{"title":"经济分析的进展、演变的范式和最新趋势","authors":"Robertas Damasevicius","doi":"10.58567/fel02020004","DOIUrl":null,"url":null,"abstract":"<p class=\"MsoNormal\" style=\"margin-top: 12pt; line-height: 14pt; text-align: justify;\"><span lang=\"EN-US\" style=\"font-family: arial, helvetica, sans-serif;\">This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as &rsquo;smart economics,&rsquo; this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.<span style=\"mso-spacerun: yes;\">&nbsp; </span>In the future, it&rsquo;s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it&rsquo;s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.</span></p>","PeriodicalId":262077,"journal":{"name":"Financial Economics Letters","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Progress, Evolving Paradigms and Recent Trends in Economic Analysis\",\"authors\":\"Robertas Damasevicius\",\"doi\":\"10.58567/fel02020004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p class=\\\"MsoNormal\\\" style=\\\"margin-top: 12pt; line-height: 14pt; text-align: justify;\\\"><span lang=\\\"EN-US\\\" style=\\\"font-family: arial, helvetica, sans-serif;\\\">This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as &rsquo;smart economics,&rsquo; this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.<span style=\\\"mso-spacerun: yes;\\\">&nbsp; </span>In the future, it&rsquo;s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. 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引用次数: 0
摘要
p class=" font - family:宋体;"行高:14 pt;text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">本文对不断变化的经济分析格局进行了全面回顾,重点介绍了最近的趋势,并预测了未来的路径。虽然传统的经济模型仍然是解释经济活动的关键,但它们正在被新的方法和跨学科的观点所补充。对使用先进统计技术和独特数据来源的不平等研究的日益关注,强调了经济分析中对公平和分配的日益重视。将行为因素纳入经济模型也扩展了我们对经济决策和市场结果的理解。值得注意的是,计算经济学的出现——将人工智能(AI)、大数据和机器学习整合到经济审查中——代表了一项重大发展。通常被称为“聪明经济学”。该领域采用技术来制定、解决复杂的经济困境,并以非常规的方式感知经济活动。然而,人工智能和机器学习在经济学中的应用在数据隐私、算法偏见和模型结果透明度方面引入了新的障碍。数字革命对经济分析的影响是巨大的,因为计算经济学的出现和大数据的激增正在改变研究技术和政策含义。与此同时,循环经济的出现表明我们对经济可持续性的看法发生了根本性的转变,对环境政策和商业策略产生了重大影响。在未来,预计这些趋势将进一步改变经济分析领域,人工智能和机器学习的整合,对可持续性和公平性的强调,以及大数据的影响将变得更加明显。随着这些变化的发生,研究人员、政策制定者和实践者必须保持适应能力和灵活性,准备好利用机遇并应对这些趋势带来的挑战。
Progress, Evolving Paradigms and Recent Trends in Economic Analysis
This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics. In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.