## ACE and Markets as Complex Adaptive Systems: Lectures, Material and Slides

Sheri's CAS lectures based on the formal mathematics of Godel incompleteness uses the Emil Post proof (discussed by Raymond Smullyan in his book *Formal Systems*) shows the key components of CAS to be the following: (i)Meta representational systems and self referential mapping capable by ' uber' computational intelligence of a Universal Turing machine (recently identified with mirror neurons in the brain that can simulate scenarios with self in it) (ii) Contrarian or self-negating structures like the Liar (iii) The consequence of (i) and (ii) can be represented in so called creative and productive sets with the latter depicting an arms race in novelty production or 'surprises'.

The most recent version of these lectures is the one Sheri gave at Ruhr University Bochum Germany 20-21 May 2010

*New Paper Meta-representation, Mirroring and the Liar: Complex Strategic Behaviour and Arms Race in Novelty and Surprises Sheri M. Markose*

## Lecture 1: Agent Based Computational Economics (ACE) A Paradigm Shift in Economics? - An over view 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**** **

**Version Published (Autumn 2004) in Physica A **__ http://authors.elsevier.com/sd/article/S0378437104009045__

- S.M Markose
*"Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems", Economic Journal*June 2005, Vol. 115 , F159-F192.

http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0297.2005.01000.x/full

- 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,
*Formal Systems .* - 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

**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 .
**THE RED QUEEN PRINCIPLE AND THE EMERGENCE OF EFFICIENT FINANCIAL MARKETS: AN AGENT BASED APPROACH***2004, Proceedings of WEHIA-8 (Workshop of Heterogeneous Interacting Agents), Edited by Thomas Lux, Springer Verlag.*

** Demos Guru Effects and Herding Model**

**Herding Simulator** - an application to study herding, guru effects and star formations with dynamic learning on networks.

**CCFEA Electronic SETS Trading Simulator**

**Labs will provide hands on exercises for Pairs Trading and VWAP Strategy**

M. Kearns and L. Ortiz, The Penn-Lehman Automated Trading Project http://www.cis.upenn.edu/

## Lecture 4: The Lucas Critique and Principles of Policy Design

This lecture investigates why policy can fail and also the failure of macro-econometric models for policy design and recommends the use of a computational agent based platform to stress test market and policy *before *implementation or for purposes of monitoring for perverse effects on an ongoing basis

## Lecture 5: 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 this WP*Too Interconnected To Fail: Financial Contagion and Systemic Risk in Network Model of CDS and Other Credit Enhancement Obligations of US Banks *(pdf version) [Abstract]

## ACE Simulators

**Smart Market for Congestion**** - Robustness Analysis - PPT** - Simulator to study the auction design for a congestion charge model.

**Flow Network Simulator** - Project with the Financial Stability Group at the Bank of England to study systemic risk in banking networks.

**Interbank Payment System Simulator** - Project with the Bank of England to study liquidity and risk in large payment systems with agents

## CCFEA - ASM

**Version**: 1.0 (24/08/2007)

**OS**: Independent

**Language**: Java

**Author**: Simone Giansante

**RUN SIMULATION**

## SONEENs

**Version**: 1.0 (30/06/2006)

**OS**: Independent

**Language**: Java

**Author**: Simone Giansante

**RUN SIMULATION**

- INTRODUCTION
- TOOLS

**IMF Conference on “Operationalizing Systemic Risk Monitoring” (Washington DC, 26-28 May)**

**ECB Workshop on**

**“Recent advances in modelling systemic risk using network analysis”** **Frankfurt**** am Main, 5 October 2009-->Workshop Programme**