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公开讲座预告 | 生成式AI助力金融与社科研究决策

金科丛林

2025-06-01

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人工智能如何帮助我们在金融与社会科学领域得到更多洞察和做出更优决策？在新加坡南洋理工大学最新 Dean’s Distinguished Speaker系列讲座中，来自康奈尔大学的Lin William Cong 教授将主要介绍GOALS框架——这是一系列生成式人工智能在大模型空间里的目标驱动算法，结合了强化学习，贪婪搜索，鲁棒性控制等方法的经济建模，旨在支持更智能、个性化、实时及时的投资管理决策制定。讲座也将引入数据驱动生成式均衡的概念和AI行为经济学的新领域。

本次公开讲座由南洋商学院（NBS）金融系主任Byoung-Hyoun Hwang教授主持，将围绕人工智能如何重塑金融理论、经济研究、企业战略以及数据驱动的社会科学展开深入探讨。

本次讲座具体信息如下：

🔹 主题：AI for Social Sciences: Generative Modelling Beyond LLMs

（人工智能与社会科学：超越大语言模型的生成建模）

🔹 主讲人：Prof. Lin William Cong, 康奈尔大学SC Johnson管理学院及金融科技中心

🔹 主持人：Prof. Byoung-Hyoun Hwang, 南洋理工大学南洋商学院

🔹 简要：I characterize modern AI development as featuring two core themes: (i) goal-oriented end-to-end optimization in large modelling space, and (ii) generative pre-trained foundational models. Combining the insights from both, I introduce Goal-Oriented Algorithms in Large Space (GOALS) involving transformer-based reinforcement learning or panel trees, which are particularly suited for answering questions related to optimal decision-making and modelling grouped heterogeneity in social science research.

In several specific financial applications, I show how GOALS can effectively and flexibly manage investment portfolios, generate test portfolios or latent factors for evaluating extant pricing models or more accurate pricing, and separate assets of higher and lower return predictabilities under different macroeconomic regimes. Among them, I highlight how GenAI based on GOALS can assist corporate decision-making that entails complex, high-dimensional, and non-linear stochastic control during which managers possessing various business objectives learn and adapt via dynamic interactions with the market environment.

With pre-trained foundational models and GOALS effectively capturing individual agents’ optimizing behavior in a given economy or market, I further introduce the concept of data-driven generative equilibrium for counterfactual analysis. Specifically, I show how one can take a data-driven approach to examine the counterfactual equilibrium in the online lending market when borrowers endogenously adopt LLMs to complete loan applications. Such a framework fully utilizes the power of generative modelling and can be applied in other social science studies.

Finally, I remark on one caveat when using AI agents for experimentation or to generate counterfactual data: we need to understand AI agents as a new species that potentially differ from humans. Therefore, it is necessary to introduce the new field of behavioral economics of AI and the foundational work therein, which promises a fruitful path for future research.

🔹 讲座时间：2025年6月4日（周三）18:30 – 21:00

📌 活动形式：线上 + 线下混合

📌 立即报名：点击"阅读原文"或扫描下方二维码进入官网获取更多信息及报名方式，链接： http://bit.ly/4junddss

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金科丛林

聚焦国际前沿研究，经济思想应用，行业发展动态，政策法规洞察，学研信息共享，学者领袖沟通。共推数字化，大数据，人工智能，Web3等在数字经济，科技金融，普惠可续领域的知识积累和创新应用。（康奈尔大学丛林教授数济金科实验室）

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金科丛林聚焦国际前沿研究，经济思想应用，行业发展动态，政策法规洞察，学研信息共享，学者领袖沟通。共推数字化，大数据，人工智能，Web3等在数字经济，科技金融，普惠可续领域的知识积累和创新应用。（康奈尔大学丛林教授数济金科实验室）

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