The core focus of my research is using agent-based modelling to understand real-world complex-adaptive systems which are composed of interacting autonomous agents. The key research question that I am interested in is how, and if, these systems maintain macroscopic homeostatic behaviour despite the fact that their constituent agents often face an incentive to disrupt the rest of the system for their own gain. This question pervades the biological and social sciences, as well as many areas of engineering and computing. Accordingly, I work with a diverse range of collaborators in different disciplines. I am particularly interested in whether models of learning and cooperation can be validated against empirical studies, and I have had the opportunity to apply many different modelling techniques to a diverse range of data.
The financial markets present a unique opportunity for studying complex-adaptive systems with the recent availability of high-frequency tick-data which records every transaction in the market, and can run to many billions of events per exchange per annum. I am interested in developing methods for using these big data sets to systematically calibrate agent-based simulation models, in order to try and better understand the role of learning and adaptation in explaining some of the phenomena that are observed in empirical financial time-series data, which cannot be accounted-for by the classical theoretical models in this field.
I also have commercial experience of the electronic-commerce and financial sectors, having worked for a number of SMEs and Blue-chip companies, with a total of over ten years of commercial software engineering experience. I co-founded a startup company, Ripple Software Ltd., which developed econometric analysis tools for power-sellers in the eBay market place, and later Victria Ltd which delivered a prototype dark-pool trading platform.
S. Phelps. An Empirical Game-Theoretic Analysis of the Dynamics of Cooperation in Small Groups. Journal of Artificial Societies and Social Simulation, 19(2):4, 2016. [HTML]
S. Phelps and Y. I. Russell. Economic drivers of biological complexity. Adaptive Behavior, 23(5):315-326, 2015. [PDF]
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]
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]
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.
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.
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, 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.
I have taught various topics in the area of Computer Science and Computational Finance, including Scientific Computing, Agent-based Modelling for Finance and Economics, Machine Learning and Computational-Intelligence, Data Science and Big Data.
I am making increasing use of Python in my teaching. Some example lecture slides are provided below, which were produced as IPython notebooks.
Economic Drivers of Biological Complexity presented at King's College London, 2016.
Dynamic Social Networks and reciprocity presented at University of Liverpool, 2015.
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.
py-abm: a simple class library for agent-based modelling in Python.
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 email@example.com.