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on Artificial Intelligence |
| By: | Suzie Grondin; Arthur Charpentier; Philipp Ratz |
| Abstract: | Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was put forward, where the human action in the pricing process is replaced by automated agents. Although experiments have shown that collusive market equilibria can be reached through such techniques, without the need for human intervention, many of the techniques developed remain susceptible to exploitation by other players, making them difficult to implement in practice. In this article, we explore a situation where an agent has a multi-objective strategy, and not only learns to unilaterally exploit market dynamics originating from other algorithmic agents, but also learns to model the behaviour of other agents directly. Our results show how common critiques about the viability of algorithmic collusion in real-life settings can be overcome through the usage of slightly more complex algorithms. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.16935 |
| By: | Simon Martin; Hans-Theo Normann; Paul Püplichhuisen; Tobias Werner |
| Abstract: | We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of asymmetry between firms. We find that both consumers and firms can benefit from asymmetry. Algorithms produce more competitive outcomes when firms are symmetric, but less when they are very asymmetric. Although the static Nash equilibrium underestimates the effect on total quantity and overestimates the effect on profits, it delivers surprisingly accurate predictions in terms of total welfare. The best description of our results is provided by the equal relative gains solution. In particular, we find algorithms to agree on profits that are on or close to the Pareto frontier for all degrees of asymmetry. Our results suggest that the common belief that symmetric industries are more prone to collusion may no longer hold when algorithms increasingly drive managerial decisions. |
| Keywords: | algorithmic collusion, Cournot duopoly, asymmetric firms |
| JEL: | C73 D43 L13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11629 |
| By: | Keenan, Michael; Koo, Jawoo; Mwangi, Christine; Karachiwalla, Naureen; Breisinger, Clemens; Kim, MinAh |
| Abstract: | Academic researchers want their research to be understood and used by non-technical audiences, but that requires communication that is more accessible in the form of non-technical and shorter summaries. The researcher must both signal the quality of the research and ensure that the content is salient by making it more readable. AI tools can improve salience; however, they can also lead to ambiguity in the signal since true effort is then difficult to observe. We implement an online factorial experiment providing non-technical audiences with a blog on an academic paper and vary the actual author of the blog from the same paper (human or ChatGPT) and whether respondents are told the blog is written by a human or AI tool. Even though AI-generated blogs are objectively of higher quality, they are rated lower, but not if the author is disclosed as AI, indicating that signaling is important and can be distorted by AI. Use of the blog does not vary by experimental arm. The findings suggest that, provided disclosure statements are included, researchers can potentially use AI to reduce effort costs without compromising signaling or salience. Academic researchers want their research to be understood and used by non-technical audiences, but that requires communication that is more accessible in the form of non-technical and shorter summaries. The researcher must both signal the quality of the research and ensure that the content is salient by making it more readable. AI tools can improve salience; however, they can also lead to ambiguity in the signal since true effort is then difficult to observe. We implement an online factorial experiment providing non-technical audiences with a blog on an academic paper and vary the actual author of the blog from the same paper (human or ChatGPT) and whether respondents are told the blog is written by a human or AI tool. Even though AI-generated blogs are objectively of higher quality, they are rated lower, but not if the author is disclosed as AI, indicating that signaling is important and can be distorted by AI. Use of the blog does not vary by experimental arm. The findings suggest that, provided disclosure statements are included, researchers can potentially use AI to reduce effort costs without compromising signaling or salience. |
| Keywords: | artificial intelligence; communication; research; Southern Asia |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:2321 |
| By: | Tavishi Choudhary (Greenwich High, Greenwich, Connecticut, US) |
| Abstract: | Artificial Intelligence large language models have rapidly gained widespread adoption, sparking discussions on their societal and political impact, especially for political bias and its far-reaching consequences on society and citizens. This study explores the political bias in large language models by conducting a comparative analysis across four popular AI mod-els—ChatGPT-4, Perplexity, Google Gemini, and Claude. This research systematically evaluates their responses to politically charged prompts and questions from the Pew Research Center’s Political Typology Quiz, Political Compass Quiz, and ISideWith Quiz. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances based on their training data sets. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate any of these biases. The most significant question surrounding political bias in AI is its consequences, particularly its influence on public discourse, policy-making, and democratic processes. The results of this study advocate for ethical implications for the development of AI models and the need for transparency to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex AI bias issue. |
| Keywords: | Large language models (LLM), Generative AI (GenAI), AI Governance and Policy, Ethical AI Systems |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0451 |
| By: | Annie Liang |
| Abstract: | Large language models, trained on personal data, may soon be able to mimic individual personalities. This would potentially transform search across human candidates, including for marriage and jobs -- indeed, several dating platforms have already begun experimenting with training "AI clones" to represent users. This paper presents a theoretical framework to study the tradeoff between the substantially expanded search capacity of AI clones and their imperfect representation of humans. Individuals are modeled as points in $k$-dimensional Euclidean space, and their AI clones are modeled as noisy approximations. I compare two search regimes: an "in-person regime" -- where each person randomly meets some number of individuals and matches to the most compatible among them -- against an "AI representation regime" -- in which individuals match to the person whose AI clone is most compatible with their AI clone. I show that a finite number of in-person encounters exceeds the expected payoff from search over infinite AI clones. Moreover, when the dimensionality of personality is large, simply meeting two people in person produces a higher expected match quality than entrusting the process to an AI platform, regardless of the size of its candidate pool. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.16996 |
| By: | Julia M. Puaschunder (Columbia University, New York, United States) |
| Abstract: | Artificial intelligence (AI) is transforming human societies. In the many attempts to capture the novel trend of AI entering in all aspects of human life these days, hardly any research covers intergenerational aspects of AI encroaching society. Intergenerational aspects concern all features of the interaction and interrelatedness of overlapping generations. In the long-term evolution of AI, intergenerational transfers that resemble human family compounds should be considered. When analyzing the relation of AI with intergenerational human features, benefits as well as costs and risks are highlighted. As for the benefits in the interaction of AI with human beings, information storage opportunities of AI allow for more intergenerational transfers and richer communication opportunities between generations than ever before in the history of humankind. Intergenerational potentials of AI must also be evaluated with caution for AI aspects that may impose negative externalities for society. The eternally living character of AI raises questions of sustainability and the fear of crowding out humanness in the artificial age exist. Overall, AI development must be staged with a logic of cost and benefits weighting in order to ensure to harvest the upsides of AI with attention for potential downfalls and risks. All these efforts will help fostering richer and more efficient collaboration among generations but also ensure equitable, sustainable and inclusive AI development. |
| Keywords: | artificial intelligence, cultural transmission, ethics, intergenerational equity, sustainable development, workforce evolution |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0459 |
| By: | Park , Donghyun (Asian Development Bank); Shin, Kwanho (Korea University) |
| Abstract: | We examine the implications of robots and artificial intelligence (AI) for employment and productivity, using a rich firm-level database from the Survey of Business Activities provided by Statistics Korea. While previous studies have explored the effects of robots and AI separately, we investigate their effects jointly within a unified framework. We deploy propensity score matching to control for firm characteristics, enabling a potential causal interpretation of the differential impacts of robots and AI. We find that the patterns of adopting robots and AI differ significantly across industries. Additionally, although the overall share of firms adopting robots is larger, AI adoption is more concentrated among bigger firms. Our main finding is that, while adopting robots and adopting AI both increase employment, only adopting AI improves labor productivity. However, such productivity gains are accompanied by a decrease in the labor share of income, suggesting a potential shift in value distribution favoring capital income. Furthermore, we find that the immediate impact of adopting both robots and AI is an increase in temporary but not permanent employment. Finally, there is no evidence that firms adopting both robots and AI improve their labor productivity, potentially reflecting a lack of synergy. |
| Keywords: | artificial intelligence; robots; employment; productivity |
| JEL: | D22 J21 J24 O33 O40 |
| Date: | 2025–02–07 |
| URL: | https://d.repec.org/n?u=RePEc:ris:adbewp:0769 |
| By: | Nii-Aponsah, Hubert (RS: GSBE other - not theme-related research, Mt Economic Research Inst on Innov/Techn); Verspagen, Bart (RS: GSBE other - not theme-related research, Mt Economic Research Inst on Innov/Techn, RS: UNU-MERIT Theme 1); Mohnen, Pierre (RS: GSBE other - not theme-related research, QE Econometrics) |
| Abstract: | Technological progress in automation technologies, such as Artificial Intelligence (AI), is expected to impact production activities beyond the home country adopting them as countries interact within the global trade system. Firms tend to offshore production activities to other countries when it is more profitable to produce elsewhere than at home. The adoption of automation technologies reduces the cost of producing in the home country, making previous offshore locations relatively less attractive. From a global perspective, the altered cost structure induces reshoring: a reorganization of production activities back home or to other lower-cost locations. Developing economies, which previously served as low-cost locations, could be adversely impacted by experiencing a drop in the production of the affected sectors and goods. This paper analyses the potential effect of automation on the global portfolio of trade specialization based on the principle of comparative advantage, employed in an extension of Duchin’s World Trade Model to include non-tradable sectors. Through scenario-based analyses within the global economic context and using data, primarily, from the World Input-Output Database (WIOD) and the International Assessment of Adult Competencies (PIAAC), we find that countries in lower-income Asia are likely to be the most adversely affected by reshoring induced by automation in advanced economies. |
| JEL: | O33 D33 E25 F14 F17 F47 J21 |
| Date: | 2023–05–22 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2023018 |
| By: | Philip Moreira Tomei; Rupal Jain; Matija Franklin |
| Abstract: | This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.17755 |
| By: | Damioli, Giacomo; Van Roy, Vincent; Vértesy, Dániel; Vivarelli, Marco |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be “enabling technologies”, a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| JEL: | O31 O33 |
| Date: | 2024–08–13 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2024018 |
| By: | Sheyan Lalmohammed |
| Abstract: | The integration of artificial intelligence (AI) into economic systems represents a transformative shift in decision-making frameworks, introducing novel dynamics between human and AI agents. This paper proposes a welfare model that incorporates both game-theoretic and behavioral dimensions to optimize interactions within human-AI ecosystems. By leveraging agent-based modeling (ABM), we simulate these interactions, accounting for trust evolution, perceived risks, and cognitive costs. The framework redefines welfare as the aggregate utility of interactions, adjusted for collaboration synergies, efficiency penalties, and equity considerations. Dynamic trust is modeled using Bayesian updating mechanisms, while synergies between agents are quantified through a collaboration index rooted in cooperative game theory. Results reveal that trust-building and skill development are pivotal to maximizing welfare, while sensitivity analyses highlight the trade-offs between AI complexity, equity, and efficiency. This research provides actionable insights for policymakers and system designers, emphasizing the importance of equitable AI adoption and fostering sustainable human-AI collaborations. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.15317 |
| By: | D'Allesandro, Francesco; Santarelli, Enrico; Vivarelli, Marco |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| JEL: | O33 L26 |
| Date: | 2024–08–12 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2024016 |
| By: | Alessandra Bonfiglioli; Rosario Crinò; Mattia Filomena; Gino Gancia |
| Abstract: | This paper investigates the determinants of comparative advantage in Artificial Intelligence (AI)-intensive industries using a comprehensive dataset of US imports from 68 countries across 79 manufacturing and service industries over the period 1999–2019. Using a novel measure of AI intensity based on the prevalence of occupations requiring expertise in machine learning and data analysis, we identify key factors influencing exports in AI-intensive industries. Our analysis reveals that countries with larger STEM graduate populations, broader Internet penetration and higher export volumes exhibit stronger export performance in AI-intensive industries. In contrast, regulatory barriers to digital trade are associated with lower AI-intensive exports. These results are robust to controlling for traditional sources of comparative advantage and addressing potential threats to identification. Our findings have implications for understanding competitiveness in the digital economy and highlight that fostering capabilities in data-driven industries may be particularly important due to their pronounced scale economies. |
| Keywords: | artificial intelligence, international trade, digital data, comparative advantage |
| JEL: | F10 F14 J23 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11642 |
| By: | Michael R. Douglas; Sergiy Verstyuk |
| Abstract: | We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant rates over long periods. Production of patents and publications doubles every ten years, by contrast with the growth of computing resources driven by Moore's Law, roughly a doubling every two years. We argue that the input of AI researchers is also crucial and its contribution can be objectively estimated. Consequently, we give a simple argument that explains the 5:1 relation between these two rates. We then discuss the application of this argument to different output measures and compare our analyses with predictions based on machine learning scaling laws proposed in existing literature. Our quantitative framework facilitates understanding, predicting, and modulating the development of these important technologies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.17894 |