Adaptive Multi-Agent Systems
I study populations of learning, heuristic, and deliberative agents interacting over evolving economic networks, studying how local adaptation shapes aggregate dynamics.
- Multi-Agent Systems
- Reinforcement Learning
- Graph Neural Networks
In this research line, I model economic systems as dynamic, decentralized networks of heterogeneous decision-makers. Rather than assuming hyper-rational global equilibrium, agents operate with bounded rationality, local interactions, and adaptive strategies (such as multi-agent reinforcement learning or heuristic decision rules). By combining network science and agent-based modeling, I analyze how micro-level adaptation gives rise to macroeconomic phenomena, market liquidity shifts, and emergent systemic patterns.
Projects
- Multi-Agent Orchestration Event-driven multi-agent coordination system for auction-based procurement, inventory allocation, and real-time fulfillment under demand uncertainty.
- Multi-Agent Systems
- Event-Driven Systems
- Auctions
- Inventory Systems
- Real Estate AI Agent Autonomous AI agent for property-market analysis, price estimation, and natural-language interaction built on predictive models and LLM orchestration.
- Side Quest
- AI Agents
- LLMs
- Predictive Modeling
- RiskSentinel - Agentic Systemic Risk Simulator Multi-agent systemic risk simulator for financial contagion analysis on 210 S&P 500 stocks, built for Microsoft AI Dev Days Hackathon 2026 and deployed as an interactive Streamlit app.
- Hackathon
- Multi-Agent Systems
- Financial AI
- Systemic Risk