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on Artificial Intelligence |
| By: | Xu Han; Zengqing Wu; Chuan Xiao |
| Abstract: | Firm competition and collusion involve complex dynamics, particularly when considering communication among firms. Such issues can be modeled as problems of complex systems, traditionally approached through experiments involving human subjects or agent-based modeling methods. We propose an innovative framework called Smart Agent-Based Modeling (SABM), wherein smart agents, supported by GPT-4 technologies, represent firms, and interact with one another. We conducted a controlled experiment to study firm price competition and collusion behaviors under various conditions. SABM is more cost-effective and flexible compared to conducting experiments with human subjects. Smart agents possess an extensive knowledge base for decision-making and exhibit human-like strategic abilities, surpassing traditional ABM agents. Furthermore, smart agents can simulate human conversation and be personalized, making them ideal for studying complex situations involving communication. Our results demonstrate that, in the absence of communication, smart agents consistently reach tacit collusion, leading to prices converging at levels higher than the Bertrand equilibrium price but lower than monopoly or cartel prices. When communication is allowed, smart agents achieve a higher-level collusion with prices close to cartel prices. Collusion forms more quickly with communication, while price convergence is smoother without it. These results indicate that communication enhances trust between firms, encouraging frequent small price deviations to explore opportunities for a higher-level win-win situation and reducing the likelihood of triggering a price war. We also assigned different personas to firms to analyze behavioral differences and tested variant models under diverse market structures. The findings showcase the effectiveness and robustness of SABM and provide intriguing insights into competition and collusion. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.10974&r=ain |
| By: | Gary Charness; Brian Jabarian; John List |
| Abstract: | We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we discuss the implementation of experiments using LLMs, focusing on enhancing causal inference by creating consistent experiences, improving comprehension of instructions, and monitoring participant engagement in real time. Third, we highlight how LLMs can help analyze experimental data, including pre-processing, data cleaning, and other analytical tasks while helping reviewers and replicators investigate studies. Each of these tasks improves the probability of reporting accurate findings. Finally, we recommend a scientific governance blueprint that manages the potential risks of using LLMs for experimental research while promoting their benefits. This could pave the way for open science opportunities and foster a culture of policy and industry experimentation at scale. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:feb:artefa:00777&r=ain |
| By: | Quentin Gallea |
| Abstract: | This paper illustrates how generative AI could give opportunities for big productivity gains but also opens up questions about the impact of these new powerful technologies on the way we work and share knowledge. More specifically, we explore how ChatGPT changed a fundamental aspect of coding: problem-solving. To do so, we exploit the effect of the sudden release of ChatGPT on the 30th of November 2022 on the usage of the largest online community for coders: Stack Overflow. Using quasi-experimental methods (Difference-in-Difference), we find a significant drop in the number of questions. In addition, the questions are better documented after the release of ChatGPT. Finally, we find evidence that the remaining questions are more complex. These findings suggest not only productivity gains but also a fundamental change in the way we work where routine inquiries are solved by AI allowing humans to focus on more complex tasks. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.11302&r=ain |
| By: | Qin Chen; Jinfeng Ge; Huaqing Xie; Xingcheng Xu; Yanqing Yang |
| Abstract: | This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s methodology. We then aggregate occupation exposure to the industry level to obtain industry exposure scores. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums, suggesting higher-paying and experience-intensive jobs may face greater displacement risks from LLM-powered software. The industry exposure scores align with expert assessments and economic intuitions. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption. Overall, this study provides an analytical basis for understanding the labor market impacts of increasingly capable AI systems in China. Key innovations include the occupation-level exposure analysis, industry aggregation approach, and economic modeling incorporating AI adoption and labor market effects. The findings will inform policymakers and businesses on strategies for maximizing the benefits of AI while mitigating adverse disruption risks. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.08776&r=ain |
| By: | Rick Steinert; Saskia Altmann |
| Abstract: | This paper investigates the potential improvement of the GPT-4 Language Learning Model (LLM) in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. We recorded daily adjusted closing prices and translated them into up-down movements. Sentiment for each day was extracted from messages on the Stocktwits platform using both LLMs. We develop a novel method to engineer a comprehensive prompt for contextual sentiment analysis which unlocks the true capabilities of modern LLM. This enables us to carefully retrieve sentiments, perceived advantages or disadvantages, and the relevance towards the analyzed company. Logistic regression is used to evaluate whether the extracted message contents reflect stock price movements. As a result, GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six months and substantially exceeding a naive buy-and-hold strategy, reaching a peak accuracy of 71.47 % in May. The study also highlights the importance of prompt engineering in obtaining desired outputs from GPT-4's contextual abilities. However, the costs of deploying GPT-4 and the need for fine-tuning prompts highlight some practical considerations for its use. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.16771&r=ain |
| By: | Andres Alonso-Robisco (Banco de España); Jose Manuel Carbo (Banco de España) |
| Abstract: | Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering. |
| Keywords: | ChatGPT, BERT, CBDC, digital money |
| JEL: | G15 G41 E58 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2321&r=ain |
| By: | Hansen, Sakina; Loftus, Joshua |
| Abstract: | Tools for interpretable machine learning (IML) or explainable artificial intelligence (xAI) can be used to audit algorithms for fairness or other desiderata. In a black-box setting without access to the algorithm’s internal structure an auditor may be limited to methods that are model-agnostic. These methods have severe limitations with important consequences for outcomes such as fairness. Among model-agnostic IML methods, visualizations such as the partial dependence plot (PDP) or individual conditional expectation (ICE) plots are popular and useful for displaying qualitative relationships. Although we focus on fairness auditing with PDP/ICE plots, the consequences we highlight generalize to other auditing or IML/xAI applications. This paper questions the validity of auditing in high-stakes settings with contested values or conflicting interests if the audit methods are model-agnostic. |
| Keywords: | artificial intelligence; black-box auditing; causal models; CEUR Workshop Proceedings (CEUR-WS.org); counterfactual fairness; individual conditional expectation; machine learning; partial dependence plots; supervised learning; visualization |
| JEL: | C1 |
| Date: | 2023–07–16 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120114&r=ain |
| By: | Joao Felix; Michel Alexandre, Gilberto Tadeu Lima |
| Abstract: | The use of machine learning models and techniques to predict economic variables has been growing lately, motivated by their better performance when compared to that of linear models. Although linear models have the advantage of considerable interpretive power, efforts have intensified in recent years to make machine learning models more interpretable. In this paper, tests are conducted to determine whether models based on machine learning algorithms have better performance relative to that of linear models for predicting the size of the informal economy. The paper also explores whether the determinants of such size detected as the most important by machine learning models are the same as those detected in the literature based on traditional linear models. For this purpose, observations were collected and processed for 122 countries from 2004 to 2014. Next, eleven models (four linear and seven based on machine learning algorithms) were used to predict the size of the informal economy in these countries. The relative importance of the predictive variables in determining the results yielded by the machine learning algorithms was calculated using Shapley values. The results suggest that (i) models based on machine learning algorithms have better predictive performance than that of linear models and (ii) the main determinants detected through the Shapley values coincide with those detected in the literature using traditional linear models. |
| Keywords: | : Informal economy; machine learning; linear models; Shapley values |
| JEL: | C52 C53 O17 |
| Date: | 2023–08–28 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2023wpecon10&r=ain |
| By: | Reinking, Ernst; Becker, Marco |
| Abstract: | The introduction of ChatGPT as one of the best-known Large Language Models not only opened a new chapter in artificial intelligence in the general perception – some authors even speak of an era of (business) informatics. It also heralds the fifth industrial revolution (Industry 5.0). The aim of this working paper is not only to objectify the contradiction between hype and reality in the context of artificial intelligence, but also to show the opportunities and perspectives for the analysis of unstructured, internal company data. To this end, the authors have developed several prototypes based on their own research work, which form the basis of this working paper. |
| Keywords: | Ai, Industry 5.0, Language Model, LLM, ChatGPT, I5.0 |
| JEL: | M15 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:275738&r=ain |