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P H Crowley

Publications and source records attributed to P H Crowley.

10 recordsLinked to original sources

Hawks, doves, and mixed-symmetry games.

The hawk-dove game has proved to be an important tool for understanding the role of aggression in social interactions. Here, the game is presented in a more general form (GHD) to facilitate analyses of interactions between individuals that may differ in "size", where size is interpreted as a surrogate for resource holding power. Three different situations are considered, based on the availability and use of information that interacting individuals have about their sizes: the classical symmetric case, in which no information about sizes is used, the asymmetric case, in which the individuals know their relative sizes and thus their chances of prevailing in combat, and a mixed-symmetry case, in which each individual only knows its own size (or only knows its opponent's size). I describe and use some recently developed methods for multitype games-evolutionary games involving two or more categories of players. With these methods and others, the evolutionarily stable strategies (ESSs) that emerge for the three different cases are identified and compared. A proof of the form and uniqueness of the ESS for the mixed-symmetry case is presented. In this situation, one size category at most can play a mixed strategy; larger individuals are aggressive and smaller individuals are not. As the number of size categories approaches infinity and the size distribution becomes continuous, there is a threshold size, above which all individuals are aggressive, and below which they are not.

Aggression↗

Solving the complementarity dilemma: evolving strategies for simultaneous hermaphroditism.

We introduce a complementarity dilemma, a two-player, binary response game in which the playoffs are highest when the two players respond differently. Using the classifier EvA, we determine the evolutionary dynamics and structure of strategies that evolve to play an iterated version of this game, and we relate the results to the evolution of major types of sexual reproduction, particularly simultaneous hermaphroditism. We find that complementarity strategies consistently evolve under a broad range of conditions, but that those most consistent with the simultaneous hermaphroditism can predominate only when a substantial cost of repeatedly adopting the female role is imposed. The cost is analogous to the fecundity reduction to be expected when a single partner must repeatedly produce the eggs in sexual reproduction.

Animals↗

Evolving cooperation: strategies as hierarchies of rules.

To better understand the evolutionary dynamics of cooperative strategies and their behavioral components in populations subjected to individual selection, a new classifier-system model (EvA) was developed. In EvA, strategies are encoded as algorithms composed of a fixed number of rules relating behavior remembered from the recent past to the present action to be taken. Each algorithm is the genotype of an individual within the population, and these individuals play the Iterated Prisoner's Dilemma game against each other to determine their relative contributions to the next generation. The rules are hierarchical, with more specific rules, when they apply, overriding more general rules. Maximal mutual cooperation was obtained when interaction sequences for each pair of individuals playing the game were long, when only the immediately preceeding plays in the game were remembered, and when the algorithms consisted of an intermediate number of rules (20-40). Under other conditions, mutual cooperation was reduced--even becoming less frequent than would be expected if behavior were completely random, with very few rules per algorithm. The algorithms that evolved could sometimes be recognized as Tit-For-Tat, Simpleton, or other well-known strategies; but when memory of several previous events was invoked by algorithms based on a substantial number of rules, the resulting strategies were considerably more complex. This approach shows considerable promise for providing a much deeper understanding of how cooperation may evolve in nature. Moreover, classifier-system models could prove to be broadly useful for addressing many optimization questions in biology.

Algorithms↗

Evolving cooperation: the role of individual recognition.

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.

Algorithms↗