Computational behavioral models in public goods games with migration between groups Articles uri icon

publication date

  • December 2021

start page

  • 1

end page

  • 13

issue

  • 4

volume

  • 2

abstract

  • In this study we have simulated numerically two models of linear public goods games where players
    are equally distributed among a given number of groups. Agents play in their group by using two
    simple sets of rules, called ‘blind" and ‘rational" model, respectively, that are inspired by the
    observed behavior of human participants in laboratory experiments. In addition, unsatisfied agents
    have the option of leaving their group and migrating to a new random one through probabilistic
    choices. Stochasticity, and the introduction of two types of players in the blind model, help
    simulate the heterogeneous behavior that is often observed in experimental work. Our numerical
    simulations of the corresponding dynamical systems show that being able to leave a group when
    unsatisfied favors contribution and avoids free-riding to a good extent in a range of the
    enhancement factor where defection would prevail without migration. Our numerical simulation
    presents results that are qualitatively in line with known experimental data when human agents are
    given the same kind of information about themselves and the other players in the group. This is
    usually not the case with customary mathematical models based on replicator dynamics or
    stochastic approaches. As a consequence, models like the ones described here may be useful for
    understanding experimental results and also for designing new experiments by first running cheap
    computational simulations instead of doing costly preliminary laboratory work. The downside is
    that models and their simulation tend to be less general than standard mathematical approaches.

keywords

  • behavioral models; cooperation; migration; public goods game