Uncertainty reduction, self-enhancement, and ingroup identification

Two experiments tested the prediction that uncertainty reduction and self-enhancement motivations have an interactive effect on ingroup identification. In Experiment 1 (N = 64), uncertainty and group status were manipulated, and the effect on ingroup identification was measured. As predicted, low-uncertainty participants identified more strongly with a high- than low-status group, whereas high-uncertainty participants showed no preference; and low-status group members identified more strongly under high than low uncertainty, whereas high-status group members showed no preference. Experiment 2 (N = 210) replicated Experiment 1, but with a third independent variable that manipulated how prototypical participants were of their group. As predicted, the effects obtained in Experiment 1 only emerged where participants were highly prototypical. Low prototypicality depressed identification with a low-status group under high uncertainty. The implications of these results for intergroup relations and the role of prototypicality in social identity processes are discussed.

Introducing uncertainty into pattern discovery in temporal event sequences

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.