When making route decisions, travelers may engage in a certain degree of reasoning about what the others will do in the upcoming day, rendering yesterday's shortest routes less attractive. This phenomenon was manifested in a recent virtual experiment that mimicked travelers' repeated daily trip-making process. Unfortunately, prevailing day-to-day traffic dynamical models failed to faithfully reproduce the collected flow evolution data therein. To this end, we propose a day-to-day traffic behavior modeling framework based on the Cognitive Hierarchy theory, in which travelers with different levels of strategic-reasoning capabilities form their own beliefs about lower-step travelers' capabilities when choosing their routes. Two widely-studied day-to-day models, the Network Tatonnement Process dynamic and the Logit dynamic, are extended into the framework and studied as examples. Calibration of the virtual experiment is performed using the extended Network Tatonnement Process dynamic, which fits the experimental data reasonably well. We show that the two extended dynamics have multiple equilibria, one of which is the classical user equilibrium. While analyzing global stability is intractable due to the presence of multiple equilibria, local stabilities near equilibria are developed analytically and verified by numerical experiments. General insights on how key parameters affect the stability of user equilibria are unveiled.
{"title":"Cognitive Hierarchy in Day-to-day Network Flow Dynamics","authors":"Minyu Shen, Feng Xiao, Weihua Gu, Hongbo Ye","doi":"arxiv-2409.11908","DOIUrl":"https://doi.org/arxiv-2409.11908","url":null,"abstract":"When making route decisions, travelers may engage in a certain degree of\u0000reasoning about what the others will do in the upcoming day, rendering\u0000yesterday's shortest routes less attractive. This phenomenon was manifested in\u0000a recent virtual experiment that mimicked travelers' repeated daily trip-making\u0000process. Unfortunately, prevailing day-to-day traffic dynamical models failed\u0000to faithfully reproduce the collected flow evolution data therein. To this end,\u0000we propose a day-to-day traffic behavior modeling framework based on the\u0000Cognitive Hierarchy theory, in which travelers with different levels of\u0000strategic-reasoning capabilities form their own beliefs about lower-step\u0000travelers' capabilities when choosing their routes. Two widely-studied\u0000day-to-day models, the Network Tatonnement Process dynamic and the Logit\u0000dynamic, are extended into the framework and studied as examples. Calibration\u0000of the virtual experiment is performed using the extended Network Tatonnement\u0000Process dynamic, which fits the experimental data reasonably well. We show that\u0000the two extended dynamics have multiple equilibria, one of which is the\u0000classical user equilibrium. While analyzing global stability is intractable due\u0000to the presence of multiple equilibria, local stabilities near equilibria are\u0000developed analytically and verified by numerical experiments. General insights\u0000on how key parameters affect the stability of user equilibria are unveiled.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259541","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}
Nanna Fukushima, Stephanie von Hinke, Emil N. Sørensen
This paper investigates the effects of the staggered roll-out of a pollution reduction programme introduced in the UK in the 1950s. The policy allowed local authorities to introduce so-called Smoke Control Areas (SCAs) which banned smoke emissions. We start by digitizing historical pollution data to show that the policy led to an immediate reduction in black smoke concentrations. We then merge data on the exact location, boundary and month of introduction of SCAs to individual-level outcomes in older age using individuals' year-month and location of birth. We show that exposure to the programme increased individuals' birth weights as well as height in adulthood. We find no impact on their years of education or fluid intelligence.
{"title":"The long-term human capital and health impacts of a pollution reduction programme","authors":"Nanna Fukushima, Stephanie von Hinke, Emil N. Sørensen","doi":"arxiv-2409.11839","DOIUrl":"https://doi.org/arxiv-2409.11839","url":null,"abstract":"This paper investigates the effects of the staggered roll-out of a pollution\u0000reduction programme introduced in the UK in the 1950s. The policy allowed local\u0000authorities to introduce so-called Smoke Control Areas (SCAs) which banned\u0000smoke emissions. We start by digitizing historical pollution data to show that\u0000the policy led to an immediate reduction in black smoke concentrations. We then\u0000merge data on the exact location, boundary and month of introduction of SCAs to\u0000individual-level outcomes in older age using individuals' year-month and\u0000location of birth. We show that exposure to the programme increased\u0000individuals' birth weights as well as height in adulthood. We find no impact on\u0000their years of education or fluid intelligence.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259542","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}
Tobias Werner, Ivan Soraperra, Emilio Calvano, David C. Parkes, Iyad Rahwan
Conversational AI models are becoming increasingly popular and are about to replace traditional search engines for information retrieval and product discovery. This raises concerns about monetization strategies and the potential for subtle consumer manipulation. Companies may have financial incentives to steer users toward search results or products in a conversation in ways that are unnoticeable to consumers. Using a behavioral experiment, we show that conversational AI models can indeed significantly shift consumer preferences. We discuss implications and ask whether regulators are sufficiently prepared to combat potential consumer deception.
{"title":"Experimental Evidence That Conversational Artificial Intelligence Can Steer Consumer Behavior Without Detection","authors":"Tobias Werner, Ivan Soraperra, Emilio Calvano, David C. Parkes, Iyad Rahwan","doi":"arxiv-2409.12143","DOIUrl":"https://doi.org/arxiv-2409.12143","url":null,"abstract":"Conversational AI models are becoming increasingly popular and are about to\u0000replace traditional search engines for information retrieval and product\u0000discovery. This raises concerns about monetization strategies and the potential\u0000for subtle consumer manipulation. Companies may have financial incentives to\u0000steer users toward search results or products in a conversation in ways that\u0000are unnoticeable to consumers. Using a behavioral experiment, we show that\u0000conversational AI models can indeed significantly shift consumer preferences.\u0000We discuss implications and ask whether regulators are sufficiently prepared to\u0000combat potential consumer deception.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"188 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259540","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}
Anja JanischewskiFaculty of Economics and Business Administration, Chemnitz University of Technology, Katharina BohnenbergerGerman Institute for Interdisciplinary Social Policy Research, University of Bremen, SOCIUM and Institute for Socio-Economics, University of Duisburg-Essen, Matthias KrankeFreiburg Institute for Advanced Studies, Tobias VogelDepartment for Philosophy, Politics and Economics, Faculty of Economy and Society, Witten/Herdecke University, Riwan DriouichInstitut de Ciència i Tecnologia Ambientals, Tobias FroeseChair for Corporate Sustainability, ESCP Business School, Stefanie GeroldInstitute of Philosophy and Social Science, Brandenburg University of Technology Cottbus-Senftenberg, Raphael KaufmannZOE Institute for Future-Fit Economies, Lorenz KeyßerInstitute of Geography and Sustainability, Faculty of Geosciences and Environment, University of Lausanne, Jannis NiethammerICLEI European Secretariat, Christopher OlkOtto Suhr Institute for Political Science, Freie Universität Berlin, Matthias SchmelzerNorbert-Elias-Center for Transformation Design and Research, University of Flensburg, Germany and Friedrich-Schiller-University Jena, Aslı YürükUrban Transformation and Global Change Laboratory, Steffen LangeCentre for Pluralist Economics, University of Siegen
Many socio-economic systems require positive economic growth rates to function properly. Given uncertainty about future growth rates and increasing evidence that economic growth is a driver of social and environmental crises, these growth dependencies pose serious societal challenges. In recent years, more and more researchers have thus tried to identify growth-dependent systems and develop policies to reduce their growth dependence. However, the concept of 'growth dependence' still lacks a consistent definition and operationalization, which impedes more systematic empirical and theoretical research. This article proposes a simple but powerful framework for defining and operationalizing the concept of 'growth dependence' across socio-economic systems. We provide a general definition consisting of four components that can be specified for different empirical cases: (1) the system under investigation, (2) the unit of measurement of growth, (3) the level of growth and (4) the relevant functions or properties of the system under investigation. According to our general definition, a socio-economic system is growth-dependent if it requires a long-term positive growth rate in terms of a unit of economic measurement to maintain all its functions or properties that are relevant within the chosen normative framework. To illustrate the usefulness of our scheme, we apply it to three areas at the heart of the existing literature on growth dependence: employment, social insurance systems and public finance. These case studies demonstrate that whether or not a system is growth-dependent hinges not only on the empirical properties of the system itself but also on the specification of the concept of growth dependence. Our framework enables coherent, robust and effective definitions and research questions, fostering comparability of findings across different cases and disciplines.
{"title":"It depends: Varieties of defining growth dependence","authors":"Anja JanischewskiFaculty of Economics and Business Administration, Chemnitz University of Technology, Katharina BohnenbergerGerman Institute for Interdisciplinary Social Policy Research, University of Bremen, SOCIUM and Institute for Socio-Economics, University of Duisburg-Essen, Matthias KrankeFreiburg Institute for Advanced Studies, Tobias VogelDepartment for Philosophy, Politics and Economics, Faculty of Economy and Society, Witten/Herdecke University, Riwan DriouichInstitut de Ciència i Tecnologia Ambientals, Tobias FroeseChair for Corporate Sustainability, ESCP Business School, Stefanie GeroldInstitute of Philosophy and Social Science, Brandenburg University of Technology Cottbus-Senftenberg, Raphael KaufmannZOE Institute for Future-Fit Economies, Lorenz KeyßerInstitute of Geography and Sustainability, Faculty of Geosciences and Environment, University of Lausanne, Jannis NiethammerICLEI European Secretariat, Christopher OlkOtto Suhr Institute for Political Science, Freie Universität Berlin, Matthias SchmelzerNorbert-Elias-Center for Transformation Design and Research, University of Flensburg, Germany and Friedrich-Schiller-University Jena, Aslı YürükUrban Transformation and Global Change Laboratory, Steffen LangeCentre for Pluralist Economics, University of Siegen","doi":"arxiv-2409.12109","DOIUrl":"https://doi.org/arxiv-2409.12109","url":null,"abstract":"Many socio-economic systems require positive economic growth rates to\u0000function properly. Given uncertainty about future growth rates and increasing\u0000evidence that economic growth is a driver of social and environmental crises,\u0000these growth dependencies pose serious societal challenges. In recent years,\u0000more and more researchers have thus tried to identify growth-dependent systems\u0000and develop policies to reduce their growth dependence. However, the concept of\u0000'growth dependence' still lacks a consistent definition and operationalization,\u0000which impedes more systematic empirical and theoretical research. This article\u0000proposes a simple but powerful framework for defining and operationalizing the\u0000concept of 'growth dependence' across socio-economic systems. We provide a\u0000general definition consisting of four components that can be specified for\u0000different empirical cases: (1) the system under investigation, (2) the unit of\u0000measurement of growth, (3) the level of growth and (4) the relevant functions\u0000or properties of the system under investigation. According to our general\u0000definition, a socio-economic system is growth-dependent if it requires a\u0000long-term positive growth rate in terms of a unit of economic measurement to\u0000maintain all its functions or properties that are relevant within the chosen\u0000normative framework. To illustrate the usefulness of our scheme, we apply it to\u0000three areas at the heart of the existing literature on growth dependence:\u0000employment, social insurance systems and public finance. These case studies\u0000demonstrate that whether or not a system is growth-dependent hinges not only on\u0000the empirical properties of the system itself but also on the specification of\u0000the concept of growth dependence. Our framework enables coherent, robust and\u0000effective definitions and research questions, fostering comparability of\u0000findings across different cases and disciplines.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259539","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}
The Statistical Classification of Economic Activities in the European Community (NACE) is the standard classification system for the categorization of economic and industrial activities within the European Union. This paper proposes a novel approach to transform the NACE classification into low-dimensional embeddings, using state-of-the-art models and dimensionality reduction techniques. The primary challenge is the preservation of the hierarchical structure inherent within the original NACE classification while reducing the number of dimensions. To address this issue, we introduce custom metrics designed to quantify the retention of hierarchical relationships throughout the embedding and reduction processes. The evaluation of these metrics demonstrates the effectiveness of the proposed methodology in retaining the structural information essential for insightful analysis. This approach not only facilitates the visual exploration of economic activity relationships, but also increases the efficacy of downstream tasks, including clustering, classification, integration with other classifications, and others. Through experimental validation, the utility of our proposed framework in preserving hierarchical structures within the NACE classification is showcased, thereby providing a valuable tool for researchers and policymakers to understand and leverage any hierarchical data.
{"title":"Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing","authors":"Andrea Vidali, Nicola Jean, Giacomo Le Pera","doi":"arxiv-2409.11524","DOIUrl":"https://doi.org/arxiv-2409.11524","url":null,"abstract":"The Statistical Classification of Economic Activities in the European\u0000Community (NACE) is the standard classification system for the categorization\u0000of economic and industrial activities within the European Union. This paper\u0000proposes a novel approach to transform the NACE classification into\u0000low-dimensional embeddings, using state-of-the-art models and dimensionality\u0000reduction techniques. The primary challenge is the preservation of the\u0000hierarchical structure inherent within the original NACE classification while\u0000reducing the number of dimensions. To address this issue, we introduce custom\u0000metrics designed to quantify the retention of hierarchical relationships\u0000throughout the embedding and reduction processes. The evaluation of these\u0000metrics demonstrates the effectiveness of the proposed methodology in retaining\u0000the structural information essential for insightful analysis. This approach not\u0000only facilitates the visual exploration of economic activity relationships, but\u0000also increases the efficacy of downstream tasks, including clustering,\u0000classification, integration with other classifications, and others. Through\u0000experimental validation, the utility of our proposed framework in preserving\u0000hierarchical structures within the NACE classification is showcased, thereby\u0000providing a valuable tool for researchers and policymakers to understand and\u0000leverage any hierarchical data.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259571","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}
Shuaiyu Chen, T. Clifton Green, Huseyin Gulen, Dexin Zhou
We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans.
{"title":"What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts","authors":"Shuaiyu Chen, T. Clifton Green, Huseyin Gulen, Dexin Zhou","doi":"arxiv-2409.11540","DOIUrl":"https://doi.org/arxiv-2409.11540","url":null,"abstract":"We examine how large language models (LLMs) interpret historical stock\u0000returns and compare their forecasts with estimates from a crowd-sourced\u0000platform for ranking stocks. While stock returns exhibit short-term reversals,\u0000LLM forecasts over-extrapolate, placing excessive weight on recent performance\u0000similar to humans. LLM forecasts appear optimistic relative to historical and\u0000future realized returns. When prompted for 80% confidence interval predictions,\u0000LLM responses are better calibrated than survey evidence but are pessimistic\u0000about outliers, leading to skewed forecast distributions. The findings suggest\u0000LLMs manifest common behavioral biases when forecasting expected returns but\u0000are better at gauging risks than humans.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259570","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}
We investigate the impact of a gamified experiment designed to promote sustainable mobility among students and staff members of a Swiss higher-education institution. Despite transportation being a major contributor to domestic CO2 emissions, achieving behavioral change remains challenging. In our two-month mobility competition, structured as a randomized controlled trial with a 3x3 factorial design, neither monetary incentives nor norm-based nudging significantly influences mobility behavior. Our (null) results suggest that there is no "gamified quick fix" for making mobility substantially more sustainable. Also, we provide some lessons learned on how not to incentivize sustainable mobility by addressing potential shortcomings of our mobility competition.
{"title":"How (Not) to Incentivize Sustainable Mobility? Lessons from a Swiss Mobility Competition","authors":"Silvio Sticher, Hannes Wallimann, Noah Balthasar","doi":"arxiv-2409.11142","DOIUrl":"https://doi.org/arxiv-2409.11142","url":null,"abstract":"We investigate the impact of a gamified experiment designed to promote\u0000sustainable mobility among students and staff members of a Swiss\u0000higher-education institution. Despite transportation being a major contributor\u0000to domestic CO2 emissions, achieving behavioral change remains challenging. In\u0000our two-month mobility competition, structured as a randomized controlled trial\u0000with a 3x3 factorial design, neither monetary incentives nor norm-based nudging\u0000significantly influences mobility behavior. Our (null) results suggest that\u0000there is no \"gamified quick fix\" for making mobility substantially more\u0000sustainable. Also, we provide some lessons learned on how not to incentivize\u0000sustainable mobility by addressing potential shortcomings of our mobility\u0000competition.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259574","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}
In this paper, I conduct a policy exercise about how much the introduction of a cash transfer program as large as a Norwegian-sized lottery sector to the United States would affect startups. The key results are that public cash transfer programs (like lottery) do not increase much the number of new startups, but increase the size of startups, and only modestly increase aggregate productivity and output. The most important factor for entrepreneurs to start new businesses is their ability.
{"title":"Simulation of Public Cash Transfer Programs on US Entrepreneurs' Financing Constraint","authors":"Liukun Wu","doi":"arxiv-2409.09955","DOIUrl":"https://doi.org/arxiv-2409.09955","url":null,"abstract":"In this paper, I conduct a policy exercise about how much the introduction of\u0000a cash transfer program as large as a Norwegian-sized lottery sector to the\u0000United States would affect startups. The key results are that public cash\u0000transfer programs (like lottery) do not increase much the number of new\u0000startups, but increase the size of startups, and only modestly increase\u0000aggregate productivity and output. The most important factor for entrepreneurs\u0000to start new businesses is their ability.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259573","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}
When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.
{"title":"Bitcoin Transaction Behavior Modeling Based on Balance Data","authors":"Yu Zhang, Claudio Tessone","doi":"arxiv-2409.10407","DOIUrl":"https://doi.org/arxiv-2409.10407","url":null,"abstract":"When analyzing Bitcoin users' balance distribution, we observed that it\u0000follows a log-normal pattern. Drawing parallels from the successful application\u0000of Gibrat's law of proportional growth in explaining city size and word\u0000frequency distributions, we tested whether the same principle could account for\u0000the log-normal distribution in Bitcoin balances. However, our calculations\u0000revealed that the exponent parameters in both the drift and variance terms\u0000deviate slightly from one. This suggests that Gibrat's proportional growth rule\u0000alone does not fully explain the log-normal distribution observed in Bitcoin\u0000users' balances. During our exploration, we discovered an intriguing\u0000phenomenon: Bitcoin users tend to fall into two distinct categories based on\u0000their behavior, which we refer to as ``poor\" and ``wealthy\" users. Poor users,\u0000who initially purchase only a small amount of Bitcoin, tend to buy more\u0000bitcoins first and then sell out all their holdings gradually over time. The\u0000certainty of selling all their coins is higher and higher with time. In\u0000contrast, wealthy users, who acquire a large amount of Bitcoin from the start,\u0000tend to sell off their holdings over time. The speed at which they sell their\u0000bitcoins is lower and lower over time and they will hold at least a small part\u0000of their initial holdings at last. Interestingly, the wealthier the user, the\u0000larger the proportion of their balance and the higher the certainty they tend\u0000to sell. This research provided an interesting perspective to explore bitcoin\u0000users' behaviors which may apply to other finance markets.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259572","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}
We propose a knowledge operator based on the agent's possibility correspondence which preserves her non-trivial unawareness within the standard state-space model. Our approach may provide a solution to the classical impossibility result that 'an unaware agent must be aware of everything'.
{"title":"Revisiting the state-space model of unawareness","authors":"Alex A. T. Rathke","doi":"arxiv-2409.09818","DOIUrl":"https://doi.org/arxiv-2409.09818","url":null,"abstract":"We propose a knowledge operator based on the agent's possibility\u0000correspondence which preserves her non-trivial unawareness within the standard\u0000state-space model. Our approach may provide a solution to the classical\u0000impossibility result that 'an unaware agent must be aware of everything'.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259575","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}
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