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. © 2016 Elsevier B.V.

Ämnesord

NATURVETENSKAP  -- Biologi -- Evolutionsbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Evolutionary Biology (hsv//eng)

Nyckelord

Agent-based modeling

Evolutionary game theory

Autonomous agents

Computational methods

Stochastic systems

Agent-based model

Communication between agents

Computational simulation

Heterogeneous populations

Mathematical frameworks

Mathematical treatments

Spatial interaction

Game theory

algorithm

animal

computer simulation

evolution

game

Markov chain

mutation

population density

population dynamics

probability

theoretical model

Algorithms

Animals

Biological Evolution

Models

Theoretical

Stochastic Processes

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