Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Inscrutable Decision Makers: Knightian Uncertainty in Machine Learning

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 10671))

Included in the following conference series:

Abstract

In building models that causally explain observed data and future data, econometricians must grapple with quantifiable uncertainty, or risk, and unquantifiable Knightian uncertainty, or ambiguity. In contrast, machine learning practitioners work with statistical models for a data set that enable predictions about data items imputed to be in the data set. Recently these two distinct modeling concepts have become topics of mutual interest in economics and machine learning. We take the viewpoint here that a data set implicitly embodies the ambiguity of the generating processes from which it arises. We present a data model incorporating ambiguity that we dub theInscrutable Decision Maker (IDM) derived from the Anscombe-Aumann model of subjective utility.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Anscombe, F.J., Aumann, R.J.: A definition of subjective probability. Ann. Math. Stat.34(1), 199–205 (1963)

    Article MathSciNet MATH  Google Scholar 

  2. Arrow, K.J.: Alternative approaches to the theory of choice in risk-taking situations. Econometrica19(4), 404–437 (1951)

    Article MathSciNet MATH  Google Scholar 

  3. Baum, L., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat.37(6), 1554–1563 (1966)

    Article MathSciNet MATH  Google Scholar 

  4. Choquet, G.: Theory of capacities. Annales de l’institut Fourier5, 131–295 (1953)

    Article MathSciNet MATH  Google Scholar 

  5. Domingos, P.: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, New York (2015)

    Google Scholar 

  6. Ellsberg, D.: Risk, ambiguity, and the savage axioms. Q. J. Econ.75(4), 643–669 (1961)

    Article MATH  Google Scholar 

  7. Getoor, L., Culler, D., de Sturler, E., Ebert, D., Franklin, M., Jagadish, H.V.: Computing Research and the Emerging Field of Data Science (2016).http://cra.org/wp-content/uploads/2016/10/Computing-Research-and-the-Emerging-Field-of-Data-Science.pdf

  8. Gilboa, I., Marinacci, M.: Ambiguity and the Bayesian paradigm. In: Arló-Costa, H., Hendricks, F.V., van Benthem, J. (eds.) Readings in Formal Epistemology. SGTP, vol. 1, pp. 385–439. Springer International Publishing, Cham (2016).https://doi.org/10.1007/978-3-319-20451-2_21

    Google Scholar 

  9. Hansen, L.P., Marinacci, M.: Ambiguity Aversion and Model Misspecification: An Economic Perspective (2016).http://didattica.unibocconi.it/mypage/dwload.php?nomefile=approximate-02-June-201620160608190839.pdf

  10. Hvistendahl, M.: Crime forecasters. Science353, 1484–1487 (2016)

    Article  Google Scholar 

  11. Angrist, J., Pischke, J.S.: The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. J. Econ. Perspect.24(2), 3–30 (2010)

    Article  Google Scholar 

  12. Kirkpatrick, K.: Battling algorithmic bias. Comm. ACM59, 16–17 (2016)

    Google Scholar 

  13. Knight, F.H.: Risk, Uncertainty, and Profit. Houghton Mifflin Co., New York (1921)

    Google Scholar 

  14. Lane, D.A., Maxfield, R.R.: Ontological uncertainty and innovation. J. Evol. Econ.15(1), 3–50 (2005)

    Article  Google Scholar 

  15. Leamer, E.E.: Let’s take the con out of econometrics. Am. Econ. Rev.73(1), 31–43 (1983)

    Google Scholar 

  16. Leamer, E.E.: Tantalus on the road to asymptopia. J. Econ. Perspect.24(2), 31–46 (2010)

    Article  Google Scholar 

  17. Liptak, A.: Sent to prison by a software program’s secret algorithms. New York Times, 1 May 2017.https://www.nytimes.com/2017/05/01/us/politics/sent-to-prison-by-a-software-programs-secret-algorithms.html

  18. McCulloch, C.E., Searle, S.R., Neuhaus, J.M.: Generalized, Linear, and Mixed Models. Wiley Series in Probability and Statistics. Wiley, Hoboken (2008)

    MATH  Google Scholar 

  19. O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York (2016)

    MATH  Google Scholar 

  20. Smith, R.E.: Idealizations of uncertainty, and lessons from artificial intelligence. Econ.: Open-Access Open-Assess. E-J.10(2016-7), 1–40 (2016).https://dx.doi.org/10.5018/economics-ejournal.ja.2016-7

  21. Smithson, M.: Ignorance and Uncertainty, Emerging Paradigms. Cognitive Science. Springer-Verlag, New York (1989).https://doi.org/10.1007/978-1-4612-3628-3

    Book  Google Scholar 

  22. Stratonovich, R.: Conditional Markov processes. Theory Probab. Appl.5(2), 156–178 (1960)

    Article MathSciNet MATH  Google Scholar 

  23. Taleb, N.: The Black Swan: The Impact of the Highly Improbable, 2nd edn. Penguin Books, London (2010)

    Google Scholar 

  24. Walker, W., Lempert, R., Kwakkel, J.H.: Deep uncertainty. In: Gass, S., Fu, M. (eds.) Encyclopedia of Operations Research and Management Science. Springer, Berlin (2013).https://doi.org/10.1007/978-1-4419-1153-7

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineer and Computer Science, Oregon State University, Corvallis, OR, 97331, USA

    Rick Hangartner & Paul Cull

Authors
  1. Rick Hangartner

    You can also search for this author inPubMed Google Scholar

  2. Paul Cull

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toRick Hangartner.

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

Rights and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hangartner, R., Cull, P. (2018). Inscrutable Decision Makers: Knightian Uncertainty in Machine Learning. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_28

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


[8]ページ先頭

©2009-2025 Movatter.jp