자료유형  

 학위논문

Control Number  

0017161399

International Standard Book Number  

9798382834887

Dewey Decimal Classification Number  

005

Main Entry-Personal Name  

Kuriksha, Artem.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Pennsylvania., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

198 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

General Note  

Advisor: Shephard, Andrew.

Dissertation Note  

Thesis (Ph.D.)--University of Pennsylvania, 2024.

Summary, Etc.  

요약This dissertation aims to apply and extend structural economic methods to contexts where agents' choices may be influenced by idiosyncratic behavioral factors. Such generalizations enable us to explain empirical economic puzzles and to better predict the effects of different policies.Chapter 1, "Illegal Drug Use and Government Policy: Evidence from a Darknet Marketplace," joint with Priyanka Goonetilleke, Anastasia Karpova, and Peter Meylakhs, studies consumption of illegal drugs. These products are highly different from the ones traditionally studied in economics, with complex patterns in how drug users consume different illegal substances. This chapter develops a structural model of demand for illegal drug varieties and studies how consumers substitute between different types of drugs in response to government policies. Our estimation procedure exploits a novel set of micro-level moment conditions to identify correlations in preferences for specific drug types and the degree of attachment to them. The estimated model is used to evaluate counterfactual drug policies. In particular, we find that policies increasing availability of cannabis have the potential of decreasing the use of riskier drugs. However, they can also cause a sharp increase in cannabis use.Chapter 2, "Hydra: Lessons from the World's Largest Darknet Market," joint with Priyanka Goonetilleke and Alex Knorre, complements the discussion of drug consumption by providing a comprehensive description of Hydra, the darknet marketplace that was the origin of data used to estimate the model in Chapter 1. We quantitatively examine the scale and the structure of the marketplace using data scraped from the platform. The phenomenon of Hydra suggests that shut-down policies applied to darknet marketplaces have a large effect and implicitly shape the whole drug market. Without these policies, a pervasive digitalization of drug trade can occur.Chapter 3, "An Economy of Neural Networks: Learning from Heterogeneous Experiences," studies behavior of economic agents who might be imperfectly rational because deriving the optimal decision rule is computationally hard or because they might have limited information about the environment in which they make decisions. It develops a dynamic model of bounded rationality, relying on ideas from deep reinforcement learning. In the model, agents improve their decision rules over time, guided by the utility flow they derived from past decisions. The approach I develop can be used to relax the assumption of rational expectations in a large set of DSGE models. I apply it to the canonical framework of Aiyagari (1994), in which consumers make saving decisions when their income is subject to idiosyncratic fluctuations. The model enables me to explain several empirical puzzles, in particular, the high share of the population with no savings.

Subject Added Entry-Topical Term  

Web studies.

Index Term-Uncontrolled  

Bounded rationality

Index Term-Uncontrolled  

Dark web

Index Term-Uncontrolled  

Demand estimation

Index Term-Uncontrolled  

Illegal drugs

Index Term-Uncontrolled  

Policy learning

Index Term-Uncontrolled  

Rational expectations

Added Entry-Corporate Name  

University of Pennsylvania Economics

Host Item Entry  

Dissertations Abstracts International. 85-12A.

Electronic Location and Access  

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Control Number  

joongbu:655629