Probing the Democratic Peace Argument Using Linguistic Fuzzy Logic

Why have the numerous debates on the “democratic peace” remained inconclusive? In addressing this question, the paper examines causality in social sciences by using propositional calculus in the framework of linguistic fuzzy logic. The paper does this by taking into account the possibility that some causal relations might be more or less of a sufficient type while others might be more or less of a necessary type, and while still others might be of both types to a lesser or greater degree of truth. The paper shows that depending on how much more or less democratic the two states are, and depending on how much more or less they feel threatened by one another, this more or less sufficiently causes a more or less possibility of fighting between the two states. Therefore, the lack of agreement on the possibility of a democratic peace is strictly speaking neither a problem of empirical validation, nor one of theoretical explanation, although these are still important issues. Instead, the lack of agreement has much to do with taking for granted a Boolean logic approach as a framework for validating the democratic peace argument. A linguistic fuzzy-logic framework predicts a much more diverse set of conclusions than just whether or not two democracies go to war.

[1]  N. D. Pidgen,et al.  The Comparative Method , 1987 .

[2]  Robert Fullér,et al.  An Analytic Approach for Obtaining Maximal Entropy Owa Operator Weights , 2001, Fuzzy Sets Syst..

[3]  A. Heyting,et al.  Intuitionism: An introduction , 1956 .

[4]  Badredine Arfi,et al.  Linguistic Fuzzy-Logic Game Theory , 2006 .

[5]  Dina A. Zinnes Constructing Political Logic , 2004 .

[6]  Badredine Arfi Fuzzy Decision Making in Politics: A Linguistic Fuzzy-Set Approach (LFSA) , 2005, Political Analysis.

[7]  Francisco Herrera,et al.  Linguistic decision analysis: steps for solving decision problems under linguistic information , 2000, Fuzzy Sets Syst..

[8]  Petr Hájek,et al.  Metamathematics of Fuzzy Logic , 1998, Trends in Logic.

[9]  Lotfi A. Zadeh,et al.  From Computing with Numbers to Computing with Words - from Manipulation of Measurements to Manipulation of Perceptions , 2005, Logic, Thought and Action.

[10]  Charles C. Ragin,et al.  Fuzzy-Set Social Science , 2001 .

[11]  S. Chan Explaining War Termination: a Boolean Analysis of Causes , 2003 .

[12]  Van-Nam Huynh,et al.  An algebraic approach to linguistic hedges in Zadeh's fuzzy logic , 2002, Fuzzy Sets Syst..

[13]  Francisco Herrera,et al.  Aggregation operators for linguistic weighted information , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Van-Nam Huynh,et al.  Hedge Algebras, Linguistic-Valued Logic and Their Application to Fuzzy Reasoning , 1999, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Harvey Starr,et al.  Necessary conditions : theory, methodology, and applications , 2003 .

[16]  Badredine Arfi,et al.  Linguistic Fuzzy-Logic Social Game of Cooperation , 2006 .