Rigid Clusters, Flexible Networks

In scenarios where objects are characterized by a combination of rigid and flexible features, we consider the problem of identifying a natural set of rigid clusters, along with a network model of flexible states per cluster. Our approach proves effective within the Allais paradox context. Our algorithm, applied to data collected in an experiment, identified personality clusters and emotion states within each cluster. This model outperforms alternative clustering models in capturing information regarding participants’ choices. Beyond the current scope, our approach is applicable to other data-sets with combined rigid-flexible attributes. Beyond prediction, a strategy that aims to achieve a result by influencing a flexible state holds the promise of enhanced effectiveness when it is tailored to a cluster.