Computational Economics - Special issue: Evolutionary processes in economics
The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma
Computational Economics
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions
Computational Economics
Scientific approaches and techniques for negotiation. A game theoretic and artificial intelligence perspective
A machine-learning approach to automated negotiation and prospects for electronic commerce
Journal of Management Information Systems - Special issue: Information technology and its organizational impact
Learning to Collude Tacitly on Production Levels by Oligopolistic Agents
Computational Economics
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In this paper the evolution of bargaining behavior is studied under the assumption that individuals might choose between obstinate and responsive strategies. Following Ellingson (1997) it is assumed that obstinate agents commit to a certain demand, whereas responsive agents adapt optimally to their opponents strategy. Agents change strategies due to imitation based on observations of the success of other individuals. An agent-based model, where the updating of the population profile is governed by tournament selection and mutation, is used to describe the evolution of behavior. In contrast to existing local and stochastic stability results, which predict robust convergence to an equal split norm in this and related frameworks, the simulations show persistent episodes of substantial deviation of behavior from the equal split. Furthermore, it is shown that parameters governing frequency and type of updating as well as selection pressure have significant impact on the qualitative features of the simulation results. This shows the importance of being able to attach economic interpretations to changes in these parameter values.