptor, a D1-like receptor that is mostly expressed in prefrontal cortex, iirc) with economic risk-taking.

But these papers are, hopefully, proof of principle for the economic community.  If papers like this can garner some influence, maybe a broad behavioral economics community can arise that studies these genetics.  God knows, listening to the first author of this paper talk makes it evident how much biology he needs to learn.