Modeling cooperative and competitive decision-making in the Tiger Task

The mathematical models underlying reinforcement learning help us understand how agents nav- igate the world and maximize future reward. Partially observable Markov Decision Processes (POMDPs)— an extension of classic RL—allow for action planning in uncertain environments. In this study we set out to investigate human decision-making under these circumstances in the context of cooperation and competition using the iconic Tiger Task (TT) in single-player and cooperative and competitive multi-player versions. The task mimics the setting of a game show, in which the participant has to choose between two doors hiding either a tiger (-100 points) or a treasure (+10 points) or taking a probabilistic hint about the tiger location (-1 point). In addition to the probabilistic location hints, the multi-player TT also includes probabilistic information about the other player’s actions. POMDPs have been successfully used in simulations of the single-player TT. A critical feature are the beliefs (probability distributions) about current position in the state space. However, here we leverage interactive POMDPs (I-POMDPs) for the modeling choice data from the cooperative and competitive multi-player TT. I-POMDPs construct a model of the other player’s beliefs, which are incorporated into the own valuation process. We demonstrate using hierarchical logis- tic regression modeling that the cooperative context elicits better choices and more accurate predictions of the other player’s actions. Furthermore, we show that participants generate Bayesian beliefs to guide their actions. Critically, including the social information in the belief updating improves model performance underlining that participants use this information in their belief computations. In the next step we will use I- POMDPs that explicitly model other players as an intentional agents to investigate the generation of mental models and Theory of Mind in cooperative and competitive decision-making in humans.

Authors

• Saurabh A Kumar
• Prashant Doshi
• Michael Spezio
• Jan P

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