Agent-based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters.This article compares two important approaches that are dominating in ACE and shows that the above practice may hinder the performance of the genetic algorithm and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.
This paper presents a multi-player multi-issue negotiation model to solve a resource allocation problem. We design a multilateral negotiation protocol, by which rational players bid sequentially in consecutive rounds till a deadline. Every player's bid is a combination of all resource allocations for himself. In this framework, we perform a thorough theoretical analysis of the negotiation with complete information, which is a preliminary for the more complex incomplete information case. We show that, under a complete information setting, we can derive the negotiation strategies that form a subgame perfect equilibrium outcome. We also show that when a discount factor exists, an agreement will be reached immediately at the end of the first negotiation round. By making trade-offs between issues, the utility that every player gets in the equilibrium outcome is maximized and the solution is Pareto optimal.
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