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Fuzzy Modeling of Economic Institutional Rules

  • Christopher FrantzEmail author
  • Martin K. Purvis
  • Maryam A. Purvis
  • Mariusz Nowostawski
  • Nathan D. Lewis
Chapter

Abstract

Modeling collective social action is challenging not only because of the opacity of the underlying social processes, but even more because of the insufficient information detail concerning the activities under investigation. Such information gaps are customarily filled using the modeler’s intuition or randomization techniques. A promising alternative is to employ fuzzy reasoning. We have built on this potential to employ fuzzy methods as an alternative mechanism to integrate numerous opinions in order to model the establishment of economic institutional rules. Our empirical application domain is based on a historic trade scenario in which traders established rules and shared information in order to prevent the sellers of their goods from cheating them. We address this modeling problem by employing two different group decision-making mechanisms—majority-based voting on the one hand (which follows the original historical case) and preference aggregation using interval type-2 fuzzy sets on the other hand. We compare the outcomes of these two approaches and identify significantly lower sensitivity of the outcomes (i.e., instability of the outcomes to small changes in parameter settings) using fuzzy-set-based approaches in contrast to majority votes. The results suggest that the use of abstract decision-making mechanisms (such as preference aggregation) may be more useful in scenarios that prescribe a decision-making mechanism, but do not provide information to model this process in its entirety. Based on our finding, the potential for a wider use of fuzzy logic in the context of social simulation is discussed and pointers for future investigations are provided.

Keywords

Membership Function Employment Level Preference Aggregation Social Choice Theory Cheat Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2015

Authors and Affiliations

  • Christopher Frantz
    • 1
    Email author
  • Martin K. Purvis
    • 1
  • Maryam A. Purvis
    • 1
  • Mariusz Nowostawski
    • 1
  • Nathan D. Lewis
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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