Pain expressiveness and altruistic behavior

Predictions which invoke evolutionary mechanisms ar
e hard to test. Agent-based modeling in artificial
life offers a way to simulate behaviors and interac
tions in specific physical or social environments o
ver
many generations. The outcomes have implications fo
r 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/non-expresser strategies) when injured,
and of helping, ignoring or exploiting another in
pain (altruistic/non-altruistic/selfish strategies)
. Agents modeled in MATLAB interacted at random
while foraging (gaining energy); random injury inte
rrupted 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, conti
ngencies evident from experimental work with a
variety of mammals, over a few interactions, were r
eplicated in the agent-based model after selection
pressure over many generations. More energy-demandi
ng expression of pain reduced its frequency in
successive generations, and increasing injury frequ
ency resulted in fewer expressers and altruists.
Allowing exploitation of injured agents decreased e
xpression of pain to near zero, but altruists
remained. Decreasing costs or increasing benefits o
f helping hardly changed its frequency, while
increasing interaction rate between injured agents
and helpers diminished the benefits to both. Agent-
based modeling allows simulation of complex behavio
urs and environmental pressures over
evolutionary time.