An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Experimental Congestion Games

The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is an asymmetric variation of the minority game with linear payoff functions. For each game, simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the player\'s behaviour.Congestion Game, Minority Game, Laboratory Experiments, Reinforcement Algorithm, Payoff Sum Model, Game Theory, Experimental Economics

Genetic Action Trees A New Concept for Social and Economic Simulation

Multi-Agent Based Simulation is a branch of Distributed Artificial Intelligence that builds the base for computer simulations which connect the micro and macro level of social and economic scenarios. This paper presents a new method of modelling the formation and change of patterns of action in social systems with the help of Multi-Agent Simulations. The approach is based on two scientific concepts: Genetic Algorithms [Goldberg 1989, Holland 1975] and the theory of Action Trees [Goldman 1971]. Genetic Algorithms were developed following the biological mechanisms of evolution. Action Trees are used in analytic philosophy for the structural description of actions. The theory of Action Trees makes use of the observation of linguistic analysis that through the preposition by a semi-order is induced on a set of actions. Through the application of Genetic Algorithms on the attributes of the actions of an Action Tree an intuitively simple algorithm can be developed with which one can describe the learning behaviour of agents and the changes in action spaces. Using the extremely simplified economic action space, in this paper called “SMALLWORLDâ€, it is shown with the aid of this method how simulated agents react to the qualities and changes of their environment. Thus, one manages to endogenously evoke intuitively comprehensible changes in the agents‘ actions. This way, one can observe in these simulations that the agents move from a barter to a monetary economy because of the higher effectiveness or that they change their behaviour towards actions of fraud.Multi agent system, genetic algorithms, actiontrees, learning, decision making, economic and social behaviour, distributed artificial intelligence

Minority Game - Experiments and Simulations of Traffic Scenarios

This paper reports laboratory experiments and simulations on a minority game. The minority game is the most important example for a classic non-zerosum- game. The game can be applied on different situations with social and economic contests. We chose an elementary traffic scenario, in which subjects had to choose between a road A and a road B. Nine subjects participated in each session. Subjects played 100 rounds and had to choose between one of the roads. The road which the minority of players chose got positive payoffs. We constructed an extended reinforcement model which fits the empirical data.