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The increase in the amount and variety of evaluations provided by the users of different websites regarding the products displayed is becoming an increasingly familiar scenario. That is, decision makers (DMs) constantly receive linguistic evaluations (LEs) from unknown evaluators when considering different choice alternatives. The imprecision of the LEs and the fact that the evaluators may have biased interests when describing a product must be considered by the DMs when computing their expected utilities. We define a Bayesian-updated probability (BUP) function that accounts for the fuzziness inherent in the LEs and the reputation of the evaluator to represent the beliefs of DMs. The proposed BUP process allows the DMs to subjectively adjust the probability mass that is shifted across evaluation intervals when updating their beliefs and computing their corresponding expected utilities. We illustrate the behavior of the BUP function numerically and describe potential decision support applications. [ABSTRACT FROM AUTHOR]

The current paper analyzes the formation of social networks determined by the preferences of their users, who are endowed with incomplete information regarding the characteristics of other users from who they receive friendship requests. The acceptance or rejection decision is determined by the limited information available when receiving the requests, the expectations of the users regarding the remaining characteristics of the requesters and the resulting improvement in network capacity derived from accepting the friendship requests. We illustrate how the similarity in preferences among users leads to more concentrated clusters within the incomplete information scenario analyzed. At the same time, the emergence of disutility costs derived from a suboptimal decision when accepting to interact with other users increments the dispersion between clusters. In this regard, the inclusion of requesters endowed with average preferences relative to those of the standard users composing the network acts as a connectivity-enhancing mechanism designed to reduce the dispersion and differences existing between clusters. [ABSTRACT FROM AUTHOR]

Consider the decision faced by the user of a social media site of whether or not to accept a friendship request from another user, given the limited amount of information available before deciding. We formalize the problem by defining the expected utility trade-offs derived from the request and simulate the resulting incentives numerically. These incentives provide the basis on which to build social networks determined by the different expectations and preferences of their users. Social networks are generated using a self-organizing map to cluster the decision makers (DMs) by their friendship acceptance behaviour. This behaviour is determined by the distribution of requesters relative to the preferences of the DMs. [ABSTRACT FROM AUTHOR]