TY - GEN

T1 - Principal-Agent Problems with Present-Biased Agents

AU - Oren, Sigal

AU - Soker, Dolav

N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We present a novel graph-theoretic principal-agent model in which the agent is present biased (a bias that was well studied in behavioral economics). Our model captures situations in which a principal guides an agent in a complex multi-step project. We model the different steps and branches of the project as a directed acyclic graph with a source and a target, in which each edge has the cost for completing a corresponding task. If the agent reaches the target it receives some fixed reward R. We assume that the present-biased agent traverses the graph according to the framework of Kleinberg and Oren (EC’14) and as such will continue traversing the graph as long as his perceived cost is less than R. We further assume that each edge is assigned a value and if the agent reaches the target the principal’s payoff is the sum of values of the edges on the path that the agent traversed. Our goal in this work is to understand whether the principal can efficiently compute a subgraph that maximizes his payoff among all subgraphs in which the agent reaches the target. For this central question we provide both impossibility results and algorithms.

AB - We present a novel graph-theoretic principal-agent model in which the agent is present biased (a bias that was well studied in behavioral economics). Our model captures situations in which a principal guides an agent in a complex multi-step project. We model the different steps and branches of the project as a directed acyclic graph with a source and a target, in which each edge has the cost for completing a corresponding task. If the agent reaches the target it receives some fixed reward R. We assume that the present-biased agent traverses the graph according to the framework of Kleinberg and Oren (EC’14) and as such will continue traversing the graph as long as his perceived cost is less than R. We further assume that each edge is assigned a value and if the agent reaches the target the principal’s payoff is the sum of values of the edges on the path that the agent traversed. Our goal in this work is to understand whether the principal can efficiently compute a subgraph that maximizes his payoff among all subgraphs in which the agent reaches the target. For this central question we provide both impossibility results and algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85075234560&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-30473-7_16

DO - 10.1007/978-3-030-30473-7_16

M3 - Conference contribution

AN - SCOPUS:85075234560

SN - 9783030304720

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 237

EP - 251

BT - Algorithmic Game Theory - 12th International Symposium, SAGT 2019, Proceedings

A2 - Fotakis, Dimitris

A2 - Markakis, Evangelos

PB - Springer

T2 - 12th International Symposium on Algorithmic Game Theory, SAGT 2019

Y2 - 30 September 2019 through 3 October 2019

ER -