nep-ain New Economics Papers
on Artificial Intelligence
Issue of 2023‒10‒16
eleven papers chosen by
Ben Greiner, Wirtschaftsuniversität Wien

  1. Decoding GPT's hidden "rationality" of cooperation By Bauer, Kevin; Liebich, Lena; Hinz, Oliver; Kosfeld, Michael
  2. Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing By Nunzio Lor\`e; Babak Heydari
  3. Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems By Xingchen Xu; Stephanie Lee; Yong Tan
  4. Beyond the Matrix: Experimental Approaches to Studying Social-Ecological Systems By Hertz, Uri; Koster, Raphael; Janssen, Marco; Leibo, Joel Z.
  5. GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice By Pawe{\l} Niszczota; Sami Abbas
  6. InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning By Yi Yang; Yixuan Tang; Kar Yan Tam
  7. GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models By Udit Gupta
  8. Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network By Peer Nagy; Sascha Frey; Silvia Sapora; Kang Li; Anisoara Calinescu; Stefan Zohren; Jakob Foerster
  9. Are Software Automation and Teleworkers Substitutes? Preliminary Evidence from Japan By Richard Baldwin; Toshihiro Okubo
  10. Artificial Intelligence and Its Impact on Information Technology (IT) Service Sector in Bangladesh By Fahmida Khatun; Nadia Nawrin
  11. A compendium of data sources for data science, machine learning, and artificial intelligence By Paul Bilokon; Oleksandr Bilokon; Saeed Amen

  1. By: Bauer, Kevin; Liebich, Lena; Hinz, Oliver; Kosfeld, Michael
    Abstract: In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT's cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT's behavior isn't random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our research highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.
    Keywords: large language models, cooperation, goal orientation, economic rationality
    Date: 2023
  2. By: Nunzio Lor\`e; Babak Heydari
    Abstract: This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or defect for individual gain. Crucially, we extend our analysis to examine the role of contextual framing, such as diplomatic relations or casual friendships, in shaping the models' decisions. Our findings reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual framing, it shows limited ability to engage in abstract strategic reasoning. Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and context, but LLaMa-2 exhibits a more nuanced understanding of the games' underlying mechanics. These results highlight the current limitations and varied proficiencies of LLMs in strategic decision-making, cautioning against their unqualified use in tasks requiring complex strategic reasoning.
    Date: 2023–09
  3. By: Xingchen Xu; Stephanie Lee; Yong Tan
    Abstract: Recent academic research has extensively examined algorithmic collusion resulting from the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e-commerce platforms employ recommendation algorithms to allocate exposure to various products, and this important aspect has been largely overlooked in previous studies on algorithmic collusion. Our study bridges this important gap in the literature and examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms. Specifically, two commonly deployed recommendation algorithms are examined: (i) a recommender system that aims to maximize the sellers' total profit (profit-based recommender system) and (ii) a recommender system that aims to maximize the demand for products sold on the platform (demand-based recommender system). We construct a repeated game framework that incorporates both pricing algorithms adopted by sellers and the platform's recommender system. Subsequently, we conduct experiments to observe price dynamics and ascertain the final equilibrium. Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives. Conversely, a demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals. Extended analyses suggest the robustness of our findings in various market scenarios. Overall, we highlight the importance of platforms' recommender systems in delineating the competitive structure of the digital marketplace, providing important insights for market participants and corresponding policymakers.
    Date: 2023–09
  4. By: Hertz, Uri (University of Haifa); Koster, Raphael; Janssen, Marco (Arizona State University); Leibo, Joel Z.
    Abstract: Studying social-ecological systems, in which agents interact with each other and their environment is a challenging but important task. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Experimental and computational approaches to studying complex social behaviors and processes have come a long way since the 1950s. However, emphasis on directly mapping the paradigms that are most computationally convenient (matrix games) to their direct analogs in the laboratory may have impoverished experimental design. Modern artificial intelligence (AI) techniques provide new avenues to model complex social worlds, preserving more of their characteristics. These techniques can be fed back to the laboratory where they help to design experiments in more complex social situations without compromising their tractability for computational modeling. This novel approach can help researchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social ecological systems such as climate change, pandemics, and conflict resolution.
    Date: 2023–09–06
  5. By: Pawe{\l} Niszczota; Sami Abbas
    Abstract: We assess the ability of GPT -- a large language model -- to serve as a financial robo-advisor for the masses, by using a financial literacy test. Davinci and ChatGPT based on GPT-3.5 score 66% and 65% on the financial literacy test, respectively, compared to a baseline of 33%. However, ChatGPT based on GPT-4 achieves a near-perfect 99% score, pointing to financial literacy becoming an emergent ability of state-of-the-art models. We use the Judge-Advisor System and a savings dilemma to illustrate how researchers might assess advice-utilization from large language models. We also present a number of directions for future research.
    Date: 2023–08
  6. By: Yi Yang; Yixuan Tang; Kar Yan Tam
    Abstract: We present a new financial domain large language model, InvestLM, tuned on LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset related to financial investment. Inspired by less-is-more-for-alignment (Zhou et al., 2023), we manually curate a small yet diverse instruction dataset, covering a wide range of financial related topics, from Chartered Financial Analyst (CFA) exam questions to SEC filings to Stackexchange quantitative finance discussions. InvestLM shows strong capabilities in understanding financial text and provides helpful responses to investment related questions. Financial experts, including hedge fund managers and research analysts, rate InvestLM's response as comparable to those of state-of-the-art commercial models (GPT-3.5, GPT-4 and Claude-2). Zero-shot evaluation on a set of financial NLP benchmarks demonstrates strong generalizability. From a research perspective, this work suggests that a high-quality domain specific LLM can be tuned using a small set of carefully curated instructions on a well-trained foundation model, which is consistent with the Superficial Alignment Hypothesis (Zhou et al., 2023). From a practical perspective, this work develops a state-of-the-art financial domain LLM with superior capability in understanding financial texts and providing helpful investment advice, potentially enhancing the work efficiency of financial professionals. We release the model parameters to the research community.
    Date: 2023–09
  7. By: Udit Gupta
    Abstract: Annual Reports of publicly listed companies contain vital information about their financial health which can help assess the potential impact on Stock price of the firm. These reports are comprehensive in nature, going up to, and sometimes exceeding, 100 pages. Analysing these reports is cumbersome even for a single firm, let alone the whole universe of firms that exist. Over the years, financial experts have become proficient in extracting valuable information from these documents relatively quickly. However, this requires years of practice and experience. This paper aims to simplify the process of assessing Annual Reports of all the firms by leveraging the capabilities of Large Language Models (LLMs). The insights generated by the LLM are compiled in a Quant styled dataset and augmented by historical stock price data. A Machine Learning model is then trained with LLM outputs as features. The walkforward test results show promising outperformance wrt S&P500 returns. This paper intends to provide a framework for future work in this direction. To facilitate this, the code has been released as open source.
    Date: 2023–09
  8. By: Peer Nagy; Sascha Frey; Silvia Sapora; Kang Li; Anisoara Calinescu; Stefan Zohren; Jakob Foerster
    Abstract: Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we propose the first end-to-end autoregressive generative model that generates tokenized limit order book (LOB) messages. These messages are interpreted by a Jax-LOB simulator, which updates the LOB state. To handle long sequences efficiently, the model employs simplified structured state-space layers to process sequences of order book states and tokenized messages. Using LOBSTER data of NASDAQ equity LOBs, we develop a custom tokenizer for message data, converting groups of successive digits to tokens, similar to tokenization in large language models. Out-of-sample results show promising performance in approximating the data distribution, as evidenced by low model perplexity. Furthermore, the mid-price returns calculated from the generated order flow exhibit a significant correlation with the data, indicating impressive conditional forecast performance. Due to the granularity of generated data, and the accuracy of the model, it offers new application areas for future work beyond forecasting, e.g. acting as a world model in high-frequency financial reinforcement learning applications. Overall, our results invite the use and extension of the model in the direction of autoregressive large financial models for the generation of high-frequency financial data and we commit to open-sourcing our code to facilitate future research.
    Date: 2023–08
  9. By: Richard Baldwin; Toshihiro Okubo
    Abstract: Digital technology is reshaping workplaces by enabling spatial separation of offices, known as telework, or remote intelligence (RI), and by facilitating automation of service sector tasks via artificial intelligence (AI). This paper is a first attempt to empirically investigate whether AI and RI are complements or substitutes in the service sector. It uses a worker-level panel of surveys collected from around 10, 000 workers from pre-COVID-19 pandemic to late 2022, we find preliminary evidence that suggests that AI and RI are complements rather than substitutes. The evidence comes first from the positive correlation of investments in AI-promoting and RI-promoting software at the firm and worker level, and second from the positive correlation of workers' expectations regarding telework and software automation. The evidence is far from definitive but suggests that the complement-substitution question is a fruitful line for future research.
    JEL: F6
    Date: 2023–08
  10. By: Fahmida Khatun; Nadia Nawrin
    Abstract: This paper has attempted to examine the 4IR’s penetration and impacts on the workforce in the IT services sector in Bangladesh. This study also discusses some of the challenges that Bangladesh IT sector faces at present. Finally, contemplating Bangladesh’s preparedness for the digital age of 4IR in terms of access to technology and policy framework, the paper makes a number of recommendations which can enable the country to reap the full benefits of 4IR.
    Keywords: Artificial Intelligence, Fourth industrial revolution, 4IR, CPD-FES Publication
    Date: 2021–11
  11. By: Paul Bilokon; Oleksandr Bilokon; Saeed Amen
    Abstract: Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are application-specific, and it is impossible to produce an exhaustive list of such data sources, it seems that a comprehensive, rather than complete, list would still benefit data scientists and machine learning experts of all levels of seniority. The goal of this publication is to provide just such an (inevitably incomplete) list -- or compendium -- of data sources across multiple areas of applications, including finance and economics, legal (laws and regulations), life sciences (medicine and drug discovery), news sentiment and social media, retail and ecommerce, satellite imagery, and shipping and logistics, and sports.
    Date: 2023–09

This nep-ain issue is ©2023 by Ben Greiner. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.