Abstract

In many situations a player may act so as to maximize a perceived utility that is not exactly her utility function, but rather some other, biased, utility. Examples of such biased utility functions are common in behavioral economics, and include risk attitudes, altruism, present-bias and so on. When analyzing a game, one may ask how inefficiency, measured by the Price of Anarchy (PoA) is a?ected by the perceived utilities. The smoothness method [16, 15] naturally extends to games with such perceived utilities or costs, regardless of the game or the behavioral bias. We show that such biasedsmoothness is broadly applicable in the context of nonatomic congestion games. First, we show that on series-parallel networks we can use smoothness to yield PoA bounds even for diverse populations with di?erent biases. Second, we identify various classes of cost functions and biases that are smooth, thereby substantially improving some recent results from the literature.

Highlights

  • Game theory is founded on the assumption that players are rational decision makers, i.e. maximizing their utility, and that groups of agents reach an equilibrium outcome

  • Human decision makers are prone to various cognitive and behavioral biases, such as risk-aversion, loss-aversion, tendency to focus on short-term utility and so on

  • A state s for an nonatomic routing game (NRG) is an equilibrium in game G if no agent can gain by selecting a different path

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Summary

INTRODUCTION

Game theory is founded on the assumption that players are rational decision makers, i.e. maximizing their utility, and that groups of agents reach an equilibrium outcome. The PoA is robust to different notions of equilibrium, and to players that are not playing exactly by the game specification This brings about two important challenges: First, it is unlikely that all of the participants in a game have exactly the same bias (see example on risk-aversion). Cost functions (general, convex, affine and quadratic) with particular behavioral biases, namely different levels of tax sensitivity, pessimism [8], or risk-aversion [12, 11]. Our results significantly improve the upper bounds of Meir and Parkes [8] and Nikolova and Stier-Moses [11], while using much simpler proofs

PRELIMINARIES
Introducing Biased Costs
Smoothness for Biased Costs
DIVERSE POPULATION
Tax-Sensitive Agents
Pessimist Agents
Risk-averse agents in the Mean-Var Model
Findings
DISCUSSION
Full Text
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