Abstract
This chapter provides a brief overview of computational thinking inEconomics and Finance. It explores the nexus between developments in Machine Learning, Artificial Intelligence and Economics. It sketches Shu-Heng Chen’s contributions to the field and gives a panoramic view of the chapters included in this volume.
Keywords
- Computational economics
- Artificial intelligence
- Machine learning
- Finance
- Learning
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Herbert Simon is the only person so far to win both the Turing Award and the Nobel Memorial Prize in Economic Sciences. Allen Newell, Cliff Shaw, and Herbert Simon developed one of the earliest programmes—Logic Theorist—which utilised insights from problem solving skills of human beings, especially heuristics, to prove theorems in Principia Mathematica. The foundational paper of the field, however, was by Alan Turing (1950). Also, see Velupillai (2018).
- 2.
It is worth distinguishing here between concerns that relate to efficiency and effectivity. As (Simon, 1983, p. 27) points out:
Artificial intelligence has two goals. First, AI is directed toward getting computers to be smart and do smart things so that human beings don’t have to do them. And second, AI (sometimes called cognitive simulation, or information processing psychology) is also directed at using computers to simulate human beings, so that we can find out how humans work and perhaps can help them to be a little better in their work.
- 3.
There is now a growing literature on the impact of AI on various aspects of the economy and society, see Agrawal et al. (2019).
- 4.
Velupillai’s contribution to this volume provides more details about Shu’s time at UCLA.
- 5.
To this list, we would also add his love for spicy noodles!.
- 6.
Especially, whenever the Deans need to take international visitors around the School for a guided tour!.
References
Agrawal A, Gans J, Goldfarb A (eds) (2019) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago
Athey S (2019) The impact of machine learning on economics. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 507–547
Chen S-H (2012) Varieties of agents in agent-based computational economics: a historical and an interdisciplinary perspective. J Econ Dyn Control 36(1):1–25
Chen S-H (2017) Agent-based computational economics: how the idea originated and where it is going. Routledge, Abingdon
Chen S-H (2020) On the ontological turn in economics: the promises of agent-based computational economics. Philos Soc Sci 50(3):238–259
Chen S-H, Chang C-L, Du Y-R (2012) Agent-based economic models and econometrics. Knowl Eng Rev 27(2):187–219
Chen S-H, Kaboudan M, Du Y-R (eds) (2018) The Oxford handbook of computational economics and finance. Oxford University Press, Oxford
Chen S-H, Kao Y-F (2016) Herbert Simon and agent-based computational economics. In: Frantz R, Marsh L (eds) Minds, models and Milieux: commemorating the centennial of the birth of Herbert Simon. Palgrave Macmillan, London, pp 113–144
Chen S-H, Kao Y-F, Venkatachalam R (2016) Computational behavioral economics. In: Frantz R, Chen S-H, Dopfer K, Heukelom F, Mousavi S (eds) Routledge handbook of behavioral economics. Routledge, Abingdon, pp 309–331
Chen S-H, Lux T, Marchesi M (2001) Testing for non-linear structure in an artificial financial market. J Econ Behav Organ 46(3):327–342
Chen S-H, Venkatachalam R (2017) Agent-based models and their development through the lens of networks. In: Aruka Y, Kirman A (eds) Economic foundations for social complexity science. Springer, Singapore, pp 89–106
Chen S-H, Wang PP (2004) Computational intelligence in economics and finance. In: Chen S-H, Wang PP (eds) Computational intelligence in economics and finance. Springer, pp 3–55
Chen S-H, Yeh C-H (1997) Toward a computable approach to the efficient market hypothesis: an application of genetic programming. J Econ Dyn Control 21(6):1043–1063
Chen S-H, Yeh C-H (2001) Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market. J Econ Dyn Control 25(3–4):363–393
Chen S-H, Yeh C-H (2002) On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. J Econ Behav Organ 49(2):217–239
Cogliano JF, Veneziani R, Yoshihara N (2021) Computational methods and Classical-Marxian economics. J Econ Surv 36(2):310–349
Davies A, Veličković P, Buesing L, Blackwell S, Zheng D, Tomašev N, Tanburn R, Battaglia P, Blundell C, Juhász A et al (2021) Advancing mathematics by guiding human intuition with AI. Nature 600(7887):70–74
Halpern JY, Pass R (2015) Algorithmic rationality: game theory with costly computation. J Econ Theory 156:246–268
Hofman JM, Watts DJ, Athey S, Garip F, Griffiths TL, Kleinberg J, Margetts H, Mullainathan S, Salganik MJ, Vazire S et al (2021) Integrating explanation and prediction in computational social science. Nature 595(7866):181–188
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589
Kao Y-F, Venkatachalam R (2021) Human and machine learning. Comput Econ 57(3):889–909
LeBaron B (2006) Agent-based computational finance. In: Tesfatsion L, Judd K (eds) Handbook of computational economics 2: agent-based computational economics, vol 2. Handbook of computational economics. North-Holland (Elsevier), Amsterdam, pp 1187–1233
Mullainathan S, Obermeyer Z (2022) Diagnosing physician error: a machine learning approach to low-value health care. Q J Econ 137(2):679–727
Mullainathan S, Spiess J (2017) Machine learning: an applied econometric approach. J Econ Perspect 31(2):87–106
Roughgarden T (2016) Twenty lectures on algorithmic game theory. Cambridge University Press
Russel S, Norvig P et al. (2013) Artificial intelligence: a modern approach. Pearson Education Limited, London
Schmid M, Moravcik M, Burch N, Kadlec R, Davidson J, Waugh K, Bard N, Timbers F, Lanctot M, Holland Z et al. (2021) Player of games. arXiv preprint arXiv:2112.03178
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A et al (2017) Mastering the game of GO without human knowledge. Nature 550(7676):354–359
Simon HA (1983) Why should machines learn? In: Michalskl R, Carbonell J, Mitchell T (eds) Machine learning - an artificial approach. Tioga Publishing Co, Palo Alto, pp 25–37
Simon HA, Newell A (1972) Human problem solving. Prentice Hall
Sraffa P (1960) Production of commodities by means of commodities. Cambridge University Press, Cambridge
Tesfatsion L, Judd KL (eds) (2006) Handbook of computational economics 2: agent-based computational economics. North-Holland (Elsevier), Amsterdam
Turing AM (1950) Computing machinery and intelligence. Mind: A Quart Rev Psychol Philos 59(236):433–460
Velupillai K (2010) Computable foundations for economics. Routledge, Abingdon
Velupillai KV (2004) Economic dynamics and computation - resurrecting the Icarus tradition. Metroeconomica 55(2–3):239–264
Velupillai KV (2018) Models of Simon. Routledge, Abingdon
Velupillai KV, Zambelli S (2015) Simulation, computation and dynamics in economics. J Econ Methodol 22(1):1–27
Venkatachalam R, Zambelli S (2022) Self-replacing prices with credit and debt. Struct Chang Econ Dyn 60:451–466
von Neumann J (1966) Theory of self-reproducing automata. University of Illinois Press, Urbana
Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 113(523):1228–1242
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Venkatachalam, R., Wang, S.G. (2023). Computational Thinking in Economics and Finance: Introductory Remarks. In: Venkatachalam, R. (eds) Artificial Intelligence, Learning and Computation in Economics and Finance . Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-15294-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-15294-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15293-1
Online ISBN: 978-3-031-15294-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)