Models of multi-agent decision making

In this thesis we formalise and study computational aspects of group decision making for rational, self-interested agents. Specifically, we are interested in systems where agents reach consensus according to endogenous thresholds. Natural groups have been shown to make collective decisions according...

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Main Author: Zappala, Julian
Format: Thesis (University of Nottingham only)
Language:English
Published: 2014
Subjects:
Online Access:https://eprints.nottingham.ac.uk/28306/
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author Zappala, Julian
author_facet Zappala, Julian
author_sort Zappala, Julian
building Nottingham Research Data Repository
collection Online Access
description In this thesis we formalise and study computational aspects of group decision making for rational, self-interested agents. Specifically, we are interested in systems where agents reach consensus according to endogenous thresholds. Natural groups have been shown to make collective decisions according to threshold-mediated behaviours. An individual will commit to some collective endeavour only if the number of others having already committed exceeds their threshold. Consensus is reached only where all individuals express commitment. We present a family of models that describe fundamental aspects of cooperative behaviour in multi-agent systems. These include: coalition formation, participation in joint actions and the achievement of individuals’ goals over time. We associate novel solution concepts with our models and present results concerning the computational complexity of several natural decision problems arising from these. We demonstrate potential applications of our work by modelling a group decision problem common to many cohesive groups: establishing the location of the group. Using model checking tools we compute the effects of agents’ thresholds upon outcomes. We consider our results within an appropriate research context.
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spelling nottingham-283062025-02-28T11:33:22Z https://eprints.nottingham.ac.uk/28306/ Models of multi-agent decision making Zappala, Julian In this thesis we formalise and study computational aspects of group decision making for rational, self-interested agents. Specifically, we are interested in systems where agents reach consensus according to endogenous thresholds. Natural groups have been shown to make collective decisions according to threshold-mediated behaviours. An individual will commit to some collective endeavour only if the number of others having already committed exceeds their threshold. Consensus is reached only where all individuals express commitment. We present a family of models that describe fundamental aspects of cooperative behaviour in multi-agent systems. These include: coalition formation, participation in joint actions and the achievement of individuals’ goals over time. We associate novel solution concepts with our models and present results concerning the computational complexity of several natural decision problems arising from these. We demonstrate potential applications of our work by modelling a group decision problem common to many cohesive groups: establishing the location of the group. Using model checking tools we compute the effects of agents’ thresholds upon outcomes. We consider our results within an appropriate research context. 2014-07-15 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/28306/1/ThesisJulianZappala.pdf Zappala, Julian (2014) Models of multi-agent decision making. PhD thesis, University of Nottingham. decision-making agents multi-agent intelligent agents
spellingShingle decision-making
agents
multi-agent
intelligent agents
Zappala, Julian
Models of multi-agent decision making
title Models of multi-agent decision making
title_full Models of multi-agent decision making
title_fullStr Models of multi-agent decision making
title_full_unstemmed Models of multi-agent decision making
title_short Models of multi-agent decision making
title_sort models of multi-agent decision making
topic decision-making
agents
multi-agent
intelligent agents
url https://eprints.nottingham.ac.uk/28306/