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Abstract: Cultural Consensus Theory (CCT) is a popular information pooling methodology used in the social and behavioral sciences. CCT consists of cognitive models designed to determine a consensus truth shared by a group of informants (respondents), and to better understand the cognitive characteristics of the informants (e.g. level knowledge, response biases). However prior to this paper, no CCT models have been developed that allow the possibility of the informant responses to come from a mixture of two or more consensus answer patterns. The major advance in the current paper is to endow one of the popular CCT models, the General Condorcet Model (GCM) for dichotomous responses, with the possibility of having several latent consensus answer patterns, each corresponding to a different, latent subgroup of informants. In addition, we augment the model to allow the possibility of questions having differential difficulty (cultural saliency). This is the first CCT finite-mixture model, and it is named the Multi-Culture GCM (MC-GCM). The model is developed axiomatically and a notable property is derived that can suggest the appropriate number of mixtures for a given data set. The model is extended in a hierarchical Bayesian framework and its application is successfully demonstrated on both simulated and real data, including a new experimental data set on political viewpoints. [Copyright &y& Elsevier]

In recent times, there has been an increased interest in theories of language evolution that have an applicability to the study of dialect formation, linguistic change, creolization, the origin of language, and animal and robot communication systems in general. One particular question that has attracted some interest has the following general form: how might a group of linguistic agents arrive at a shared communication system purely through local patterns of interaction and without any global agency enforcing uniformity? In this paper, we consider a natural model of language (or more precisely, word) evolution on a social network, prove several theoretical properties, and establish connections to related phenomena in biology, social sciences, and physics. [ABSTRACT FROM AUTHOR]

Abstract: Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct “smart” experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond. [Copyright &y& Elsevier]

Abstract: The use of quantum mechanical concepts in social science is a fairly new phenomenon. This paper uses one of quantum mechanics’ most basic concepts, probability interference, to explain the violation of an important decision theory principle (the ‘sure-thing principle’). We also attempt to introduce other quantum mechanical concepts in relation to the sure-thing principle violation. [Copyright &y& Elsevier]

Abstract: The security level models of Gilboa [1988. A combination of expected utility and maxmin decision criteria. Journal of Mathematical Psychology, 32, 405–420] and of Jaffray [1988. Choice under risk and the security factor: An axiomatic model. Theory and Decision, 24, 169–200] as well as the security and potential level model of Cohen [1992. Security level, potential level, expected utility: A three-criteria decision model under risk. Theory and Decision, 33, 101–104] and Essid [1997. Choice under risk with certainty and potential effects: A general axiomatic model. Mathematical Social Sciences, 34, 223–247] successfully accommodate classical Allais paradoxes while they offer an interesting explanation for their occurrence. However, experimental data suggest a systematic violation of these models when lotteries with low probabilities of bad or good outcomes are involved. In our opinion, one promising candidate for the explanation of these violations is the assumption of thresholds in the perception of security and potential levels. The present paper develops an axiomatic model that allows for such thresholds, so that the derived representation of preferences can accommodate the observed violations of the original security and potential level models. [Copyright &y& Elsevier]