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Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory

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  • Steve J. Bickley
  • Benno Torgler
Abstract
In this chapter, we ask (conceptually and methodologically) what exactly is behavioural economics and what are its roots? And further, what may we have missed along the way? We argue that revisiting “classical” behavioural economics concepts and methods will benefit the wider behavioural economics program by questioning its yardstick approach to ‘Olympian’ rationality and optimisation and in doing so, exploring the ‘how’ and ‘why’ of economic behaviours (micro, meso, and macro) in greater detail and clarity. We also do the same for fields which share similar ontological and epistemological roots with “classical” behavioural economics. In particular, cognitive psychology, complexity theory, and artificial intelligence. By engaging in debate and investing thought into multiple layers of the ontology-epistemology- methodology, we look to engage in ‘deeper’ (and potentially more profound) scientific discussions. We also explore the utility and implications of mixed methods in behavioural economics research, policy, and practice.

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  • Steve J. Bickley & Benno Torgler, 2021. "Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory," CREMA Working Paper Series 2021-21, Center for Research in Economics, Management and the Arts (CREMA).
    • Handle: RePEc:cra:wpaper:2021-21
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    1. Ying-Fang Kao & K. Vela Velupillai, 2015. "Behavioural economics: Classical and modern," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 22(2), pages 236-271, April.
    2. Foster, John & Metcalfe, J. Stan, 2012. "Economic emergence: An evolutionary economic perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 420-432.
    3. Steve J. Bickley & Ho Fai Chan & Sascha L. Schmidt & Benno Torgler, 2021. "Quantum-Sapiens: The Quantum Bases for Human Expertise, Knowledge, and Problem-Solving (Extended Version with Applications)," CREMA Working Paper Series 2021-14, Center for Research in Economics, Management and the Arts (CREMA).
    4. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    5. John Foster, 2005. "From simplistic to complex systems in economics," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 29(6), pages 873-892, November.
    6. Kenneth E. Boulding, 1956. "General Systems Theory--The Skeleton of Science," Management Science, INFORMS, vol. 2(3), pages 197-208, April.
    7. Enrico Petracca, 2017. "A cognition paradigm clash: Simon, situated cognition and the interpretation of bounded rationality," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(1), pages 20-40, January.
    8. Wolfram Elsner, 2017. "Complexity Economics as Heterodoxy: Theory and Policy," Journal of Economic Issues, Taylor & Francis Journals, vol. 51(4), pages 939-978, October.
    9. Peter M. Spiegler & William Milberg, 2013. "Methodenstreit 2013? Historical Perspective on the Contemporary Debate Over How to Reform Economics," Forum for Social Economics, Taylor & Francis Journals, vol. 42(4), pages 311-345, November.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    11. David Colander & Roland Kupers, 2014. "Complexity and the Art of Public Policy: Solving Society’s Problems from the Bottom Up," Economics Books, Princeton University Press, edition 1, number 10207.
    12. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    13. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    14. Alan Kirman, 2010. "The Economic Crisis is a Crisis for Economic Theory ," CESifo Economic Studies, CESifo Group, vol. 56(4), pages 498-535, December.
    15. Claudius Gräbner, 2017. "The Complementary Relationship Between Institutional and Complexity Economics: The Example of Deep Mechanismic Explanations," Journal of Economic Issues, Taylor & Francis Journals, vol. 51(2), pages 392-400, April.
    16. Ramos-Martin, Jesus, 2003. "Empiricism in ecological economics: a perspective from complex systems theory," Ecological Economics, Elsevier, vol. 46(3), pages 387-398, October.
    17. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    18. Benno Torgler, 2021. "The Power of Public Choice in Law and Economics," CREMA Working Paper Series 2021-04, Center for Research in Economics, Management and the Arts (CREMA).
    19. Jean-Philippe Bouchaud, 2008. "Economics need a scientific revolution," Papers 0810.5306, arXiv.org.
    20. Ariel Rubinstein, 2003. ""Economics and Psychology"? The Case of Hyperbolic Discounting," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(4), pages 1207-1216, November.
    21. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    22. Jean-Philippe Bouchaud, 2008. "Economics needs a scientific revolution," Nature, Nature, vol. 455(7217), pages 1181-1181, October.
    23. Shlomo Benartzi & Richard Thaler, 2007. "Heuristics and Biases in Retirement Savings Behavior," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 81-104, Summer.
    24. Steve J. Bickley & Alison Macintyre & Benno Torgler, 2021. "Artificial Intelligence and Big Data in Sustainable Entrepreneurship," CREMA Working Paper Series 2021-11, Center for Research in Economics, Management and the Arts (CREMA).
    25. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    26. Max Boisot & Agustí Canals, 2004. "Data, information and knowledge: have we got it right?," Journal of Evolutionary Economics, Springer, vol. 14(1), pages 43-67, January.
    27. Dr. Peter Kenning & Hilke Plassmann, 2004. "NeuroEconomics," Experimental 0412005, University Library of Munich, Germany.
    28. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    29. Steve J. Bickley & Ho Fai Chan & Sascha L. Schmidt & Benno Torgler, 2020. "Quantum-Sapiens: The Quantum Bases for Human Expertise, Knowledge, and Problem-Solving," CREMA Working Paper Series 2020-18, Center for Research in Economics, Management and the Arts (CREMA).
    30. Smith, Vernon L, 1989. "Theory, Experiment and Economics," Journal of Economic Perspectives, American Economic Association, vol. 3(1), pages 151-169, Winter.
    31. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    32. Helbing, Dirk, 2009. "Managing Complexity in Socio-Economic Systems," European Review, Cambridge University Press, vol. 17(2), pages 423-438, May.
    33. Smith, Vernon L., 2005. "Behavioral economics research and the foundations of economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 34(2), pages 135-150, March.
    34. Torgler, Benno, 2016. "Can Tax Compliance Research Profit from Biology?," Review of Behavioral Economics, now publishers, vol. 3(1), pages 113-144, April.
    35. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    36. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    37. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    38. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    39. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    40. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    41. Benno Torgler, 2021. "Symbiotics > Economics?," CREMA Working Paper Series 2021-15, Center for Research in Economics, Management and the Arts (CREMA).
    42. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    43. Ceddia, M.G. & Bardsley, N.O. & Goodwin, R. & Holloway, G.J. & Nocella, G. & Stasi, A., 2013. "A complex system perspective on the emergence and spread of infectious diseases: Integrating economic and ecological aspects," Ecological Economics, Elsevier, vol. 90(C), pages 124-131.
    44. Cristiano Castelfranchi, 2000. "Through the agents' minds: Cognitive mediators of social action," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 109-140, March.
    45. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, April.
    46. Matthew Rabin, 1998. "Psychology and Economics," Journal of Economic Literature, American Economic Association, vol. 36(1), pages 11-46, March.
    47. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    48. Esther-Mirjam Sent, 2004. "Behavioral Economics: How Psychology Made Its (Limited) Way Back Into Economics," History of Political Economy, Duke University Press, vol. 36(4), pages 735-760, Winter.
    49. Tomer, John F., 2007. "What is behavioral economics?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 36(3), pages 463-479, June.
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    Behavioural Economics; Cognitive Psychology; Complexity Theory; Artificial Intelligence;
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