nep-gth 2005-07-18 papers

on Game Theory

Issue of 2005‒07‒18
three papers chosen by
László Á. Kóczy
Universiteit Maastricht

Genetic Action Trees A New Concept for Social and Economic Simulation

By:	Thomas Pitz (Laboratory of Experimental Economics University of Bonn); Thorsten Chmura (Laboratory of Experimental Economics University of Bonn)
Abstract:	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 â€œSMALLWORLDâ€, 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 agentsâ€˜ 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.
Keywords:	Multi agent system, genetic algorithms, actiontrees, learning, decision making, economic and social behaviour, distributed artificial intelligence
JEL:	C8
Date:	2005–07–14
URL:	http://d.repec.org/n?u=RePEc:wpa:wuwpco:0507002&r=gth

Beat The Market

By:	Fan Wang (Stony Brook University, JP Morgan Chase & Co.)
Abstract:	Speculation in asset market is modelled as a stochastic betting game played by finite number of players and repeated infinite times. With stochastic asset return and unkown quality of public signal, a generic adaptive learning rule is proposed and the corresponding evolutionary dynamics is analyzed. The impact of historical events on players' belief decays over time. It is proved to be a robust approach to adapt to stochastic regime shifts in the market. The market dynamics has characteristics, i.e. endogenous boom-bust cycle, positive correlation in return and volume, and negative first order autocorrelation in return series, commonly observed in financial market but inexplicable by conventional rational expectations theory.
Keywords:	Evolutionary Dynamics, Adaptive Learning, Behavioral Finance
JEL:	C7 D8
Date:	2005–07–12
URL:	http://d.repec.org/n?u=RePEc:wpa:wuwpga:0507006&r=gth

Social Learning in Market Games

By:	Carlo Altavilla; Luigi Luini; Patrizia Sbriglia
Abstract:	The aim of our experiments is to test the effect of different information settings on firms’ behaviour in duopoly price and quantity games. We find that, when players have full information on their rivals’ choices, the imitation rule prevails and such learning behaviour induces more competitive outcomes in the Cournot market designs. By the same token, when information on the average industrial profit is provided, there is evidence of an increase in cooperation, and the majority of players experiment with new strategies when their payoff falls below the average profit (F. Palomino and F. Vega-Redondo, 1999; H. Dixon, 2000)
Keywords:	Learning, Cournot and Bertrand experiments
JEL:	D83 C91
Date:	2005–05
URL:	http://d.repec.org/n?u=RePEc:usi:labsit:003&r=gth

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