Behavioral Network Economics

Soham Phade

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2021-216
September 14, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-216.pdf

Game theoretic models are prevalent in the study of interactions between autonomous agents. Given the pervasive role of humans as agents in networks (e.g. social networks) and markets (e.g. labor markets), building mechanisms based on presumably more accurate models of human behavior is of great interest both for increasing human welfare and for building more efficient commercial systems that interact with humans. Cumulative prospect theory (CPT), one of the leading models for decision-making under risk and uncertainty, introduced by Kahneman and Tversky, combines several psychological insights into decision theory. Theoretical economics has primarily focused on expected utility theory (EUT) to model human behavior. On the other hand, CPT has been observed to be a better fit in empirical studies, it is a generalization of EUT, and has a nice mathematical formulation convenient for theoretical studies. It provides a way to incorporate psychological aspects into the concrete frameworks of game theory and economics which is required in building large scale systems that are better aligned with human preferences and needs and are also robust to their emotional traits. A systematic and principled approach is needed. This thesis aims to build work in this direction by studying the following three problems through the lens of CPT: 1. resource allocation over networks, 2. notions of equilibrium in non-cooperative games, and 3. mechanism design. In this thesis, we develop theoretical tools and establish fundamental results that would support real-world applications and future research in behavioral network economics.

Advisor: Venkat Anantharam


BibTeX citation:

@phdthesis{Phade:EECS-2021-216,
    Author = {Phade, Soham},
    Title = {Behavioral Network Economics},
    School = {EECS Department, University of California, Berkeley},
    Year = {2021},
    Month = {Sep},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-216.html},
    Number = {UCB/EECS-2021-216},
    Abstract = {Game theoretic models are prevalent in the study of interactions between autonomous agents. Given the pervasive role of humans as agents in networks (e.g. social networks) and markets (e.g. labor markets), building mechanisms based on presumably more accurate models of human behavior is of great interest both for increasing human welfare and for building more efficient commercial systems that interact with humans. Cumulative prospect theory (CPT), one of the leading models for decision-making under risk and uncertainty, introduced by Kahneman and Tversky, combines several psychological insights into decision theory. Theoretical economics has primarily focused on expected utility theory (EUT) to model human behavior. On the other hand, CPT has been observed to be a better fit in empirical studies, it is a generalization of EUT, and has a nice mathematical formulation convenient for theoretical studies. It provides a way to incorporate psychological aspects into the concrete frameworks of game theory and economics which is required in building large scale systems that are better aligned with human preferences and needs and are also robust to their emotional traits. A systematic and principled approach is needed. This thesis aims to build work in this direction by studying the following three problems through the lens of CPT:
1. resource allocation over networks,
2. notions of equilibrium in non-cooperative games, and 
3. mechanism design.
In this thesis, we develop theoretical tools and establish fundamental results that would support real-world applications and future research in behavioral network economics.}
}

EndNote citation:

%0 Thesis
%A Phade, Soham
%T Behavioral Network Economics
%I EECS Department, University of California, Berkeley
%D 2021
%8 September 14
%@ UCB/EECS-2021-216
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-216.html
%F Phade:EECS-2021-216