2 citations · 6 across the 11 of their papers we have counts for
11 papers
EconSimulacra: A Digital Twin Platform of Socio-Economic Systems Powered by LLM Agents
Ryuji Hashimoto, Masahiro Kaneko, Kentaro Ueda +2
Real-world social behavior emerges from tightly coupled domains: economic conditions shape mobility and social interactions, while online attention and offline activity feed back i…
Constitutional Arms Races in the Public Goods Game: Co-Evolving LLM Constitutions Under Cooperation-Defection Pressure
Ujwal Kumar, Arth Singh, Hershraj Niranjani +5
Frontier LLM agents engage in blackmail, sabotage, and document leaks under goal conflicts in agentic settings, exposing limitations of alignment methods built around single-agent…
From Heard to Lived Opinions: Simulating Opinion Dynamics with Grounded LLM Agents in Economic Environments
Ryuji Hashimoto, Masahiro Kaneko, Ryosuke Takata +2
Opinion dynamics (OD) studies how individual opinions evolve and generate collective patterns such as consensus and polarization. While recent work explores OD using populations of…
Agent-Based Simulation of a Financial Market with Large Language Models
Ryuji Hashimoto, Takehiro Takayanagi, Masahiro Suzuki +1
In real-world stock markets, certain chart patterns -- such as price declines near historical highs -- cannot be fully explained by fundamentals alone. These phenomena suggest the…
PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation
Kentaro Ueda, Takehiro Takayanagi
Personalizing story generation to individual users remains a core challenge in natural language generation. Existing approaches typically require explicit user feedback or fine-tun…
Are Generative AI Agents Effective Personalized Financial Advisors?
Takehiro Takayanagi, Kiyoshi Izumi, Javier Sanz-Cruzado +2
Large language model-based agents are becoming increasingly popular as a low-cost mechanism to provide personalized, conversational advice, and have demonstrated impressive capabil…
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation
Yuki Tanaka, Ryuji Hashimoto, Takehiro Takayanagi +3
The generation of synthetic financial data is a critical technology in the financial domain, addressing challenges posed by limited data availability. Traditionally, statistical mo…
The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making
Takehiro Takayanagi, Ryuji Hashimoto, Chung-Chi Chen +1
In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper a…
Beyond Turing Test: Can GPT-4 Sway Experts' Decisions?
Takehiro Takayanagi, Hiroya Takamura, Kiyoshi Izumi +1
In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human…
SETN: Stock Embedding Enhanced with Textual and Network Information
Takehiro Takayanagi, Hiroki Sakaji, Kiyoshi Izumi
Stock embedding is a method for vector representation of stocks. There is a growing demand for vector representations of stock, i.e., stock embedding, in wealth management sectors,…
Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis
Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi +2
Causality is fundamental in human cognition and has drawn attention in diverse research fields. With growing volumes of textual data, discerning causalities within text data is cru…