http://plato.stanford.edu/entries/game-theory/#Neuro
In the best publicized example, Glimcher (2003) and colleagues have fMRI-scanned monkeys they had trained to play so-called ‘inspection games’ against computers. In an inspection game, one player faces a series of choices either to work for a reward, in which case he is sure to receive it, or to perform another, easier action ("shirking"), in which case he will receive the reward only if the other player (the "inspector") is not monitoring him. Assume that the first player's (the "worker's") behavior reveals a utility function bounded on each end as follows: he will work on every occasion if the inspector always monitors and he will shirk on every occasion if the inspector never monitors. The inspector prefers to obtain the highest possible amount of work for the lowest possible monitoring rate, thus deriving profits from her private information. In this game, the only Nash equilibria (NE) for both players are in mixed strategies, since any pattern in one player's strategy that can be detected by the other can be exploited. For any given pair of specific utility functions for the two players meeting the constraints described above, any pair of strategies in which, on each trial, either the worker is indifferent between working and shirking or the inspector is indifferent between monitoring and not monitoring, is a NE.
Applying inspection game analyses to pairs or groups of agents requires us to have either independently justified their utility functions over all variables relevant to their play, in which case we can define NE and then test to see whether they successfully maximize expected utility; or to assume that they maximize expected utility, or obey some other rule such as a matching function, and then infer their utility functions from their behavior. Either such procedure can be sensible in different empirical contexts. But epistemological leverage increases greatly if the utility function of the inspector is exogenously determined, as it often is. (Police implementing random roadside inspections to catch drunk drivers, for example, typically have a maximum incidence of drunk driving assigned to them as a target by policy, and an exogenously set budget. These determine their utility function, given a distibution of preferences and attitudes to risk among the population of drivers.) In the case of Glimcher's experiments the inspector is a computer, so its program is under experimental control and its side of the payoff matrix is known. Proxies for the subjects' expected utility, in this case squirts of fruit juice for the monkeys, can be antecedently determined in parametric test settings. The computer is then programmed with the economic model of the monkeys, and can search the data in their behavior in game conditions for exploitable patterns, varying its strategy accordingly. With these variables fixed, expected-utility-maximizing NE behavior by the monkeys can be calculated and tested by manipulating the computer's utility function in various runs of the game.
Monkey behavior after training tracks NE very robustly (as does the behavior of people playing similar games for monetary prizes; Glimcher 2003, pp. 307-308). Working with trained monkeys, Glimcher and colleagues could perform the experiments of significance here. Working and shirking behaviors for the monkeys had been associated by their training with staring either to the right or to the left on a visual display. In earlier experiments, Platt and Glimcher (1999) had established that, in parametric settings, as juice rewards varied from one block of trials to another, firing rates of each parietal neuron that controls eye movements could be trained to encode the expected utility to the monkey of each possible movement relative to the expected utility of the alternative movement. Thus "movements that were worth 0.4 ml of juice were represented twice as strongly [in neural firing probabilities] as movements worth 0.2 ml of juice" (p. 314). Unsurprisingly, when amounts of juice rewarded for each movement were varied from one block of trials to another, firing rates also varied.
Against this background, Glimcher and colleagues could investigate the way in which monkeys' brains implemented the tracking of NE. When the monkeys played the inspection game against the computer, the target associated with shirking could be set at the optimal location, given the prior training, for a specific neuron under study, while the work target would appear at a null location. This permitted Glimcher to test the answer to the following question: did the monkeys maintain NE in the game by keeping the firing rate of the neuron constant while the actual and optimal behavior of the monkey as a whole varied? The data robustly gave the answer ‘yes’. Glimcher reasonably interprets these data as suggesting that neural firing rates, at least in this cortical region for this task, encode expected utility in both parametric and nonparametric settings. Here is a vindication of the empirical applicability of classical game theory in a context independent of institutions or social conventions.
http://plato.stanford.edu/entries/game-theory/#Human
Henrich et al. (2004, 2005) have run a series of experimental games with populations drawn from fifteen small-scale human societies in South America, Africa, and Asia, including three groups of foragers, six groups of slash-and-burn horticulturists, four groups of nomadic herders, and two groups of small-scale agriculturists. The games (Ultimatum, Dictator, Public Goods) they implemented all place subjects in situations broadly resembling that of the Trust game discussed earlier in this section. That is, Ultimatum and Public Goods games are scenarios in which social welfare can be maximized, and each individual's welfare maximized (Pareto efficiency achieved) if and only if at least some players use strategies that are not sub-game perfect equilibrium strategies (see Section 2.6). In Dictator games, a narrowly selfish first mover would capture all available profits. Thus in each of the three game types, SPE players who cared only about their own monetary welfare would get outcomes that would involve massively inegalitarian payoffs. In none of the societies studied by Henrich et al. (or any other society in which games of this sort have been run) are such outcomes observed. The players whose roles are such that they would take away all but epsilon of the monetary profits if they and their partners played SPE always offered the partners substantially more than epsilon, and even then partners sometimes refused such offers at the cost of receiving no money. Furthermore, unlike the traditional subjects of experimental economics — university students in industrialized countries — Henrich et al.'s subjects did not even play Nash equilibrium strategies with respect to monetary payoffs. (That is, strategically advantaged players offered larger profit splits to strategically disadvantaged ones than was necessary to induce agreement to their offers.) Henrich et al. interpret these results by saying that all actual people, unlike ‘rational economic man’, value egalitarian outcomes to some extent. However, their experiments also show that this extent varies significantly with culture, and is correlated with variations in two specific cultural variables: typical payoffs to cooperation (the extent to which economic life in the society depends on cooperation with non-immediate kin) and aggregate market integration (a construct built out of independently measured degrees of social complexity, anonymity, privacy, and settlement size). As the values of these two variables increase, game behavior shifts (weakly) in the direction of Nash equilibrium play. Thus the researchers conclude that people are genetically endowed with a preference for egalitarianism, but that the relative weight of this preference is programmable by social learning processes conditioned on local cultural cues.
In evaluating Henrich et al.'s interpretation of the data, we should first note that the axioms defining ‘rational economic man’, which are incorporated into game theory in the way discussed in Section 2.1, do not include the property of selfishness. (See Ross (2005a) ch. 4; Binmore (2005b); and any economics or game theory text that lets the mathematics do the talking and doesn't insist on ‘spinning’ it in one idealogical direction or another.) (Emphasis mine) Orthodox game theory thus does not predict that people will play SPE or NE strategies derived by treating monetary payoffs as equivalent to utility. Binmore (2005b) is therefore justified in taking Henrich et al. to task over their fashionable rhetoric suggesting that their empirical work embarrasses orthodox theory. It does not.
May not come as a great surprise to some people, but still...as far as I see this, it appears to me as though these results from neuro- and experimental economics (at least as far as game theory is concerned) suggest that we are trained in some way due to our existence in a large social setting to behave in ways that are sub-optimal now. Of course, there may be a simple explanation for this: in the Prisoner's Dilemma, it pays for both agents not to do the "selfish" thing, and things like the Sicilian Omerta are basically attempts to preempt the inability of two captured fugitives to communicate with each other. So an intelligent human being, if he or she really can't expect any future pay-off from others, may choose to play a subgame perfect choice and thus ruin things for everyone. The choice not to do so is then due to self-interest in thinking beyond the immediate game.
So what do you reckon? Genuine altruism, or deferred self-interest?
