Essays in Economic Theory

Author:

Guryev, Konstantin

Graduate Program:

Economics

Degree:

Doctor of Philosophy

Document Type:

Dissertation

Date of Defense:

October 07, 2023

Committee Members:

Ran Shorrer, Major Field Member
Sona Golder, Outside Unit & Field Member
Miaomiao Dong, Major Field Member
Kalyan Chatterjee, Chair & Dissertation Advisor
Barry Ickes, Program Head/Chair

Keywords:

Constrained inference
Changing worlds
Availability heuristic
Regime change
R&D race
Independent treasures

Abstract:

This dissertation is comprised of three chapters the first two of which deal with learning in decision problems with bounded memory and the third one analyzes an R&D race between two firms. In Chapter 1 and Chapter 2 a decision-maker faces a decision problem to choose an action, at a randomly determined time, to match an unknown state of nature. She has access to a sequence of signals partially informative of the current state of nature. The state of nature evolves according to a Markov chain. The decision-maker is subject to constraints on information-processing capacity, modelled here by a finite set of memory states. We characterize when optimal inference is possible with these constraints and, when it is not, what the optimal constrained inference is in two broad classes of environments. In the first class where the signals have similar strengths, optimal inference is analyzed in Chapter 1 and can be represented by simple rules corresponding to heuristics, like the “recency bias”, which have been studied by experimental researchers. In the second class where one signal is very informative, the constrained optimal rule ignores the possibility of regime changes - this environment is thoroughly studied in Chapter 2. In Chapter 3, I examine an R&D race involving two firms, where each firm decides which of two research projects to invest in. Assuming that the success of each project is independent of the other, I utilize a straightforward dynamic model to define a pure-strategy equilibrium in a two-period noncooperative game. Additionally, I establish a socially optimal policy for the finite-horizon problem, considering any given number of search periods.