Learning and Decision Making in Groups.

Many important real-world decision-making problems involve group interactions among individuals with purely informational interactions. Such situations arise for example in jury deliberations, expert committees, medical diagnoses, etc. We model the purely informational interactions of group members, where they receive private information and act based on that information while also observing other people's beliefs or actions. In the first part of the thesis, we address the computations that a rational (Bayesian) decision-maker should undertake to realize her optimal actions, maximizing her expected utility given all available information at every decision epoch. We use an approach called iterated eliminations of infeasible signals (IEIS) to model the thinking process as well as the calculations of a Bayesian agent in a group decision scenario. Accordingly, as the Bayesian agent attempts to infer the true state of the world from her sequence of observations, she recursively refines her belief about the signals that other players could have observed and beliefs that they would have hold given the assumption that other players are also rational. We show that IEIS algorithm runs in exponential time; however, when the group structure is a partially ordered set the Bayesian calculations simplify and polynomial-time computation of the Bayesian recommendations is possible. We also analyze the computational complexity of the Bayesian belief formation in groups and show that it is NP-hard. We investigate the factors underlying this computational complexity and show how belief calculations simplify in special network structures or cases with strong inherent symmetries. We finally give insights about the statistical efficiency (optimality) of the beliefs and its relations to computational efficiency. In the second part, we propose the "no-recall" model of inference for heuristic decision-making that is rooted in the Bayes rule but avoids the complexities of rational inference in group interactions. Accordingly to this model, the group members behave rationally at the initiation of their interactions with each other; however, in the ensuing decision epochs, they rely on heuristics that replicate their experiences from the first stage and can be justified as optimal responses to simplified versions of their complex environments. We study the implications of the information structure, together with the properties of the probability distributions, which determine the structure of the so-called ``Bayesian heuristics'' that the agents follow in this model. We also analyze the group decision outcomes in two classes of linear action updates and log-linear belief updates and show that many inefficiencies arise in group decisions as a result of repeated interactions between individuals, leading to overconfident beliefs as well as choice-shifts toward extreme actions. Nevertheless, balanced regular structures demonstrate a measure of efficiency in terms of aggregating the initial information of individuals. Finally, we extend this model to a case where agents are exposed to a stream of private data in addition to observing each other's actions and analyze properties of learning and convergence under the no-recall framework. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Electrical & Systems Engineering This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/2880 First Advisor Ali Jadbabaie