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Selection in Coevolutionary Algorithms and the Inverse Problem

In: Collectives and the Design of Complex Systems

Author

Listed:
  • Sevan Ficici

    (Department of Computer Science Brandeis University)

  • Ofer Melnik

    (Rutgers University, DIMACS)

  • Jordan Pollack

    (Center for Complex Systems, Brandeis University, Computer Science Department)

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.

Suggested Citation

  • Sevan Ficici & Ofer Melnik & Jordan Pollack, 2004. "Selection in Coevolutionary Algorithms and the Inverse Problem," Springer Books, in: Kagan Tumer & David Wolpert (ed.), Collectives and the Design of Complex Systems, chapter 12, pages 277-294, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-8909-3_12
    DOI: 10.1007/978-1-4419-8909-3_12
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