Adaptive Coding is the property of the brain to adjust its response to statistical properties of the environment. Its effect is an improved discrimination among signals under the constraints on the dynamic range of its response. It can thus be considered the neural correspondent of Rational Attention, which models how a rational decisionmaker allocates attention among different informative signals. There is strong evidence of existence of widespread adaptive coding. Adaptive coding introduces a dependence of choice from the environment which is not observed in behavior. We discuss potential solutions and propose Hebbian learning as a potentially satisfactory answer.

We introduce a game of complete information with multiple principals and multiple common agents. Each agent makes a decision that can affect the payoffs of all principals. Each principal offers monetary transfers to each agent conditional on the action taken by the agent. We characterize pure-strategy equilibria and we provide conditions—in terms of game balancedness—for the existence of an equilibrium with an efficient outcome. Games played through agents display a type of strategic inefficiency that is absent when either there is a unique principal or there is a unique agent.

People who are members of a group and identify with it behave differently from people who perceive themselves as isolated individuals. This paper shows that group membership affects preferences over outcomes, and saliency of the group affects the perception of the environment. We manipulate the saliency of group membership by letting a player's own group watch as a passive audience as decisions are made, and/or by making part of the payoff common for members of the group. In contrast to the minimal-group paradigm, minimal groups alone do not affect behavior in our strategic environments. However, salient group membership significantly increases the aggressive stance of the hosts (people who have their group members in the audience), and tends to reduce that of the guests. (JEL D71, Z13)

Abstract We study how strategic interaction and cooperation are affected by the heterogeneity of cognitive skills of groups of players, over consecutive plays of repeated games with randomly matched opponents using Prisoner’s Dilemma as stage game. We observe overall higher cooperation rates and average final payoffs in integrated treatment groups—where subjects of different IQ levels interact together—than in separated treatment groups. Lower IQ subjects are better off and higher IQ subjects are worse off in integrated groups than in separated groups. Higher IQ subjects adopt harsher strategies when they are pooled with lower IQ subjects than when they play separately. We demonstrate that this outcome should be expected in learning and evolutionary models where higher intelligence subjects exhibit lower frequency of errors in the implementation of strategies. Estimations of errors and strategies in our experimental data are consistent with the model’s assumptions and predictions.

Abstract We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation $$\beginalign* p_t\left( a,A\right) =\dfrace^\fracu\left( a\right) λ\left( t\right) +α\left( a\right) \sum_b\in Ae^\fracu\left( b\right) λ\left( t\right) +α\left( b\right) , \endalign*$$ where $p_t\left( a,A\right)$ is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, λ is a time-dependent noise parameter measuring the unit cost of information, u is a time-independent utility function, and α is an alternative-specific bias that determines the initial choice probabilities (reflecting prior information and memory anchoring). Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model when α is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behaviour. Jointly, the two approaches provide a thorough understanding of softmaximization in terms of internal causes (neuro-physiological mechanisms) and external effects (testable implications).

We study how intelligence and personality affect the outcomes of groups, focusing on repeated interactions that provide the opportunity for profitable cooperation. Our experimental method creates two groups of subjects who have different levels of certain traits, such as higher or lower levels of Intelligence, Conscientiousness, and Agreeableness, but who are very similar otherwise. Intelligence has a large and positive long-run effect on cooperative behavior. The effect is strong when at the equilibrium of the repeated game there is a trade-off between short-run gains and long-run losses. Conscientiousness and Agreeableness have a natural, significant but transitory effect on cooperation rates.