Dr Steve Phelps


My research focuses on the application of agent-based simulation to economic analysis, and encompasses topics crossing several disciplines: finance (trading strategies & market structure), economics (game theory, auction theory, mechanism design) and computer-science/complexity (multi-agent reinforcement-learning, design of co-evolutionary algorithms, complex adaptive systems).

Before coming to Essex, I worked on the EPSRC-funded Market-based Control of Complex Computational Systems project developing a novel technique for market design (evolutionary mechanism design) in which I applied techniques from agent-based modeling, evolutionary computation and heuristic optimization to the mechanism design problem.   More recently, I have been applying agent-based modeling and evolutionary game-theory to the study of social networks and the emergence of cooperation via indirect reciprocity.

I also have commercial experience of the E-business sector, having worked as a consultant for a professional-services company delivering web-based auction solutions, with over ten years of commercial software engineering experience.  I co-founded two startup companies: Ripple Software Ltd. which developed econometric analysis tools for power-sellers in the eBay market place, and Victria Ltd, which developed a prototype dark-pool trading platform.


M. K. Nguyen, S. Phelps, and W. L. Ng. Simulation based calibration using extended balanced augmented empirical likelihood. Statistics and Computing, pages 1-20, 2014.

S. Phelps and W. L. Ng. A simulation analysis of herding and unifractal scaling behaviour. Intelligent Systems in Accounting, Finance and Management, 21(1):39-58, 2014. [PDF]

N. Rayner, S. Phelps, and N. Constantinou. Learning is Neither Sufficient Nor Necessary: An Agent-Based Model of Long Memory in Financial Markets. AI Communications, 27(4):437-452, 2014.

S. Phelps and M. Wooldridge. Game Theory and Evolution. IEEE Intelligent Systems, 28(4):76-81, 2013.

E. Sbruzzi and S. Phelps. Testing leverage-based trading strategies under an adaptive-expectations agent-based model. In T. Ito, C. Jonker, M. Gini, and O. Shehory, editors, Proceedings of the twelfth international conference on Autonomous Agents and Multiagent Systems, pages 1161-1162, Saint Paul, 2013. ACM.

S. Phelps. Emergence of social networks via direct and indirect reciprocity. Journal of Autonomous Agents and Multiagent Systems, 27(3):355-374, 2013. [PDF]

Y. I. Russell and S. Phelps. How do you measure pleasure? A discussion about intrinsic costs and benefits in primate allogrooming. Biology and Philosophy, 28(6):1005-1020, 2013. [PDF]

N. Rayner, S. Phelps, and N. Constantinou. Testing adaptive expectations models of a double auction market against empirical facts. In Lecture Notes on Business Information Processing: Agent-Mediated Electronic Commerce Designing Trading Strategies and Mechanisms for Electronic Markets, pages 44-56. Springer, Barcelona, 2013.

I. Palit, S. Phelps, and W. L. Ng. Can a Zero-Intelligence Plus Model Explain the Stylized Facts of Financial Time Series Data? In Proceedings of the Eleventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) - Volume 2, pages 653-660, Valencia, Spain, 2012. International Foundation for Autonomous Agents and Multiagent Systems. [PDF]

J. Cartlidge and S. Phelps. Estimating Demand for Dynamic Pricing in Electronic Markets. GSTF International Journal on Computing (JoC), 1(2):128- 133, 2011.

K. Adamu and S. Phelps. Modelling Financial Time Series Using Grammatical Swarm. In P. Kellenberger, editor, Proceedings of the International Conference on Financial Theory and Engineering (ICFTE), pages 27-31, Dubai, United Emirates, Dec. 2010. IEEE.

S. Phelps, P. McBurney, and S. Parsons. A Novel Method for Strategy Acquisition and its application to a double-action market game. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 40(3):668-674, June 2010.

S. Phelps, P. McBurney, and S. Parsons. Evolutionary mechanism design: a review. Autonomous Agents and Multi-Agent Systems, 21(2):237-264, 2010. [PDF]

K. Adamu and S. Phelps. A Coevolutionary Grammatical Evolution Approach for Developing Technical Trading Rules. In T. Oyabu and M. Gen, editors, Proceedings of Asia Pacific Industrial Engineering and Management Systems Conference, pages 1475-1482, Kitakyushu, Japan, Dec. 2009.

K. Adamu and S. Phelps. Coevolutionary Grammatical Evolution. In Sixth International Conference on Computational Management Science, Geneva, May 2009. University of Geneva.

S. Phelps, G. Nevarez, and A. Howes. The effect of group size and frequency of encounter on the evolution of cooperation. In LNCS, Volume 5778, ECAL 2009, Advances in Artificial Life: Darwin meets Von Neumann, pages 37-44, Budapest, 2009. Springer.

K. Adamu and S. Phelps. Modelling Financial Time Series using Grammatical Evolution. In D. R. Hardoon, J. Shawe-Taylor, P. Treveaven, and L. Zangeneh, editors, International Workshop on Advances in Machine Learning for Computational Finance, London, 2009.

J. Niu, K. Cai, S. Parsons, E. Gerding, P. McBurney, T. Moyaux, S. Phelps, and D. Shield. JCAT: A platform for the TAC Market Design Competition. In L. Padgham, D. Parkes, J. P. Mueller, and S. Parsons, editors, Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1649-1650, Estroril, Portugal, 2008.

S. Phelps, K. Cai, P. McBurney, J. Niu, S. Parsons, and E. Sklar. Auctions, evolution, and multi-agent learning. In Z. G. K. Tuyls and D. Kudenko, editors, LNCS 4865 Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning, number 4865 in Lecture Notes in Artificial Intelligence, pages 188-210. Springer, 2008.

Y. Chevaleyre, P. E. Dunne, U. Endriss, J. Lang, M. Lemâitre, N. Maudet, J. Padget, S. Phelps, J. A. Rodríguez-Aguilar, and P. Sousa. Issues in Multiagent Resource Allocation. Informatica, 30:3-31, 2006.

S. Phelps, M. Marcinkiewicz, S. Parsons, and P. McBurney. A novel method for automatic strategy acquisition in N-player non-zero-sum games. In H. Nakashima, M. P. Wellman, G. Weiss, and P. Stone, editors, Fifth International Conference on Autonomous Agents and Multiagent Systems, pages 705-712, Hakadate, Japan, 2006.

V. Tamma, S. Phelps, I. Dickinson, and M. Wooldridge. Ontologies for supporting negotiation in e-commerce. Engineering Applications of Artificial Intelligence, 18(2):223-236, Mar. 2005.

S. Phelps, S. Parsons, and P. McBurney. An Evolutionary Game-Theoretic Comparison of Two Double-Auction Market Designs. In P. Faratin and J. A. Rodriguez-Aguílar, editors, Agent-Mediated Electronic Commerce VI, pages 101-114. Springer Verlag, 2005.

S. Phelps, V. Tamma, M. Wooldridge, and I. Dickinson. Toward Open Negotiation. IEEE Internet Computing, 8:70-76, 2004.

S. Phelps, S. Parsons, E. Sklar, and P. McBurney. Using Genetic Programming to Optimise Pricing Rules for a Double Auction Market. In Proceedings of the workshop on Agents for Electronic Commerce, Pitsburgh, PA,, Pitsburgh, PA, 2003.

S. Phelps, S. Parsons, E. Sklar, and P. McBurney. Applying Genetic Programming to Economic Mechanism Design: Evolving a pricing rule for a continuous double auction. In J. S. Rosenschein, T. Sandholm, M. Wooldridge, and M. Yokoo, editors, Proceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), pages 1096-1097, Melbourne, Australia, 2003.

S. Phelps, S. Parsons, P. McBurney, and E. Sklar. Co-Evolutionary Mechanism Design: A Preliminary Report. In J. Padget, O. Shehory, D. Parkes, N. Sadeh, and W. E. Walsh, editors, Agent-Mediated Electronic Commerce IV: Designing Mechanisms and Systems, pages 123-143. Springer Verlag, 2002.

S. Phelps, S. Parsons, P. McBurney, and E. Sklar. Co-Evolution of Auction Mechanisms and Trading Strategies: Towards a Novel Approach to Microeconomic Design. pages 65-72. AAAI, 2002.


I have taught various topics in the area of Computer Science and Computational Finance, including scientific computing, agent-based modelling for finance and economics, learning and computational-intelligence. I currently teach Big Data for Computational Finance and Computer Security.

I am making increasing use of Python in my teaching. Some example lecture slides are provided below, which were produced as IPython notebooks.


  • Java Agent-Based Modelling (JABM) toolkit: JABM is a Java framework for building agent-based simulation models using a discrete-event simulation framework.

  • Java Auction Simulator API (JASA): JASA allows researchers in agent-based computational economics to write high-performance trading simulations using a number of different auction protocols. The software also provides base classes for implementing simple adaptive trading agents.


If you would like to get in touch to discuss potential collobration or to ask questions on any aspect of my research please do not hesitate to contact me via email at sphelps@sphelps.net.