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<p>This paper presents the Trump Worldview Generative Model (TWGM). This theory-driven computational framework formalizes Donald J. Trump’s ontology of power as a system of three fixed priors: hierarchy, power, and transactionalism. Drawing on thinkers like Schmitt, Weber, Bourdieu, and related traditions, the model shows that Trump’s seemingly unpredictable behavior reflects a coherent, low-entropy worldview where power restores natural order. Using hermeneutic coding of 450 statements and a transformer-inspired algorithmic architecture constrained by four theoretical lemmas—transitivity, hierarchy boundaries, asymmetry, and centrality—TWGM reduces predictive entropy by 23% while maintaining high accuracy (87.3%). Empirical validation confirms distinct prior activations, ranking transitivity, and boundary detection in hierarchical contexts, enabling reliable predictions of Trump’s responses in new situations. Beyond the Trump case, the study emphasizes how theory-guided machine learning can embed political ontology into computational models, providing new tools for analyzing populist leaders whose reasoning extends beyond institutionalist frameworks.</p>

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Modeling Trump’s Worldview with Algorithms: Power, Hierarchy, and Transactionalism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Dwayne Woods

摘要

This paper presents the Trump Worldview Generative Model (TWGM). This theory-driven computational framework formalizes Donald J. Trump’s ontology of power as a system of three fixed priors: hierarchy, power, and transactionalism. Drawing on thinkers like Schmitt, Weber, Bourdieu, and related traditions, the model shows that Trump’s seemingly unpredictable behavior reflects a coherent, low-entropy worldview where power restores natural order. Using hermeneutic coding of 450 statements and a transformer-inspired algorithmic architecture constrained by four theoretical lemmas—transitivity, hierarchy boundaries, asymmetry, and centrality—TWGM reduces predictive entropy by 23% while maintaining high accuracy (87.3%). Empirical validation confirms distinct prior activations, ranking transitivity, and boundary detection in hierarchical contexts, enabling reliable predictions of Trump’s responses in new situations. Beyond the Trump case, the study emphasizes how theory-guided machine learning can embed political ontology into computational models, providing new tools for analyzing populist leaders whose reasoning extends beyond institutionalist frameworks.