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on Artificial Intelligence |
| By: | Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt |
| Abstract: | Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this work, we study this issue from both an economic and an algorithmic point of view, focusing on a phenomenon that reduces barriers to entry. Specifically, an incumbent company risks reputational damage unless its model is sufficiently aligned with safety objectives, whereas a new company can more easily avoid reputational damage. To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry -- the required number of data points can be significantly smaller than the incumbent company's dataset size. En route to proving these results, we develop scaling laws for high-dimensional linear regression in multi-objective environments, showing that the scaling rate becomes slower when the dataset size is large, which could be of independent interest. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.03734 |
| By: | Zhang Xu; Wei Zhao |
| Abstract: | Two issues of algorithmic collusion are addressed in this paper. First, we show that in a general class of symmetric games, including Prisoner's Dilemma, Bertrand competition, and any (nonlinear) mixture of first and second price auction, only (strict) Nash Equilibrium (NE) is stochastically stable. Therefore, the tacit collusion is driven by failure to learn NE due to insufficient learning, instead of learning some strategies to sustain collusive outcomes. Second, we study how algorithms adapt to collusion in real simulations with insufficient learning. Extensive explorations in early stages and discount factors inflates the Q-value, which interrupts the sequential and alternative price undercut and leads to bilateral rebound. The process is iterated, making the price curves like Edgeworth cycles. When both exploration rate and Q-value decrease, algorithms may bilaterally rebound to relatively high common price level by coincidence, and then get stuck. Finally, we accommodate our reasoning to simulation outcomes in the literature, including optimistic initialization, market design and algorithm design. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.01147 |
| By: | Caleb Peppiatt |
| Abstract: | Generative Artificial Intelligence constitutes a new wave of automation. There is broad agreement among economists that humanity is potentially entering into a period of profound change. However, significant uncertainties and disagreements exist concerning a variety of overlapping topics: the share of jobs in which human labour is displaced and/or reinstated through automation; the effects on income inequality; the effects on job satisfaction; and, finally, what policy changes ought to be pursued to reduce potential negative impacts. This literature review seeks to clarify this landscape by mapping out key disagreements between positions, and to identify the critical elements upon which such disagreements rest. By surveying the current literature, the effects of AI on the future of work will be clarified. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13300 |
| By: | Gianluca Biggi; Martina Iori; Julia Mazzei; Andrea Mina |
| Abstract: | This paper investigates the contribution of Artificial Intelligence (AI) to environmental innovation. Leveraging a novel dataset of USPTO patent applications from 1980 to 2019, it explores the domain of Green Intelligence (GI), defined as the application of AI algorithms to green technologies. Our analyses reveal an expanding landscape where AI is indeed used as a general purpose technology to address the challenge of sustainability and acts as a catalyst for green innovation. We highlight transportation, energy, and control methods as key applications of GI innovation. We then examine the impact of inventions by using measures and econometric tests suitable to establish 1) how AI and green inventions differ from other technologies and 2) what specifically distinguishes GI technologies in terms of quality and value. Results show that AI and green technologies have a greater impact on follow-on inventions and display greater originality and generality. GI inventions stand out even further in these dimensions. However, when we examine the market response to these inventions, we find positive results only for AI, indicating a mismatch between the technological vis-Ã -vis market potential of green and GI technologies, arguably due to greater uncertainty in their risk-return profiles. |
| Keywords: | Artificial Intelligence, Environmental innovation, Green Intelligence (GI), Twin transition, Digitalization, Green technologies |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/23 |
| By: | Zhizhuo Kou; Holam Yu; Jingshu Peng; Lei Chen |
| Abstract: | Despite significant progress in deep learning for financial trading, existing models often face instability and high uncertainty, hindering their practical application. Leveraging advancements in Large Language Models (LLMs) and multi-agent architectures, we propose a novel framework for quantitative stock investment in portfolio management and alpha mining. Our framework addresses these issues by integrating LLMs to generate diversified alphas and employing a multi-agent approach to dynamically evaluate market conditions. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data, ensuring a comprehensive understanding of market dynamics. The first module extracts predictive signals by integrating numerical data, research papers, and visual charts. The second module uses ensemble learning to construct a diverse pool of trading agents with varying risk preferences, enhancing strategy performance through a broader market analysis. In the third module, a dynamic weight-gating mechanism selects and assigns weights to the most relevant agents based on real-time market conditions, enabling the creation of an adaptive and context-aware composite alpha formula. Extensive experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines across multiple financial metrics. The results underscore the efficacy of combining LLM-generated alphas with a multi-agent architecture to achieve superior trading performance and stability. This work highlights the potential of AI-driven approaches in enhancing quantitative investment strategies and sets a new benchmark for integrating advanced machine learning techniques in financial trading can also be applied on diverse markets. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06289 |