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on Artificial Intelligence |
| By: | Eli Ben-Michael; D. James Greiner; Melody Huang; Kosuke Imai; Zhichao Jiang; Sooahn Shin |
| Abstract: | The use of Artificial Intelligence (AI) based on data-driven algorithms has become ubiquitous in today's society. Yet, in many cases and especially when stakes are high, humans still make final decisions. The critical question, therefore, is whether AI helps humans make better decisions as compared to a human alone or AI an alone. We introduce a new methodological framework that can be used to answer experimentally this question with no additional assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on the baseline potential outcome. We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions. Under this experimental design, we show how to compare the performance of three alternative decision-making systems--human-alone, human-with-AI, and AI-alone. We apply the proposed methodology to the data from our own randomized controlled trial of a pretrial risk assessment instrument. We find that AI recommendations do not improve the classification accuracy of a judge's decision to impose cash bail. Our analysis also shows that AI-alone decisions generally perform worse than human decisions with or without AI assistance. Finally, AI recommendations tend to impose cash bail on non-white arrestees more often than necessary when compared to white arrestees. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.12108&r=ain |
| By: | Inkoo Cho; Noah Williams |
| Abstract: | We develop a model of algorithmic pricing that shuts down every channel for explicit or implicit collusion while still generating collusive outcomes. We analyze the dynamics of a duopoly market where both firms use pricing algorithms consisting of a parameterized family of model specifications. The firms update both the parameters and the weights on models to adapt endogenously to market outcomes. We show that the market experiences recurrent episodes where both firms set prices at collusive levels. We analytically characterize the dynamics of the model, using large deviation theory to explain the recurrent episodes of collusive outcomes. Our results show that collusive outcomes may be a recurrent feature of algorithmic environments with complementarities and endogenous adaptation, providing a challenge for competition policy. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.07177&r=ain |
| By: | Bruno de Melo |
| Abstract: | Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant aspect of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies of this analytical technique are extensively documented across numerous academic research papers and books. The integration of large language models (LLMs) and AI agents marks a groundbreaking development in this field. These agents are designed to automate and enhance the performance attribution analysis by accurately calculating and analyzing portfolio performances against benchmarks. In this study, we introduce the application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answer (QA) exercises. Leveraging advanced prompt engineering techniques such as Chain-of-Thought (CoT) and Plan and Solve (PS), and employing a standard agent framework from LangChain, the research achieves promising results: it achieves accuracy rates exceeding 93% in analyzing performance drivers, attains 100% in multi-level attribution calculations, and surpasses 84% accuracy in QA exercises that simulate official examination standards. These findings affirm the impactful role of AI agents, prompt engineering and evaluation in advancing portfolio management processes, highlighting a significant advancement in the practical application and evaluation of AI technologies within the domain. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.10482&r=ain |
| By: | Emin Dinlersoz; Can Dogan; Nikolas Zolas |
| Abstract: | Using comprehensive administrative data on business applications over the period 2004-2023, we study emerging business ideas for developing AI technologies or producing goods or services that use, integrate, or rely on AI. The annual number of new AI business applications is stable between 2004 and 2012 but begins to rise after 2012, and increases faster from 2016 onward into the pandemic, with a large, discrete jump in 2023. The distribution of AI business applications is highly uneven across states and sectors. AI business applications have a higher likelihood of becoming employer startups and higher expected initial employment compared to other business applications. Moreover, controlling for application characteristics, employer businesses originating from AI business applications exhibit higher employment, revenue, payroll, average pay per employee, and labor share, but have similar labor productivity and lower survival rate, compared to those originating from other business applications. While these early patterns may change as the diffusion of AI progresses, the rapid rise in AI business applications, combined with their generally higher rate of transition to employers and better performance in some post-transition outcomes, suggests a small but growing contribution from these applications to business dynamism. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:24-09&r=ain |
| By: | Drydakis, Nick |
| Abstract: | There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term 'AI capital' as a vector of knowledge, skills and capabilities related to AI technologies, which could boost individuals' productivity, employment and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms. |
| Keywords: | Artificial Intelligence, Artificial Intelligence Capital, Employment, Wages, Higher Education, Education |
| JEL: | E24 I26 O14 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1408&r=ain |
| By: | Thanos Konstantinidis; Giorgos Iacovides; Mingxue Xu; Tony G. Constantinides; Danilo Mandic |
| Abstract: | There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading techniques, to arrive at better informed trading decisions. Standard lexicon based sentiment approaches have demonstrated their power in aiding financial decisions. However, they are known to suffer from issues related to context sensitivity and word ordering. Large Language Models (LLMs) can also be used in this context, but they are not finance-specific and tend to require significant computational resources. To facilitate a finance specific LLM framework, we introduce a novel approach based on the Llama 2 7B foundational model, in order to benefit from its generative nature and comprehensive language manipulation. This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data, so as to jointly handle the complexities of financial lexicon and context, and further equipping it with a neural network based decision mechanism. Such a generator-classifier scheme, referred to as FinLlama, is trained not only to classify the sentiment valence but also quantify its strength, thus offering traders a nuanced insight into financial news articles. Complementing this, the implementation of parameter-efficient fine-tuning through LoRA optimises trainable parameters, thus minimising computational and memory requirements, without sacrificing accuracy. Simulation results demonstrate the ability of the proposed FinLlama to provide a framework for enhanced portfolio management decisions and increased market returns. These results underpin the ability of FinLlama to construct high-return portfolios which exhibit enhanced resilience, even during volatile periods and unpredictable market events. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.12285&r=ain |
| By: | Bertin Martens |
| Abstract: | The licensing of training inputs slows down economic growth compared to what it could be with competitive and high-quality GenAI |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:bre:wpaper:node_9853&r=ain |
| By: | Anton Korinek; Donghyun Suh |
| Abstract: | We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If the distribution of task complexity exhibits a sufficiently thick infinite tail, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.12107&r=ain |