Economía del dato: luces y sombras

  1. David Ríos Insua 1
  1. 1 ICMAT-CSIC
Aldizkaria:
Economía industrial

ISSN: 0422-2784

Argitalpen urtea: 2022

Zenbakien izenburua: Economía del dato

Zenbakia: 423

Orrialdeak: 15-24

Mota: Artikulua

Beste argitalpen batzuk: Economía industrial

Laburpena

This paper presents several lights and shadows from Data Economy through examples from projects oriented towards Public Economics. Several relevant principles and relevant future research directions are identified.

Erreferentzia bibliografikoak

  • Ashton, M. C., Lee, K., y de Vries, R. E. (2014). The HEXACO Honesty-Humility, Agreeableness, and Emotionality Factors: A Review of Research and Theory. Personality and Social Psychology Review, 18(2):139–152.
  • Aurentz, J., Navarro, A., y Rios Insua, D. (2022). Learning the rules of the game: An interpretable ai for learning how to play. IEEE Transactions on Games.
  • Banks, D. L., Rios, J., y Rios Insua, D. (2015). Adversarial risk analysis. CRC Press.
  • Bjornsson, Y. (2012). Learning rules of simplified boardgames by observing. In Proc. ECAI 2012, pages 175–180.
  • Brynjolfsson, E. y Kahin, B. (2002). Understanding the digital economy: data, tools, and research. MIT press.
  • Burkart, N. y Huber, M. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70:245–317.
  • Burns, L. y Shulgan, C. (2019). Autonomy: The Quest to Build the Driverless Car—And How It Will Reshape Our World. ECCO.
  • Caballero, W., Naveiro, R., y Rios Insua, D. (2022). Modeling ethical and operational preferences in automated driving systems. Decision Analysis.
  • Castillo, E., Gutierrez, J. M., y Hadi, A. S. (2012). Expert Systems and Probabilistic Networks. Springer.
  • Claussmann, L., Revilloud, M., Gruyer, D., y Glaser, S. (2019). A review of motion planning for highway autonomous driving. IEEE Transactions on Intelligent Transportation Systems, pages 1–23.
  • Comiter, M. (2019). Attacking Artificial Intelligence. Belfer Center Paper.
  • Cox, T. (2008). What’s wrong with risk matrices. Risk Analysis, 28:497–512.
  • Daniell, K., Morton, A., y Rios Insua, D. (2016). Policy analysis and policy analytics. Annals of Operations Research, pages 1–13.
  • Elvira, V., Bernal, F., Hernandez-Coronado, P., Herraiz, E., Alfaro, C., Gómez, J., y Ríos Insua, D. (2020). Safer skies over spain. INFORMS Journal Applied Analytics, 50:21–36.
  • French, S. y Rios Insua, D. (2000). Statistical Decision Theory. Wiley.
  • Gallego, V., Naveiro, R., Roca, C., Campillo, N., y Rios Insua, D. (2022). Ai in drug development: a multidisciplinary perspective. Molecular Diversity.
  • Gardner, H. (2011). Frames of mind: the theory of multiple intelligences. Hachette UK.
  • Goodfellow, I. J., Shlens, J., y Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.
  • Jarvis Thomson, J. (1985). The trolley problem. Yale Law Journal, pages 1395–1415.
  • LeCun, Y., Cortes, C., y Burges, C. (1998). THE MNIST database of handwritten digits. http://yann.lecun.com/exdb/ mnist/.
  • Liu, S. y Rios Insua, D. (2020). An affective decision-making model with applications to social robotics. EURO J Decis Process, 8:13–39.
  • Loewenstein, G. y Lerner, J. S. (2003). The role of affect in decision making. Handbook of affective science, 619(642):3.
  • Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4):370.
  • McAllister, R., Gal, Y., Kendall, A., van der Wilk, M., Shah, A., Cipolla, R., y Weller, A. (2017). Concrete problems for autonomous vehicle safety: advantages of bayesian deep learning. In Proc. 26th IJCAI.
  • Nielsen, T. y Jensen, F. (2008). Bayesian Networks and Decision Graphs. Springer, New York.
  • Picard, R. W. (1997). Affective Computing. Encyclopedia of Multimedia Technology and Networking, Second Edition, (321):15–21.
  • Rios Insua, D., Caballero, W., y Naveiro, R. (2021a). Managing driving modes in automated driving systems. arXiv: 2107.00280.
  • Rios Insua, D., Camacho, J. M., Santos, A., y Lozano, A. (2021b). A predictive bayesian network model for cardiovascular diseases. Technical report, ICMAT.
  • Rios Insua, D. y Gómez-Ullate, D. (2019). ¿Qúe sabemos de? Big Data. La Catarata.
  • Rios Insua, D., Naveiro, R., y Gallego, V. (2020a). Perspectives on adversarial classification. Mathematics, 8(11).
  • Rios Insua, D., Naveiro, R., Gallego, V., y Poulos, J. (2020b). Adversarial machine learning: Perspectives from adversarial risk analysis. arXiv preprint arXiv:2003.03546.
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206– 215.
  • Russell, J. A. y Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant. Journal of Personality and Social Psychology, 76(5):805–819.
  • Society of Automobile Engineers (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Technical report, SAE.
  • Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., y Fergus, R. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.
  • Wilkins, E., Wilson, L., Wickramasinghe, K., Bhatnagar, P., Rayner, M., y Townsend, N. (2017). European cardiovascular disease statistics. Technical report, European Heart Network.
  • Wu, B., Iandola, F., Jin, P. H., y Keutzer, K. (2017). Squeeze- det: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 129–137.