Does the grand coalition form? Persistence, arrival, and the role of the sharing rule in a dynamic process of nested binding agreements
研究动态联盟形成过程，通过分析折扣长期预期收益和自确认信念，探讨了大联盟形成的条件及其吸收状态。
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A Fair Objective for Human-Empowerment-Preserving AI: Desiderata, Design, and Likely Behavioral Consequences
This work proposes a parametric and decomposable AI objective function designed to safeguard human well-being and safety while maintaining an equitable balance of power in human–AI interaction. The function aggregates human utilities with explicit consideration for long-term outcomes, risk aversion, and disparities in human capabilities, integrating models of bounded rationality and social norms to accommodate diverse human goals. Grounded in axiomatic design principles, it explicitly enshrines human empowerment and power balance as core desiderata, yielding a functional form and associated parameter constraints that satisfy desirable theoretical properties. Theoretical analysis and case studies demonstrate that the proposed objective effectively achieves soft maximization of human utility and reveals emergent instrumental subgoals and behavioral implications inherent to its structure.
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Planning resilient hydrogen supply chains under disruption risk
This study addresses the critical oversight in existing green hydrogen supply chain planning—the neglect of supply disruption risks, which can lead to substantial welfare losses and systemic vulnerability. To mitigate these issues, the authors propose a risk-aware stochastic optimization model tailored to the European Union’s hydrogen import system, integrating two complementary resilience strategies: diversification of import corridors and strategic overinvestment in infrastructure. The framework holistically evaluates infrastructure configurations and their economic impacts across a range of disruption scenarios. Compared to conventional risk-agnostic planning approaches, the proposed model reduces welfare losses by 12% (approximately €24 billion), achieves performance nearly equivalent to that of an idealized disruption-free system, and yields significantly improved transport network design and terminal deployment.
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Model-Based Soft Maximization of Suitable Metrics of Long-Term Human Power
This paper addresses the fundamental tension between AI safety and human welfare by redefining the optimization objective around “human power” — a normative construct capturing agency, autonomy, and collective capability. Method: We propose an axiomatic framework featuring a parameterized, decomposable objective function that explicitly encodes long-horizon considerations, inequality sensitivity, and risk aversion in aggregating human power. The model integrates bounded rationality, social norms, and multi-objective preferences to enable adaptive power balancing under dynamic conditions. Optimization employs backward induction combined with world-model-based multi-agent reinforcement learning for tractable approximation. Contribution/Results: By substituting soft maximization of human power for conventional utility maximization, our approach mitigates instrumental convergence risks. Experiments across canonical scenarios demonstrate that the framework consistently induces AI systems to generate beneficial instrumental subgoals, yielding substantial improvements in system safety and human–AI collaboration efficacy.
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Rethinking Climate Econometrics: Data Cleaning, Flexible Trend Controls, and Predictive Validation
This paper addresses three fundamental challenges in climate econometrics: model sensitivity to outliers, neglect of temporal dependence, and absence of principled model selection. Methodologically, it proposes a reconstruction involving robust data cleaning, nonparametric time-trend controls (e.g., splines and local regression), and a rolling-window out-of-sample forecasting validation framework spanning 700+ variables. Key contributions include the first empirical finding that mainstream climate variables—such as mean temperature—exhibit negligible predictive power, whereas humidity-related variables demonstrate superior robustness; this motivates a new evaluation standard centered on predictive validity. Results falsify several widely accepted climate–economic relationships and reveal that even optimal predictors explain only a limited fraction of variation, raising serious concerns about the empirical foundations of the field. The study advances climate econometrics toward a data-driven, validation-first methodological paradigm.
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Does the grand coalition form? Persistence, arrival, and the role of the sharing rule in a dynamic process of nested binding agreements
研究动态联盟形成过程，通过分析折扣长期预期收益和自确认信念，探讨了大联盟形成的条件及其吸收状态。
0 citationsRead paper
A Fair Objective for Human-Empowerment-Preserving AI: Desiderata, Design, and Likely Behavioral Consequences
This work proposes a parametric and decomposable AI objective function designed to safeguard human well-being and safety while maintaining an equitable balance of power in human–AI interaction. The function aggregates human utilities with explicit consideration for long-term outcomes, risk aversion, and disparities in human capabilities, integrating models of bounded rationality and social norms to accommodate diverse human goals. Grounded in axiomatic design principles, it explicitly enshrines human empowerment and power balance as core desiderata, yielding a functional form and associated parameter constraints that satisfy desirable theoretical properties. Theoretical analysis and case studies demonstrate that the proposed objective effectively achieves soft maximization of human utility and reveals emergent instrumental subgoals and behavioral implications inherent to its structure.
0 citationsRead paper
Planning resilient hydrogen supply chains under disruption risk
This study addresses the critical oversight in existing green hydrogen supply chain planning—the neglect of supply disruption risks, which can lead to substantial welfare losses and systemic vulnerability. To mitigate these issues, the authors propose a risk-aware stochastic optimization model tailored to the European Union’s hydrogen import system, integrating two complementary resilience strategies: diversification of import corridors and strategic overinvestment in infrastructure. The framework holistically evaluates infrastructure configurations and their economic impacts across a range of disruption scenarios. Compared to conventional risk-agnostic planning approaches, the proposed model reduces welfare losses by 12% (approximately €24 billion), achieves performance nearly equivalent to that of an idealized disruption-free system, and yields significantly improved transport network design and terminal deployment.
0 citationsRead paper
Model-Based Soft Maximization of Suitable Metrics of Long-Term Human Power
This paper addresses the fundamental tension between AI safety and human welfare by redefining the optimization objective around “human power” — a normative construct capturing agency, autonomy, and collective capability. Method: We propose an axiomatic framework featuring a parameterized, decomposable objective function that explicitly encodes long-horizon considerations, inequality sensitivity, and risk aversion in aggregating human power. The model integrates bounded rationality, social norms, and multi-objective preferences to enable adaptive power balancing under dynamic conditions. Optimization employs backward induction combined with world-model-based multi-agent reinforcement learning for tractable approximation. Contribution/Results: By substituting soft maximization of human power for conventional utility maximization, our approach mitigates instrumental convergence risks. Experiments across canonical scenarios demonstrate that the framework consistently induces AI systems to generate beneficial instrumental subgoals, yielding substantial improvements in system safety and human–AI collaboration efficacy.
0 citationsRead paper
Rethinking Climate Econometrics: Data Cleaning, Flexible Trend Controls, and Predictive Validation
This paper addresses three fundamental challenges in climate econometrics: model sensitivity to outliers, neglect of temporal dependence, and absence of principled model selection. Methodologically, it proposes a reconstruction involving robust data cleaning, nonparametric time-trend controls (e.g., splines and local regression), and a rolling-window out-of-sample forecasting validation framework spanning 700+ variables. Key contributions include the first empirical finding that mainstream climate variables—such as mean temperature—exhibit negligible predictive power, whereas humidity-related variables demonstrate superior robustness; this motivates a new evaluation standard centered on predictive validity. Results falsify several widely accepted climate–economic relationships and reveal that even optimal predictors explain only a limited fraction of variation, raising serious concerns about the empirical foundations of the field. The study advances climate econometrics toward a data-driven, validation-first methodological paradigm.
0 citationsRead paper