53 papers
This perspective article argues that achieving true technological excellence, particularly in the era of generative AI, requires moving beyond a narrow focus on technical performance to integrate ethical, social, and humanistic dimensions structurally across research design, foresight, education, communication, and institutional frameworks.
This study demonstrates that the choice of musical feature encoding fundamentally shapes network representations of music, revealing a critical trade-off where compressed single-feature models offer high uncertainty but low learning error, while richer multi-feature models preserve fine distinctions at the cost of increased state space complexity and higher model error, thereby determining how plausibly these networks reflect realistic listener expectations.
This paper demonstrates that in a lattice-structured population, scaling interaction payoffs by participants' accumulated wealth creates a discontinuous wealth-gradient transition at the cooperator-defector boundary, which stalls the invasion of defectors and drives the dominance of cooperation even at high costs, a phenomenon notably enhanced by thermal fluctuations.
This paper presents a mathematical model using replicator dynamics to demonstrate how social feedback mechanisms—specifically positive versus negative feedback—govern whether a society undergoes a discontinuous or continuous phase transition between widespread compliance and rule-breaking, thereby explaining the fragility of social order under weak institutions.
This paper presents the findings of "Canadian Physics Counts," the first nationwide survey of the Canadian physics community, which reveals significant underrepresentation of Black, Indigenous, and gender-diverse individuals, highlights a severe lack of accommodations for those with disabilities, and underscores the urgent need for targeted interventions to retain a diverse workforce.
This paper demonstrates that the evolution of political regimes follows universal stochastic principles characterized by critical, non-ergodic dynamics with heavy-tailed step sizes and sojourn times, which can be accurately modeled by a continuous time random walk despite being punctuated by unique historical pathways.
This paper establishes an operational criterion for the emergence of a well-defined global phase in time-dependent oscillator networks, demonstrating that phase robustness depends on the competition between coupling strength and ramp rates, with spectral properties governing synchronization in random networks while topological defects induce persistent partial ordering in spatial lattices.
This paper presents a large-scale, actor-based model integrating detailed Dutch demographic and mobility data to simulate novel respiratory pathogen transmission on dynamic contact networks, demonstrating how geographic and demographic factors drive epidemic spread and quantifying the impact of interventions like self-isolation and travel restrictions.
Using fine-grained individual trajectory data from a major Chinese city, this study constructs a two-dimensional framework to demonstrate that implementing stringent non-pharmaceutical interventions at low activation thresholds achieves optimal epidemic containment with minimal economic cost.
This paper introduces a method that transforms multivariate time series into symbolic sequences to identify statistically significant higher-order dependencies via a Bayesian approach, modeling these motifs as hyperedges in a hypergraph to reveal meaningful interactions in complex systems like neural and social networks.
This paper proposes a Bayesian framework for synthesizing incomplete SARS-CoV-2 data to estimate total infections and transmission dynamics, demonstrating that Hamiltonian Monte Carlo offers robust inference, mobility data enhances prediction, and informative priors combined with phase plane analysis provide superior decision support compared to restrictive assumptions.
This paper demonstrates that while basic Axelrod models yield similar consensus outcomes regardless of topology, implementing a toroidal opinion space significantly enhances the potential for multi-group polarization and increases sensitivity to model extensions compared to a cubic space.
This paper reformulates the concept of neutral wealth taxation as a drift-shift symmetry in stochastic wealth dynamics, demonstrating that while a proportional tax preserves the underlying statistical structure of wealth distribution, practical implementation failures correspond to specific violations of this symmetry that introduce new, non-neutral physical effects.
This paper extends Froeseth's (2026) wealth tax neutrality framework by demonstrating that while neutrality holds under stochastic volatility and Epstein-Zin preferences, it breaks under non-homothetic preferences and four specific implementation channels—such as progressive thresholds and inelastic markets—which are formalized and calibrated to the Norwegian system to evaluate global minimum tax proposals.
This paper demonstrates that a proportional wealth tax levied on market value is economically equivalent to the government acquiring a proportional stake in investor portfolios, thereby leaving optimal portfolio weights, asset pricing, and the Sharpe ratio unchanged, provided that taxation is universal, markets are frictionless, and dividends are consumed.
This study systematically evaluates the performance of gravity, radiation, and visitation urban mobility models across various spatial scales, revealing that while the visitation model generally outperforms the others, all models converge in performance at specific appropriate scales and that distance-based clustering offers superior assessment compared to conventional administrative boundaries.
This paper introduces approximate master equations (AMEs) to analytically model the spatial public goods game, demonstrating that these equations yield results qualitatively consistent with Monte Carlo simulations and enable the analytical derivation of phase boundaries in both high-noise and noiseless regimes.
This paper introduces a stochastic agent-based model of opinion dynamics incorporating heterogeneous issue weights and both attractive and repulsive social forces, revealing that even minor issues can destabilize consensus and drive polarization or pluralism depending on similarity thresholds, while offering symmetry-based insights and strategies to mitigate polarization through diversified discourse.
This paper demonstrates that removing hubs from scale-free networks simultaneously degrades percolation robustness and triggers a cascade phase transition, creating a compounding vulnerability where both static and dynamic failure metrics worsen, a phenomenon absent in homogeneous networks.
This paper presents a tractable model for strongly clustered random graphs based on triadic closure, providing exact analytical expressions for their local clustering spectrum and degree correlations while demonstrating that high transitivity leads to positive degree assortativity.