nep-ain New Economics Papers
on Artificial Intelligence
Issue of 2023‒12‒04
nine papers chosen by
Ben Greiner, Wirtschaftsuniversität Wien

  1. Awareness of artificial intelligence: Diffusion of information about AI versus ChatGPT in the United States By Goel, Rajeev K.; Nelson, Michael A.
  2. Application of Artificial Intelligence for Monetary Policy-Making By Mariam Dundua; Otar Gorgodze
  3. Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in By Mamalis, Marios; Kalampokis, Evangelos; Karamanou, Areti; Brimos, Petros; Tarabanis, Konstantinos
  4. Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&D Project Assessment By Lee, Sangkyu
  5. Brief for the Canada House of Commons Study on the Implications of Artificial Intelligence Technologies for the Canadian Labor Force: Generative Artificial Intelligence Shatters Models of AI and Labor By Morgan R. Frank
  6. Evaluating Local Language Models: An Application to Bank Earnings Calls By Thomas R. Cook; Sophia Kazinnik; Anne Lundgaard Hansen; Peter McAdam
  7. Central banks and policy communication: How emerging markets have outperformed the Fed and ECB By Tatiana Evdokimova; Piroska Nagy Mohacsi; Olga Ponomarenko; Elina Ribakova
  8. Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques By Xiong Xiong; Fan Yang; Li Su
  9. The A.I. Dilemma: Growth versus Existential Risk By Charles I. Jones

  1. By: Goel, Rajeev K.; Nelson, Michael A.
    Abstract: This paper addresses the awareness about the artificial intelligence across states in the United States. We uniquely create indices of Google internet search results for general AI awareness and about ChatGPT, normalizing alternatively by internet users and land area. An understanding of the awareness about AI would provide useful insights into regulatory attempts to monitor and guard the AI technologies, besides suggesting alternatives for laggard states to catch up. Econometric results to explain the drivers of AI awareness show that, ceteris paribus, more prosperous states had greater awareness about AI and ChatGPT. On the other hand, states with greater economic freedom had a lower awareness. States with more men to women has lower AI awareness when hits were normalized by area, but the reverse was true when weighted by internet users. States with a higher proportion of the elderly population were no different from the other states, while those with greater urbanization had more AI/ChatGPT awareness when the internet hits were weighted by land area. Finally, states bordering Canada were no different from other states, while states bordering Mexico generally had a lower AI/ChatGPT awareness.
    Keywords: artificial intelligence, AI, ChatGPT, Internet, machine learning, Google search, economic freedom, urbanization, gender
    JEL: O33 D83 L86
    Date: 2023
  2. By: Mariam Dundua (Financial and Supervisory Technology Development Department, National Bank of Georgia); Otar Gorgodze (Head of Financial and Supervisory Technologies Department, National Bank of Georgia)
    Abstract: The recent advances in Artificial Intelligence (AI), in particular, the development of reinforcement learning (RL) methods, are specifically suited for application to complex economic problems. We formulate a new approach looking for optimal monetary policy rules using RL. Analysis of AI generated monetary policy rules indicates that optimal policy rules exhibit significant nonlinearities. This could explain why simple monetary rules based on traditional linear modeling toolkits lack the robustness needed for practical application. The generated transition equations analysis allows us to estimate the neutral policy rate, which came out to be 6.5 percent. We discuss the potential combination of the method with state-of-the-art FinTech developments in digital finance like DeFi and CBDC and the feasibility of MonetaryTech approach to monetary policy.
    Keywords: Artificial Intelligence; Reinforcement Learning; Monetary policy
    JEL: C60 C61 C63 E17 C45 E52
    Date: 2022–11
  3. By: Mamalis, Marios; Kalampokis, Evangelos; Karamanou, Areti; Brimos, Petros; Tarabanis, Konstantinos
    Abstract: Large language models possess tremendous natural language understanding and generation abilities. However, they often lack the ability to discern between fact and fiction, leading to factually incorrect responses. Open Government Data are repositories of, often times linked, information that is freely available to everyone. By combining these two technologies in a proof of concept designed application utilizing the GPT3.5 OpenAI model and the Scottish open statistics portal, we show that not only is it possible to augment the large language model's factuality of responses, but also propose a novel way to effectively access and retrieve statistical information from the data portal just through natural language querying. We anticipate that this paper will trigger a discussion regarding the transformation of Open Government Portals through large language models.
    Date: 2023–10–24
  4. By: Lee, Sangkyu (Korea Institute for Industrial Economics and Trade)
    Abstract: When employed in artificial intelligence (AI) applications, machine learning (ML) allows AI to recognize patterns in data and predict future outcomes based on these patterns, supporting decision-making. This additionally allows ML to be utilized in the formulation of industrial policies (IPs). However, overreliance on AI for all pol-icies presents several challenges. To harness AI effectively, it is essential to ensure logical clarity and measurability that can be digitally transformed into data, along with the availability of a sufficient amount of data to ensure accuracy and reliability. On the other hand, it is more difficult to use AI in IP design when policies must take into normative as well as economic considerations, or when it becomes necessary to define new norms. These are typically cases in which simple pattern recognition fails to grasp the complexity of various issues at play, making the immediate application of AI application impossible. For instance, situations in which numerous stakeholders hold diverse perspectives can make it challenging to establish clear policy objectives. Additionally, any given problem may include some issues that are fundamentally subjective or normative, and thus incapably of being quantified or measured. This also presents challenges to the effective use of AI. This paper explores the ways in which machine learning (ML) techniques in the field of object classification can contribute to formulating industrial policies. Thank you for reading this abstract of a report from the Korea Institute for Industrial Economics and Trade! Visit us on YouTube: Visit us on Instagram: Visit our website:
    Keywords: artificial intelligence; AI; machine larning (ML); patterns; data; data analysis; pattern recognition; neural networks; industrial policy; policy design; Korea
    JEL: E61 E69 I28 L52 L52 L86 L88
    Date: 2023–10–31
  5. By: Morgan R. Frank
    Abstract: Exciting advances in generative artificial intelligence (AI) have sparked concern for jobs, education, productivity, and the future of work. As with past technologies, generative AI may not lead to mass unemployment. But, unlike past technologies, generative AI is creative, cognitive, and potentially ubiquitous which makes the usual assumptions of automation predictions ill-suited for today. Existing projections suggest that generative AI will impact workers in occupations that were previously considered immune to automation. As AI's full set of capabilities and applications emerge, policy makers should promote workers' career adaptability. This goal requires improved data on job separations and unemployment by locality and job titles in order to identify early-indicators for the workers facing labor disruption. Further, prudent policy should incentivize education programs to accommodate learning with AI as a tool while preparing students for the demands of the future of work.
    Date: 2023–11
  6. By: Thomas R. Cook; Sophia Kazinnik; Anne Lundgaard Hansen; Peter McAdam
    Abstract: This study evaluates the performance of local large language models (LLMs) in interpreting financial texts, compared with closed-source, cloud-based models. We first introduce new benchmarking tasks for assessing LLM performance in analyzing financial and economic texts and explore the refinements needed to improve its performance. Our benchmarking results suggest local LLMs are a viable tool for general natural language processing analysis of these texts. We then leverage local LLMs to analyze the tone and substance of bank earnings calls in the post-pandemic era, including calls conducted during the banking stress of early 2023. We analyze remarks in bank earnings calls in terms of topics discussed, overall sentiment, temporal orientation, and vagueness. We find that after the banking stress in early 2023, banks tended to converge to a similar set of topics for discussion and to espouse a distinctly less positive sentiment.
    Keywords: data; large language models; quantitative methods; banking and finance
    JEL: C45 G21
    Date: 2023–11–06
  7. By: Tatiana Evdokimova (Joint Vienna Institute); Piroska Nagy Mohacsi (London School of Economics and Political Science); Olga Ponomarenko (Caplight); Elina Ribakova (Peterson Institute for International Economics)
    Abstract: This paper uses innovative natural language processing techniques to analyze central bank communication in emerging-market (EM) central banks and compare it with that of the Federal Reserve (Fed) and the European Central Bank (ECB). Once laggards of the central banking policy scene, EM central banks have made remarkable progress in improving their policy frameworks in the past two decades. They adopted many of the principles of advanced-economy (AE) central banks both in policy conduct and communication, but with modifications that reflect their specific circumstances of capital flow volatility, financial dollarization, and traditionally weaker credibility. The authors find that EM central banks' transparency has improved dramatically; their statements' readability has overall been better than in AEs; their focus on inflation has been sharper; and they have used data-shy "forward guidance" sparingly and flexibly. Worryingly though, most central banks do not communicate on inflationary pressures until after inflation already happens. EMs have outperformed AEs in two critical respects recently: addressing rising post-COVID inflationary pressures in a timely manner and, related, avoiding banking sector stress during the monetary policy tightening cycle. Systemic support in the form of currency swaps and repo operations by the Fed and the ECB with powerful signaling at times of acute market stress also helped. EM central banks have also started moving towards easing monetary policy already, ahead of the Fed and the ECB. Bringing down inflation fast and sustainably will be the ultimate test for the quality of EM central bank frameworks. The authors conclude with policy lessons for both EM and AE central banks. These include better forecasting and communication of inflation by the majority of central banks; more consistent delivery by EM central banks of communicated policy action; discarding pure "forward guidance" that hampers data dependency and thus fast policy action particularly at times of rapid change; consistent focus on supply-side factors of inflation; and for multiple-goal central banks, a clear choice and communication of policy priorities at times of possible conflict among some of the goals. The paper also suggests a more transparent communication of coordination with fiscal authorities that would improve the credibility of both the monetary and fiscal authorities.
    Keywords: central banking, monetary policy, emerging markets, Federal Reserve, ECB, communication, inflation-targeting, currency swaps, supply-side inflation, forward guidance, Chat GPT, AI
    JEL: B22 C55 E42 E52 E58
    Date: 2023–10
  8. By: Xiong Xiong; Fan Yang; Li Su
    Abstract: Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19, 175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.
    Date: 2023–10
  9. By: Charles I. Jones
    Abstract: Advances in artificial intelligence (A.I.) are a double-edged sword. On the one hand, they may increase economic growth as A.I. augments our ability to innovate. On the other hand, many experts worry that these advances entail existential risk: creating a superintelligence misaligned with human values could lead to catastrophic outcomes, even possibly human extinction. This paper considers the optimal use of A.I. technology in the presence of these opportunities and risks. Under what conditions should we continue the rapid progress of A.I. and under what conditions should we stop?
    JEL: J17 O40
    Date: 2023–11

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