Collective Decision Making through Self-regulation

Mechanisms and Algorithms for Self-regulation in Decision-Theoretic Planning

Doctoral thesis (2020)

Authors

J.C.D. Scharpff Algorithmics - EEMCS
Research Group
Algorithmics (EEMCS) (TU Delft)
Copyright: © 2020 J.C.D. Scharpff
DOI: https://doi.org/10.4233/uuid:63d60259-e0bf-4852-b38b-c1157c390b0d
Serious gaming Self-regulation Decision-theoretic planning under uncertainty Dynamic mechanism design
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Published Date

2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

Abstract

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
making.