Collective Decision Making through Self-regulation: Mechanisms and Algorithms for Self-regulation in Decision-Theoretic Planning

J.C.D. Scharpff

Research output: ThesisDissertation (TU Delft)

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This thesis explores the potential of self-regulation in collective decision making to align interests and optimise joint performance. Demonstrated in the domain of road maintenance planning, this research contributes novel incentive mechanisms and algorithmic techniques to incite self-regulation and coordinate agent interactions, paired with a practical validation of the concept through serious gaming. The learnings of this work guide the design and implementation of future performance-based partnerships and advance the current state-of-the-art in sequential decision
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • de Weerdt, M.M., Supervisor
  • Spaan, M.T.J., Supervisor
Thesis sponsors
Award date20 Nov 2020
Print ISBNs 978-90-5584-274-2
Publication statusPublished - 2020

Bibliographical note

TRAIL Thesis Series no. T2020/17, the Netherlands TRAIL Research School


  • Self-regulation
  • Decision-theoretic planning under uncertainty
  • Dynamic mechanism design
  • Serious gaming

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