SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series

Junyan Cheng, Peter Chin

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Keywords: Large Langauge Models, Agent, Prompt Tunning, Time series forcasting

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TL;DR: We introduce SocioDojo, a lifelong learning environment that lets agents continuously make investment decisions based on news and a novel agent architecture with Hypothesis & Proof prompting for generating in-depth analyses to assist decision-making.

Abstract: We introduce SocioDojo, an open-ended lifelong learning environment for developing ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics such as economics, finance, politics, and culture. It consists of (1) information sources from news, social media, reports, etc., (2) a knowledge base built from books, journals, and encyclopedias, plus a toolbox of Internet and knowledge graph search interfaces, (3) 30K high-quality time series in finance, economy, society, and polls, which support a novel task called "hyperportfolio", that can reliably and scalably evaluate societal analysis and decision-making power of agents, inspired by portfolio optimization with time series as assets to "invest". We also propose a novel Analyst-Assistant-Actuator architecture for the hyperportfolio task, and a Hypothesis & Proof prompting for producing in-depth analyses on input news, articles, etc. to assist decision-making. We perform experiments and ablation studies to explore the factors that impact performance. The results show that our proposed method achieves improvements of 32.4% and 30.4% compared to the state-of-the-art method in the two experimental settings.

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Primary Area: datasets and benchmarks

Submission Number: 2968