Part V. Agent-based models vs ODE models

V-1. Introduction

Many models in Evolutionary Game Theory are described as systems of Ordinary Differential Equations (ODEs). The most famous example is the Replicator Dynamics (Taylor and Jonker, 1978), which reads:

 \dot{x}_i =  x_i (\pi_i - \pi)

where:

  • x_i is the fraction of i-strategists in the population.
  • \dot{x}_i = \frac{dx_i}{dt} describes how the fraction of i-strategists x_i changes in time.
  • \pi_i is the expected payoff of strategy i. That is, if \pi_{ij} is the payoff that an i-strategist obtains against a j-strategist, then \pi_i = \sum_{j} x_j \pi_{ij}. This is also the average payoff that an i-strategist would obtain if he played with the whole population.
  • \pi = \sum_{j} x_j \pi_j is the average payoff in the population.

ODE models are very different from the agent-based models we have considered in this book. Our goal in this Part V is to clarify the relationship between these two kinds of models. We will see that most ODE models in Evolutionary Game Theory can be seen as the mean dynamic of an agent-based model where agents follow a certain decision rule in a well-mixed population.[1] This implies that those ODE models provide a good deterministic approximation to the dynamics of the corresponding agent-based model over finite time spans when the number of agents is sufficiently large (Benaïm & Weibull, 2003; Sandholm, 2010a, chapter 10; Roth & Sandholm, 2013). For this reason, ODE models in Evolutionary Game Theory are often called infinite-population models (because they describe dynamics of populations whose size tends to infinity), while agent-based models are sometimes called finite-population models.[2]

It is also clear that the ODE models represent a higher level of abstraction than the agent-based models, in the sense that the variables in the ODE models are population-level aggregates, while the agent-based models are defined at the individual level (fig. 1).

Figure. 1. Agent-based models are described at the individual level, while ODE models are described at the population level

In fact, in many cases, there is a wide range of different agent-based models which share the same mean dynamic. For instance, consider the replicator dynamics, which is often derived as the infinite-population limit of a certain model of biological evolution (see derivations in e.g. Weibull (1995, section 3.1.1)Vega-Redondo (2003, section 10.3.1) or Alexander (2023, section 3.2.1)). In the following chapters we will see that the replicator dynamics is also the mean dynamic of the following disparate agent-based models:[3]

In the next chapter, we extend the (well-mixed population) model we developed in Part II by including different decision rules that have been studied in the literature and different ways of computing payoffs. Then, in chapter V-3, we derive the mean dynamic of each of the possible parameterizations of this extended agent-based model. We will see that some parameterizations that generate different stochastic dynamics share the same mean dynamic. In this way, we hope that the (many-to-one) relationship between agent-based models and ODEs will be perfectly clear.


  1. The term well-mixed population refers to a population where all individuals are equally likely to interact with each other.
  2. Another reason is that the fraction of i-strategists x_i in ODE models changes continuously in the interval [0,1], while in a finite population of N individuals, this fraction would have to be a multiple of 1/N. See footnote 31 in Alexander (2023).
  3. Some of these models lead to the replicator dynamics up to a speed factor, i.e., a constant may appear multiplying the whole right-hand side of the equation of the replicator dynamics. This constant can be interpreted as a change of time scale.

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Agent-Based Evolutionary Game Dynamics Copyright © 2024 by Luis R. Izquierdo, Segismundo S. Izquierdo & William H. Sandholm is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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