Joint commitment in human cooperative hunting through an “Imagined We”

Cooperation involves the challenge of jointly selecting one from multiple
goals while maintaining the team’s joint commitment to it. We test joint
commitment in a multi-player hunting game, combining psychophysics and
computational modeling. Joint commitment is modeled through an
"Imagined We" (IW) approach, where each agent uses Bayesian
inference to infer the intention of “We”, an imagined supraindividual
agent controlling all agents as its body parts. This is compared against a
Reward Sharing (RS) model, which frames cooperation through reward sharing
via multi-agent reinforcement learning (MARL). Both humans and IW, but not
RS, maintained high performance by jointly committing to a single prey,
regardless of prey quantity or speed. Human observers rated all hunters in
both human and IW teams as making high contributions to the catch,
regardless of their proximity to the prey, suggesting that high-quality
hunting stemmed from sophisticated cooperation rather than individual
strategies. Unlike RS hunters, IW hunters are capable of cooperating not
only with one another, but also with human participants actively engaged
in the same hunting game. In conclusion, this study demonstrates that
humans achieve cooperation through joint commitment that enforces a single
goal, rather than simply motivating members through reward sharing.