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<p>Societal diversification – the increasing heterogeneity of decision makers’ (DMs’) cultural backgrounds, expertise, values, and risk perceptions – inevitably breeds social conflict, here characterized as task-related disagreements and trust-based doubts. These intertwined conflicts, amplified by extreme risks and distributional uncertainty, can derail group decisions. To address these challenges, we develop a Wasserstein distributionally robust, risk-averse minimum cost consensus model (DRO-R-MCCM). Pairwise task and trust conflicts are first quantified and converted into node strength and degree centrality, which determine DMs’ weights and initial unit adjustment costs within the social conflict network. Furthermore, to account for risk-averse DMs, the Conditional Value at Risk (CVaR) method is employed for risk assessment. Moreover, an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(L_1\)</EquationSource> </InlineEquation>-Wasserstein ambiguity set manages uncertainty by considering all probability distributions that are ’close enough’ to the empirical distribution, where the closeness is measured by the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(L_1\)</EquationSource> </InlineEquation>-Wasserstein distance, which calculates the Manhattan distance (the sum of absolute differences) between two probability distributions. The resulting formulation reduces to a tractable linear program. Finally, a simulation analysis of China’s new energy vehicle subsidy policy is conducted to validate the proposed model’s effectiveness. Sensitivity and comparative analyses further confirm the model’s superiority in handling complex social conflicts, risks and uncertain conditions.</p>

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Social conflict network-driven Wasserstein distributionally robust minimum cost consensus model under uncertain cost with risk aversion

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Kai Zhu,
  • Shaojian Qu,
  • Ying Ji,
  • Yifan Ma

摘要

Societal diversification – the increasing heterogeneity of decision makers’ (DMs’) cultural backgrounds, expertise, values, and risk perceptions – inevitably breeds social conflict, here characterized as task-related disagreements and trust-based doubts. These intertwined conflicts, amplified by extreme risks and distributional uncertainty, can derail group decisions. To address these challenges, we develop a Wasserstein distributionally robust, risk-averse minimum cost consensus model (DRO-R-MCCM). Pairwise task and trust conflicts are first quantified and converted into node strength and degree centrality, which determine DMs’ weights and initial unit adjustment costs within the social conflict network. Furthermore, to account for risk-averse DMs, the Conditional Value at Risk (CVaR) method is employed for risk assessment. Moreover, an \(L_1\) -Wasserstein ambiguity set manages uncertainty by considering all probability distributions that are ’close enough’ to the empirical distribution, where the closeness is measured by the \(L_1\) -Wasserstein distance, which calculates the Manhattan distance (the sum of absolute differences) between two probability distributions. The resulting formulation reduces to a tractable linear program. Finally, a simulation analysis of China’s new energy vehicle subsidy policy is conducted to validate the proposed model’s effectiveness. Sensitivity and comparative analyses further confirm the model’s superiority in handling complex social conflicts, risks and uncertain conditions.