Macquarie University
Browse

Mechanistic complexity economics: a methodological framework for economic science

Download (3.7 MB)
thesis
posted on 2022-03-28, 17:48 authored by Matthew Tuxford
The central argument of this thesis is that economic science requires a methodological reorientation in order to realign with contemporary philosophy of science. This is argued with reference to both the the history of general philosophy of science - in particular the literatures on scientific explanation and the structure of scientific theories - and the history of the methodology of economics. It is also argued that the heterodox school of economic thought, known as complexity economics, offers a valid basis for achieving such a reorientation.

History

Table of Contents

Part 1. Philosophy of science. Chapter 1. Scientific explanation & the structure of scientific theories Chapter 2. The mechanistic model of scientific explanation & theory structure -- Part 2. Philosophy of economics. Chapter 3. Methodology of economics Chapter 4. Positivist economics -- Part 3. Complexity economics. Chapter 5. Central themes of complexity economics Chapter 6. Does complexity economics incorporate a mechanistic methodology? -- Part 4. Case study. Chapter 7. Asset pricing models -- Conclusion -- Endnotes -- Bibliography.

Notes

Theoretical thesis. Bibliography: pages 448-523

Awarding Institution

Macquarie University

Degree Type

Thesis PhD

Degree

PhD, Macquarie University, Faculty of Arts, Department of Philosophy

Department, Centre or School

Department of Philosophy

Year of Award

2019

Principal Supervisor

Albert Atkin

Additional Supervisor 1

Colin Klein

Additional Supervisor 2

Wylie Bradford

Rights

Copyright Matthew Tuxford 2019. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (523 pages)

Former Identifiers

mq:71140 http://hdl.handle.net/1959.14/1271267

Usage metrics

    Macquarie University Theses

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC