2016 Lectures on Complex Adaptive Systems
Lecture 1: Agent Based Computational Economics (ACE) A Paradigm Shift in Economics? – An overview of some ACE Modelling Applications
Lecture 2: Limits of Formalistic Deduction and Introduction to Markets As Complex Adaptive Systems (CAS)
The main aim of this lecture is to give the foundations based on mathematical logic that are little known to economists for why incompleteness and non-computability is the norm when highly intelligent agents interact. This is a new framework for uncertainty and for why perfect rationality cannot exist. The sine qua non of a complex adaptive system is to produce novelty or surprises and the Nash equilibrium of a game in which players strategically innovate will be shown to challenge received norms in game theory where given fixed action sets innovation is not feasible.
Readings for Lecture 2
- S.M Markose (2004). “Novelty And Surprises In Complex Adaptive System (CAS) Dynamics: A Computational Theory of Actor Innovation”.
- S.M Markose (2005). “Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems”, Economic Journal June 2005, Vol. 115 , F159-F192.
- Arthur, W.B. (1994). “Inductive behaviour and bounded rationality”, American Economic Review, 84, pp.406-411.
- Binmore, K. (1987). “Modelling Rational Players: Part 1”, Journal of Economics and Philosophy, vol. 3, pp. 179-214.
- Smullyan, R.,1961. “Theory of Formal Systems”
- Sheri M. Markose (2014). Logical and Neuro-physiological Foundations of Strategic and Complex Adaptive Behaviour With Novelty and Surprises
Lecture 3: Contrarian agents, heterogeneity and the absence of a Homogenous Rational Expectations: Rationale behind the Santa Fe Institute Artificial Stock Market Model
The significance of contrarian agents or structures which are germane to incompleteness in formal systems was first highlighted to pose problems for perfect economic rationality by Brian Arthur. In stock market environments, the fact that most money is made when one is in the minority or following a contrarian strategy renders a homogenous rational expectations to be a logical impossibility. Two Demos of ACE stock market modelling will be given. One entails herding and guru effects and the other is a real time rebuild of the Electronic Order Book of the London Stock Exchange.
Readings for Lecture 3
- Arthur, W.B, Holland, J., Le Baron, B., Palmer, R., Taylor, P. (1997). “Asset pricing under endogenous expectations in an artificial stock market”, In Arthur W.B., Durlauf S., Lane, D. (Eds) The Economy as an Evolving Complex System II, Addison Wesley, pp. 15-44.
- Chen Shu-Heng, and Chia-Hsuan Yeh (2001). “Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market”, Journal of Economic Dynamics and Control, 25, 363-393.
- Sheri Markose, Edward Tsang and Serafin Martinez (2004). “The Red Queen Principle and the Emergence of Efficient Financial Markets: An Agent Based Approach”. Proceedings of WEHIA-8 (Workshop of Heterogeneous Interacting Agents), Edited by Thomas Lux, Springer Verlag.
Lecture 4: How to build large scale data driven ACE macro-policy models: Systemic Risk, Financial Contagion and Financial Networks
This lecture will unpack the sort of modelling tools such as the use of financial networks to understand and analyse financial contagion and quantify systemic risk. The lecture will be based on the working paper from Markose, Sheri & Giansante, Simone & Shaghaghi, Ali Rais (2012). “Too Interconnected To Fail: Financial Contagion and Systemic Risk in Network Model of CDS and Other Credit Enhancement Obligations of US Banks”