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Motivating Time-Inconsistent Agents: A Computational Approach.

Susanne Albers1, Dennis Kraft1

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Summary
This summary is machine-generated.

Motivating time-inconsistent agents on long-term projects is NP-complete. Researchers explored two strategies for guiding agents with present-bias, finding complexity in budget sufficiency for task completion.

Keywords:
Approximation algorithmsBehavioral economicsCommitment devicesComputational complexityTime-inconsistent preferences

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithmic Game Theory

Background:

  • Studies agent motivation in long-term projects using a graph-based planning model.
  • Addresses challenges posed by time-inconsistent agents with present-bias.

Purpose of the Study:

  • To analyze the computational complexity of motivating time-inconsistent agents.
  • To evaluate two distinct strategies for guiding agents towards project completion within budget constraints.

Main Methods:

  • Investigated NP-completeness for budget sufficiency in two agent guidance strategies.
  • Developed approximation algorithms and hardness proofs for budget optimization.

Main Results:

  • Determined NP-completeness for budget sufficiency in both considered scenarios.
  • Established approximation bounds for the first strategy, showing a O(log n)-approximation and NP-hardness for ratios above 2.
  • Demonstrated that the second strategy does not allow efficient approximation unless P=NP.

Conclusions:

  • Motivating time-inconsistent agents presents significant computational challenges.
  • The complexity of guiding agents depends heavily on the available intervention strategies (edge deletion vs. reward placement).
  • Approximation algorithms offer partial solutions, but inherent hardness limits efficient optimization in certain settings.