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Item type:Publication, Access status: with files , Opinion dynamics with median aggregation(Elsevier, 2026-03-30); ; ; ; Understanding the formation and evolution of opinions is of broad interdisciplinary interest. Many classical models for opinion formation focus on the impact of different notions of locality, e.g., locality due to network effects among agents or the role of the proximity of opinions. In practice, however, opinion formation is often governed by the interplay of local and global influences. In this paper, we study these influences with a model for opinion formation of agents embedded in a social network. Each agent has a static intrinsic opinion as well as a public opinion that is updated asynchronously over time. Moreover, agents have access to a global aggregate (e.g., the outcome of a vote) of all public opinions. We focus on the popular median voting rule and show that pure Nash equilibria always exist. For every initial state of the dynamics, a pure equilibrium can be reached. The set of reachable equilibria forms a complete lattice, and extremal equilibria can be computed in polynomial time. We show that by uniformly increasing the influence of the global median we can enforce that the median opinion is the same in every reachable equilibrium. We can compute the increase scheme that achieves this property in polynomial time. In contrast, when we can increase the influence of the global median for a set of at most k agents, finding the set that leads to a unique median opinion in every reachable equilibrium is NP-complete.Publicationtype: Journal Article14 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A computational framework for conceptual blending(Elsevier, 2017-12-02); ; ; ; We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles, described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music.Publicationtype: Journal ArticleScopus© Citations 63 69 256