The decision to explore a novel option or exploit a known one — referred to as the explore-exploit trade-off — has received much attention from diverse fields of research, ranging from computer science to developmental psychology. However, much of the work on this topic has focused exclusively on an individual agent acting alone, a scenario that does not fully capture the rich social dynamics of human decision-making. In particular, the presence and participation of others can theoretically influence the decision to explore or exploit. One factor which may affect how individuals navigate the explore-exploit tradeoff is the presence of caregivers, who can help buffer the downside costs of more exploratory decision making. Across two pre-registered studies, we investigated whether children and adults predicted more or less exploratory behavior in the presence of a caregiver. In Study 1, we presented U.S. American children (N=87, ages 4 to 8) with vignettes of other children faced with the choice of exploring a novel option or exploiting a known one across a range of domains. In the vignettes, the characters either faced these decisions alone or in the presence of a parent. In Study 2, we presented the same vignettes to U.S. American adults (N=79). Across both studies, and as predicted, we found that both children and adults believed others would be more exploratory in the presence of caregivers. These results add important nuance to our understanding of how individuals navigate the explore-exploit tradeoff, and highlight the role of the social context in shaping these decisions. We aim to build on these results on future work centralizing the role and function of care in decision-making and exploration.

Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for this success. However, it is unclear what causal roles are played by this reward-sharing mechanism. In order to systematically examine the effects of sharing rewards in animal coordinated hunting, we conduct a suite of modeling experiments using a state-of-the-art multi-agent reinforcement learning algorithm. The models are trained and evaluated with a task that simulates real-world collective hunting. We manipulate four evolutionarily important variables: reward distribution, hunting party size, free-rider problems, and hunting difficulty. Our results indicate that individually rewarded predators outperform predators that share rewards, especially when the hunting is difficult, the group size is large, and the action cost is high. Moreover, predators with shared rewards suffer from the free-rider problem. We conclude that sharing reward is neither necessary nor sufficient for modeling animal coordinated hunting through reinforcement learning.

Although pointing is sparse, overloaded, and indirect, it allows humans to effectively decode shared information, (ex)change their minds, and plan accordingly. Pointing is an invitation to jointly attend to an object, which triggers the mutual inference between agents of each other’s mind. Relevance is a fundamental assumption underlying all human communication, including pointing. We define relevance as how much a signaler’s belief can make a positive difference to its receiver’s well being. We build a Theory of Mind (ToM) model to test our definition of relevance and use pointing as a case study. In two experiments, we test our relevance model in a classic artificial intelligence (AI) task, the Wumpus world, with the key difference that there is a guide that points to help a hunter. Agents with our relevance model gain significantly higher rewards than agents who ignore signals from the guide. Agents with our model also achieve better performance than agents who receive an additional observation of the environment. The results show that the power of pointing comes from the ToM inference of relevance, rather than providing more precise individual perception.