Abstract
Predictions which invoke evolutionary mechanisms are hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interactions in specific physical or social environments over many generations. The outcomes have implications for understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/nonexpresser strategies) when injured and of helping, ignoring, or exploiting another in pain (altruistic/nonaltruistic/selfish strategies). Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury interrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, contingencies that evident from experimental work with a variety of mammals, over a few interactions, were replicated in the agent-based model after selection pressure over many generations. More energy-demanding expression of pain reduced its frequency in successive generations, and increasing injury frequency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased expression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits of helping hardly changed its frequency, whereas increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent-based modeling allows simulation of complex behaviors and environmental pressures over evolutionary time.
Highlights
Injury represents a major threat to animals’ survival and fitness, and pain serves to prioritize efforts to escape and to promote recovery.[52,57] behavior associated with pain in animals is of particular interest, but the evolutionary perspective has been neglected.[55,57]Testing evolutionary explanations is difficult, whereas fitting speculative explanations to observational data is unsatisfactory.[24]
We introduced variability in sociability of agents, not unlike dimensions of shyness or boldness described, for instance, in some fish and cephalopods.[1,6,10,13]. We used these variables to address the following questions: (1) What was the effect of increasing the energy costs of expressing pain? (2) What was the effect of increasing or decreasing the costs of helping an agent in pain? (3) What was the effect of increasing agents’ sociability by increasing interaction rate? (4) What was the effect of exploitation by healthy agents of injured agents that expressed pain? (5) What is the effect of increasing the frequency of injury on expression of pain? (6) What is the effect of increasing recovery time from injury on expression of pain?
We considered the outcomes of 1 initial agent population, rather than many, examining the outcomes both of 10 repeated simulations of a single, default initial agent population, and of balt, energy benefit of being helped when injured, 0 to 1, default 0.5; calt, energy cost of helping, 0 to 20, default 1; cexp, energy cost of expressing pain, 0 to 20, default 1; cself, cost to connectedness of being nonaltruistic, ie, not helping an injured agent, 0 to 1, default 0.5
Summary
Injury represents a major threat to animals’ survival and fitness, and pain serves to prioritize efforts to escape and to promote recovery.[52,57] behavior associated with pain in animals is of particular interest, but the evolutionary perspective has been neglected.[55,57]. Testing evolutionary explanations is difficult, whereas fitting speculative explanations to observational data is unsatisfactory.[24] Computer simulation of the effects of selection pressures on behaviors over generations, using agent-based models and in silico experiments,[2] offers a viable alternative.[3,38] Whole system. Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
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