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on Artificial Intelligence |
| By: | Ayato Kitadai; Sinndy Dayana Rico Lugo; Yudai Tsurusaki; Yusuke Fukasawa; Nariaki Nishino |
| Abstract: | Economic experiments offer a controlled setting for researchers to observe human decision-making and test diverse theories and hypotheses; however, substantial costs and efforts are incurred to gather many individuals as experimental participants. To address this, with the development of large language models (LLMs), some researchers have recently attempted to develop simulated economic experiments using LLMs-driven agents, called generative agents. If generative agents can replicate human-like decision-making in economic experiments, the cost problem of economic experiments can be alleviated. However, such a simulation framework has not been yet established. Considering the previous research and the current evolutionary stage of LLMs, this study focuses on the reasoning ability of generative agents as a key factor toward establishing a framework for such a new methodology. A multi-agent simulation, designed to improve the reasoning ability of generative agents through prompting methods, was developed to reproduce the result of an actual economic experiment on the ultimatum game. The results demonstrated that the higher the reasoning ability of the agents, the closer the results were to the theoretical solution than to the real experimental result. The results also suggest that setting the personas of the generative agents may be important for reproducing the results of real economic experiments. These findings are valuable for the future definition of a framework for replacing human participants with generative agents in economic experiments when LLMs are further developed. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.11426&r= |
| By: | Bnaya Dreyfuss; Raphael Raux |
| Abstract: | How do humans assess the performance of Artificial Intelligence (AI) across different tasks? AI has been noted for its surprising ability to accomplish very complex tasks while failing seemingly trivial ones. We show that humans engage in ``performance anthropomorphism'' when assessing AI capabilities: they project onto AI the ability model that they use to assess humans. In this model, observing an agent fail an easy task is highly diagnostic of a low ability, making them unlikely to succeed at any harder task. Conversely, a success on a hard task makes successes on any easier task likely. We experimentally show that humans project this model onto AI. Both prior beliefs and belief updating about AI performance on standardized math questions appear consistent with the human ability model. This contrasts with actual AI performance, which is uncorrelated with human difficulty in our context, and makes such beliefs misspecified. Embedding our framework into an adoption model, we show that patterns of under- and over-adoption can be sustained in an equilibrium with anthropomorphic beliefs. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.05408&r= |
| By: | Hein, Ilka; Cecil, Julia (Ludwig-Maximilians-Universität München); Lermer, Eva (LMU Munich) |
| Abstract: | Artificial intelligence (AI) is increasingly taking over leadership tasks in companies, including the provision of feedback. However, the effect of AI-driven feedback on employees and its theoretical foundations are poorly understood. We aimed to reduce this research gap by comparing perceptions of AI and human feedback based on construal level theory and the feedback process model. A 2 x 2 between-subjects design with vignettes was applied to manipulate feedback source (human vs. AI) and valence (negative vs. positive). In a preregistered experimental study (S1) and subsequent direct replication (S2), responses from NS1 = 263 and NS2 = 449 participants who completed a German online questionnaire were studied. Regression analyses showed that AI feedback was rated as less accurate and led to lower performance motivation, acceptance of the feedback provider, and intention to seek further feedback. These effects were mediated by perceived social distance. Moreover, for feedback acceptance and performance motivation, the differences were only found for positive but not for negative feedback in the first study. This implies that AI feedback may not inherently be perceived as more negatively than human feedback as it depends on the feedback’s valence. Furthermore, the mediation effects indicate that the shown negative evaluations of the AI can be explained by higher social distance and that increased social closeness to feedback providers may improve appraisals of them and of their feedback. Theoretical contributions of the studies and implications for the use of AI for providing feedback in the workplace are discussed, emphasizing the influence of effects related to construal level theory. |
| Date: | 2024–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:uczaw&r= |
| By: | Yuqi Nie; Yaxuan Kong; Xiaowen Dong; John M. Mulvey; H. Vincent Poor; Qingsong Wen; Stefan Zohren |
| Abstract: | Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of data, and generating human-preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We provide a discussion of the progress and advantages of LLMs in financial contexts, analyzing their advanced technologies as well as prospective capabilities in contextual understanding, transfer learning flexibility, complex emotion detection, etc. We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications. For each application area, we delve into specific methodologies, such as textual analysis, knowledge-based analysis, forecasting, data augmentation, planning, decision support, and simulations. Furthermore, a comprehensive collection of datasets, model assets, and useful codes associated with mainstream applications are presented as resources for the researchers and practitioners. Finally, we outline the challenges and opportunities for future research, particularly emphasizing a number of distinctive aspects in this field. We hope our work can help facilitate the adoption and further development of LLMs in the financial sector. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.11903&r= |
| By: | Liyang Wang; Yu Cheng; Ao Xiang; Jingyu Zhang; Haowei Yang |
| Abstract: | This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.09765&r= |
| By: | Viorel Silaghi; Zobaida Alssadi; Ben Mathew; Majed Alotaibi; Ali Alqarni; Marius Silaghi |
| Abstract: | Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations. |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.18434&r= |
| By: | Gaétan de Rassenfosse (Ecole polytechnique federale de Lausanne); Adam Jaffe (Brandeis University); Joal Waldfogel (University of Minnesota) |
| Abstract: | The arrival of creative machines—software capable of producing human-like creative content—has triggered a series of legal challenges about intellectual property. The outcome of these legal challenges will shape the future of the creative industry in ways that could enhance or jeopardize welfare. Policymakers are already tasked with creating regulations for a post-generative AI creative industry. Economics may offer valuable insights, and this paper is our attempt to contribute to the discussion. We identify the main economic issues and propose a framework and some tools for thinking about them. |
| Keywords: | generative AI; machine learning; copyright; fair use |
| JEL: | O34 K20 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iip:wpaper:27&r= |
| By: | Maria José Sousa (ISCTE Instituto Universitário de Lisboa) |
| Abstract: | Artificial Intelligence (AI) has emerged as a focal point for researchers and industry experts, continuously redefined by technological advancements. AI encompasses the development of machines impersonating human cognitive processes, such as learning, reasoning, and self-correction. Its wide-ranging applications across industries have showcased its increasing precision and efficiency, and Agriculture has also embraced AI to increase income and efficiency. In this regard a literature review to comprehensively understand the concept, existing research, and projects related to AI in agriculture was performed. Moreover, this paper approaches the potential of AI in agriculture practically, addressing the emergence of new methods and practices, using a case study approach, and analyzing the perceptions of impacts of AI in agriculture, from experts, academics, and agriculture professionals regarding the application of AI. It contributes to real application development, offering insights that resonate within academic and practical dimensions. |
| Keywords: | Artificial Intelligence, Agriculture, Efficiency, Quantitative analysis |
| JEL: | D20 Q16 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:180&r= |
| By: | Anabela Marques Santos; Francesco Molica; Carlos Torrecilla Salinas (European Commission, Joint Research Centre, Sevilla, Spain; European Commission, Joint Research Centre, Brussels, Belgium; European Commission, Joint Research Centre, Sevilla, Spain) |
| Abstract: | Artificial Intelligence (AI) is seen as a disruptive and transformative technology with the potential to impact on all societal aspects, but particularly on competitiveness and growth. While its development and use has grown exponentially over the last decade, its uptake between and within countries is very heterogeneous. The paper assesses the geographical distribution at NUTS2-level of EU-funded investments related to AI during the programming period 2014-2020. It also examines the relationship between this specialization pattern and regional characteristics using a spatial autoregressive model. Such an analysis provides a first look at the geography of public investment in AI in Europe, which has never been done before. Results show that in the period 2014-2020, around 8 billion EUR of EU funds were targeted for AI investments in the European regions. More developed regions have a higher specialization in AI EU-funded investments. This specialization also generates spillover effects that enhance similar specialization patterns in neighboring regions. AI-related investments are more concentrated in regions with a higher concentration of ICT activities and that are more innovative, highlighting the importance of agglomeration effects. Regions that have selected AI as an innovation priority for their Smart Specialization Strategies are also more likely to have a higher funding specialization in AI. Such findings are very relevant for policymakers as they show that AI-related investments are already highly spatially concentrated. This highlights the importance for less-developed regions to keep accessing to sufficient amounts of pre-allocated cohesion funds and to devote them for AI-related opportunities in the future. |
| Keywords: | Artificial intelligence; Public subsidy; Territorial specialization; Europe |
| JEL: | O31 R58 R12 O52 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:181&r= |