Skip to main content

Bayesian Games: Games with Incomplete Information

  • Reference work entry
Encyclopedia of Complexity and Systems Science

Definition of the Subject

Bayesian games (also known as Games with Incomplete Information) are models of interactive decision situations inwhich the decision makers (players) have only partial information about the data of the game and about the other players. Clearly this is typically thesituation we are facing and hence the importance of the subject: The basic underlying assumption of classical game theory according to which the data ofthe game is common knowledge(CK) among the players, is too strong and often implausible in real situations. Theimportance of Bayesian games is in providing the tools and methodology to relax this implausible assumption, to enable modeling of the overwhelmingmajority of real‑life situations in which players have only partial information about the payoff relevant data. As a result of the interactivenature of the situation, this methodology turns out to be rather deep and sophisticated, both conceptually and mathematically: Adopting the...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 3,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Notes

  1. 1.

    The projective limit (also known as the inverse limit ) of the sequence \( (T_k)_{k=1}^\infty\) is the space T of all sequences \( (\mu_1,\mu_2,\dots)\in \times_{k=1}^\infty T_k \) whichsatisfy: For any \( k\in \mathbb N \), there isa probability distribution \( \nu_k\in\Delta (S\times T_k^{n-1}) \) such that \( \mu_{k+1}=(\mu_k,\nu_k) \).

Abbreviations

Bayesian game:

An interactive decision situation involving several decision makers (players) in which each player has beliefs about (i. e. assigns probability distribution to) the payoff relevant parameters and the beliefs of the other players.

State of nature:

Payoff relevant data of the game such as payoff functions, value of a random variable, etc. It is convenient to think of a state of nature as a full description of a ‘game-form’ (actions and payoff functions).

Type:

Also known as state of mind, is a full description of player's beliefs (about the state of nature), beliefs about beliefs of the other players, beliefs about the beliefs about his beliefs, etc. ad infinitum.

State of the world:

A specification of the state of nature (payoff relevant parameters) and the players' types (belief of all levels). That is, a state of the world is a state of nature and a list of the states of mind of all players.

Common prior and consistent beliefs:

The beliefs of players in a game with incomplete information are said to be consistent if they are derived from the same probability distribution (the common prior) by conditioning on each player's private information. In other words, if the beliefs are consistent, the only source of differences in beliefs is difference in information.

Bayesian equilibrium:

A Nash equilibrium of a Bayesian game: A list of behavior and beliefs such that each player is doing his best to maximize his payoff, according to his beliefs about the behavior of the other players.

Correlated equilibrium:

A Nash equilibrium in an extension of the game in which there is a chance move, and each player has only partial information about its outcome.

Bibliography

  1. Aumann R (1974) Subjectivity and Correlation in RandomizedStrategies. J Math Econ 1:67–96

    MathSciNet  MATH  Google Scholar 

  2. Aumann R (1976) Agreeing to disagree. Ann Stat4:1236–1239

    MathSciNet  MATH  Google Scholar 

  3. Aumann R (1987) Correlated equilibrium as an expression of Bayesianrationality. Econometrica 55:1–18

    MathSciNet  MATH  Google Scholar 

  4. Aumann R (1998) Common priors: A reply to Gul. Econometrica66:929–938

    MathSciNet  MATH  Google Scholar 

  5. Aumann R (1999) Interactive epistemology I: Knowledge. Intern J Game Theory28:263–300

    MathSciNet  MATH  Google Scholar 

  6. Aumann R (1999) Interactive epistemology II: Probability. Intern J GameTheory 28:301–314

    MathSciNet  MATH  Google Scholar 

  7. Aumann R, Heifetz A (2002) Incomplete Information. In: Aumann R, Hart S (eds)Handbook of Game Theory, vol 3. Elsevier, pp 1666–1686

    Google Scholar 

  8. Aumann R, Maschler M (1995) Repeated Games with Incomplete Information. MITPress, Cambridge

    MATH  Google Scholar 

  9. Brandenburger A, Dekel E (1993) Hierarchies of beliefs and commonknowledge. J Econ Theory 59:189–198

    MathSciNet  MATH  Google Scholar 

  10. Gul F (1998) A comment on Aumann's Bayesian view. Econometrica66:923–927

    MathSciNet  MATH  Google Scholar 

  11. Harsanyi J (1967–8) Games with incomplete information played by‘Bayesian’ players, parts I–III. Manag Sci 8:159–182, 320–334, 486–502

    Google Scholar 

  12. Heifetz A (1993) The Bayesian formulation of incomplete information, thenon‐compact case. Intern J Game Theory 21:329–338

    MathSciNet  MATH  Google Scholar 

  13. Heifetz A, Mongin P (2001) Probability logic for type spaces. Games Econ Behav35:31–53

    MathSciNet  MATH  Google Scholar 

  14. Heifetz A, Samet D (1998) Topology‐free topology of beliefs. J EconTheory 82:324–341

    MathSciNet  MATH  Google Scholar 

  15. Maskin E, Riley J (2000) Asymmetric auctions. Rev Econ Stud67:413–438

    MathSciNet  MATH  Google Scholar 

  16. Meier M (2001) An infinitary probability logic for type spaces. COREDiscussion Paper 2001/61

    Google Scholar 

  17. Mertens J-F, Sorin S, Zamir S (1994) Repeated Games, Part A: BackgroundMaterial. CORE Discussion Paper No. 9420

    Google Scholar 

  18. Mertens J-F, Zamir S (1985) Foundation of Bayesian analysis for games withincomplete information. Intern J Game Theory 14:1–29

    MathSciNet  MATH  Google Scholar 

  19. Milgrom PR, Stokey N (1982) Information, trade and commonknowledge. J Eco Theory 26:17–27

    MATH  Google Scholar 

  20. Milgrom PR, Weber RJ (1982) A Theory of Auctions and CompetitiveBidding. Econometrica 50:1089–1122

    MATH  Google Scholar 

  21. Nyarko Y (1991) Most games violate the Harsanyi doctrine. C.V. Starr workingpaper #91–39, NYU

    Google Scholar 

  22. Reny P, Zamir S (2004) On the existence of pure strategy monotone equilibriain asymmetric first price auctions. Econometrica 72:1105–1125

    MathSciNet  MATH  Google Scholar 

  23. Sorin S, Zamir S (1985) A 2‑person game with lack of information on\( { 1\frac{1}{2} } \) sides. Math Oper Res10:17–23

    MathSciNet  MATH  Google Scholar 

  24. Vassilakis S, Zamir S (1993) Common beliefs and common knowledge. J MathEcon 22:495–505

    MathSciNet  MATH  Google Scholar 

  25. Wolfstetter E (1999) Topics in Microeconomics. Cambridge University Press,Cambridge

    Google Scholar 

Download references

Acknowledgments

I am grateful to two anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag

About this entry

Cite this entry

Zamir, S. (2009). Bayesian Games: Games with Incomplete Information. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_29

Download citation

Publish with us

Policies and ethics