Learning to Respond: The Use of Heuristics in Dynamic Games

While many learning models have been proposed in the game theoretic literature to track individuals’ behavior, surprisingly little research has focused on how well these models describe human adaptation in changing dynamic environments. Analysis of human behavior demonstrates that people are often remarkably responsive to changes in their environment, on time scales ranging from millennia (evolution) to milliseconds (reflex). The goal of this paper is to evaluate several prominent learning models in light of a laboratory experiment on responsiveness in a lowinformation dynamic game subject to changes in its underlying structure. While history-dependent reinforcement learning models track convergence of play well in repeated games, it is shown that they are ill suited to these environments, in which sastisficing models accurately predict behavior. A further objective is to determine which heuristics, or “rules of thumb,” when incorporated into learning models, are responsible for accurately capturing responsiveness. Reference points and a particular type of experimentation are found to be important in both describing and predicting play.learning, limited information, dynamic games

A simplicial algorithm approach to Nash equilibria in concave games

In this paper we demonstrate a new method for computing approximate Nash equilibria in n-person games. Strategy spaces are assumed to be represented by simplices, while payoff functions are assumed to be concave. Our procedure relies on a simplicial algorithm that traces paths through the set of strategy profiles using a new variant of Sperner's Lemma for labelled triangulations of simplotopes, which we prove in this paper. Our algorithm uses a labelling derived from the satisficing function of Geanakoplos (2003) and can be used to compute approximate Nash equilibria for payoff functions that are not necessarily linear. Finally, in bimatrix games, we can compare our simplicial algorithm to the combinatorial algorithm proposed by Lemke and Howson (1964).simplicial algorithm, Nash equilibria, strategy labelling

Behavioural Economics: Classical and Modern

In this paper, the origins and development of behavioural economics, beginning with the pioneering works of Herbert Simon (1953) and Ward Edwards (1954), is traced, described and (critically) discussed, in some detail. Two kinds of behavioural economics – classical and modern – are attributed, respectively, to the two pioneers. The mathematical foundations of classical behavioural economics is identified, largely, to be in the theory of computation and computational complexity; the corresponding mathematical basis for modern behavioural economics is, on the other hand, claimed to be a notion of subjective probability (at least at its origins in the works of Ward Edwards). The economic theories of behavior, challenging various aspects of 'orthodox' theory, were decisively influenced by these two mathematical underpinnings of the two theoriesClassical Behavioural Economics, Modern Behavioural Economics, Subjective Probability, Model of Computation, Computational Complexity. Subjective Expected Utility

Bargaining and Negotiations What should experimentalists explore more thoroughly?

A long time ago most economists would have limited themselves to stating that agreements should be individually rational and efficient and that selecting a specific agreement from that set depends on bargaining and negotiation power whatever that may be. Nowadays hardly any economist will argue that way. The change has been brought about by the strategic approach to bargaining and cooperation and the parallel experimental studies of bargaining and negotiation. When arguing what should be explored more thoroughly, we will point out directions where previous efforts may have been misdirected, where importing new methods may be helpful or even needed, and where new research questions need to be asked and answered.(un)bounded rationality, (non-)cooperative game theory, bargaining and negotiation (theory and experiments)

Herbert Simon (1916-2001). The scientist of the artificial

With the disappearance of Herbert A. Simon, we have lost one of the most original thinkers of the 20th century. Highly influential in a number of scientific fields—some of which he actually helped create, such as artificial intelligence or information-processing psychology—Simon was a true polymath. His research started in management science and political science, later encompassed operations research, statistics and economics, and finally included computer science, artificial intelligence, psychology, education, philosophy of science, biology, and the sciences of design. His often controversial ideas earned him wide scientific recognition and essentially all the top awards of the fields in which he researched, including the Turing award from the Association of Computing Machinery, with Allen Newell, in 1975, the Nobel prize in economics, in 1978, and the Gold Medal Award for Psychological Science from the American Psychological Foundation, in 1988

Essays in Economic Theory

This dissertation consists of three research papers on cheap talk game and satisficing behaviour. The first chapter examines the potential for communication via cheap talk between an expert and a decision maker whose type (preferences) is uncertain. The expert privately observes states for each type of the decision maker and wants to persuade the decision maker to choose an action in his favour by informing her of the states. The decision maker privately observes her type and chooses an action. An optimal action for the decision maker depends upon both her type and type-specific states. In equilibrium the expert can always inform the decision maker in the form of comparative statements and the decision maker also can partially reveal her type to the expert or public. The second and third chapters build a dynamic model of satisficing behaviour in which an agent’s “expected” payoff is explicitly introduced, where this expectation is adaptively formed. If the agent receives a payoff above her satisficing level she continues with the current action, updating her valuation of the action. If she receives a payoff below her satisficing level and her valuation falls below her satisficing level she updates both her action and satisficing level. In the second chapter, we find that in the long run, all players satisfice. In individual decision problems, satisficing behaviour results in cautious, maximin choice and in normal form games like the Prisoner’s Dilemma and Stag Hunt, they in the long run play either cooperative or defective outcomes conditional on past plays. In coordination games like the Battle of the Sexes, Choosing Sides and Common Interest, they in the long run coordinate on Pareto optimal outcomes. In the third chapter, we find that satisficing players in the long run play subgame dominant paths, which is a refinement of subgame perfection, and identify conditions with which they ‘always cooperate’ or ‘fairly coordinate’ in repeated Prisoner’s Dilemma and Battle of the Sexes games, respectively, and truthfully communicate in sender-receiver games. Proofs and simulations are provided in appendices