Matching at the Midpoint: A Strategic Agent Based Analysis

Author(s): Gabriel Smithline, Anri Gu, and Michael Wellman

University of Michigan | 2025 | ACM International Conference on AI in Finance 2025 (ICAIF) | [PDF]

We study midpoint matching through Nasdaq’s Midpoint Extended-Life Order (M-ELO) mechanism, which offers non-displayed midpoint execution subject to a mandatory holding period to balance liquidity and adverse-selection concerns. We conduct an empirical game-theoretic analysis using an agent-based simulation in PyMarketSim that models both M-ELO and a traditional lit order book, simulating traders’ venue choices across a range of holding periods and enumerating pure and mixed Nash equilibria for each setting. We analyze welfare outcomes, strategic stability via deviation graphs, and basins of attraction across these equilibria. Our findings reveal that shorter holding periods can insufficiently deter higher frequency small lot trading, while excessively long delays erode the midpoint premium for large traders. This highlights the fundamental trade-off in designing holding periods that effectively screen predatory trading without undermining value for intended users.

Measuring Competition and Cooperation in LLM Bargaining: An Empirical Meta-Game Analysis

Author(s): Gabriel Smithline, Chris Mascioli, Mithun Chakraborty, and Michael Wellman

University of Michigan | 2025 | Full Paper Under Review | Presented at ICML 2025 Workshop on MAS: Multi-Agent Systems in the Era of Foundation Models: Opportunities, Challenges and Futures

We conduct an empirical game-theoretic analysis of how large-language models (LLMs) negotiate the allocation of a set of subjectively valued items as a case-study of advanced AI assessment in mixed-motive scenarios. We devise an incremental prompting framework with graduated levels to adapt select proprietary LLMs to our negotiation task and empirically identify levels for each LLM that are most effective in reducing blatant mistakes. We formulate and estimate empirical meta-game models from bargaining game trajectories over a range of parameter configurations, where these LLM agents act as meta-strategies mapping a prompt describing the bargaining scenario to an implemented bargaining strategy. We include three heuristic approaches, representing extreme negotiation attitudes, and a policy derived by reinforcement learning as baseline (meta-)strategies. We measure the agents' individual effectiveness, induced social welfare, and fairness at equilibria of these meta-games, using bootstrapping to estimate uncertainty. Our analysis reveals a strong positive correlation between individual effectiveness and social metrics across models. OpenAI models (especially o3-mini) deliver the best overall performance metrics and robustness.

Fast and the Furious: Hot Starts in Pursuit-Evasion Games

Author(s): Gabriel Smithline and Scott Nivison

AFRL Research Project | 2024 | Accepted to ARMS (Autonomous Robots and Multirobot Systems) Workshop at AAMAS 2025 and selected for oral presentation | [PDF]

Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph Characteristic Space is constructed via multi-objective optimization to identify Pareto-optimal configurations. A Graph Convolutional Network (GCN) is trained on these Pareto-optimal graphs to generate strategically effective initial configurations, termed "hot starts". Empirical evaluations demonstrate that the GCN-generated hot starts provide a significant advantage over random configurations. In scenarios considering multiple pursuers and evaders, this method hastens the decline in evader survival rates, reduces pursuer travel distances, and enhances containment, showcasing clear strategic benefits.

Creating a Cooperative AI Policymaking Platform through Open Source Collaboration

Preprint arXiv:2412.06936

Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking.

Quantal Response-Based Defense in One vs. Many Stackelberg Security Games

Tufts University | 2023-2024 | [PDF]

During the 2023-2024 academic year, I researched Stackelberg Security Games and Deception. My work focused on finding reductions from Stackelberg Security Games to social planner problems in macroeconomic models.

Understanding Eviction Rates Induced by COVID: A Statistical, Mechanism Design, and Game Theoretic Perspective

Lafayette College | 2021

During the 2021 academic year, I researched the impacts of COVID-19 on the rental market in Pennsylvania and Easton. I approached this through statistics, econometrics, mechanism design, and game theory. I worked with the city of Easton to help implement these Mechanism Design solutions.