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on Artificial Intelligence |
| By: | Pataranutaporn, Pat (Massachusetts Institute of Technology); Powdthavee, Nattavudh (Nanyang Technological University, Singapore); Maes, Pattie (Massachusetts Institute of Technology) |
| Abstract: | We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27, 090 evaluations of 9, 030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g., top-tier, mid-tier, low-tier, AI-generated papers). The results indicate that LLMs effectively distinguish paper quality but exhibit biases favoring prominent institutions, male authors, and renowned economists. Additionally, LLMs struggle to differentiate high-quality AI-generated papers from genuine top-tier submissions. While LLMs offer efficiency gains, their susceptibility to bias necessitates cautious integration and hybrid peer review models to balance equity and accuracy. |
| Keywords: | Artificial Intelligence, peer review, large language model (LLM), bias in academia, economics publishing, equity-efficiency trade-off |
| JEL: | A11 C63 O33 I23 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17659 |
| By: | Iuliia Alekseenko; Dmitry Dagaev; Sofia Paklina; Petr Parshakov |
| Abstract: | A Keynesian beauty contest is a wide class of games of guessing the most popular strategy among other players. In particular, guessing a fraction of a mean of numbers chosen by all players is a classic behavioral experiment designed to test iterative reasoning patterns among various groups of people. The previous literature reveals that the level of sophistication of the opponents is an important factor affecting the outcome of the game. Smarter decision makers choose strategies that are closer to theoretical Nash equilibrium and demonstrate faster convergence to equilibrium in iterated contests with information revelation. We replicate a series of classic experiments by running virtual experiments with modern large language models (LLMs) who play against various groups of virtual players. We test how advanced the LLMs' behavior is compared to the behavior of human players. We show that LLMs typically take into account the opponents' level of sophistication and adapt by changing the strategy. In various settings, most LLMs (with the exception of Llama) are more sophisticated and play lower numbers compared to human players. Our results suggest that LLMs (except Llama) are rather successful in identifying the underlying strategic environment and adopting the strategies to the changing set of parameters of the game in the same way that human players do. All LLMs still fail to play dominant strategies in a two-player game. Our results contribute to the discussion on the accuracy of modeling human economic agents by artificial intelligence. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03158 |
| By: | Ceballos, Francisco; Chugh, Aditi; Kramer, Berber |
| Abstract: | The rise of artificial intelligence (AI) has heightened interest in digital models to strengthen agricultural extension. Such tools could help provide personalized advisories tailored to a farmer's unique conditions at scale and at a low cost. This study evaluates the fundamental assumption that personalized crop advisories are more effective than generic ones. By means of a large-scale randomized controlled trial (RCT), we assess the impact of personalized picture-based advisories on farmers’ perceptions, knowledge and adoption of recommended inputs and practices, and other downstream outcomes. We find that personalizing advisories does not significantly improve agricultural outcomes compared to generic ones. While farmers who engage relatively more with advisories (i.e., those who receive and read a substantial number of messages based on self-reports) tend to achieve better outcomes, this is irrespective of whether the advisories they receive are tailored to their specific situation or not. We conclude that investments in digital extension tools should aim to enhance engagement with advisories rather than focusing solely on personalization. |
| Keywords: | agricultural extension; artificial intelligence; farmers; inputs; Asia; Southern Asia; Africa; Eastern Africa; India; Kenya |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:2322 |
| By: | Jing Yuan (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China); Teng Ma; Yinghui Wang (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China); Jinxin Cao (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA) |
| Abstract: | Using the Chinese CFPS database, this paper analyzes the impact of AI on occupational income inequality in China by using the Pareto coefficient. The empirical results show that AI has significantly widened the occupational income gap in China in recent years. Also, using results based on the mediation effect test concludes that AI widens the income gap significantly through the upgrading of the industrial structure and technological innovation. Furthermore, the analysis of regional heterogeneity reveals that the impact of AI on occupational income inequality is strongest in the northeastern region, followed by the western region, while the impacts in the central and eastern regions are relatively smaller. Finally, our analysis suggests that China should strengthen the supervision and adjustment mechanism of occupational income, establish a monitoring system for occupational income, and deepen the reform of the income distribution system, among other measures, to narrow the occupational income gap caused by the skill premium. |
| Keywords: | Artificial intelligence; Industrial structure; Mediation analysis; Occupational income inequality; Regional heterogeneity; Technological innovation. |
| JEL: | D31 D33 E25 O30 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202504 |
| By: | Marydas, Sneha (RS: GSBE MGSoG, Maastricht Graduate School of Governance); Mathew, Nanditha (Maastricht Graduate School of Governance, RS: GSBE MORSE, RS: GSBE MGSoG); De Marzo, Giordano; Pietrobelli, Carlo (RS: GSBE other - not theme-related research, Mt Economic Research Inst on Innov/Techn) |
| Abstract: | In this study, using a novel dataset that matches firm-level data with online job vacancy data, we investigate the effects of firms’ digital technology adoption on future hiring and the dynamics of hiring and training, focusing on different types of technologies and categories of occupations. First, we examine the impact of adopting different types of digital technologies, namely AI, Advanced ICT, and Basic ICT, on future firm hiring. Our findings reveal that less advanced digital jobs (eg. Basic ICT, Advanced ICT) are substituted by more advanced digital jobs (eg. AI), while the advanced technology adoption by firms leads to increased overall hiring of non-digital roles. Second, we show that there is a positive relationship between training and new hiring only for one occupational category, namely, managers, with no significant relationship for other occupations. Third, we investigate the joint effect of training and technology adoption for firm performance. Our findings reveal that digital technology adoption enhances a firm’s financial performance only when combined with internal staff training. The sole exception is AI, which yields positive performance benefits even in the absence of training. |
| JEL: | O33 O12 L20 D22 |
| Date: | 2025–02–07 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025004 |
| By: | Kubitza, Dennis Oliver; Weßling, Katarina |
| Abstract: | Transitions from school to further education, training, or work are among the most extensively researched topics in the social sciences. Success in such transitions is influenced by predictors operating at multiple levels, such as the individual, the institutional, or the regional level. These levels are intertwined, creating complex inter-dependencies in their influence on transitions. To unravel them, researchers typically apply (multilevel) regression techniques and focus on mediating and moderating relations between distinct predictors. Recent research demonstrates that machine learning techniques can uncover previously overlooked patterns among variables. To detect new patterns in transitions from school to vocational training, we apply artificial neural networks (ANNs) trained on survey data from the German National Educational Panel Study (NEPS) linked with regional data. For an accessible interpretation of complex patterns, we use explainable artificial intelligence (XAI) methods. We establish multiple non-linear interactions within and across levels, concluding that they have the potential to inspire new substantive research questions. We argue that adopting ANNs in the social sciences yields new insights into established relationships and makes complex patterns more accessible |
| Keywords: | school-to-work transitions, VET, machine learning, explainable artificial neuronal networks, SHAP values, rule extraction |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:310974 |
| By: | Thomas MELONIO; Peter Martey Addo,; Anastesia Taieb,; Laura Landrein |
| Abstract: | The AI Investment Potential Index (AIIPI) 2025 represents a significant advancement in the systematic evaluation of global readiness and attractiveness for artificial intelligence (AI) investments. Building upon the foundational framework established in 2024, AIIPI 2025 integrates cutting-edge methodologies, advanced machine learning models, and comprehensive datasets to provide a nuanced and globally comparable assessment of AI ecosystems.This multidimensional framework analyzes key dimensions, including economic environment, governance quality, infrastructure resilience, human capital development, and data governance, with an enhanced emphasis on statistical capacity and data privacy. By addressing regional disparities and identifying strategic opportunities, AIIPI 2025 highlights critical factors driving AI readiness and investment potential worldwide.This paper explores the index’s theoretical underpinnings, methodological advancements, and empirical findings. It provides actionable insights and evidence-based recommendations for policymakers, investors, and researchers, aiming to harness AI’s transformative potential. By fostering strategic interventions and addressing global inequities, AIIPI 2025 serves as an essential instrument for advancing inclusive economic growth, fostering innovation, and shaping a sustainable and equitable global AI ecosystem. |
| JEL: | Q |
| Date: | 2025–02–10 |
| URL: | https://d.repec.org/n?u=RePEc:avg:wpaper:en17879 |
| By: | Yoontae Hwang; Yaxuan Kong; Stefan Zohren; Yongjae Lee |
| Abstract: | This paper addresses the critical disconnect between prediction and decision quality in portfolio optimization by integrating Large Language Models (LLMs) with decision-focused learning. We demonstrate both theoretically and empirically that minimizing the prediction error alone leads to suboptimal portfolio decisions. We aim to exploit the representational power of LLMs for investment decisions. An attention mechanism processes asset relationships, temporal dependencies, and macro variables, which are then directly integrated into a portfolio optimization layer. This enables the model to capture complex market dynamics and align predictions with the decision objectives. Extensive experiments on S\&P100 and DOW30 datasets show that our model consistently outperforms state-of-the-art deep learning models. In addition, gradient-based analyses show that our model prioritizes the assets most crucial to decision making, thus mitigating the effects of prediction errors on portfolio performance. These findings underscore the value of integrating decision objectives into predictions for more robust and context-aware portfolio management. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00828 |
| By: | George Fatouros; Kostas Metaxas; John Soldatos; Manos Karathanassis |
| Abstract: | MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00415 |
| By: | Felix Drinkall; Janet B. Pierrehumbert; Stefan Zohren |
| Abstract: | Large language models (LLMs) have shown remarkable success in language modelling due to scaling laws found in model size and the hidden dimension of the model's text representation. Yet, we demonstrate that compressed representations of text can yield better performance in LLM-based regression tasks. In this paper, we compare the relative performance of embedding compression in three different signal-to-noise contexts: financial return prediction, writing quality assessment and review scoring. Our results show that compressing embeddings, in a minimally supervised manner using an autoencoder's hidden representation, can mitigate overfitting and improve performance on noisy tasks, such as financial return prediction; but that compression reduces performance on tasks that have high causal dependencies between the input and target data. Our results suggest that the success of interpretable compressed representations such as sentiment may be due to a regularising effect. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02199 |