Essays in behavioral economics

Abstract

In Chapter 1, we model a finite population of agents, each learning about the real-world state through private signals and by observing the actions of a random sample of other agents over time. This differs significantly from the infinite case, where the impact of an individual on others is negligible. In a finite situation, the mutual interaction becomes quite intricate: the behavior of a given agent influences the inference of others, subsequently affecting their behaviors, which in turn affects the likelihood of observing that behavior in the future. Our findings indicate that when there are infinite agents, they can eventually discern the true state. However, should the agents in finite groups ignore finitude and simply apply the law of large numbers, they may collectively herd towards an incorrect state, despite the potential for accurate learning through exclusive reliance on their own signals. This phenomenon can be attributed to the increasing importance agents place on the actions of others as time progresses, at the expense of their private information. Additional information on others' behavior may do harm to learning efficiency.

Chapter 2 introduces the Dimensional Attention Effect (DAE), a framework that explains how the allocation of attention across different attributes within a consideration set can drive context-dependent decision making. Unlike existing models that focus on either attribute salience or range in isolation, the DAE integrates both mechanisms and highlights the interaction between attention shifts and the structure of the consideration set. Through theoretical modeling and experimental evidence, we demonstrate that introducing specific attractor options can disproportionately increase the attention paid to certain dimensions-not choices-leading to preference reversals not predicted by standard economic theories. Our results illustrate the importance of attention distribution in decision making and offer new insights into how context influence economic behavior.

Chapter 3 develops a general theory of attention-driven utility distortion in economic decision making. By modeling attention as a limited and dynamically allocated cognitive resource, we show how the marginal allocation of attention across attributes leads to context-dependent preferences and observed behavioral anomalies. Our framework formalizes the marginal attention function, which distorts traditional utility in response to reference point, salience, context, and the structure of choice sets. This unified approach nests prospect theory, salience models, and relative thinking as special cases, explaining a wide range of empirical and experimental findings such as preference reversals, range effects, and framing effects. We demonstrate the explanatory power of the model through applications to well-known experimental puzzles and discuss implications for future research and policy design.