Two-Person Second-Order Games, Part 2: Restructuring Operations to Reach a Win-Win Profile

Article

Abstract

In Part 1 of the paper, using habitual domains theory and finite Markov chain theory, we have introduced a new model for describing the evolution of the states of mind of players over time, the two-person second-order game. The concepts of focal mind profile as well as the solution concept of win-win mind profile have been introduced as solution concepts for these games. In Part 2 of the paper, we address the problem of restructuring a game where the focal profile (1,1) is not reachable or is not a win-win profile into a game where the profile (1,1) is a reachable win-win profile. Precisely, under some reasonable assumptions, we derive the possibility theorem that it is always possible to reach a win-win mind profile in a two-person second-order game. Moreover, we provide practical operations for restructuring games for reaching a win-win profile.

Keywords

Games Habitual domains Second-order games Focal mind profiles Win-win mind profiles Markov chains 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Larbani, M., Yu, P.L.: Second order games, I: formulation and anatomy of transition. J. Optim. Theory Appl. (2008, accepted for publication) Google Scholar
  2. 2.
    Aumann, R.J., Hart, S.: Handbook of Game Theory with Economic Applications, vols. 1–3. Elsevier Science/North-Holland, Amsterdam (1992, 1994, 2002) Google Scholar
  3. 3.
    Yu, P.L.: Towards second order games: decision dynamics in gaming phenomena. J. Optim. Theory Appl. 27, 147–166 (1979) MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Yu, P.L.: Forming Win-Win Strategies, an Integrated Theory of Habitual Domains. Springer, Berlin (1990) Google Scholar
  5. 5.
    Yu, P.L.: Habitual Domains and Forming Win-Win Strategies. NCTU Press, Hshinchu (2002) Google Scholar
  6. 6.
    Kwon, Y.K., Yu, P.L.: Conflict resolution by reframing game payoffs using linear perturbations. J. Optim. Theory Appl. 39, 187–214 (1983) MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Bhat, U.N., Miller, K.G.: Elements of Applied Stochastic Processes. Wiley Series in Probability and Statistics. Wiley, New York (2002) MATHGoogle Scholar
  8. 8.
    Dawson, D.: Introduction to Markov Chains. Canadian Mathematical Monographs, vol. 2. Mc Gill University (1970) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Business AdministrationKainan UniversityTaoyuanTaiwan
  2. 2.Department of Business AdministrationIIUM UniversityKuala LumpurMalaysia
  3. 3.Institute of Information ManagementNational Chiao Tung UniversityHsinchuTaiwan
  4. 4.School of BusinessUniversity of KansasLawrenceUSA

Personalised recommendations