Evolving cooperation: the role of individual recognition.

TitleEvolving cooperation: the role of individual recognition.
Publication TypeJournal Article
JournalBio Systems
Volume37
Issue1-2
Pagination49-66
Date Published1996
ISSN0303-2647
Abstract

To evaluate the role of individual recognition in the evolution of cooperation, we formulated and analyzed genetic algorithm model (EvCo) for playing the Iterated Prisoner's Dilemma (IPD) game. Strategies compete against each other during each generation, and successful strategies contribute more of their attributes to the next generation. Each strategy is encoded on a 'chromosome' that plays the IPD, responding to the sequences of most recent responses by the interacting individuals (chromosomes). The analysis reported in this paper considered different memory capabilities (one to five previous interactions), pairing continuities (pairs of individuals remain together for about one, two, five, or 1000 consecutive interactions), and types of individual recognition (recognition capability was maximal, nil, or allowed to evolve between these limits). Analysis of the results focused on the frequency of mutual cooperation in pairwise interactions (a good indicator of overall success in the IPD) and on the extent to which previous responses by the focal individual and its partner were associated with the partner's identity (individual recognition). Results indicated that a fixed, substantial amount of individual recognition could maintain high levels of mutual cooperation even at low pairing continuities, and a significant but limited capability for individual recognition evolved under selection. Recognition generally increased mutual cooperation more when the recent responses of individuals other than the current partner were ignored. Titrating recognition memory under selection using a fitness cost suggested that memory of the partner's previous responses was more valuable than memory of the focal's previous responses. The dynamics produced to date by EvCo are a step toward understanding the evolution of social networks, for which additional benefits associated with group interactions must be incorporated.

Short TitleBiosystems
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