Welfare Effects of Market Making in Continuous Double Auctions
E Wah, M Wright, and MP Wellman
Journal of Artificial Intelligence Research 59:613–650, 2017.
Revised and significantly extended version of a paper presented at:
International Conference on Autonomous Agents and Multiagent Systems, May…
Strategic Modeling of Dynamic Credit Networks
Principal Investigator
Michael Wellman
Students
Frank Cheng
Junming Liu (MS ECE, 2015)
Project Goals
The 2008 financial crisis demonstrated that complex and opaque networks of credit relationships among firms can set the…
Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model
E Wah and MP Wellman
Proceedings of the 14th ACM Conference on Electronic Commerce, pages 855–872, June 2013.
Abstract
We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We…
An empirical game-theoretic analysis of credit network formation
MP Wellman and B Wiedenbeck
Fiftieth Annual Allerton Conference on Communication, Control, and Computing, October 2012.
Abstract
The framework of credit networks provides a flexible and robust model of distributed trust, based on pairwise…
Strategic Formation of Credit Networks
P Dandekar, A Goel, MP Wellman, and B Wiedenbeck
ACM Transactions on Internet Technology 15(1): 3:1–3:41, 2015.
Abstract
Credit networks are an abstraction for modeling trust among agents in a network. Agents who do not directly trust each…
Trading Agents
MP Wellman
Morgan & Claypool Publishers, Synthesis Lectures on Artificial Intelligence and Machine Learning
Abstract
Automated trading in electronic markets is one of the most common and consequential applications of autonomous software…
Asset pricing under ambiguous information: An empirical game-theoretic analysis
B-A Cassell and MP Wellman
Computational and Mathematical Organization Theory 18:445–462, 2012
preliminary version presented at SpringSim Agent-Directed Simulation Symposium, April 2011.
Abstract
In a representative agent model, the…
Stronger CDA Strategies through Empirical Game-Theoretic Analysis and Reinforcement Learning
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-theoretic analysis with reinforcement learning. We apply this methodology to the classic Continuous Double Auction game, conducting the most comprehensive CDA strategic study published to date. Empirical game analysis confirms prior findings about the relative performance of known strategies. Reinforcement learning derives new bidding strategies from the empirical equilibrium environment. Iterative application of this approach yields strategies stronger than any other published CDA bidding policy, culminating in a new Nash equilibrium supported exclusively by our learned strategies.
