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Title: An agent-based modelling approach to spatial economic theory
Author: Olner, Dan
ISNI:       0000 0004 5359 018X
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2014
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This thesis presents an agent-based modelling framework, leading to a ‘transmission belt’ model connecting supply and demand in a spatial setting, as a step towards understanding the “complex, integrated, tightly-coupled global fabric of exchange” (Korowicz 2010 p.2) that makes up the modern spatial economy. Informed by a careful unpacking of economic modelling ideas, the ‘transmission belt’ model succeeds in producing a stable equilibrium of consumption and production across space. The motivation for creating the model framework is as follows. While some argue ‘it is better to assume that moving goods is essentially costless’ (Glaeser and Kohlhase 2004) because space costs are so low, others point out that a future of ‘oil depletion’ (Sorrell et al. 2009) and high carbon prices due to climate change mean that the implications of future cost changes are not well understood. The thesis examines the most prominent economic approach to space costs, geographical economics (GE), which finds ways to avoid key modelling problems imposed by space but has a tight ‘mathematical straightjacket’(Martin 1999) of assumptions. This thesis keeps to simple utility functions for describing actors’ preferences, but uses an agent-based modelling approach to break out of these assumptions. While spatial agent models have dealt with a huge range of actor-environment interactions, very few examine traditional spatial economic problems. As a consequence, simple but powerful spatial economic ideas have been neglected. Much can be learned from issues that faced economists throughout the twentieth century. By closely examining these ideas, the thesis asks: what obstacles have stopped agent-based modelling from tackling the ‘big questions’ of spatial economics? The answers suggested are: a lack of research directly tackling the space of ‘dependencies’ in agent model development and a broader sense that it has broken all ties with history and can learn nothing from past modelling efforts.
Supervisor: Evans, A. ; Heppenstall, A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available