Human and Machine Learning

In this paper, we consider learning by human beings and machines in the light of Herbert Simon’s pioneering contributions to the theory of Human Problem Solving. Using board games of perfect information as a paradigm, we explore differences in human and machine learning in complex strategic environments. In doing so, we contrast theories of learning in classical game theory with computational game theory proposed by Simon. Among theories that invoke computation, we make a further distinction between computable and computational or machine learning theories. We argue that the modern machine learning algorithms, although impressive in terms of their performance, do not necessarily shed enough light on human learning. Instead, they seem to take us further away from Simon’s lifelong quest to understand the mechanics of actual human behaviour

Tractable consumer choice

We derive a rational model of separable consumer choice which can also serve as a behavioral model. The central construct is [lambda] , the marginal utility of money, derived from the consumer's rest-of-life problem. We present a robust approximation of [lambda], and show how to incorporate liquidity constraints, indivisibilities and adaptation to a changing environment. We fi nd connections with numerous historical and recent constructs, both behavioral and neoclassical, and draw contrasts with standard partial equilibrium analysis. The result is a better grounded, more flexible and more intuitive description of consumer choice

Towards the computation of a Nash equilibrium

Game theory has played a progressively more noticeable and important role in computer science topics, such as artificial intelligence, computer networking, and distributed computing, in recent years. In this paper, we provide a preliminary review of where efforts on this topic have been focused over the past several decades and find that currently, the most remarkable interface between algorithmic game theory and theoretical computer science is the computational complexity of computing a Nash equilibrium

Playing Games with Genetic Algorithms

Abstract. In 1987 the first published research appeared which used the Genetic Algorithm as a means of seeking better strategies in playing the repeated Prisoner’s Dilemma. Since then the application of Genetic Algorithms to game-theoretical models has been used in many ways. To seek better strategies in historical oligopolistic interactions, to model economic learning, and to explore the support of cooperation in repeated interactions. This brief survey summarises related work and publications over the past thirteen years. It includes discussions of the use of gameplaying automata, co-evolution of strategies, adaptive learning, a comparison of evolutionary game theory and the Genetic Algorithm, the incorporation of historical data into evolutionary simulations, and the problems of economic simulations using real-world data.