A Survey on Entropy and Economic Behaviour Entropy plays a significant role in the study of games and economic behaviour in several ways. A decision maker faced with an nfold repetition of a decisionmaking problem needs to apply strategies that become increasingly complex as n increases. When several players are involved in selecting strategies in interactive games, bounds on the memories and cognitive capacities of the players can affect possible outcomes. A player who can recall only the last k periods of history is said to have bounded recall of capacity k. We present here a brief survey of results of games played by players with different bounded recall capacities, in particular those indicating surprisingly strong relations between memory and entropy in the study of the minmax values of repeated games with bounded recall. In addition, we consider uses of entropy in measuring the value of information of noisy signal structures, also known as experiments. These are represented by stochastic matrices, with the rows representing states of the world and the columns possible signals. The classic ordering of experiments, due to David Blackwell and based on decisionmaking criteria, is a partial ordering, which has led to attempts to extend this ordering to a total ordering. If a decision maker has a prior distribution over the states, receipt of a signal yields a posterior. The difference between the entropy of a prior and the expected entropy of the set of possible posteriors has been proposed as a natural extension of the Blackwell ordering. We survey this alongside the theory of rational inattention, which posits that, since individuals have limited attention, they do not always follow every single piece of economic news in planning their economic behaviour. By modelling attention limits as finite channel capacity in the sense of Shannon, economists have developed a theory that explains a range of observed economic behavioural phenomena well.

Generalizing Information to the Evolution of Rational Belief Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcomexe2x80x99s plausibility. Information measures based on Shannonxe2x80x99s concept of entropy include realization information, Kullbackxe2x80x93Leibler divergence, Lindleyxe2x80x99s information in experiment, cross entropy, and mutual information. We derive a general theory of information from first principles that accounts for evolving belief and recovers all of these measures. Rather than simply gauging uncertainty, information is understood in this theory to measure change in belief. We may then regard entropy as the information we expect to gain upon realization of a discrete latent random variable. This theory of information is compatible with the Bayesian paradigm in which rational belief is updated as evidence becomes available. Furthermore, this theory admits novel measures of information with welldefined properties, which we explored in both analysis and experiment. This view of information illuminates the study of machine learning by allowing us to quantify information captured by a predictive model and distinguish it from residual information contained in training data. We gain related insights regarding feature selection, anomaly detection, and novel Bayesian approaches.