Behavioral Game Theory

Last Sunday the sun was out and the children's playground was full of kids and their families. As usual the ice cream van was nearby with a steady stream of willing customers. Then something unexpected happening - another ice cream van turned into the car park. What would happen? Well, the driver saw he was not alone, turned around and left. So, we missed out on any particular excitement. Even so, this brief encounter is a nice illustration of the concept of contestable markets.     The standard textbook typically associates the extent of competition with the number of firms in the market. A monopoly has one firm and perfect competition has a large number of firms. Simple enough. But, also misleading, bordering on plain wrong. It is more accurate to measure competition, not by the number of firms, but by the restrictions on entry to the market and the standardization of goods in the market.     To illustrate the issues consider our ice crea...

The basic idea behind social value orientation (SVO) is to gain a snapshot of someone's social preferences. Are they selfish and simply do the best for themselves without caring about the payoff of others? Are they competitive and want to earn more than others (even if that means sacrificing own payoff)? Are they inequality averse and want to earn the same as others? Or are they pro-social and want to maximize the payoff of others? SVO is a tool most closely associated with social psychology, but there is no doubt that it has a useful role to play in economics. A contribution that should be particularly interesting to economists is a recent meta-analysis published in the European Journal of Personality by Jan Luca Pletzer and co-authors. The analysis provides evidence on the connection between SVO, beliefs and behavior, which could feed into debates around reciprocity and psychological game theory. But I'm not going to talk about that study yet. Instead, I will do a co...

One of the more famous and intriguing results of game theory is that cooperation can be sustained in a repeated prisoners dilemma as long as nobody knows when the last game will be played. To set out the basic issue consider the following game between Bob and Francesca. If they both cooperate they get a nice payoff of 10 each. If they both defect they get 0 each. Clearly mutual cooperation is better than mutual defection. But, look at individual incentives. If Francesca cooperates then Bob does best to defect and get 15 rather than 10. If Francesca defects then Bob does best to defect and get 0 rather than -5. Bob has a dominant strategy to choose defect. So does Francesca. We are likely to end up with mutual defection. But what if Bob and Francesca are going to play the game repeatedly with each other? Intuitively there is now an incentive to cooperate in one play of the game in order to encourage cooperation in subsequent plays of the game. To formalize that logic suppose that...

How many subjects should there be in an economic experiment? One answer to that question would be to draw on power rules for statistical significance. In short, you need enough subjects to be able to reasonably reject the null hypothesis you are testing. This approach, though, has never really been standard in experimental economics. There are two basic reasons for this - practical and theoretical.  From a practical point of view the power rules may end up suggesting you need a lot of subjects. Suppose, for instance, you want to test cooperation within groups of 5 people. Then the unit of observation is the group. So, you need 5 subjects for 1 data point. Let's suppose that you determine you need 30 observations for sufficient power (which is a relatively low estimate). That is 30 x 5 = 150 subjects per treatment. If you want to compare 4 treatments that means 600 subjects. This is a lot of money (at least $10,000) and also a lot of subjects to recruit to a lab. In simple term...

I have finally got around to reading Richard Thaler's fantastically wonderful book on Misbehaving . One thing that surprised me in the early chapters is how Thaler backs expected utility theory as the right way to think . Deviations from expected utility are then interpreted as humans not behaving 'as they should'. While I am familiar with this basic argument it still came as a surprise to me how firmly Thaler backed expected utility theory. And I'm not sure I buy this argument.  To appreciate the issue consider some thought experiments. Thaler gives the following example: Stanley mows his lawn every weekend and it gives him terrible hay fever. I ask Stan why he doesn't hire a kid to mow his lawn. Stan says he doesn't want to pay the $10. I ask Stan whether he would mow his neighbor's lawn for $20 and Stan says no, of course not. From the point of view of expected utility theory Stan's behavior makes no sense. What we should do is calculate t...

My holiday reading started with the book Behavioral Economics: A History by Floris Heukelom. The book provides a interesting take on how behavioral economics has grown from humble beginnings to the huge phenomenon that it now is. A nice review of the book has been written by Andreas Ortmann and so I will not delve too deeply into general comment here, other than to say I enjoyed reading the book.  But in terms of more specific comment, one theme running throughout the book is the distinction between behavioral economics and experimental economics. Heukelom makes clear that he thinks there is a very sharp distinction between these two fields. Personally I have always thought of them both as part of one big entangled blob. There are people who clearly prefer to label themselves a behavioral economist or an experimental economist but this seemed to me more a matter of personal preference than any grand design. So, what is the difference between behavioral and experime...

Prospect theory is most well known for its assumption that gains are treated differently to losses. Another crucial part of the theory, namely that probabilities are weighted, typically attracts much less attention. Recent evidence, however, is suggesting that probability weighting has a crucial role to play in many applied settings. So, what is probability weighting and why does it matter? The basic idea of probability weighting is that people tend to overestimate the likelihood of events that happen with small probability and underestimate the likelihood of events that happen with medium to large probability. In their famous paper on ' Advances in prospect theory ', Amos Tversky and Daniel Kahneman quantified this effect. They fitted experiment data to equation where  γ is a parameter to be estimated. In interpretation, p is the actual probability and  π (p)  the weighted probability. The figure below summarizes the kind of effect you get. Tversky and Kah...