Relación entre economía y algunos paradigmas de inteligencia artificial

Alfredo Olguín Gallardo

Authors

Alfredo Olguín Gallardo Centro de Ciencias de la Complejidad

Keywords:

Computational Economics, Complexity, Artificial Intelligence

Abstract

This paper explores some relationships between the Economics and some of the current paradigms that define the methodologies and models of artificial intelligence. The approach that stands out is the paradigm of mathematical principles of automated learning or machine learning, as well as the contribution of computational economics and economy of complexity on models based on agents in the paradigm of biological principles. In this research are shown some information schemes that distinguish a standard model of automated learning and conventional econometrics, later the visions are developed. Finally, the importance of precision in the machine learning classifier models in the technology industry is explained.

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Author Biography

Alfredo Olguín Gallardo, Centro de Ciencias de la Complejidad

Alfredo Olguín Gallardo. Alfredo, Economista por la Facultad de Economía UNAM. Actualmente ejerciendo como Científico de Datos (Data Scientist) en Uber Technologies Inc. Estancia de investigación de posgrado en el Centro de Ciencias de la Complejidad (C3) en Minería de Datos. Escritos y publicaciones académicas disponibles en https://www.researchgate.net/profile/Alfredo_ Olguin. Correo electrónico: alfredo.olguin.g@gmail.com

References

Bediako-Asare, H., Buffett, S. and Fleming, M. W. (2016) “Advances in Artificial Intelligence”, Canadian Conference on AI. doi: 10.1007/978-3-642-21043-3.

Breiman, L. (2001) “Statistical Modeling: The Two Cultures”, Statistical Science, 16(3), pp. 199–215. doi: 10.2307/2676681.

Brewer, P. J., Huang, M., Nelson, B. and Plott, C. R. (2002) “On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness”, Experimental Economics, 5, pp. 179–208. doi: 10.1023/A:1020871917917.

Chia-Hsuan, Y. 2007, “The role of intelligence in time series properties”, Computational Economics, 2, p. 95.

Cockshott, W, Cottrell, A, Michaelson, G, Wright, I, & Yakovenko, V 2009, “Classical Econophysics”, n.p.: London: Routledge, 2009.

Diestel, R, & Diestel, R 2010, “Graph Theory. Reinhard Diestel”, n.p.: Heidelberg: Springer.

Goldman, A 1980, “The Internalist Conception of Justification”, Midwest Studies In Philosophy, 5, 1, p. 27, Complementary Index, EBSCOhost, viewed 9 May 2017.

Kirman, A. P. (1992) “Whom or What Does the Representative Individual Represent?”, Journal of Economic Perspectives, 6(2), pp. 117–136.

Lucas, R. E. (1988) “On the Mechanics of Economic Development”, Journal of Monetary Economics, 22(August 1987), pp. 3–42. doi: 10.1016/0304- 3932(88)90168-7.

Gallegati, M. and Kirman, A. (2012) “Reconstructing economics: Agent based models and complexity”, Complexity Economics, 1, pp. 5–31. doi: 10.7564/12-COEC2.

“The logic theory machine--A complex information processing system” (1956), IRE Transactions On Information Theory, Information Theory, IRE Transactions On, IRE Trans. Inf. Theory, 3, p. 61.

Olguin, A. (2016) “Economía computacional, complejidad y ciencia de datos. Conociendo el éxito empresarial en el desafío Yelp para 61 mil compañías”, Oikonómika, 1(2), pp. 4–16.

Olguin, A. (2017) “Aproximaciones de inteligencia artificial a mercados de Fintech” presentado en Noveno coloquio de finanzas aplicadas. Facultad de Economía UNAM, 4 Mayo 2017.

O’Regan, G. (2016) “Introduction to the History of Computing”. doi: 10.1007/978-3- 319-33138-6.

Parkes, D. C., and M. P. Wellman. (2015). “Economic Reasoning and Artificial Intelligence.” Science 349 (6245) (July 16): 267–272. doi:10.1126/science. aaa8403. http://dx.doi.org/10.1126/ science.aaa8403.

Patcha, A. and Park, J. M. (2007) “An overview of anomaly detection techniques: Existing solutions and latest technological trends”, Computer Networks, 51(12), pp. 3448–3470. doi: 10.1016/j.comnet.2007.02.001.

Paul, B, Maria, H, Brad, N, & Charles, P (2002), “On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness”, Experimental Economics, 3, p. 179.

Peng, R. D. and Matsui, E. (2015) “The Art of Data Science: A Guide for Anyone Who Works with Data”, Journal of Chemical Information and Modeling, 53, p. 159. doi: 10.1017/ CBO9781107415324.004.

Rüdiger, E., Yoshihiko, D., Stefan, N. and David, S. (2011) “Cognitive Systems Monographs”, Control, 3.

Singh, S. and Markou, M. (2004). “An approach to novelty detection applied to the classification of image regions”. IEEE Transactions on Knowledge and Data Engineering, 16(4), pp.396-406.