Abstract

This dissertation leverages methodologies from computer science to understand sources of complexity in economic theory. Chapter 1 considers time complexity: how much time is needed to make a decision. Chapters 2 and 3 consider informational complexity: how much and what kind of data is needed to make a decision. I argue that this line of work is critical for our understanding of economic behavior. The fact that economic models often assume agents are capable of very complex behaviors can lead to predictions and policy recommendations that are themselves quite complex. Through applications in decision theory and mechanism design, this work aims to help distinguish predictions and recommendations that are unrealistically complex from those that are at least plausible.

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