Using reinforcement learning to validate empirical game-theoretic analysis: A continuous double auction study

M Wright - arXiv preprint arXiv:1604.06710, 2016 - arxiv.org
Empirical game-theoretic analysis (EGTA) has recently been applied successfully to analyze
the behavior of large numbers of competing traders in a continuous double auction market …

Stronger bidding strategies through empirical game-theoretic analysis and reinforcement learning

LJ Schvartzman - 2009 - search.proquest.com
Empirical game-theoretic analysis (EGTA) combines tools from simulation, search, statistics,
and game-theoretic concepts to study strategic properties of large multiagent scenarios. One …

Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics

M Wright - 2019 - deepblue.lib.umich.edu
In environments governed by the behavior of strategically interacting agents, game theory
provides a way to predict outcomes in counterfactual scenarios, such as new market …

[PDF][PDF] Evaluating the stability of non-adaptive trading in continuous double auctions: A reinforcement learning approach

M Wright, MP Wellman - Workshops at the Thirty-Second AAAI …, 2018 - cdn.aaai.org
The continuous double auction (CDA) is the predominant mechanism in modern securities
markets. Despite much prior study of CDA strategies, fundamental questions about the CDA …

[PDF][PDF] Agent-based Modeling And Market Microstructure

B Liu, C Ventre, L Li - 2024 - core.ac.uk
In most modern financial markets, traders express their preferences for assets by making
orders. These orders are either executed if a counterparty is willing to match them or …

Evaluating the stability of non-adaptive trading in continuous double auctions

M Wright, MP Wellman - … on Autonomous Agents and Multiagent Systems, 2018 - par.nsf.gov
The continuous double auction (CDA) is the predominant mechanism in modern securities
markets. Many agent-based analyses of CDA environments rely on simple non-adaptive …

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning

G d'Eon, N Newman, K Leyton-Brown - arXiv preprint arXiv:2402.19420, 2024 - arxiv.org
Iterative combinatorial auctions are widely used in high stakes settings such as spectrum
auctions. Such auctions can be hard to understand analytically, making it difficult for bidders …

Learning equilibria in asymmetric auction games

M Bichler, N Kohring… - INFORMS Journal on …, 2023 - pubsonline.informs.org
Computing Bayesian Nash equilibrium strategies in auction games is a challenging problem
that is not well-understood. Such equilibria can be modeled as systems of nonlinear partial …

Learning in continuous double auction market

M Posada, C Hernandez, A López-Paredes - Artificial Economics: Agent …, 2006 - Springer
We start from the fact, that individual behaviour is always mediated by social relations. A
heuristic is not good or bad, rational or irrational, but only relative to an institutional …

How to evaluate trading strategies: Single agent market replay or multiple agent interactive simulation?

TH Balch, M Mahfouz, J Lockhart, M Hybinette… - arXiv preprint arXiv …, 2019 - arxiv.org
We show how a multi-agent simulator can support two important but distinct methods for
assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS) …