Management Talk: Tony Ke (MIT)

T. Tony Ke is an Assistant Professor of Marketing at the MIT Sloan School of Management. 

 

His research is in the area of marketing analytics, social networks, and marketing strategy.  His current work focuses on modeling consumer search for information, especially on multiple attributes of products, and analyzing firms’ pricing and product strategies. 

 

Talk Title: “Optimal Learning Before Choice”

 

Abstract: A Bayesian decision maker is choosing among multiple alternatives with uncertain payoffs and an outside option with known payoff. Before deciding which one to adopt, the decision maker can purchase sequentially multiple informative signals on each of the available alternatives. To maximize the expected payoff, the decision maker solves the problem of optimal dynamic allocation of learning efforts as well as optimal stopping of the learning process. We show that the optimal learning strategy is of the type of consider-then-decide. The decision maker considers an alternative for learning or adoption if and only if the expected payoff of the alternative is above a threshold. Given several alternatives in the decision maker’s consideration set, we find that sometimes, it is optimal for him to learn information from an alternative that does not have the highest expected payoff, given all other characteristics of all the alternatives being the same. If the decision maker subsequently receives enough positive informative signals, the decision maker will switch to learning the better alternative; otherwise the decision maker will rule out this alternative from consideration and adopt the currently most preferred alternative. We find that this strategy works because it minimizes the decision maker’s learning efforts. It becomes the optimal strategy when the outside option is weak, and the decision maker’s beliefs about the different alternatives are in an intermediate range.