Physics and Society Papers of the Week

Market interactions can make the two diverge, potentially biasing long-term price assumptions. We quantify this gap using an 81-region global hydrogen-market model, comparing each region's costs and prices when only its demand is active and when all regions' demands are active.

In particular, we introduce a type of adaptive agents who incur an observation cost to infer the risk level during game interactions, and subsequently decide whether to cooperate or defect based on their tolerance thresholds.

Our paper aims to provide a unified framework for understanding mobility as a combined physical and informational driving force in the evolution of cooperation.

How do large language models (LLMs) perform gender under zero-shot prompting, and how do people interpret those performances in conversation?

We introduce a stochastic eco-evolutionary model in which harvesting strategies and resource availability shape one another.

In this paper, we introduce a generalized framework for Sierpiński network families by allowing arbitrary intercopy edge connection patterns, and derive two associated families: touching-type and skeletal-type, formed by contracting inter-copy edges and intra-copy edges, respectively.

Studying these effects in cities is challenging because, unlike online resources, travel constraints and limited venue capacity shape where people can go, while each recommended visit also changes whom other visitors encounter. To capture such features, we build a long-term simulation framework based on fine-grained human mobility data from four U.S. metropolitan areas, with distance and capacity constraints.

Here, we analyse the spatial distribution and duration of walking habits of 1,318 participants, tracked during a 4-week time use survey in major Swiss cities - which are very close to the 15-minute city model.

We present AgenticMobility, an open-source experimental platform for studying how mobility AI agents and other AI agents interact, adapt through repeated experience, and collectively shape mobility outcomes.

This review provides a unified, mathematically rigorous account of the theory, algorithms, and cross-domain applications of quantum-transition-based network simulation.

Governing equations provide an interpretable description of the mechanisms underlying complex network dynamics, yet their discovery from observational data remains challenging when shared community-level dynamics coexist with individual-level variations and the relevant functional structures may be absent from the initial candidate library or not explicitly specified in advance. Here we formulate this task as a hierarchical law-discovery problem and introduce SAGE, a large language model-guided self-reflective agent framework for discovering community-shared governing laws and individual-specific dynamical variations.

We apply the pipeline to the Web of Science Core Collection, classifying 22,936 ABM articles into ecology, epidemiology, oncology, other-biology, or not-biology.

In this work, we demonstrate that restructuring the organization of groups within a population can be used as an effective strategy to decrease the severity of an epidemic.

In this study, we examine how the negative sign ratio of a signed Erdos-Renyi reservoir affects the internal dynamics and forecasting performance of an echo state network on the Mackey-Glass benchmark.

Hierarchical scoring amplifies small advantages on points into large advantages on matches, but also allows a player to win with fewer points than the opponent. Using Monte Carlo simulations and exact Markov chain calculations, we show that in tennis, badminton, volleyball and table tennis this amplification is set almost entirely by the number of points per match.

We prove that the lower bound of maximum HHI is reached when the tournament consists of independent round-robin groups, while the upper bound of maximum HHI is reached when the set of matches is represented by a bipartite graph.

To understand how the bargaining process shapes the outcomes, we introduce a behavioral taxonomy that jointly characterizes numerical bidding actions and communicative attitudes.

We combined process simulation, life cycle assessment, and Monte Carlo simulation to identify hotspots in the manufacturing of ultrafiltration hollow-fiber membranes for pharmaceutical applications and evaluate strategies to mitigate impact and reduce waste generation.