
AbstractSocial interactions involving coordination between individuals are subject to an “evolutionary trap.” Once a suboptimal strategy has evolved, mutants playing an alternative strategy are counterselected because they fail to coordinate with the majority. This creates a detrimental situation from which evolution cannot escape, preventing the evolution of efficient collective behaviors. Here, we study this problem using evolutionary robotics simulations. We first confirm the existence of an evolutionary trap in a simple setting. We then, however, reveal that evolution can solve this problem in a more realistic setting where individuals need to coordinate with one another. In this setting, simulated robots evolve an ability to adapt plastically their behavior to one another, as this improves the efficiency of their interaction. This ability has an unintended evolutionary consequence: a genetic mutation affecting one individual's behavior also indirectly alters their partner's behavior because the two individuals influence one another. As a consequence of this indirect genetic effect, pairs of partners can virtually change strategy together with a single mutation, and the evolutionary barrier between alternative strategies disappears. This finding reveals a general principle that could play a role in nature to smoothen the transition to efficient collective behaviors in all games with multiple equilibriums.
equilibrium selection, Evolution, [SDV] Life Sciences [q-bio], plasticity, Collective action; equilibrium selection; evolution of cooperation; evolutionary game theory; evolutionary robotics; plasticity, QH359-425, Letters, Collective action, evolution of cooperation, evolutionary game theory, evolutionary robotics
equilibrium selection, Evolution, [SDV] Life Sciences [q-bio], plasticity, Collective action; equilibrium selection; evolution of cooperation; evolutionary game theory; evolutionary robotics; plasticity, QH359-425, Letters, Collective action, evolution of cooperation, evolutionary game theory, evolutionary robotics
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