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Related Experiment Video

Updated: Jul 7, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Agents that react to changing market situations.

Kwang Mong Sim¹, Chung Yu Choi

¹Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

Market-driven agents adapt their concession rates to market dynamics using mathematical functions. This approach enables agents to achieve realistic trading outcomes in simulated large markets.

Related Experiment Videos

Last Updated: Jul 7, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

Artificial Intelligence
Computational Economics
Agent-Based Modeling

Background:

Negotiation agents traditionally employ fixed strategies.
Market dynamics necessitate adaptive negotiation behaviors.
Existing models lack robust mechanisms for real-time market responsiveness.

Purpose of the Study:

To establish foundations for designing market-driven agent strategies.
To develop and evaluate a testbed for market-driven agents.
To analyze agent performance in large-scale market simulations.

Main Methods:

Agents utilize four mathematical functions: eagerness, remaining trading time, trading opportunity, and competition.
Strategies are dynamically adjusted based on market conditions and trading progress.
Performance is assessed through simulations and theoretical analyses in large markets.

Main Results:

Market-driven agents effectively adjust concession rates based on market situations.
Agents demonstrate prudent and appropriate concession behaviors.
Simulated trading outcomes align with real-world trading intuitions.

Conclusions:

Market-driven strategies provide a robust framework for adaptive negotiation.
The proposed approach enhances agent performance in dynamic trading environments.
This research offers insights into optimizing agent behavior in complex markets.