Applications in agent-based computational economics

A constituent feature of adaptive complex systems are non-linear feedback mechanisms between actors. These mechanisms are often difficult to model and analyse. One pos- sibility of modelling is given by Agent-based Computational Economics (ACE), which uses computer simulation methods to represent su...

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Main Author: Schuster, Stephan
Published: University of Surrey 2012
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Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556466
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spelling ndltd-bl.uk-oai-ethos.bl.uk-5564662015-03-20T05:28:54ZApplications in agent-based computational economicsSchuster, Stephan2012A constituent feature of adaptive complex systems are non-linear feedback mechanisms between actors. These mechanisms are often difficult to model and analyse. One pos- sibility of modelling is given by Agent-based Computational Economics (ACE), which uses computer simulation methods to represent such systems and analyse non-linear processes. The aim of this thesis is to explore ways of modelling adaptive agents in ACE models. Its major contribution is of a methodological nature. Artificial intelligence and machine learning methods are used to represent agents and learning processes in economics domains by means of learning mechanisms. In this work, a general reinforcement learning framework is developed and realised in a simulation system. This system is used to implement three models of increasing complexity in two different economic domains. One of these domains are iterative games in which agents meet repeatedly and interact. In an experimental labour market, it is shown how statistical discrimination can be generated simply by the learning algorithm used. The results resemble actual patterns of observed human behaviour in laboratory settings. The second model treats strategic network formation. The main contribution here is to show how agent-based modelling helps to analyse non-linearity that is introduced when assumptions of perfect information and full rationality are relaxed. The other domain has a Health Economics background. The aim here is to provide insights of how the approach might be useful in real-world applications. For this, a general model of primary care is developed, and the implications of different consumer behaviour patterns (based on the learning features introduced before) analysed.330.285University of Surreyhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556466Electronic Thesis or Dissertation
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topic 330.285
spellingShingle 330.285
Schuster, Stephan
Applications in agent-based computational economics
description A constituent feature of adaptive complex systems are non-linear feedback mechanisms between actors. These mechanisms are often difficult to model and analyse. One pos- sibility of modelling is given by Agent-based Computational Economics (ACE), which uses computer simulation methods to represent such systems and analyse non-linear processes. The aim of this thesis is to explore ways of modelling adaptive agents in ACE models. Its major contribution is of a methodological nature. Artificial intelligence and machine learning methods are used to represent agents and learning processes in economics domains by means of learning mechanisms. In this work, a general reinforcement learning framework is developed and realised in a simulation system. This system is used to implement three models of increasing complexity in two different economic domains. One of these domains are iterative games in which agents meet repeatedly and interact. In an experimental labour market, it is shown how statistical discrimination can be generated simply by the learning algorithm used. The results resemble actual patterns of observed human behaviour in laboratory settings. The second model treats strategic network formation. The main contribution here is to show how agent-based modelling helps to analyse non-linearity that is introduced when assumptions of perfect information and full rationality are relaxed. The other domain has a Health Economics background. The aim here is to provide insights of how the approach might be useful in real-world applications. For this, a general model of primary care is developed, and the implications of different consumer behaviour patterns (based on the learning features introduced before) analysed.
author Schuster, Stephan
author_facet Schuster, Stephan
author_sort Schuster, Stephan
title Applications in agent-based computational economics
title_short Applications in agent-based computational economics
title_full Applications in agent-based computational economics
title_fullStr Applications in agent-based computational economics
title_full_unstemmed Applications in agent-based computational economics
title_sort applications in agent-based computational economics
publisher University of Surrey
publishDate 2012
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556466
work_keys_str_mv AT schusterstephan applicationsinagentbasedcomputationaleconomics
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