Evolutionary game theory using agent-based methods.
Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations.
Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA; Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA; BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA. Electronic address: adami@msu.edu.
Christoph
C
Adami
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA. Electronic address: jory@msu.edu.
Jory
J
Schossau
Department of Integrative Biology, Michigan State University, East Lansing, MI, USA; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA. Electronic address: hintze@msu.edu.
Arend
A
Hintze
D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D005716:Game Theory; D008962:Models, Theoretical; D009154:Mutation; D011156:Population Density; D011157:Population Dynamics; D011336:Probability; D013269:Stochastic Processes
Agent-based modeling; Evolutionary game theory