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Articles by Max Pellert
A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags
Abstract We present a large-scale, longitudinal dataset capturing user activity on the online platform of DerStandard, a major Austrian newspaper. The dataset spans ten years (2013–2022) and includes over 75 million user comments, more than 400 million votes, and detailed metadata on articles and user interactions.
Generative AI for climate governance and acceptability-constrained policy design
Abstract Climate policies often fail when they clash with cultural values, social identities, and fairness perceptions. We propose Acceptability-Constrained Climate Policy Design (ACCPD), using large language models as “cultural world models” to simulate public responses before implementation. By embedding LLMs in generative agent-based models and physical system simulators, ACCPD aims to enable policymakers to co-optimize for climate-policy efficacy and social legitimacy.
[2511.04500] Large language models replicate and predict human cooperation across experiments in game theory
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AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories
In recent years, large language models (LLMs) have been processing an ever-increasing amount of human-generated data. Neural models of language such as GloVe (Pennington et al., 2014), BERT (Devlin et al., 2019), GPT-2 (Radford et al., 2018), XLNet (Yang et al., 2019), RoBERTa (Y. Liu et al., 2019), BART (Lewis et al., 2019), or ChatGPT and GPT-3 (Brown et al., 2020) have come to play a transformative role in several applications of societal relevance.
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