
In empirical game-theoretic analysis (EGTA), game models are extended iteratively through a process of generating new strategies based on experience with prior strategies. The strategy exploration problem in EGTA is how to direct this process so to construct effective models with minimal iteration. A variety of approaches have been proposed in the literature, including methods based on classic techniques and novel concepts. Comparing the performance of these alternatives can depend sensitively on criteria adopted and measures employed. We investigate some of the methodological considerations in evaluating strategy exploration, proposing and justifying new evaluation methods based on examples and experimental observations. In particular, we emphasize the fact that empirical games create a space of strategies and evaluation should reflect how well it covers the strategically relevant space. Based on this fact, we suggest that the minimum regret constrained profile(MRCP) provides a particularly robust basis for evaluating a space of strategies, and propose a local search method for computing MRCP. However, MRCP computation is not always feasible especially in large games. To evaluate strategy exploration in large games, we propose a new evaluation scheme that measures the strategic coverage of an empirical game. Specifically, we highlight consistency considerations for comparing across different approaches. We show that violation of the consistency considerations could yield misleading conclusions on the performance of different approaches. In accord with consistency considerations, we propose a profile-selection method, which effectively discovers the profile that can represent the strategic coverage of an empirical game through its regret information. We show that our evaluation scheme reveals the authentic learning performance of different approaches compared to previous evaluation methods.

Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

We analyze portfolio compression, the netting of cycles in a financial network, as a strategic decision made by firms within a debt network. We define a network game in which firms have only local information and ask what criteria the firms should consider in their decision to compress. We propose a variety of heuristic strategies and evaluate them using agent-based simulation and empirical game-theoretic analysis. Our results show that some simple strategies based on local information perform better than the unconditional strategies of always agreeing or disagreeing to a compression and that when the decision is made strategically, the price of anarchy is always high.

A Strategic Analysis of Portfolio Compression


**Session Chair:** <br><br> Jivko Sinapov

Oral Session 2A3-1

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

Poster/Demo Session 4 

poster

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Michael P. Wellman

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Presentations

Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

A Strategic Analysis of Portfolio Compression

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