arXiv - ECON - General Economics最新文献_第6页

The Green Peace Dividend: the Effects of Militarization on Emissions and the Green Transition 绿色和平红利：军事化对排放和绿色转型的影响

arXiv - ECON - General Economics Pub Date : 2024-08-29 DOI: arxiv-2408.16419

Balázs Markó

引用次数: 0

The Turing Valley: How AI Capabilities Shape Labor Income 图灵谷：人工智能能力如何影响劳动收入

arXiv - ECON - General Economics Pub Date : 2024-08-29 DOI: arxiv-2408.16443

Enrique Ide, Eduard Talamàs

{"title":"The Turing Valley: How AI Capabilities Shape Labor Income","authors":"Enrique Ide, Eduard Talamàs","doi":"arxiv-2408.16443","DOIUrl":"https://doi.org/arxiv-2408.16443","url":null,"abstract":"Do improvements in Artificial Intelligence (AI) benefit workers? We study how\u0000AI capabilities influence labor income in a competitive economy where\u0000production requires multidimensional knowledge, and firms organize production\u0000by matching humans and AI-powered machines in hierarchies designed to use\u0000knowledge efficiently. We show that advancements in AI in dimensions where\u0000machines underperform humans decrease total labor income, while advancements in\u0000dimensions where machines outperform humans increase it. Hence, if AI initially\u0000underperforms humans in all dimensions and improves gradually, total labor\u0000income initially declines before rising. We also characterize the AI that\u0000maximizes labor income. When humans are sufficiently weak in all knowledge\u0000dimensions, labor income is maximized when AI is as good as possible in all\u0000dimensions. Otherwise, labor income is maximized when AI simultaneously\u0000performs as poorly as possible in the dimensions where humans are relatively\u0000strong and as well as possible in the dimensions where humans are relatively\u0000weak. Our results suggest that choosing the direction of AI development can\u0000create significant divisions between the interests of labor and capital.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"396 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A General Framework for Optimizing and Learning Nash Equilibrium 优化和学习纳什均衡的一般框架

arXiv - ECON - General Economics Pub Date : 2024-08-29 DOI: arxiv-2408.16260

Di Zhang, Wei Gu, Qing Jin

引用次数: 0

Pareto's Limits: Improving Inequality Estimates in America, 1917 to 1965 帕累托的局限：改进 1917 年至 1965 年美国的不平等估算

arXiv - ECON - General Economics Pub Date : 2024-08-29 DOI: arxiv-2408.16861

Vincent Geloso, Alexis Akira Toda

引用次数: 0

Can AI Replace Human Subjects? A Large-Scale Replication of Psychological Experiments with LLMs 人工智能能否取代人类实验对象？大规模复制 LLM 心理实验

arXiv - ECON - General Economics Pub Date : 2024-08-29 DOI: arxiv-2409.00128

Ziyan Cui, Ning Li, Huaikang Zhou

{"title":"Can AI Replace Human Subjects? A Large-Scale Replication of Psychological Experiments with LLMs","authors":"Ziyan Cui, Ning Li, Huaikang Zhou","doi":"arxiv-2409.00128","DOIUrl":"https://doi.org/arxiv-2409.00128","url":null,"abstract":"Artificial Intelligence (AI) is increasingly being integrated into scientific\u0000research, particularly in the social sciences, where understanding human\u0000behavior is critical. Large Language Models (LLMs) like GPT-4 have shown\u0000promise in replicating human-like responses in various psychological\u0000experiments. However, the extent to which LLMs can effectively replace human\u0000subjects across diverse experimental contexts remains unclear. Here, we conduct\u0000a large-scale study replicating 154 psychological experiments from top social\u0000science journals with 618 main effects and 138 interaction effects using GPT-4\u0000as a simulated participant. We find that GPT-4 successfully replicates 76.0\u0000percent of main effects and 47.0 percent of interaction effects observed in the\u0000original studies, closely mirroring human responses in both direction and\u0000significance. However, only 19.44 percent of GPT-4's replicated confidence\u0000intervals contain the original effect sizes, with the majority of replicated\u0000effect sizes exceeding the 95 percent confidence interval of the original\u0000studies. Additionally, there is a 71.6 percent rate of unexpected significant\u0000results where the original studies reported null findings, suggesting potential\u0000overestimation or false positives. Our results demonstrate the potential of\u0000LLMs as powerful tools in psychological research but also emphasize the need\u0000for caution in interpreting AI-driven findings. While LLMs can complement human\u0000studies, they cannot yet fully replace the nuanced insights provided by human\u0000subjects.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach 评估多个 DER 聚合器对能源批发市场的影响：混合均值场方法

arXiv - ECON - General Economics Pub Date : 2024-08-27 DOI: arxiv-2409.00107

Jun He, Andrew L. Liu

{"title":"Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach","authors":"Jun He, Andrew L. Liu","doi":"arxiv-2409.00107","DOIUrl":"https://doi.org/arxiv-2409.00107","url":null,"abstract":"The integration of distributed energy resources (DERs) into wholesale energy\u0000markets can greatly enhance grid flexibility, improve market efficiency, and\u0000contribute to a more sustainable energy future. As DERs -- such as solar PV\u0000panels and energy storage -- proliferate, effective mechanisms are needed to\u0000ensure that small prosumers can participate meaningfully in these markets. We\u0000study a wholesale market model featuring multiple DER aggregators, each\u0000controlling a portfolio of DER resources and bidding into the market on behalf\u0000of the DER asset owners. The key of our approach lies in recognizing the\u0000repeated nature of market interactions the ability of participants to learn and\u0000adapt over time. Specifically, Aggregators repeatedly interact with each other\u0000and with other suppliers in the wholesale market, collectively shaping\u0000wholesale electricity prices (aka the locational marginal prices (LMPs)). We\u0000model this multi-agent interaction using a mean-field game (MFG), which uses\u0000market information -- reflecting the average behavior of market participants --\u0000to enable each aggregator to predict long-term LMP trends and make informed\u0000decisions. For each aggregator, because they control the DERs within their\u0000portfolio under certain contract structures, we employ a mean-field control\u0000(MFC) approach (as opposed to a MFG) to learn an optimal policy that maximizes\u0000the total rewards of the DERs under their management. We also propose a\u0000reinforcement learning (RL)-based method to help each agent learn optimal\u0000strategies within the MFG framework, enhancing their ability to adapt to market\u0000conditions and uncertainties. Numerical simulations show that LMPs quickly\u0000reach a steady state in the hybrid mean-field approach. Furthermore, our\u0000results demonstrate that the combination of energy storage and mean-field\u0000learning significantly reduces price volatility compared to scenarios without\u0000storage.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time is Knowledge: What Response Times Reveal 时间就是知识：响应时间揭示了什么

arXiv - ECON - General Economics Pub Date : 2024-08-27 DOI: arxiv-2408.14872

Jean-Michel Benkert, Shuo Liu, Nick Netzer

引用次数: 0

Regional emission dynamics across phases of the EU ETS 欧盟排放交易计划各阶段的地区排放动态

arXiv - ECON - General Economics Pub Date : 2024-08-27 DOI: arxiv-2408.15438

Marco Dueñas, Antoine Mandel

引用次数: 0

The Climate Cost of Climate Investment: A Two-Period Perspective 气候投资的气候成本：两期视角

arXiv - ECON - General Economics Pub Date : 2024-08-26 DOI: arxiv-2408.14359

Shaunak Kulkarni, Rohan Ajay Dubey

{"title":"The Climate Cost of Climate Investment: A Two-Period Perspective","authors":"Shaunak Kulkarni, Rohan Ajay Dubey","doi":"arxiv-2408.14359","DOIUrl":"https://doi.org/arxiv-2408.14359","url":null,"abstract":"A one-size-fits-all paradigm that only adapts the scale and immediate outcome\u0000of climate investment to economic circumstances will provide a short-lived,\u0000economically inadequate response to climate issues; given the limited resources\u0000allocated to green finance, it stands to reason that the shortcomings of this\u0000will be exacerbated by the fact that it comes at the cost of long-term,\u0000self-perpetuating, systemic solutions. Financial commitments that do not\u0000consider the capital structure of green finance in an economy will cumulatively\u0000dis-aggregate the economic cost of climate investment, to erode the competitive\u0000advantage of the most innovative economies, while simultaneously imposing the\u0000greatest financial burden on economies that are most vulnerable to the impact\u0000of climate change; such disaggregation will also leave 'middle' economies in a\u0000state of flux - honouring similar financial commitments to vulnerable or highly\u0000developed peers, but unable to generate comparable return, yet sufficiently\u0000insulated from the impact of extreme climate phenomena to not organically\u0000develop solutions. In the face of these changing realities, green innovation needs to expand\u0000beyond technology and address systemic inefficiencies - lack of clear\u0000responsibility, ambiguously defined commitments, and inadequate checks &\u0000balances to name a few. Clever application of financial engineering demonstrates promise, and simple\u0000measures like carbon-credit exchanges have been effective in mitigating\u0000imperfections at the grassroots level. We believe that information- and\u0000incentive-centric systemic advancements can usher a fresh wave of green\u0000innovation that stands on the shoulders of giants to ensure effective\u0000implementation of technological breakthroughs; economic development that will\u0000create an international community equipped with a robust framework to deal with\u0000long-term crises in a strategic manner.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insuring Long-Term Care in Developing Countries: The Interaction between Formal and Informal Insurance 发展中国家的长期护理保险：正规与非正规保险之间的相互作用

arXiv - ECON - General Economics Pub Date : 2024-08-26 DOI: arxiv-2408.14243

Jiayi Wen, Xiaoqing Yu

引用次数: 0