Stochastic win-stay-lose-shift strategy with dynamic aspirations in evolutionary social dilemmas
| Autor: | Matjaž Perc, Marco A. Amaral, Jafferson K. L. da Silva, Lucas Wardil |
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| Rok vydání: | 2016 |
| Předmět: |
Physics - Physics and Society
Differential equation Monte Carlo method FOS: Physical sciences Physics and Society (physics.soc-ph) Network topology 01 natural sciences Models Biological 010305 fluids & plasmas Interaction network 0103 physical sciences Master equation Statistics Humans Computer Simulation Statistical physics Cooperative Behavior 010306 general physics Quantitative Biology - Populations and Evolution Condensed Matter - Statistical Mechanics Mathematics Stochastic Processes Statistical Mechanics (cond-mat.stat-mech) Stochastic game Populations and Evolution (q-bio.PE) Social dilemma Biological Evolution Reciprocity (network science) FOS: Biological sciences Monte Carlo Method |
| Zdroj: | ResearcherID |
| DOI: | 10.48550/arxiv.1609.07118 |
| Popis: | In times of plenty expectations rise, just as in times of crisis they fall. This can be mathematically described as a Win-Stay-Lose-Shift strategy with dynamic aspiration levels, where individuals aspire to be as wealthy as their average neighbor. Here we investigate this model in the realm of evolutionary social dilemmas on the square lattice and scale-free networks. By using the master equation and Monte Carlo simulations, we find that cooperators coexist with defectors in the whole phase diagram, even at high temptations to defect. We study the microscopic mechanism that is responsible for the striking persistence of cooperative behavior and find that cooperation spreads through second-order neighbors, rather than by means of network reciprocity that dominates in imitation-based models. For the square lattice the master equation can be solved analytically in the large temperature limit of the Fermi function, while for other cases the resulting differential equations must be solved numerically. Either way, we find good qualitative agreement with the Monte Carlo simulation results. Our analysis also reveals that the evolutionary outcomes are to a large degree independent of the network topology, including the number of neighbors that are considered for payoff determination on lattices, which further corroborates the local character of the microscopic dynamics. Unlike large-scale spatial patterns that typically emerge due to network reciprocity, here local checkerboard-like patterns remain virtually unaffected by differences in the macroscopic properties of the interaction network. Comment: 9 two-column pages, 7 figures; accepted for publication in Physical Review E |
| Databáze: | OpenAIRE |
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