Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/154980
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dc.titleSYSTEMIC INEQUITIES AND DYNAMICS IN EVOLUTIONARY PUBLIC GOODS GAMES ON COMPLEX NETWORKS
dc.contributor.authorANDREW JOHNATHAN SCHAUF
dc.date.accessioned2019-05-31T18:02:42Z
dc.date.available2019-05-31T18:02:42Z
dc.date.issued2018-08-20
dc.identifier.citationANDREW JOHNATHAN SCHAUF (2018-08-20). SYSTEMIC INEQUITIES AND DYNAMICS IN EVOLUTIONARY PUBLIC GOODS GAMES ON COMPLEX NETWORKS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/154980
dc.description.abstractWe use a heterogeneous mean-field (HMF) model to study how degree-based class hierarchies, as distinct from spatial effects, determine how the statistical properties of a population structure influence the evolution of cooperation in network public goods games (PGGs). By construction, this model precludes capturing the spatial variations in local cooperativity that drive cooperation in lattice models. By comparing its numerical predictions with Monte Carlo simulations of PGGs played out on full network realizations, the HMF model can thus help to distinguish the roles of spatial and degree-hierarchical effects in these games, as well as providing an analytical tool to help understand observations drawn from simulations. The HMF model successfully predicts many qualitative features of the dynamics observed in network simulations, including the relative shifts in the phase transitions to cooperation observed among networks with different types of agent degree heterogeneity, game size heterogeneity, degree-degree correlations, and transitivity patterns.
dc.language.isoen
dc.subjectcomplex networks, evolutionary game theory, public goods, heterogeneous mean-field model, social networks, Prisoner's Dilemma
dc.typeThesis
dc.contributor.departmentDEAN'S OFFICE (NGS FOR INTGR SCI & ENGG)
dc.contributor.supervisorLAI CHOY HENG
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
Appears in Collections:Ph.D Theses (Open)

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