Decision-making under Imperfect Information with Bayesian Learning or Heuristic Rules

Information is one of the most important ingredients for decision-making. While the neoclassical assumption of perfect information is surely an important conceptual benchmark for discussing efficient allocations, it is obviously far from describing a rational choice under real conditions. In reality, optimal choices should be considered choices under imperfect information. Thus, decision-makers' information problem can be solved by two strategies. Either they collect an optimal set of information to make an optimal allocation choice under this imperfect information set or they can apply heuristic reasoning. In this paper, we suggest a formal model framework for the example of a simple consumer decision for the allocation of differentiated goods to explore information acquisition strategies in such a simple standard choice situation. Using the model variation under perfect information as a benchmark, we answer the following questions. First and most importantly, under imperfect information, can a heuristic rule substitute information acquisition as an optimal choice? Second, what is the role of risk aversion in the information acquisition process? Finally, we explore the differences to the benchmark, both ex ante the first purchase decision and ex post when repeated purchases and consumption allows for experiences with the choices made.