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Coalitions and learning: Applications to a simple game in the triad
Author(s) -
Nichols Albert L.
Publication year - 1977
Publication title -
behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 0005-7940
DOI - 10.1002/bs.3830220604
Subject(s) - simple (philosophy) , computer science , bayesian probability , rational expectations , domain (mathematical analysis) , process (computing) , null model , artificial intelligence , machine learning , mathematical economics , econometrics , mathematics , mathematical analysis , philosophy , epistemology , combinatorics , operating system
This paper concerns decision processes in systems at the group level. The accuracy and domain of a model of rational choice developed by Siegel are increased through the use of complementary models of the process by which subjects estimate the probabilities of reciprocated choice in a simple coalitions game. Two alternative models are presented, one a simple linear stochastic learning model, the other a Bayesian model. Both are tested in conjunction with the Siegel model using data from experiments on coalition formation conducted by Ofshe and Ofshe. Although both models perform significantly better than a static null model, which makes predictions corresponding closely to the original Siegel model, the performance of the linear model is superior to that of the Bayesian model in all but one case. The results illustrate the way in which models developed in one area of research, probability learning, may find useful application in another, coalition formation.