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dc.contributor.authorde Castro, Luciano I.en_US
dc.description.abstractAffiliation has been a prominent assumption in the study of economic models with statistical dependence. Despite its large number of applications, especially in auction theory, affiliation has limitations that are important to be aware of. This paper shows that affiliation is a restrictive condition and the intuition usually given for its adoption may be misleading. Also, other usual justifications for affiliation are not compelling. Moreover, some implications of affiliation do not generalize to other definitions of positive dependence. These results show the need to consider alternatives to affiliation. The results of this paper suggest new directions for the study of dependence in economics. The main result classifies economic models of information and proves the existence of a minimally informative random variable that makes types conditionally independent. If this variable is known, then all results that are valid under independence are also valid for these models with statistically dependent types. Complementing this result, we describe a method to study general forms of dependence using grid distributions, which are distributions whose densities are constant in squares. This method allows a comprehensive investigation on the revenue ranking of auctions under general dependence.en_US
dc.publisher|aNorthwestern Univ., Kellogg Graduate School of Management, Center for Mathematical Studies in Economics and Management Science |cEvanstonen_US
dc.relation.ispartofseries|aDiscussion Paper, Center for Mathematical Studies in Economics and Management Science |x1479en_US
dc.subject.keywordpositive dependenceen_US
dc.subject.keywordstatistical dependence of typesen_US
dc.subject.keywordconditional independenceen_US
dc.subject.keywordde Finetti's theoremen_US
dc.subject.keywordminimally informative random variableen_US
dc.subject.keywordpure strategy equilibriumen_US
dc.subject.keywordrevenue rankingen_US
dc.titleAffiliation and dependence in economic modelsen_US
dc.typeWorking Paperen_US

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