Selection in Coevolutionary Algorithms and the Inverse Problem

Sevan Ficici (), Ofer Melnik () and Jordan Pollack ()
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Sevan Ficici: Department of Computer Science Brandeis University
Ofer Melnik: Rutgers University, DIMACS
Jordan Pollack: Center for Complex Systems, Brandeis University, Computer Science Department

Chapter 12 in Collectives and the Design of Complex Systems, 2004, pp 277-294 from Springer

Abstract: Summary The inverse problem in the collective intelligence framework concerns how the private utility functions of agents can be engineered so that their selfish behaviors collectively give rise to a desired world state. In this chapter we examine several selection and fitnesssharing methods used in coevolution and consider their operation with respect to the inverse problem. The methods we test are truncation and linear-rank selection and competitive and similarity-based fitness sharing. Using evolutionary game theory to establish the desired world state, our analyses show that variable-sum games with polymorphic Nash are problematic for these methods. Rather than converge to polymorphic Nash, the methods we test produce cyclic behavior, chaos, or attractors that lack game-theoretic justification and therefore fail to solve the inverse problem. The private utilities of the evolving agents may thus be viewed as poorly factored—improved private utility does not correspond to improved world utility.

Keywords: Nash Equilibrium; Pure Strategy; Payoff Matrix; Evolutionary Stable Strategy; Evolutionary Game Theory (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-8909-3_12

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DOI: 10.1007/978-1-4419-8909-3_12

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