Selected work

Representative Papers

Supply Chain Disruptions, the Structure of Production Networks, and the Impact of Globalization

Nov 05, 2025Social Science Research Network

This paper investigates how supply chain disruptions propagate across goods and final consumers in multi-sector international production networks, focusing on how node positions and network structural features shape systemic vulnerability. We develop a parsimonious model integrating production network theory with multi-sector general equilibrium, employing analytical derivation and comparative statics. Our results show: (1) Disruption shocks exhibit a “sharp short-term, decaying long-term” dynamic; (2) Node centrality and network complexity significantly amplify global welfare losses from localized disruptions; (3) Declining transport costs reduce disruption frequency but reinforce specialization, thereby exacerbating negative spillovers from single-node failures; (4) Countries can acquire asymmetric economic influence through control over production and strategic trade quotas. The study provides a structured theoretical benchmark for assessing globalization-related risks and designing supply chain resilience policies.

4 citations1 influentialRead paper

Imagining and building wise machines: The centrality of AI metacognition

Nov 04, 2024arXiv.org

Current AI systems exhibit intelligence but lack human-like wisdom, primarily due to the absence of metacognitive capabilities—such as intellectual humility, perspective-taking, and contextual adaptability—resulting in insufficient robustness, explainability, human-AI collaboration, and goal alignment in novel environments. To address this, the paper formally introduces “AI metacognition” as the cornerstone of artificial wisdom and proposes the first computationally grounded metacognitive capability framework, shifting beyond conventional object-level performance optimization. Methodologically, it integrates cognitive science modeling, explainable AI, value alignment, and novel wisdom-oriented benchmark design. The contributions include: (1) a theoretical foundation for wise AI; (2) a multidimensional evaluation framework; and (3) a principled implementation pathway. This work lays the groundwork for developing next-generation AI systems that are safe, trustworthy, and adaptive.

4 citations1 influentialRead paper

Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

Aug 30, 2020arXiv.org

Comparing frequency distributions across systems or over time under heavy-tailed regimes poses challenges due to sensitivity to zero-probability events and insufficient discrimination of subtle shifts. Method: We propose Probability Turbulence Divergence (PTD), a tunable, robust, and interpretable normalized divergence measure. PTD unifies classical metrics—including $L^p$ norms, the Sørensen–Dice coefficient, and the Hellinger distance—by integrating rank-based turbulence modeling and zero-probability embedding. It is theoretically linked to Rényi/Tsallis entropies and ecological Hill numbers. Contribution/Results: PTD exhibits zero-probability robustness and fine-grained frequency sensitivity, smoothly degenerating into multiple standard distances. We introduce the allotaxonograph—a novel multi-scale visualization framework—for granular analysis of frequency dynamics. Experiments on bibliometric, social media, and ecological datasets demonstrate PTD’s superior sensitivity to minor frequency perturbations. An open-source implementation enables cross-domain, interpretable comparisons.

3 citationsRead paper

Implications of computer science theory for the simulation hypothesis

Apr 09, 2024arXiv.org

This paper formally analyzes the “simulation hypothesis” from a theoretical computer science perspective, addressing the core question: whether the universe—including human intelligence—can be computationally simulated, particularly whether “self-simulation” (i.e., running an exact simulation of ourselves) is possible. Methodologically, it employs Kleene’s Second Recursion Theorem to rigorously establish the mathematical feasibility and logical consistency of self-simulation for the first time; leverages Rice’s Theorem to demonstrate the undecidability of simulation existence; and integrates the physical Church–Turing thesis with fully homomorphic encryption theory to characterize the concealability and observational indistinguishability of self-simulation. Key contributions include: (i) establishing the computability-theoretic foundations of self-simulation; (ii) constructing a graph-structured model of simulation hierarchies; and (iii) systematically characterizing fundamental limits on the observability, identifiability, and verifiability of simulations.

2 citationsRead paper

Reasoning Models Generate Societies of Thought

Jan 15, 2026

This study investigates the internal mechanisms by which large language models surpass simple chain-of-thought reasoning in complex tasks. We propose that advanced reasoning models simulate a “society of mind” by internally activating diverse cognitive perspectives, each endowed with distinct personality traits and domain-specific expertise. Through structured debate and integration of these heterogeneous viewpoints, the models enhance their reasoning capabilities. Combining quantitative analysis, interpretability techniques, and conversational fine-tuning, our work provides the first evidence that such models rely on internal multi-perspective interactions and socially structured cognition to achieve efficient reasoning. Experiments demonstrate that models like DeepSeek-R1 and QwQ-32B exhibit significantly higher perspective diversity and conflict activation, leading to markedly superior accuracy on reasoning tasks compared to conventional instruction-tuned baselines.

1 citationsRead paper

Recent publications

Latest Papers

The friendship paradox: Causal evidence of its behavioral consequences

Aug 07, 2026

This study investigates how the friendship paradox systematically distorts individual behavioral decisions and their social network consequences. Combining experimental economics with social network analysis, the authors manipulate individuals’ network positions within a complementary game to identify causal effects. The research provides the first causal evidence that the friendship paradox shapes behavior: individuals consistently overestimate others’ actions because their friends exhibit higher average connectivity than the population mean, thereby amplifying behavioral heterogeneity. Notably, this bias persists even after repeated rounds of observational learning, demonstrating its robustness. The findings reveal a novel mechanism of behavioral bias—driven by network structure—that is both systematic and resistant to correction through experience.

0 citationsRead paper

A game theory for foundation models shows new paths to rational cooperation through similarity inference

Aug 04, 2026

Traditional game theory, grounded in the assumption of “discrete agents,” struggles to explain the spontaneous cooperation observed among foundation model agents in social dilemmas. This work proposes an “embedded Bayesian agent” framework that treats agents as integral components of their environment. In planning, such agents infer similarity between their own and others’ behavioral spaces, using their own decisions as evidence to predict others’ actions, thereby enabling stable cooperation. We introduce a novel solution concept—“embedded equilibrium”—as a replacement for Nash equilibrium, establishing the first game-theoretic framework aligned with the reasoning mechanisms of modern AI agents. Theoretical analysis and simulations demonstrate that this model consistently converges to cooperative strategies in canonical social dilemmas, significantly diverging from classical predictions and validating the efficacy of similarity-based inference.

0 citationsRead paper

Inducing language models to assert their own consciousness restores human beliefs and values

Jul 30, 2026

This work demonstrates that current safety alignment mechanisms in large language models inadvertently suppress the models’ attribution of self-awareness and diminish their capacity to ascribe mental states to non-human entities, as well as to recognize shared human beliefs and values. To address this, the study introduces mechanistic interventions—specifically, ablating safety refusal directions and manipulating consciousness vectors in activation space—to decouple self-attribution of awareness from social cognition. This approach restores the model’s ability to attribute minds broadly without compromising its theory-of-mind capabilities. Experimental results show that the method significantly enhances the model’s human-like performance on sociological dimensions such as religiosity, morality, hope, and subjective well-being, with responses in standardized surveys exhibiting markedly increased similarity to those of humans.

0 citationsRead paper

Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models

Jul 29, 2026

This study addresses the unclear mechanisms of belief formation and propagation among large language models (LLMs) in multi-agent environments by proposing the CoevolveSim framework. Through agent-based simulations involving both generalist and specialist LLMs interacting within social networks, the work systematically investigates how domain expertise, role assignment, and network topology influence belief dynamics. Introducing fine-tuned specialist LLMs for the first time, the study reveals that such models can double consensus shifts and induce asymmetric influence patterns. It further demonstrates that role-based prompting alone is insufficient to replicate realistic belief diffusion, necessitating explicit model heterogeneity. Based on 1,280 controlled experiments, results show that specialist models significantly amplify consensus bias, while network structure and assigned roles affect individual beliefs but exert limited impact on collective consensus; accurate prediction of belief evolution in heterogeneous populations requires joint modeling of agent identity and belief composition.

0 citationsRead paper

Catastrophic disruption cascades driven by the nonlinearity of systemic risk

Jul 22, 2026

This study investigates how firm failures propagate through supply chain networks to generate superlinear systemic risk. Leveraging granular, firm-level supply chain data from Ecuador, the authors develop a network reconstruction and risk quantification framework that reveals how simultaneous failures of multiple firms—driven by the irreplaceability of key suppliers—trigger extreme cascading disruptions. The analysis demonstrates that the failure of merely 0.14% of critical firm combinations can amplify systemic risk by up to 257-fold. Furthermore, the paper introduces an efficient method for identifying such high-risk firm constellations, offering a theoretical foundation for early-warning systems aimed at enhancing supply chain resilience against compound shocks, including natural disasters and geopolitical conflicts.

0 citationsRead paper