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Snap Out of It? Governmental Instability and Far-Right Mainstreaming in the Dutch and French Elections of 2023/2024
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Hyperedge overlap drives explosive transitions in systems with higher-order interactions
Full text linkEntropy-based random models for hypergraphs
No full textNetwork theory has primarily focused on pairwise relationships, disregarding many-body interactions: neglecting them, however, can lead to misleading representations of complex systems. Hypergraphs represent an increasingly popular alternative for describing polyadic interactions: our innovation lies in leveraging the representation of hypergraphs based on the incidence matrix for extending the entropy-based framework to higher-order structures. In analogy with the Exponential Random Graphs, we name the members of this novel class of models Exponential Random Hypergraphs. Here, we focus on two explicit examples, i.e. the generalisations of the Erd¨ os-R´enyi Model and of the Configuration Model. After discussing their asymptotic properties, we employ them to analyse real-world configurations: more specifically, i) we extend the definition of several network quantities to hypergraphs, ii) compute their expected value under each null model and iii) compare it with the empirical one, in order to detect deviations from random behaviours. Differently from currently available techniques, ours is analytically tractable, scalable and effective in singling out the structural patterns of real-world hypergraphs differing significantly from those emerging as a consequence of simpler, structural constraints
Entropy-based models to randomise real-world hypergraphs
Full text linkNetwork theory has often disregarded many-body relationships, solely focusing on pairwise interactions: neglecting them, however, can lead to misleading representations of complex systems. Hypergraphs represent a suitable framework for describing polyadic interactions. Here, we leverage the representation of hypergraphs based on the incidence matrix for extending the entropy-based approach to higher-order structures: in analogy with the Exponential Random Graphs, we introduce the Exponential Random Hypergraphs (ERHs). After exploring the asymptotic behaviour of thresholds generalising the percolation one, we apply ERHs to study real-world data. First, we generalise key network metrics to hypergraphs; then, we compute their expected value and compare it with the empirical one, in order to detect deviations from random behaviours. Our method is analytically tractable, scalable and capable of revealing structural patterns of real-world hypergraphs that differ significantly from those emerging as a consequence of simpler constraints
Concept and Feature Change in Scientific and Deep Neural Net Representations
Full text linkScientific representations and their constituent concepts
change over time to reflect improvements in our understanding
of the world. Similar improvements in understanding lead to
changes in DNN-procured representations and their features. In
this paper, we investigate whether useful methodological
practices in concept change and in feature change carry across
the two types of representations. We argue that there is indeed considerable potential for methodological cross-pollination and offer some examples of how such benefit may be derived
Digital Sovereignism: A Comparative Analysis of Italian Parties’ Positioning on Transnational Data Governance
Full text linkAssessing the Role of Honour Culture and Image Concerns in Impeding Apologies
Full text linkDespite the known benefits of apologies, people often fail to apologize for wrongdoings. We examined the role of a cultural logic of honor—where apologizing may clash with concerns about maintaining an image of strength and toughness—in reluctance to apologize. Using general population samples from 14 societies in Mediterranean, East Asian, and Anglo-Western regions (N = 5,471), we explored links between honor values and norms, image concerns, and apology outcomes using multilevel mediation analyses. Members of groups with stronger honor endorsement reported stronger image concerns about apologizing relative to their concerns about not apologizing, which in turn predicted greater reluctance to apologize and fewer past apologies. However, groups with stronger honor endorsement did not show greater reluctance to apologize overall, and some individual-level facets of honor predicted better apology outcomes. Our results highlight the importance of considering honor as a multifaceted construct and including contextual factors and processes when studying reconciliation processes and obstacles to apologies
The 3D model of ethical AI practice
Full text linkIn recent years, there have been growing calls to operationalize artificial intelligence (AI) ethics - to move from theory to practice, or (as one group of authors has put it) ‘from what to how’ (Morley et al. Sci Eng Ethics 26(4):2141–2168, 2020. https://doi.org/10.1007/s11948-019-00165-5). In this paper, we propose a novel account of what ethical AI practice might look like, which we call the 3D model, named for its recognition, within the overall AI design cycle, of the three stages of design, development, and deployment. This model aims to embed ethics throughout this cycle, offering questions that should be addressed at each stage. We articulate the benefits of this approach to ethical AI practice: that it is pro-ethical and value-aware, amenable to implementation, it embeds ethics at every stage of the development process, it embeds a culture and language of ethics in organizations and provides clear decision points. Our model is not a panacea, of course, and we accordingly provide an indication of the context in which the implementation of our model might be most effective in ensuring ethical AI practice
