MS01 - ECOP-10 Part 1 of 2

Applications of Evolutionary Game Theory and Related Frameworks: From Cells to Societies (Part 1)

Monday, July 14 at 10:20am in Salon 2

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Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Daniel Cooney (University of Illinois Urbana-Champaign), Olivia Chu (Bryn Mawr College) and Alex McAvoy (University of North Carolina, Chapel Hill)

Description:

Evolutionary game theory is a mathematical framework that describes how traits or behaviors can spread through populations, modeling frequency-dependent selection due to either natural selection or social imitation. Models of evolutionary games have found use in modeling biological and social systems in applications ranging from the study of adaptive cancer therapies to understanding the emergence of cooperative social norms in human populations. In this session, we aim to bring together researchers working on evolutionary game theory and related modeling frameworks for emergent phenomena arising from social interactions in populations, exploring a range of scientific applications across levels of biological organization and building a common understanding of mathematical approaches that can be used to explore evolutionary dynamics across a range of spatial and temporal scales.

Room assignment: Salon 2

Jeremy Van Cleve

University of Kentucky

"The co-evolution of external punishment and internal punishment via social preferences as mechanisms to stabilize cooperation"

Mathematical modeling using game theoretic and population genetic approaches has been crucial to helping biologists understand how costly cooperation evolves and what conditions and mechanisms promote its evolution. One important mechanism is the punishment of noncooperative behavior. While there have been numerous models of the punishment of noncooperation by other individuals, there has been little study of how individuals might psychologically internalize such punishment, which could be construed as “guilt” in the case of humans. In this work, we analyze a model of the evolution of internal punishment as a function of group size and genetic relatedness among group members when external punishment by other individuals is also possible. Generally, we find that given sufficient genetic relatedness to outweigh some of the costs of cooperation and external punishment, internal punishment evolves robustly. Even when external punishment by other individuals does evolve, internal punishment often replaces it. After internal punishment initially evolves, its strength per unit of cooperation often declines. However, this decline is offset by an increase in the expected level of cooperation, which leads to a long-term constant amount of internal punishment. These results suggest psychological punishment mechanisms may be an important pathway for stabilizing cooperation in those species where such mechanisms are possible.

Jakub Svoboda

IST Austria

"Density amplifiers of cooperation for spatial games"

Spatial games provide a simple and elegant mathematical model to study the evolution of cooperation in networks. In spatial games, individuals reside in vertices, adopt simple strategies, and interact with neighbors to receive a payoff. Depending on their own and neighbors’ payoffs, individuals can change their strategy. The payoff is determined by the Prisoners’ Dilemma, a classical matrix game, where players cooperate or defect. While cooperation is the desired behavior, defection provides a higher payoff for a selfish individual. There are many theoretical and empirical studies related to the role of the network in the evolution of cooperation. However, the fundamental question of whether there exist networks that for low initial cooperation rate ensure a high chance of fixation, i.e., cooperation spreads across the whole population, has remained elusive for spatial games with strong selection. In this work, we answer this fundamental question in the affirmative by presenting network structures that ensure high fixation probability for cooperators in the strong selection regime. Besides, our structures have many desirable properties: (a) they ensure the spread of cooperation even for a low initial density of cooperation and high temptation of defection, (b) they have constant degrees, and (c) the number of steps, until cooperation spreads, is at most quadratic in the size of the network.

Folashade Agusto

University of Kansas

"Leveraging Population Mobility to Model Drug Overdose Deaths in the United States amid COVID-19"

Drug overdose fatalities have become a significant health issue in many countries, with the United States experiencing a particularly alarming rise over the past two decades. In this study, we examine the geographical patterns of drug overdose deaths at the county level across the United States by utilizing five newly defined spatial weights, developed using mobility data from Google and Facebook. Google Mobility Data, derived from users' location services, provides insights into how populations move between various categories of places, while Facebook Mobility Data, collected through its Data for Good program, tracks population movements between geographic areas. These spatial weights are based on the correlation of mobility data between two spatial units and a threshold distance decay between them. We analyze the spatial distribution of drug overdose deaths using datasets from County Health Rankings and Roadmaps, as well as the Centers for Disease Control and Prevention, focusing on the COVID-19 era spanning 2020, 2021, and 2022. By incorporating spatial covariate information into the new spatial weight definitions, these methods more accurately represent the relationships between spatial units and enhance the performance of spatial analysis techniques. Three of the methods effectively captured nearly all high-incident counties and accurately identified hot and cold spot clusters over the years. In contrast, the other two methods failed to identify many counties with high cases, classifying them as insignificant.

Feng Fu

Dartmouth College

"Evolutionary branching and consistency in human group cooperation"

Understanding the origins of volunteerism and free-riding is crucial in collective action situations where a suﬃcient number of cooperators is necessary to achieve shared benefits, such as in vaccination campaigns and social change movements. In this talk, we will explore how individual behavior responses in multi-round threshold public goods games are shaped by incentives, with and without rewards or punishment. We demonstrate that rewarding consistent cooperators can lead to the emergence of two distinct populations: volunteers who consistently cooperate and free-riders who consistently defect. In contrast, punishing consistent defectors does not lead to similar evolutionary branching. Our results help oﬀer insights into designing eﬀective interventions that promote collective action and address collective risk dilemmas ranging from climate mitigation to pandemic control.

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Annual Meeting for the Society for Mathematical Biology, 2025.