TY - GEN

T1 - Motivating time-inconsistent agents

T2 - 12th International Conference on Web and Internet Economics, WINE 2016

AU - Albers, Susanne

AU - Kraft, Dennis

N1 - Publisher Copyright:
© Springer-Verlag GmbH Germany 2016.

PY - 2016

Y1 - 2016

N2 - We study the complexity of motivating time-inconsistent agents to complete long term projects in a graph-based planning model as proposed by Kleinberg and Oren [5]. Given a task graph G with n nodes, our objective is to guide an agent towards a target node t under certain budget constraints. The crux is that the agent may change its strategy over time due to its present-bias. We consider two strategies to guide the agent. First, a single reward is placed at t and arbitrary edges can be removed from G. Secondly, rewards can be placed at arbitrary nodes of G but no edges must be deleted. In both cases we show that it is NP-complete to decide if a given budget is sufficient to guide the agent. For the first setting, we give complementing upper and lower bounds on the approximability of the minimum required budget. In particular, we devise a (1+√n)-approximation algorithm and prove NP-hardness for ratios greater than √/3. Finally, we argue that the second setting does not permit any efficient approximation unless P = NP.

AB - We study the complexity of motivating time-inconsistent agents to complete long term projects in a graph-based planning model as proposed by Kleinberg and Oren [5]. Given a task graph G with n nodes, our objective is to guide an agent towards a target node t under certain budget constraints. The crux is that the agent may change its strategy over time due to its present-bias. We consider two strategies to guide the agent. First, a single reward is placed at t and arbitrary edges can be removed from G. Secondly, rewards can be placed at arbitrary nodes of G but no edges must be deleted. In both cases we show that it is NP-complete to decide if a given budget is sufficient to guide the agent. For the first setting, we give complementing upper and lower bounds on the approximability of the minimum required budget. In particular, we devise a (1+√n)-approximation algorithm and prove NP-hardness for ratios greater than √/3. Finally, we argue that the second setting does not permit any efficient approximation unless P = NP.

KW - Approximation algorithms

KW - Behavioral economics

KW - Computational complexity

KW - Planning and scheduling

KW - Time-inconsistency

UR - http://www.scopus.com/inward/record.url?scp=85007353745&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-54110-4_22

DO - 10.1007/978-3-662-54110-4_22

M3 - Conference contribution

AN - SCOPUS:85007353745

SN - 9783662541098

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

SP - 309

EP - 323

BT - Web and Internet Economics - 12th International Conference, WINE 2016, Proceedings

A2 - Vetta, Adrian

A2 - Cai, Yang

PB - Springer Verlag

Y2 - 11 June 2016 through 14 July 2016

ER -