Group decision making and social learning

Social learning or learning from actions of others is a key focus of microeconomics; it studies how individuals aggregate information in social networks. Following the seminal work of Aumann, a large literature studies the strategic interaction of agents in a social network, where they receive private information and act based upon that information while also observing the actions of each other. These observations are in turn informative about other agents' private signals; information that can be then used in making future decisions. By the same token, agents engage in group discussions to benefit from private information of others and come up with better decisions that aggregate every body's information as efficiently as possible. We begin by considering the decision problems of a Bayesian agent in a social learning scenario. As the Bayesian agent attempts to infer the true state of the world from her sequence of private signals and observations of actions of others, her decision problems at every epoch can be cast recursively; curbing some of the complexities of the decision scenario, but only to a limited extent. In a group decision scenario, the initial private signals of the agents constitute a state space and the ultimate goal of the agents is get informed about the private signals of each other. The Bayesian agent is initially informed of only her own signal; however, as her history of interactions with other group members becomes enriched, her knowledge of the possible private signals that others may have observed also gets refined; thus enabling her to make better decisions. Bayesian calculations in the social learning setting are notoriously difficult. Successive applications of Bayes rule to the entire history of past observations lead to forebodingly complex inferences: due to lack of knowledge about the global network structure, and unavailability of private observations, as well as third-party interactions that precede every decision. This has given rise to a large literature on non-Bayesian social learning that suggest the use of decision-making heuristics, not only for mathematical tractability but also on the grounds that they are better descriptors of the bounded rational behaviors that are observed in reality: people rely on some simplifications of their environment to be able to analyze it faster and more reliably. These heuristics may be fitted for simple one-shot decision making but they are often not suited for handling the complexities of a public discussion or for making group decisions. As a result, many inefficiencies arise in the outcome of such discussions and their roots can be traced to the underlying heuristic decisionmaking mechanisms. These and other issues relating to heuristic decision making constitute the main focus of this paper.