Evolutionary Games as Interacting Particle Systems

abstract: This dissertation investigates the dynamics of evolutionary games based on the framework of interacting particle systems in which individuals are discrete, space is explicit, and dynamics are stochastic. Its focus is on 2-strategy games played on a d-dimensional integer lattice with a ran...

Full description

Bibliographic Details
Other Authors: Evilsizor, Stephen (Author)
Format: Doctoral Thesis
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.38550
id ndltd-asu.edu-item-38550
record_format oai_dc
spelling ndltd-asu.edu-item-385502018-06-22T03:07:11Z Evolutionary Games as Interacting Particle Systems abstract: This dissertation investigates the dynamics of evolutionary games based on the framework of interacting particle systems in which individuals are discrete, space is explicit, and dynamics are stochastic. Its focus is on 2-strategy games played on a d-dimensional integer lattice with a range of interaction M. An overview of related past work is given along with a summary of the dynamics in the mean-field model, which is described by the replicator equation. Then the dynamics of the interacting particle system is considered, first when individuals are updated according to the best-response update process and then the death-birth update process. Several interesting results are derived, and the differences between the interacting particle system model and the replicator dynamics are emphasized. The terms selfish and altruistic are defined according to a certain ordering of payoff parameters. In these terms, the replicator dynamics are simple: coexistence occurs if both strategies are altruistic; the selfish strategy wins if one strategy is selfish and the other is altruistic; and there is bistability if both strategies are selfish. Under the best-response update process, it is shown that there is no bistability region. Instead, in the presence of at least one selfish strategy, the most selfish strategy wins, while there is still coexistence if both strategies are altruistic. Under the death-birth update process, it is shown that regardless of the range of interactions and the dimension, regions of coexistence and bistability are both reduced. Additionally, coexistence occurs in some parameter region for large enough interaction ranges. Finally, in contrast with the replicator equation and the best-response update process, cooperators can win in the prisoner's dilemma for the death-birth process in one-dimensional nearest-neighbor interactions. Dissertation/Thesis Evilsizor, Stephen (Author) Lanchier, Nicolas (Advisor) Kang, Yun (Committee member) Motsch, Sebastien (Committee member) Smith, Hal (Committee member) Thieme, Horst (Committee member) Arizona State University (Publisher) Applied mathematics Bootstrap Percolation Death-Birth Updating Process Evolutionary Game Theory Evolutionary Stable Strategy Interacting Particle Systems Prisoner's Dilemma eng 89 pages Doctoral Dissertation Applied Mathematics 2016 Doctoral Dissertation http://hdl.handle.net/2286/R.I.38550 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2016
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Applied mathematics
Bootstrap Percolation
Death-Birth Updating Process
Evolutionary Game Theory
Evolutionary Stable Strategy
Interacting Particle Systems
Prisoner's Dilemma
spellingShingle Applied mathematics
Bootstrap Percolation
Death-Birth Updating Process
Evolutionary Game Theory
Evolutionary Stable Strategy
Interacting Particle Systems
Prisoner's Dilemma
Evolutionary Games as Interacting Particle Systems
description abstract: This dissertation investigates the dynamics of evolutionary games based on the framework of interacting particle systems in which individuals are discrete, space is explicit, and dynamics are stochastic. Its focus is on 2-strategy games played on a d-dimensional integer lattice with a range of interaction M. An overview of related past work is given along with a summary of the dynamics in the mean-field model, which is described by the replicator equation. Then the dynamics of the interacting particle system is considered, first when individuals are updated according to the best-response update process and then the death-birth update process. Several interesting results are derived, and the differences between the interacting particle system model and the replicator dynamics are emphasized. The terms selfish and altruistic are defined according to a certain ordering of payoff parameters. In these terms, the replicator dynamics are simple: coexistence occurs if both strategies are altruistic; the selfish strategy wins if one strategy is selfish and the other is altruistic; and there is bistability if both strategies are selfish. Under the best-response update process, it is shown that there is no bistability region. Instead, in the presence of at least one selfish strategy, the most selfish strategy wins, while there is still coexistence if both strategies are altruistic. Under the death-birth update process, it is shown that regardless of the range of interactions and the dimension, regions of coexistence and bistability are both reduced. Additionally, coexistence occurs in some parameter region for large enough interaction ranges. Finally, in contrast with the replicator equation and the best-response update process, cooperators can win in the prisoner's dilemma for the death-birth process in one-dimensional nearest-neighbor interactions. === Dissertation/Thesis === Doctoral Dissertation Applied Mathematics 2016
author2 Evilsizor, Stephen (Author)
author_facet Evilsizor, Stephen (Author)
title Evolutionary Games as Interacting Particle Systems
title_short Evolutionary Games as Interacting Particle Systems
title_full Evolutionary Games as Interacting Particle Systems
title_fullStr Evolutionary Games as Interacting Particle Systems
title_full_unstemmed Evolutionary Games as Interacting Particle Systems
title_sort evolutionary games as interacting particle systems
publishDate 2016
url http://hdl.handle.net/2286/R.I.38550
_version_ 1718701081082462208