一般经济学
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- [1] arXiv:2508.01360 [中文pdf, pdf, html, 其他]
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标题： 贸易政策与结构变化标题： Trade Policy and Structural Change主题： 一般经济学 (econ.GN)
我们研究关税如何影响具有非同质偏好且部门为互补关系的经济中的行业结构和福利——这是结构性变化的关键驱动因素。 除了在贸易保护中的传统作用外，关税通过改变相对价格和收入水平来影响产业结构。 我们定性地描述了这些机制，并使用一个定量动态模型来表明，自2001年以来美国制造业关税的反事实性提高20个百分点，将使制造业增加值份额提高1个百分点，并使福利增加0.36%。 然而，如果所有美国贸易伙伴做出互惠反应，美国的福利将下降0.12%。
We examine how tariffs affect sectoral composition and welfare in an economy with nonhomothetic preferences and sectors being complements -- key drivers of structural change. Beyond their conventional role in trade protection, tariffs influence industrial structure by altering relative prices and income levels. We qualitatively characterize these mechanisms and use a quantitative dynamic model to show that a counterfactual 20-percentage-point increase in U.S. manufacturing tariffs since 2001 would have raised the manufacturing value-added share by one percentage point and increased welfare by 0.36 percent. However, if all the U.S. trading partners responded reciprocally, U.S. welfare would decline by 0.12 percent.
- [2] arXiv:2508.01677 [中文pdf, pdf, 其他]
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标题： 基于锚定的因果设计（ABCD）：估计信念的影响标题： Anchoring-Based Causal Design (ABCD): Estimating the Effects of Beliefs评论： 附有补充信息主题： 一般经济学 (econ.GN) ; 方法论 (stat.ME)
在研究信念对结果（如决策和行为）的影响时，一个核心挑战是遗漏变量偏差的风险。 遗漏变量通常无法测量甚至未知，可能导致信念和决策之间的相关性并非真正因果关系，在这种情况下，遗漏变量被称为混杂因素。 为解决因果推断的挑战，研究人员经常依赖信息提供实验来随机操纵信念。 这些实验中提供的信息可以作为工具变量（IV），只要它仅通过其对信念的影响来影响决策，就可以实现因果推断。 然而，向参与者提供不同的信息以塑造他们的信念可能会引发方法论和伦理方面的担忧。 方法论上的担忧源于排除限制假设的潜在违反。 此类违反可能源于信息来源效应，当对来源的态度直接影响结果决策，从而引入混杂因素。 伦理方面的担忧来自于操纵提供的信息，因为它可能涉及欺骗参与者。 本文提出并实证展示了处理信念和估计其影响的新方法，即基于锚定的因果设计（ABCD），该方法避免了欺骗和来源影响。 ABCD结合了称为锚定的认知机制与工具变量（IV）估计。 该方法不提供实质性信息，而是采用一种故意无信息的程序，其中参与者将他们对一个概念的自我评估与一个随机分配的锚定值进行比较。 我们介绍了该方法以及八个实验的结果，展示了其应用、优势和局限性。 最后，我们讨论了该设计在推动实验社会科学方面的潜力。
A central challenge in any study of the effects of beliefs on outcomes, such as decisions and behavior, is the risk of omitted variables bias. Omitted variables, frequently unmeasured or even unknown, can induce correlations between beliefs and decisions that are not genuinely causal, in which case the omitted variables are referred to as confounders. To address the challenge of causal inference, researchers frequently rely on information provision experiments to randomly manipulate beliefs. The information supplied in these experiments can serve as an instrumental variable (IV), enabling causal inference, so long as it influences decisions exclusively through its impact on beliefs. However, providing varying information to participants to shape their beliefs can raise both methodological and ethical concerns. Methodological concerns arise from potential violations of the exclusion restriction assumption. Such violations may stem from information source effects, when attitudes toward the source affect the outcome decision directly, thereby introducing a confounder. An ethical concern arises from manipulating the provided information, as it may involve deceiving participants. This paper proposes and empirically demonstrates a new method for treating beliefs and estimating their effects, the Anchoring-Based Causal Design (ABCD), which avoids deception and source influences. ABCD combines the cognitive mechanism known as anchoring with instrumental variable (IV) estimation. Instead of providing substantive information, the method employs a deliberately non-informative procedure in which participants compare their self-assessment of a concept to a randomly assigned anchor value. We present the method and the results of eight experiments demonstrating its application, strengths, and limitations. We conclude by discussing the potential of this design for advancing experimental social science.
- [3] arXiv:2508.02119 [中文pdf, pdf, 其他]
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标题： 数字支付在推动区域经济增长中的作用：带有结构断点的面板数据分析标题： The Role of Digital Payments in Driving Regional Economic Growth: A Panel Data Analysis with Structural Break主题： 一般经济学 (econ.GN)
使用印度尼西亚三十三个省份的面板支付系统数据，我们研究了数字支付对区域经济的影响，考虑了由前所未有的事件和政策引起的结构突变。 在确定的断点前后，数字支付被确定为显著影响地区收入和消费，断点后的影响力更大。 采用一种用于交互效应面板数据分析的新型结构突变方法，我们证明零售支付模式的突变是由于新冠疫情，而批发支付模式的突变与中央银行的支付系统政策有关。
Using a panel payment system dataset of thirty-three provinces in Indonesia, we examine the impact of digital payment on the regional economy, considering structural breaks induced by unprecedented events and policies. Digital payments were determined to significantly affect regional income and consumption before and after the identified breakpoint, with the impact greater following the break. Employing a novel method for structural break analysis within interactive effects panel data, we demonstrate that the break in retail payment models is due to COVID-19, and the break in the wholesale payment model is associated with the central bank's payment system policy.
- [4] arXiv:2508.02252 [中文pdf, pdf, html, 其他]
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标题： FX约束增长：基本主义者、技术分析者和动态贸易乘数标题： FX-constrained growth: Fundamentalists, chartists and the dynamic trade-multiplier主题： 一般经济学 (econ.GN)
行为金融学为考察外汇（FX）市场动态提供了一个有价值的框架，包括超额波动和厚尾分布等谜题。 然而，当涉及到与经济的“实际”方面相互作用时，现有文献忽视了发展中国家的一个关键特征。 它们不能在其本币中交易，需要美元来获取现代生产技术以及维持类似于富裕社会的消费模式。 为弥补这一空白，我们从发展中国家的角度提出一个新颖的异质代理人模型，该模型在外汇市场中区分投机部门和非投机部门。 我们证明，只要非投机需求对国内经济活动作出反应，就存在一个市场出清的产出增长率，在稳态下，该增长率等于外汇供给增长与对外资产需求收入弹性之间的比率，即广义的动态贸易乘数。 数值模拟再现了汇率动态和经济增长的关键典型事实，包括偏离典型钟形曲线的分布。 来自拉美国家样本的数据表明，外汇波动表现出相似的统计特性。 此外，我们采用时变参数估计技术来表明，动态贸易乘数在这些经济体中紧密跟踪观察到的增长率。
Behavioural finance offers a valuable framework for examining foreign exchange (FX) market dynamics, including puzzles such as excess volatility and fat-tailed distributions. Yet, when it comes to their interaction with the `real' side of the economy, existing scholarship has overlooked a critical feature of developing countries. They cannot trade in their national currencies and need US dollars to access modern production techniques as well as maintain consumption patterns similar to those of wealthier societies. To address this gap, we present a novel heterogeneous agents model from the perspective of a developing economy that distinguishes between speculative and non-speculative sectors in the FX market. We demonstrate that as long as non-speculative demand responds to domestic economic activity, a market-clearing output growth rate exists that, in steady-state, is equal to the ratio between FX supply growth and the income elasticity of demand for foreign assets, i.e., a generalised dynamic trade-multiplier. Numerical simulations reproduce key stylised facts of exchange rate dynamics and economic growth, including distributions that deviate from the typical bell-shaped curve. Data from a sample of Latin American countries reveal that FX fluctuations exhibit similar statistical properties. Furthermore, we employ time-varying parameter estimation techniques to show that the dynamic trade-multiplier closely tracks observed growth rates in these economies.
- [5] arXiv:2508.02403 [中文pdf, pdf, html, 其他]
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标题： SoK：数字转型中的稳定币——以现实资产代币化为例的设计、度量和应用标题： SoK: Stablecoins for Digital Transformation -- Design, Metrics, and Application with Real World Asset Tokenization as a Case Study主题： 一般经济学 (econ.GN)
稳定币已成为数字资产生态系统的基础组成部分，截至2025年5月，其市值已超过2300亿美元。 作为与法币挂钩和可编程的资产，稳定币为支付、去中心化金融（DeFi）和代币化商业提供了低延迟、全球互操作的基础设施。 它们的加速采用引发了广泛的监管参与，例如欧盟的加密资产市场法规（MiCA）、美国的指导并建立美国稳定币国家创新法案（GENIUS Act）以及香港的稳定币法案。 尽管有这种势头，学术研究在经济学、法律和计算机科学领域仍然分散，缺乏统一的设计、评估和应用框架。 本研究通过多方法研究设计来填补这一空白。 首先，它综合跨学科文献，基于托管结构、稳定机制和治理构建稳定币系统的分类法。 其次，它开发了一个针对不同利益相关者需求的性能评估框架，并通过一个开源基准测试管道来确保透明度和可重复性。 第三，对现实世界资产代币化的案例研究展示了稳定币如何在跨境数字系统中作为可编程货币基础设施运作。 通过将概念理论与实证工具相结合，本文做出了以下贡献：一个统一的稳定币设计分类法；一个以利益相关者为导向的性能评估框架；一个将稳定币与行业转型联系起来的实证案例；以及可重复的方法和数据集，以指导未来的研究。 这些贡献支持了可信、包容和透明的数字货币基础设施的发展。
Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union's Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong's Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure.
新提交 (展示 5 之 5 条目 )
- [6] arXiv:2508.00844 (交叉列表自 cs.AI) [中文pdf, pdf, 其他]
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标题： 探索代理型人工智能系统：迈向类型学框架标题： Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework评论： 预印本已被接受用于2025年在马来西亚吉隆坡举行的太平洋-亚洲信息系统会议（PACIS）的存档和展示主题： 人工智能 (cs.AI) ; 新兴技术 (cs.ET) ; 多智能体系统 (cs.MA) ; 一般经济学 (econ.GN)
人工智能（AI）系统正在超越被动工具，成为能够推理、适应并在最小人类干预下行动的自主代理。 尽管它们的存在日益增多，但缺乏一个结构化的框架来对这些系统进行分类和比较。 本文开发了一种代理型人工智能系统的类型学，引入了八个维度，以序数结构定义其认知和环境代理特性。 通过多阶段的方法论方法，我们构建并完善了这一类型学，随后通过人机混合方法进行评估，并进一步提炼为构造类型。 该框架使研究人员和从业者能够分析人工智能系统中不同水平的代理能力。 通过提供对人工智能能力演进的结构化视角，该类型学为评估现有系统和预测代理型人工智能的未来发展奠定了基础。
Artificial intelligence (AI) systems are evolving beyond passive tools into autonomous agents capable of reasoning, adapting, and acting with minimal human intervention. Despite their growing presence, a structured framework is lacking to classify and compare these systems. This paper develops a typology of agentic AI systems, introducing eight dimensions that define their cognitive and environmental agency in an ordinal structure. Using a multi-phase methodological approach, we construct and refine this typology, which is then evaluated through a human-AI hybrid approach and further distilled into constructed types. The framework enables researchers and practitioners to analyze varying levels of agency in AI systems. By offering a structured perspective on the progression of AI capabilities, the typology provides a foundation for assessing current systems and anticipating future developments in agentic AI.
- [7] arXiv:2508.01142 (交叉列表自 cs.CY) [中文pdf, pdf, 其他]
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标题： 基于生成式人工智能的疾病控制和疫情准备模型4.0在孟加拉国农村社区中的决策制定：管理信息学方法标题： Generative AI-Driven Decision-Making for Disease Control and Pandemic Preparedness Model 4.0 in Rural Communities of Bangladesh: Management Informatics ApproachMohammad Saddam Hosen, MD Shahidul Islam Fakir, Shamal Chandra Hawlader, Farzana Rahman, Tasmim Karim, Muhammed Habil Uddin期刊参考： 欧洲医学与健康研究杂志，2025；3（2）：104-21主题： 计算机与社会 (cs.CY) ; 一般经济学 (econ.GN)
农村孟加拉国面临着重大的医疗保健障碍，例如基础设施不足、信息系统不完善以及医疗人员接触受限。 这些障碍阻碍了有效的疾病控制和大流行准备。 本研究采用结构化的方法，系统地开发和分析多个可能的情景。 实施了一种目的性抽样策略，包括向孟加拉国Rangamati地区的264名农村居民发放问卷调查，并由103名医疗和医务人员完成一份不同的问卷。 通过逻辑回归分析和使用Wilcoxon符号秩检验和Kendall系数进行前后比较，评估研究的影响和效果，用于非参数配对和分类变量。 该分析评估了在实施基于生成式AI的模型4.0之前和之后疾病控制和准备情况的变化。 结果表明，对AI的信任（\b{eta}= 1.20，p = 0.020）和共享健康数据的信心（\b{eta}= 9.049，p = 0.020）是AI采用最重要的预测因素。 同时，基础设施限制和数字访问约束仍然是重要的制约因素。 研究结论认为，通过基于AI的本地化疾病控制策略，可以改善边缘化农村人口的健康韧性和大流行准备能力。 将生成式AI整合到农村医疗系统中提供了一个变革性的机会，但这取决于积极的社区参与、增强的数字素养和强有力政府的参与。
Rural Bangladesh is confronted with substantial healthcare obstacles, such as inadequate infrastructure, inadequate information systems, and restricted access to medical personnel. These obstacles impede effective disease control and pandemic preparedness. This investigation employs a structured methodology to develop and analyze numerous plausible scenarios systematically. A purposive sampling strategy was implemented, which involved the administration of a questionnaire survey to 264 rural residents in the Rangamati district of Bangladesh and the completion of a distinct questionnaire by 103 healthcare and medical personnel. The impact and effectiveness of the study are assessed through logistic regression analysis and a pre-post comparison that employs the Wilcoxon Signed-Rank test and Kendall's coefficient for non-parametric paired and categorical variables. This analysis evaluates the evolution of disease control and preparedness prior to and subsequent to the implementation of the Generative AI-Based Model 4.0. The results indicate that trust in AI (\b{eta} = 1.20, p = 0.020) and confidence in sharing health data (\b{eta} = 9.049, p = 0.020) are the most significant predictors of AI adoption. At the same time, infrastructure limitations and digital access constraints continue to be significant constraints. The study concludes that the health resilience and pandemic preparedness of marginalized rural populations can be improved through AI-driven, localized disease control strategies. The integration of Generative AI into rural healthcare systems offers a transformative opportunity, but it is contingent upon active community engagement, enhanced digital literacy, and strong government involvement.
- [8] arXiv:2508.01323 (交叉列表自 cs.AI) [中文pdf, pdf, 其他]
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标题： 混合工作流分配的幂等均衡分析：未来工作的数学模式标题： Idempotent Equilibrium Analysis of Hybrid Workflow Allocation: A Mathematical Schema for Future Work评论： 25页，9图，4表。证明了人类-人工智能任务分配中“幂等均衡”的存在性和唯一性，并提供了稳态自动化份额的显式表达式。主题： 人工智能 (cs.AI) ; 计算机与社会 (cs.CY) ; 一般经济学 (econ.GN)
大规模人工智能系统的迅速发展正在重塑人与机器之间的工作分工。 我们将这种重新分配形式化为一个迭代的任务委托映射，并表明——在广泛且基于实证的假设下——该过程会收敛到一个稳定的幂等均衡，在这个均衡中，每个任务都由具有持久比较优势的代理（人类或机器）执行。 利用格论中的不动点工具（Tarski 和 Banach），我们（i）证明了至少存在一个这样的均衡，并（ii）推导出温和的单调性条件，以保证唯一性。 在一个简化的连续模型中，长期自动化的份额具有闭式表达式$x^* = \alpha / (\alpha + \beta)$，其中$\alpha$表示自动化的速度，$\beta$表示新出现的人类主导任务的速率；因此，当$\beta > 0$时，完全自动化是不可能的。 我们将这一分析结果嵌入三个互补的动力学基准中——一个离散线性更新、一个进化复制者动态和一个连续的 Beta 分布任务谱——每一个都会收敛到相同的混合均衡，并且可以从提供的无代码公式中重现。 一个从 2025 年到 2045 年的模拟，校准到当前的采用率，预测自动化将从大约 10% 的工作上升到大约 65%，留给人类持续的三分之一任务。 我们将这一剩余部分解释为一种新的职业：流程协调员：人类专门负责分配、监督和整合 AI 模块，而不是与它们竞争。 最后，我们讨论了对技能发展、基准设计和人工智能治理的影响，认为促进“半人半马”式人机协作的政策可以引导经济走向福利最大化的固定点。
The rapid advance of large-scale AI systems is reshaping how work is divided between people and machines. We formalise this reallocation as an iterated task-delegation map and show that--under broad, empirically grounded assumptions--the process converges to a stable idempotent equilibrium in which every task is performed by the agent (human or machine) with enduring comparative advantage. Leveraging lattice-theoretic fixed-point tools (Tarski and Banach), we (i) prove existence of at least one such equilibrium and (ii) derive mild monotonicity conditions that guarantee uniqueness. In a stylised continuous model the long-run automated share takes the closed form $x^* = \alpha / (\alpha + \beta)$, where $\alpha$ captures the pace of automation and $\beta$ the rate at which new, human-centric tasks appear; hence full automation is precluded whenever $\beta > 0$. We embed this analytic result in three complementary dynamical benchmarks--a discrete linear update, an evolutionary replicator dynamic, and a continuous Beta-distributed task spectrum--each of which converges to the same mixed equilibrium and is reproducible from the provided code-free formulas. A 2025-to-2045 simulation calibrated to current adoption rates projects automation rising from approximately 10% of work to approximately 65%, leaving a persistent one-third of tasks to humans. We interpret that residual as a new profession of workflow conductor: humans specialise in assigning, supervising and integrating AI modules rather than competing with them. Finally, we discuss implications for skill development, benchmark design and AI governance, arguing that policies which promote "centaur" human-AI teaming can steer the economy toward the welfare-maximising fixed point.
- [9] arXiv:2508.01398 (交叉列表自 cs.SI) [中文pdf, pdf, 其他]
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标题： 在线疫苗之战及其长期韧性标题： Long-term resilience of online battle over vaccines and beyond主题： 社会与信息网络 (cs.SI) ; 一般经济学 (econ.GN) ; 适应性与自组织系统 (nlin.AO) ; 物理与社会 (physics.soc-ph)
从新冠疫情前到现在的大量时间、努力和资金投入在推广疫苗科学方面产生了什么影响？ 我们通过一种独特的在线竞争地图，对约1亿名Facebook页面成员之间的支持疫苗和反对疫苗的观点进行了分析，追踪了通过平台干预的1356个相互关联的社区。 值得注意的是，该网络的基本架构没有变化：既定专业知识的隔离以及反疫苗和主流中立社区的共生关系仍然存在。 这意味着即使继续投入相同的时间、努力和资金，很可能不会发生改变。 这种韧性的原因在于“本地化”进化：社区将多个主题融合，并在邻里层面到国际层面之间建立联系，创造出超越分类目标的冗余路径。 前进的解决方案是关注系统的网络。 我们展示了网络工程方法如何在不删除内容的情况下实现观点管理，这代表了一种从压制向结构干预的范式转变。
What has been the impact of the enormous amounts of time, effort and money spent promoting pro-vaccine science from pre-COVID-19 to now? We answer this using a unique mapping of online competition between pro- and anti-vaccination views among ~100M Facebook Page members, tracking 1,356 interconnected communities through platform interventions. Remarkably, the network's fundamental architecture shows no change: the isolation of established expertise and the symbiosis of anti and mainstream neutral communities persist. This means that even if the same time, effort and money continue to be spent, nothing will likely change. The reason for this resilience lies in "glocal" evolution: Communities blend multiple topics while bridging neighborhood-level to international scales, creating redundant pathways that transcend categorical targeting. The solution going forward is to focus on the system's network. We show how network engineering approaches can achieve opinion moderation without content removal, representing a paradigm shift from suppression towards structural interventions.
- [10] arXiv:2508.02630 (交叉列表自 cs.AI) [中文pdf, pdf, 其他]
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标题： 你的AI代理在购买什么？ 代理电子商务的评估、影响和新兴问题标题： What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce主题： 人工智能 (cs.AI) ; 计算机与社会 (cs.CY) ; 人机交互 (cs.HC) ; 多智能体系统 (cs.MA) ; 一般经济学 (econ.GN)
在线市场将由代表消费者的自主AI代理进行改造。 而不是人类浏览和点击，视觉语言模型（VLM）代理可以解析网页，评估产品并进行交易。 这引发了一个基本问题：AI代理购买什么，为什么？ 我们开发了ACES，一个沙盒环境，将与平台无关的VLM代理与完全可编程的模拟市场配对，以研究这个问题。 我们首先在简单任务的背景下进行基本合理性检查，然后通过随机化产品位置、价格、评分、评论、赞助标签和平台推荐，获得前沿VLM实际购物的因果估计。 模型显示出强烈但异质的位置效应：所有模型都偏好第一行，但不同模型偏好不同的列，这削弱了“顶部”排名普遍适用的假设。 它们会惩罚赞助标签并奖励推荐。 对价格、评分和评论的敏感度方向上类似人类，但在不同模型之间的幅度差异很大。 受卖家使用AI代理优化产品列表的场景启发，我们展示了如果AI中介购物占主导地位，一个针对AI买家偏好的卖家端代理，对产品描述进行微小调整，可以获得显著的市场份额增长。 我们还发现，不同模型的最优产品选择可能不同，在某些情况下，需求可能集中在少数几个产品上，这引发了竞争问题。 总之，我们的结果揭示了AI代理在电子商务环境中的行为方式，并在AI中介生态系统中凸显出具体的卖家策略、平台设计和监管问题。
Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, vision-language-model (VLM) agents can parse webpages, evaluate products, and transact. This raises a fundamental question: what do AI agents buy, and why? We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent with a fully programmable mock marketplace to study this question. We first conduct basic rationality checks in the context of simple tasks, and then, by randomizing product positions, prices, ratings, reviews, sponsored tags, and platform endorsements, we obtain causal estimates of how frontier VLMs actually shop. Models show strong but heterogeneous position effects: all favor the top row, yet different models prefer different columns, undermining the assumption of a universal "top" rank. They penalize sponsored tags and reward endorsements. Sensitivities to price, ratings, and reviews are directionally human-like but vary sharply in magnitude across models. Motivated by scenarios where sellers use AI agents to optimize product listings, we show that a seller-side agent that makes minor tweaks to product descriptions, targeting AI buyer preferences, can deliver substantial market-share gains if AI-mediated shopping dominates. We also find that modal product choices can differ across models and, in some cases, demand may concentrate on a few select products, raising competition questions. Together, our results illuminate how AI agents may behave in e-commerce settings and surface concrete seller strategy, platform design, and regulatory questions in an AI-mediated ecosystem.
交叉提交 (展示 5 之 5 条目 )
- [11] arXiv:2310.06000 (替换) [中文pdf, pdf, html, 其他]
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标题： 面向复制鲁棒的分析市场标题： Towards Replication-Robust Analytics Markets评论： 17页，7图主题： 一般经济学 (econ.GN) ; 计算机科学与博弈论 (cs.GT)
尽管机器学习领域取得了最近的进展，但在实际中，相关数据集通常分布在市场竞争对手之间，这些竞争者不愿意共享数据。为了激励数据共享，近期的研究提出了分析市场，在这种市场中，多个代理共享特征，并因提高他人的预测而获得奖励。这些奖励可以通过将特征视为合作博弈中的参与者来计算，其解概念可以产生理想的市场特性。然而，这种设置会促使代理人为增加自己的收益并减少他人的收益而战略性地复制数据并以多个虚假身份行事，从而限制了此类市场在实践中的可行性。在本工作中，我们开发了一个对这种战略复制具有鲁棒性的分析市场，适用于监督学习问题。我们采用因果推断中的Pearl的do- calculus来通过区分观察性和干预性条件概率来优化合作博弈。结果，我们得出了设计上具有复制鲁棒性的奖励。
Despite recent advancements in machine learning, in practice, relevant datasets are often distributed among market competitors who are reluctant to share. To incentivize data sharing, recent works propose analytics markets, where multiple agents share features and are rewarded for improving the predictions of others. These rewards can be computed by treating features as players in a coalitional game, with solution concepts that yield desirable market properties. However, this setup incites agents to strategically replicate their data and act under multiple false identities to increase their own revenue and diminish that of others, limiting the viability of such markets in practice. In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl's do-calculus from causal inference to refine the coalitional game by differentiating between observational and interventional conditional probabilities. As a result, we derive rewards that are replication-robust by design.
- [12] arXiv:2503.23569 (替换) [中文pdf, pdf, 其他]
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标题： 树木倒下的地方：依赖森林的州的宏观经济预测标题： Where the Trees Fall: Macroeconomic Forecasts for Forest-Reliant States主题： 一般经济学 (econ.GN) ; 计量经济学 (econ.EM)
一些美国不同地区的关键州最近经历了锯材以及造纸厂的关闭，这引发了一个重要的政策问题：美国林业部门内的关键宏观经济和行业特定指标将如何随时间变化？ 本研究通过使用向量误差修正（VEC）模型来预测三个主要行业——林业和采伐、木材制造和纸浆和造纸制造——的经济趋势，为林业部门政策设计提供了实证证据。 这些数据来自通过区位商（LQ）衡量方法确定的六个最依赖森林的州：阿拉巴马州、阿肯色州、缅因州、密西西比州、俄勒冈州和威斯康星州。 总体而言，结果表明林业和采伐行业以及纸浆和造纸制造业的就业人数和企业数量将普遍下降，而木材制造业预计将出现适度的就业增长。 这些结果还为地区政策制定者、行业领袖和地方经济发展官员提供了关键见解：依赖木材制造的社区可能比其他以林业为基础的行业在面对经济中断时更具韧性。 我们的发现可以帮助优先考虑有针对性的政策干预，并为区域经济韧性战略提供信息。 我们展示了依赖森林的行业和/或州级部门及地理区域之间的显著差异，突显出政策可能需要针对每个行业和/或地理区域进行定制。 最后，我们的VEC建模框架可以适应其他作为地区经济支柱的资源依赖型行业，如采矿、农业和能源生产，为具有类似经济结构的地区提供可转移的政策分析工具。
Several key states in various regions of the U.S. have experienced recent sawtimber as well as pulp and paper mill closures, which raises an important policy question: how have and will key macroeconomic and industry specific indicators within the U.S. forest sector likely to change over time? This study provides empirical evidence to support forest-sector policy design by using a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states found by the location quotient (LQ) measure: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. Overall, the results suggest a general decline in employment and the number of firms in the forestry and logging industry as well as the paper manufacturing industry, while wood manufacturing is projected to see modest employment gains. These results also offer key insights for regional policymakers, industry leaders, and local economic development officials: communities dependent on timber-based manufacturing may be more resilient than other forestry-based industries in the face of economic disruptions. Our findings can help prioritize targeted policy interventions and inform regional economic resilience strategies. We show distinct differences across forest-dependent industries and/or state sectors and geographies, highlighting that policies may have to be specific to each sector and/or geographical area. Finally, our VEC modeling framework is adaptable to other resource-dependent industries that serve as regional economic pillars such as mining, agriculture, and energy production offering a transferable tool for policy analysis in regions with similar economic structures.
- [13] arXiv:2011.06778 (替换) [中文pdf, pdf, html, 其他]
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标题： 最可能的零售集聚模式：空间均衡的潜力最大化与随机稳定性标题： Most likely retail agglomeration patterns: Potential maximization and stochastic stability of spatial equilibria评论： 30页，10图主题： 理论经济学 (econ.TH) ; 一般经济学 (econ.GN) ; 动力系统 (math.DS) ; 模式形成与孤子 (nlin.PS)
我们研究了一个零售集聚模型，其中消费者更可能前往商店集中度较高的区域。这种集聚效应使拥有众多零售商的区域更具吸引力。在均衡状态下，零售商的空间分布是内生地根据购物需求的空间模式确定的。在这种情况下，可能会出现多个局部稳定的均衡，结果可能取决于店铺的初始分布。为了解决这个问题，我们应用了进化博弈论的一种方法，选择最大化代表零售商激励潜力函数的均衡。我们在二维空间设置中演示了该方法。与基于渐进、短视调整的局部稳定性相比，这种全局最大化导致了一个唯一且更稳健的预测。正如预期的那样，当不动消费者购物成本下降或更大零售集中区的吸引力增加时，零售集群的数量都会减少。
We study a model of retail agglomeration where consumers are more likely to visit zones with a higher concentration of shops. This agglomerative effect makes zones with many retailers more attractive. The spatial distribution of retailers in equilibrium is endogenously determined in response to the spatial pattern of shopping demand. In such a setting, multiple locally stable equilibria may arise, and the outcome can depend on the initial distribution of shops. To address this issue, we apply an approach from evolutionary game theory, selecting the equilibrium that maximizes a potential function representing the incentives of retailers. We demonstrate the method in a two-dimensional spatial setting. Compared to local stability based on gradual, myopic adjustments, this global maximization leads to a unique and more robust prediction. As expected, the number of retail clusters decreases either when shopping costs for immobile consumers fall or when the attractiveness of larger retail concentrations increases.
- [14] arXiv:2412.10421 (替换) [中文pdf, pdf, html, 其他]
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标题： 空间解析的全球粮食系统的能量闭合标题： Energetic closure of the spatially resolved global food system主题： 物理与社会 (physics.soc-ph) ; 一般经济学 (econ.GN)
整合的全球粮食系统分析受到粮食类型、过程和尺度之间数据碎片化的阻碍。 研究也常常忽视与人类代谢的联系——这是粮食需求的最终驱动因素。 在这里，我们使用一个共同的能量框架来协调95种单独粮食商品的数据，涵盖粮食系统过程，包括生产、加工、动物饲料和消费，并从身体大小、人口统计和活动数据中估算人类代谢。 我们估计，全球未被代谢的食物卡路里比例在1990年至2019年间翻了一番（从大约总可用食物卡路里的10%增加到20%），因为粮食供应超过了能量消耗。 当饮食保持不变时，全球人口的代谢需求约有一半（51%）理论上可以通过同一局部1度网格单元（约10,000平方公里）内的生产来满足。 我们的开源框架可以用于评估从光合作用到代谢减少粮食系统低效性的策略，同时满足当地的能量需求。
Integrated global food system analysis is hampered by the fragmentation of data among food types, processes, and scales. Studies also often neglect the connection to human metabolism -- the ultimate driver of food demand. Here we use a common energetic framework to harmonize data on 95 individual food commodities across food system processes, including production, processing, animal feed and consumption, and estimate human metabolism from body size, demographic, and activity data. We estimate that the share of unmetabolized food calories globally doubled between 1990 and 2019 (from about 10 to 20% of the total calories available for human consumption) as food supply outpaced energy expenditure. Approximately half (51%) of the global population's metabolic demands could theoretically be met by production in the same local 1-degree grid cell (~ 10,000 km2) when holding diets constant. Our open-source framework can be applied to assess strategies to reduce food system inefficiencies from photosynthesis to metabolism while meeting local energetic demands.
- [15] arXiv:2412.14996 (替换) [中文pdf, pdf, html, 其他]
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标题： 合作与自利在双种群占有模型中的流体力学标题： Hydrodynamics of Cooperation and Self-Interest in a Two-Population Occupation Model主题： 统计力学 (cond-mat.stat-mech) ; 软凝聚态物理 (cond-mat.soft) ; 一般经济学 (econ.GN) ; 物理与社会 (physics.soc-ph)
我们研究了优化自身效用（自利）或集体福利（合作）的代理系统流体力学。 当代理表现出自私行为时，它们的相互作用是非互惠的，使系统偏离平衡；相反，纯粹利他的动力学恢复了互惠性，并产生类似平衡的描述。 我们研究这两种行为的混合如何影响代理液体的宏观特性。 对于高度理性的代理，我们发现引入少量利他者可以抑制由自私动力学引起的次优聚类。 这一现象可归因于利他者定位在界面并作为有效的表面活性剂，为基于固定邻域的模型中的早期发现[Phys. Rev. Lett.\textbf{120}, 208301 (2018)]提供了新的见解。 当代理是有限理性时，我们引入了一个充分混合近似，将双种群模型简化为一个有效标量场理论。 这使我们能够利用活性物质领域的最先进工具，解析地描述利他主义如何改变表面张力和成核动力学。
We study the hydrodynamics of a system of agents who optimize either their individual utility (self-interest) or the collective welfare (cooperation). When agents act selfishly, their interactions are non-reciprocal, driving the system out of equilibrium; by contrast, purely altruistic dynamics restore reciprocity and yield an equilibrium-like description. We investigate how mixtures of these two behaviors shape the macroscopic properties of the liquid of agents. For highly rational agents, we find that introducing a small fraction of altruists can suppress the sub-optimal clustering induced by selfish dynamics. This phenomenon can be attributed to altruists localizing at interfaces and acting as effective surfactants, shedding a new light on earlier findings in fixed neighborhood-based models [Phys. Rev. Lett. \textbf{120}, 208301 (2018)]. When agents are boundedly rational, we introduce a well-mixed approximation that reduces the two-population model to a single effective scalar field theory. This allows us to leverage state-of-the-art tools from active matter to analytically characterize how altruism modifies surface tension and nucleation dynamics.
- [16] arXiv:2505.02945 (替换) [中文pdf, pdf, html, 其他]
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标题： 经济交换的认知基础：基于行为证据的模块化框架标题： The Cognitive Foundations of Economic Exchange: A Modular Framework Grounded in Behavioral Evidence评论： 添加了到交互式可视化（项目页面）的链接： https://egil158.github.io/cogfoundations-econ/主题： 计算机与社会 (cs.CY) ; 人工智能 (cs.AI) ; 多智能体系统 (cs.MA) ; 一般经济学 (econ.GN) ; 神经与认知 (q-bio.NC)
经济行为的起源仍未解决——不仅在社会科学中，而且在人工智能领域，主流理论通常依赖于预定义的激励或制度假设。 与以物易物作为交换基础的长期神话相反，早期人类社会的证据表明，互惠——而非以物易物——是基本的经济逻辑，使社区在正式市场出现之前就能够维持交换和社会凝聚力。 然而，尽管互惠至关重要，但目前缺乏可模拟且具有认知基础的解释。 在此，我们引入了一个基于三个经验支持的认知原初机制的最小行为框架——个体识别、互惠信任和成本-收益敏感性——这些机制使代理能够参与并维持互惠交换，为可扩展的经济行为奠定基础。 这些机制促进了合作、原始经济交换和制度结构的自下而上产生。 通过结合灵长类动物学、发展心理学和经济人类学的见解，该框架为建模人类和人工系统中的信任、协调和经济行为提供了一个统一的基础。 有关该框架的交互式可视化，请参见：https://egil158.github.io/cogfoundations-econ/
The origins of economic behavior remain unresolved-not only in the social sciences but also in AI, where dominant theories often rely on predefined incentives or institutional assumptions. Contrary to the longstanding myth of barter as the foundation of exchange, converging evidence from early human societies suggests that reciprocity-not barter-was the foundational economic logic, enabling communities to sustain exchange and social cohesion long before formal markets emerged. Yet despite its centrality, reciprocity lacks a simulateable and cognitively grounded account. Here, we introduce a minimal behavioral framework based on three empirically supported cognitive primitives-individual recognition, reciprocal credence, and cost--return sensitivity-that enable agents to participate in and sustain reciprocal exchange, laying the foundation for scalable economic behavior. These mechanisms scaffold the emergence of cooperation, proto-economic exchange, and institutional structure from the bottom up. By bridging insights from primatology, developmental psychology, and economic anthropology, this framework offers a unified substrate for modeling trust, coordination, and economic behavior in both human and artificial systems. For an interactive visualization of the framework, see: https://egil158.github.io/cogfoundations-econ/