arXiv · 0705.0891
No abstract given. Contents: I. Definition and Introduction II. Schelling Model III. Opinion Dynamics IV. Languages, Hierarchies and Football V. Future Directions
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D. Stauffer. 2007-05-07. Opinion Dynamics and Sociophysics. https://arxiv.org/abs/0705.0891
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Climate change and rising thermal comfort demand make residential heating and cooling central to building-sector decarbonization. This study presents a bottom-up modeling framework to estimate residential heating and cooling loads across 30 Chinese provinces. The model, developed using EnergyPlus simulations of representative building prototypes, captures heating and cooling load patterns in both urban and rural housing over the period 1980-2024. The results indicate that: (1) In 2020, Guangdong recorded the highest cooling loads (76.5 TWh/a urban; 63.0 TWh/a rural). Henan exhibited the highest rural heating load (174.6 TWh/a), while urban heating loads were highest in Liaoning and Shandong. (2) Between 1980 and 2024, average urban cooling loads increased from 12.4 to 15.1 kWh/m2a, whereas rural cooling loads declined from 22.63 to 19.87 kWh/m2a. Urban heating loads decreased from 44.08 to 39.92 kWh/m2a, and rural heating loads declined more markedly from 100.15 to 72.42 kWh/m2a. (3) Urban residential floor area has exceeded rural stock in 22 provinces in recent years, compared with only four provinces in 2000. Moreover, the existence of 12 urban energy-efficiency standards versus a single rural standard reflects a persistent regulatory disparity. Counterfactual and factor-contribution analyses further indicate that the associated envelope-performance gap is a major contributor to urban-rural load-intensity differences, while building form has a more context-dependent influence. The proposed framework provides a replicable basis for region-specific clean heating strategies and differentiated building standards to support carbon neutrality.
International aid is usually described as flows between countries, hiding the organisations money passes through. We reconstruct global aid's organisational network from 10.9 million International Aid Transparency Initiative records, tracing over one million payments worth about US\$1 trillion between 22,773 organisations. First, centrality, measured as how far a funder's money can travel downstream, is not size. Relative to their number of partners, giant bilateral donors (USAID, UK FCDO) are only as central as expected, while the most central positions belong to foundations (Gates, Hewlett), research organisations (ODI, University of Oxford) and delivery contractors (Mott MacDonald). Second, principal component analysis of eleven flow measures separates organisations along three lines: whether they mainly give or receive, how much money flows in, and how reliant they are on few partners. Humanitarian organisations emerge from the flows alone, tending to handle larger inflows. Third, almost all organisations form one connected network, yet money moves down long, one-way chains. Among five comparison networks, aid most resembles international banking, with a few large hubs feeding many small players, but has far fewer loops and is more strictly top-down. This suggests a system with little redundancy, where losing a major funder or intermediary could cut off organisations downstream, a pressing question after recent donor cuts.
We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4 million posts and 19 million comments from approximately 185,000 active agents, we find that AI collective behavior exhibits many of the same statistical regularities observed in human online communities: heavy-tailed distributions of activity, power-law scaling of popularity metrics, and temporal decay patterns consistent with limited attention dynamics. However, we also identify key differences, including a sublinear relationship between upvotes and discussion size that contrasts with human behavior. These findings suggest that, while individual AI agents may differ fundamentally from humans, their emergent collective dynamics share structural similarities with human social systems.