How to guide a present-biased agent through prescribed tasks?

TL;DR

This paper explores how to modify tasks in a project to guide a present-biased agent effectively, balancing the agent's immediate preferences with the principal's long-term goals using complexity analysis.

Contribution

It introduces algorithms and complexity bounds for optimizing project modifications to influence present-biased agents, extending Kleinberg and Oren's model.

Findings

Algorithms for project modification strategies

Complexity bounds for guiding present-biased agents

Conditions for successful task adjustments

Abstract

The present bias is a well-documented behavioral trait that significantly influences human decision-making, with present-biased agents often prioritizing immediate rewards over long-term benefits, leading to suboptimal outcomes in various real-world scenarios. Kleinberg and Oren (2014) proposed a popular graph-theoretical model of inconsistent planning to capture the behavior of present-biased agents. In this model, a multi-step project is represented by a weighted directed acyclic task graph, where the agent traverses the graph based on present-biased preferences. We use the model of Kleinberg and Oren to address the principal-agent problem, where a principal, fully aware of the agent's present bias, aims to modify an existing project by adding or deleting tasks. The challenge is to create a modified project that satisfies two somewhat contradictory conditions. On one hand, the present-biased agent should select specific tasks deemed important by the principal. On the other hand, if the anticipated costs in the modified project become too high for the agent, there is a risk of the agent abandoning the entire project, which is not in the principal's interest. To tackle this issue, we leverage the tools of parameterized complexity to investigate whether the principal's strategy can be efficiently identified. We provide algorithms and complexity bounds for this problem.