complexity matters

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The Future of Computer Trading inFinancial Markets

Complexity Matters Workshop Report v1.1,

November 2010

Written by Michael Reilly

Additional notes: Yasmin Hossain, Chris Griffin, Isabel Hacche(All Foresight) and Zubin Siganporia (University of Oxford)


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Contents

Executive Summary ............................................................................................................................. 3

1. Introduction ...................................................................................................................................... 4

2. What emerged? ................................................................................................................................ 5

2.1 Stasis in modelling .......................................................................................................................... 5

2.2 Endogenous risk in market microstructure ...................................................................................... 6

2.3 The human-algorithm interface ....................................................................................................... 9

2.4 Network and social structures ....................................................................................................... 11

2.5 Engineered socio-technical systems ............................................................................................. 12

2.6 The politics of decision-making ..................................................................................................... 14

2.7 Risk and intervention .................................................................................................................... 14

3. Chairs perspective ........................................................................................................................ 15

4. What work could the project commission? .................................................................................. 17

Appendix A: Workshop Agenda ........................................................................................................ 19


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Executive Summary

The computer-based trading system offers an ideal laboratory for studying complex systems:variation and selection processes are operating at rapid evolutionary timescales because ofthe intense pressure to stay in business.

The main finding of the workshop was that modelling of the computer-based trading systemis inadequate, and that this is principally because of a paucity of high-quality data with whichto calibrate and validate predictive models. Without accessible high-quality datasets(especially with identity data) many of the important hypotheses raised on the day could notbe tested. Participants called upon the Foresight project to address this by acquiring a high-quality dataset that could be made available for comparative research. Models that canassume deviations from market efficiency are necessary: inefficiencies need to beunderstood because deviations can be dramatic.

There may be a fundamental transition in market microstructure at the sub-second scale.Nanex data for the S&P 500 index revealed - over a 2-year period - 150,000 sharp dips andspikes in price. The unusual distribution of these dips and spikes might be explained as the

ecology of machines. A small strategy space of algorithms is likely to be inducing phasetransitions through crowding. If the evolution for algorithmic agents is occurring at muchfaster timescales than for biological agents, the question arose as to how the processes ofvariation, selection and amplification are modulated by human agents. This human-algorithmprocess of learning may be a crucial driver of change.

Diversity is a risk driver and more analysis of diversity and its effects throughout the systemis required. More insights were needed not just on the diversity of strategies and market

participants but also on the operational risks surrounding the diversity of software andplatforms. Phase transitions are possible if a liquidity-shock on one venue results in capitalconstrained high-frequency trading firms reducing network connections across venues.There is an urgent need for investigation of this network topology.

Fragmented social structures within firms, between firms, between firms and regulators,between exchanges, between academic disciplines, and between academics and regulators,have repercussions for risk. If these structures fracture then risk may escape from these

social fissures. When risk is managed across fragmented social structures it can also lead tooperational complexity. Ways are needed to rebuild bridging social capital across thesystem. Without this, a serious evaluation of the social costs and benefits of the financialsystem and the opportunity costs to society of its growth is likely to be obscured.

The computer-based trading system is a highly-engineered, possibly unknowable, complexi t h i l t l bl t t i i k A i th h t t t


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1. Introduction

The Foresight Future of Computer Trading in Financial Markets Complexity MattersWorkshop in London on 26th October 2011 had the following objectives1:

Understand better how complexity manifests itself in the computer generated tradingsystem

Learn lessons from other complex systems on how risk can be managed andregulation can be effective

Sketch out work that the project could commission to embed complexity in its analysisand findings

Several of the Driver Reviews commissioned by the project have used the lens of complexityto explore the future of computer-based trading and they highlight the need for furtherresearch2. In comparison with other disciplines, the study of complexity systems is nascentand definitions of complexity are imprecise. As a working definition for this report, we havedefined a system to be complex if it consists of many heterogeneous agents behaving with a

high degree of interdependence. But this is a necessary and not a sufficient condition forcomplexity. Complex systems are further identified by the surprising collective behaviour oftheir individual entities. In particular, complex systems exhibit properties that appear only inthe aggregate (emergence), small changes can through non-linear effects induce muchlarger changes in system behaviour (phase transitions), and long-term system behaviourcan be influenced by short-term events (path-dependence)3. In the aftermath of the FlashCrash of 6th May 2010, this fuller description of a complex system is highly relevant tocomputer-based trading. More than this, complexity is of interest to economists because it is

an attempt to deepen our understanding of the relationship between micro-level behaviourand macro-level system behaviour. In so much as this relationship remains enigmatic, thestudy of complex systems can offer insights into the dilemma of managing agent andsystem-level risk and thereby help to craft more effective approaches to intervention.

Professor Sir John Beddington, the UK Government Chief Scientific Adviser, encouraged thegroup at the beginning of the day to bring ecological and engineering perspectives to theproject. Many Foresight projects take as their horizons 50 years in the future but ten years is

a long time for a rapidly evolving financial system. Professor J. Doyne Farmer, in his role asworkshop chair, provided valuable context on the computer-based trading system. Thecomputer-based trading system offers an ideal laboratory for studying complex systems:variation and selection processes are operating at rapid evolutionary timescales because ofthe intense pressure to stay in business4. In contrast, natural selection in biology can takethousands of years to produce noticeable effects. The computer-based trading system has


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currently programme the algorithms usually based on rules-of-thumb and a few lines ofcode.

Professor Farmer argued that while it is impossible to anticipate the behaviour of all theindividual agents, it may be possible to anticipate the behaviour of their system. Rationalityhas been the underlying paradigm of economic theory for some time but it is now beingquestioned including by some economists based on new understandings aroundasymmetric information and institutional constraints. At the other extreme of rationality, whatwould it mean if market participants were just coin-flippers? The concept of boundedrationality is somewhere in between the two extremes.

Finally, Professor Philip Bond, an ex-trader and one of the projects Lead Expert Group,provided background and context on the project to the workshop participants. The projecthas an eight-month commissioning window for new work.

2. What emerged?

2.1 Stasis in modelling

Professor Robin Bloomfield - one of the four workshop speakers - put forward a usefulcategorisation of models for the group to explore:

Conceptual

Analytical

Predictive

Operational

Concepts and metaphors analogous (and dis-analogous) to computer-based trading wereidentified, such as evolution, market ecology, social independence, financial engineering anddiversity. The most influential metaphor for the group on the day proved to be a digging thetunnel at both ends5 this was a call by Professor Farmer for economists and physicists to

bolster inter-disciplinary engagement in order to face the considerable challenge ofmodelling the economy. But the metaphor could equally apply to co-ordination challengesfacing market participants, exchanges, academics, regulators and politicians. Although real-time surveillance operational models were considered by several participants to be highlydesirable, others were less sanguine about their prospects. Toy models - which can be bothanalytical and predictive - had the potential to offer insights into system dynamics, if not


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hypotheses raised on the day could not be tested. Identity data were crucial because withoutit there would be no way to tell foxes from rabbits or who is eating whom. Severalparticipants suggested that the FSA has the necessary data for analysis but observed that isnot available for research. Exploring how it could be accessed would be worthwhile and thegroup argued that there should be greater advocacy to release this data6. The Foresightproject might address this externality by acquiring a high-quality dataset that could be madeavailable for comparative research. Any costs were considered to be greatly outweighed bythe benefits of improving knowledge of the system and potentially averting systemic eventsof value destruction.

This data challenge apart, an analytical model or framework for understanding complexity in

the computer-based trading system did emerge and is described in sections 2.2-2.7. Thisframework suggests a system of (interdependent) systems each operating at very differentevolutionary timescales (from very fast to very slow)7.

2.2 Endogenous risk in market microstructure

The project had previously identified several feedback loops that may contribute to

endogenous risk in market microstructure but this was based on limited empirical evidence8

.Professor Neil Johnson began the workshop with an (unpublished) analysis of Nanex datafor the S&P 500 index time-stamped at the sub-second scale. The data revealed 150,000sharp dips and spikes in price over a 2-year period. Given that these price volatilities wereoccurring at 10 millisecond timescales - far beyond the reaction times of humans and theimpact of exogenous news - Professor Johnson argued that this is an endogenousphenomenon.


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There may be a fundamental transition in market microstructure at the sub-second scale. Ifthese sharp dips and spike were random we would expect their cumulative distribution to belinear. Evidence was presented that this was far from the case (see Figure 1). Theirdistribution is not fat-tailed and is not exactly a power-law distribution. Professor Johnsonconcluded that these dips and spikes are dragon-kings, following a definition developed byProf. Didier Sornette (see Box 1).

Box 1, What is a dragon-king?

Dragon-kings are statistical outliers that co-exist with power law distributions. Forexample, the size of urban agglomerations in France have a power-law distribution butan extraordinary city such as Paris is an anomaly to this distribution (see Figure 2).Dragon-kings have been associated with phase transitions or tipping points9.

Figure 2, Rank-ordering plot of the population size of French cities as a function of theirrank with sizes ordered in decreasing values. Source: Laherre and Sornette (1998).10


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meaning for humans? Most participants agreed that since exogenous information at the sub-second scale was unlikely to be producing these micro-fractures then relatively simple,homogenous algorithms were the cause. Others wondered about the influence of marketconditions and herding effects.

There were some reservations expressed at the quality of Nanex time-stamped data at themillisecond scale. To test these hypotheses robustly would inevitably require better andmore granular data. Nevertheless, the group agreed that eliminating exogenous informationwas a useful method for assessing endogenous risk.

Dr Ryan Woodard hypothesised in a subsequent presentation that endogenous feedback

effects in financial systems might be identified and monitored. The specific mechanism isless important than identifying instability in the system11. He agreed with Professor Johnsonthat phase transitions in the financial systems can resemble dragon-kings and, with thisinsight, explained the structure of a log periodic power law model that attempts to forecastthe end of financial bubbles. The dynamics of bubbles were illustrated for an ExchangedTrade Fund (ETF) for silver and for the repo market before the 2008 financial crash at thetimescale of years (see Figure 3); such dynamics were also present at very short-termtimescales of 1 week and even intra-day for the price of crude oil.


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consistently successful then the experiment could result in a system for early warningsignals. Dr Woodward was also open to using high-frequency data to test these hypothesesat the sub-second scale. He concluded by raising the intriguing question that if a system withearly warning signals for the end of financial bubbles was available to policymakers, whatsubsequent actions would improve market outcomes? Should they do nothing? Issue awarning to the market? Suspend trading? Dictate trading limits?

In a way, this last question anticipated feedback from the group who were quick to point outthat trading on a forecast will make it go away. In other words the model will change byrevealing results: financial markets are not invariant under observation.

2.3 The human-algorithm interface

The implications of a lack of diversity of algorithms for endogenous risk vexed workshopparticipants. If the evolution for algorithmic agents is occurring at much faster timescalesthan for biological agents, the related question arose as to how the processes of variation,selection and amplification are modulated by human agents. Towards the end of theworkshop one participant argued that this human-algorithm process of learning was a crucial

driver of change.Earlier in the day, Professor Lord Robert May had explored the relationship betweencomplexity and stability in both ecological and financial systems. Ecosystems such as foodwebs have survived hundreds of millions of years and may provide clues to thecharacteristics of complex systems that correlate with resilience.

Lord May took as his main theme the perils of unexamined assumptions as he comparedmisunderstandings in ecology with those in finance. Ecological wisdom in the 1960s was

based on an incorrect assumption that stability in food webs was positively correlated withconnectivity. On the contrary, there appears to be a trade-off between agent-level diversityand system diversity. Similarly, Arbitrage Pricing Theory (APT), a theory that owes more toengineering than science, has underpinnings (e.g. perfect competition, market liquidity) thatbreak under market stress. The theory has also become intertwined in the system it purportsto observe14. Lord May highlighted some toy modelling work that finds the proliferation offinancial instruments erodes systemic stability and it drives the market to a critical state

characterized by large susceptibility, strong fluctuations and enhanced correlations amongrisks.15 In other words, diversification can be good at an agent-level yet bad at the system-level. When systems go beyond a critical level of complexity they may become vulnerable tophase transitions (see Figure 4)16.


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Figure 4, Discontinuous transition to instability of derivatives as complexity increases.Source: Cacciolo et al (2009)17.

Toy modelling was also used to explain that excessive homogeneity (banks doing the samething) within a financial system may seemingly minimise risk for agents (individual banks)but unintentionally maximise systemic risk for all18. Lord May argued that recent bankingregulation (Basel I,II) had - by virtue of encouraging institutional-level resilience - the

unintended consequence of making the banking system more fragile. In other words,regulation that imposes the same constraints on individual banks makes their decisionsmore highly correlated, which can lead to herding effects. Lord May compared the higherconcentrations of assets between five largest banks in the world with those of the five largesthedge funds to support this hypothesis.

The implications of these insights for computer-based trading were discussed. Oneparticipant had anecdotal evidence that worldwide only a few thousand people were

designing algorithmic strategies for financial markets. The group wished to know in moredetail how many firms were developing in-house algorithms and how many others weresimply adopting their packages. In other words, is the diversity of strategies increasing ordecreasing?

Dis-analogies between ecosystems and financial systems were also discussed. Ecosystemstend to have a very large population of heterogeneous agents with local spatial distributions


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2.4 Network and social structures

Lord May also posed the question of what kind of network configuration could optimise thetrade-off between complexity and stability. He encouraged the group to investigate thecomplex network topologies of financial systems. Analysis of the topology of the FedwiresFunds Service had found that the network is scale-free with a tightly connected core ofbanks to which most other banks connect; and that the properties of the network changedmarkedly in the immediate aftermath of the events of September 11, 200120. Toy modellingsuggests that the resilience of the banking system depends not only on the relative size ofcapital reserves but also on the network topology (see Figure 5).

Fig 5, Results from a toy model of the banking system showing the effect on failures of therelative size of capital reserves of small and big banks. Diagram B shows the impact onbank failures of a more realistic network topology.

The importance of the structures and networks connecting human and institutional agentsfor market outcomes emerged in discussions. The Project Lead Expert Group has

expressed interest in understanding the impact of liquidity shocks of the highly dynamicnetwork topology created by algorithms trading across multiple venues. Phase transitionsare possible if a liquidity-shock on one venue results in capitalconstrained high-frequencytrading firms reducing network connections across venues. Several participants highlightedthe need for investigation of this network topology (see Figure 6).


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Figure 6, Systematic divergence feedback loop. Source: FCTFM Working Paper21.

But the social structures in the computer-based trading systems, evolving at markedlyslower timescales, were also considered important. Fragmented social structures withinfirms, between firms, between firms and regulators, between exchanges, between academic

disciplines, and between academics and regulators, had repercussions for risk. If thesestructures fracture then risk may escape from social fissures. In many ways, the Flash Crashcan be understood from a sociological perspective as well as from a financial andtechnological view of the system. In social science terms, the system may be suffering fromlow levels of bridging social capital22. An early seminal example of complexity researchexamined neighbourhood formation in the US: even if agents desire some degree ofintegration, at the system level there can be complete segregation23. A symptom of thisdisjuncture might be the emotive discourse around the future of financial system, which LordMay described as a Socratic dialogue appealing to philosophical beliefs supported byappropriate authorities. It may also mean that a serious evaluation of the social costs andbenefits of the financial system and the opportunity costs to society of its growth is beingobscured24.

2.5 Engineered socio-technical systems

The trade-off between complexity and stability was explored further but this time in thecontext of the nuclear industry in the UK. Professor Robin Bloomfield, an expert onengineered socio-technical systems, suggested that in many ways finance was similar to thenuclear industry in the 1980s. The nuclear industry has learned lessons since this era(including from judicial inquiries) by examining systemic risk, governance and trust. Therehas been a positive correlation between safety and profitability Diversity of views (and of


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Safety Assessment Principles (SAPs). Computers need not necessarily be mistrusted. Thenuclear industry has to rely on computers but it uses specialised safety systems to do so(see Figure 7).

Figure 7, Relying on computers requires a different level of protection based on a differentlevel of trust. Source: Professor Robin Bloomfield.

Professor Bloomfield suggested a systems of systems approach to safety in the computer-based trading system with different layers of protection based on different levels of trust. Forexample, algorithms most likely merit a different level of trust from platforms andapplications. There should also be an emphasis on black start the likelihood of systemfailure means that there needs to be preparation in how to recover and on learning lessons

from extreme events and crashes. If complex systems are built on experimentation andimperfect learning, then systematic learning from failures should be a universal principle.The need to evaluate and communicate risk in the financial system is urgent.

Differences between the nuclear industry and the computer-based trading system wereinformative:


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Some participants in the financial system benefit from deviations from the norm andfrom system failure

2.6 The politics of decision-making

Further differences between the systems included regulatory capture, or the political abilityof the financial system to override regulators. Black start in the financial system could beimpeded if it is a political decision not to look at the worst case scenario. Regulators may bewary of the responsibility of acquiring and managing the data required to develop operationalmodels that provide real-time surveillance of the market. They may be wary because they do

not have sufficient resources to carry this out and because of the political consequences ifsystemic events are not anticipated. In the US, when hurricane forecasting provesinaccurate, the temporarily displaced population can almost be disappointed if their homesare not destroyed. Another participant pointed to the seismologists currently on trial in Italyfor failing to anticipate the earthquake in LAquila in 2009.

Based on these observations, there was a call for a study on the politics of decision-makingin the computer-based trading system from the perspective of a political scientist.

2.7 Risk and intervention

Professor Johnson hypothesised that sharp sub-second price dips and spikes in the Nanexdataset were the result of crowding in a relatively small strategy space. If these phasetransitions were indeed a serious endogenous risk, what interventions would beappropriate? Rather than stopping trading, or taking traders out of the market he suggested

that it may be best to explore ways to create diversity. The challenge, however, is notstraightforward. For example, shifting traders around may reduce risk but the computer-based trading network structure is highly dynamic. The network structure created bycomputer-based trading across multiple trading venues itself may increase risks to marketoutcomes.

Lord May, similarly, highlighted diversity as a risk driver and there was a consensus thatmore analysis of diversity and its effects throughout the system was necessary. Moreinsights were needed not just on the diversity of strategies and market participants but alsoon the operational risks surrounding the diversity of software and platforms. The risks of afalse or forced diversity were also raised.

Participants identified the human-algorithm interface as a risk driver because computer-based trading depends on rule-of-thumb experiments in a highly-connected environment.Participants were sceptical that algorithms were being tested rigorously The computer-


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serve as an exemplar to the UK25. Another way forward could be to facilitate more multi-disciplinary thinking among academics and regulators26. On the fractured relationshipbetween regulators and market participants there was a telling quotation: policing is what

you do with people not to them.

It was argued that the low quality of the discourse around the future of the financial systemwas a major risk driver if this meant that a serious evaluation of the social costs and benefitsof the financial system was being obscured27.

As the workshop progressed, an engineering perspective proved helpful: participantsincreasingly perceived the computer-based trading system to be a highly engineered,possibly unknowable, complex socio-technical system vulnerable to systemic risk. It followedthat in the short-term at least contingency and resolution ought to be the predominantprinciples of intervention. This notwithstanding, several participants maintained thepossibility of developing early warning signals through improved modelling and scenarioanalysis.

Principles-based regulation was perceived by some to be more difficult to define in thefinancial system but was preferable to the risks of rigid tick box or checklist regulation. Theunintended consequences of Basel I and II, where bank-level heterogeneity actually madethe system more homogenous, meant that a sharper focus on the implementation ofregulation was required.

There was a call for a set of systemic risk indicators and for better regulatory oversight butdifficult questions arose as to who owns the risk, and what is safety? There needs to beclear oversight for the properties of the system that emerge from the complex interactions ofinterdependent market participants. Perhaps the risks (and the opportunities) associatedwith complexity in the computer-based trading system are not well understood because the

regulatory system is not evolving as quickly as the markets being monitored.

3. Chairs perspective

Professor Farmer also spoke later in the afternoon to stimulate participants and to providehis own perspective as an expert on complex systems. He produced a long list of financial

crises and then considered whether they were primarily exogenous or endogenous in origin.The fundamental point made was that returns have power law tails and the need to explainthis has not been adequately appreciated by economists. Comparing the list of crises withtheir corresponding New York Times headlines is revealing because the crises are oftendescribed using psychological terms such as fear28. The standard explanation of market

t b i t i t l t t i f ti O th t th


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shifts of trend followers and value investors can endogenously create these effects. A largenumber of financial crises are endogenous: there is often no exogenous news that canexplain such market crashes.

Computer-based trading will increase and some research suggests that at faster timescalesendogenous events will be more frequent29. An evolutionary approach to computer-basedtrading can help to illustrate complexity in models and explore risks to social welfare.Professor Farmer mentioned some work he had done with a value investor leverage ABMmodel. It was able to reproduce the clustered volatility and heavy tails in returns that areobserved in real-life markets. It is leverage that causes positive feedback as banks react torisk controls, recall loans and generate adverse price pressure (see Figure 8)30. Evolutionary

pressure favours aggressive hedge funds but less aggressive funds can also survivecrashes. In this way, ABMs based on evolutionary processes can provide more realisticinsights into the workings of the economy than existing models. They can be used to studyhow populations of agents change over time, to examine how such changes correlate withvolatility and crashes, and to assess the value of diversity. Better calibrated ABMs couldprove useful to policymakers because rules such as maximum leverage can be imposed tosee what impact they may have on the system31.


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This perspective provoked a debate about the development of economic thinking onfinancial crises. One participant defended economics methods by explaining that even ifinterest in ABMs may have waned since the 1960s, much work had been done on heavy-

tailed data. He also highlighted Eugene Famas nuanced research on the weak, semi-strongand strong forms of the efficient markets hypothesis32.

Milton Friedmans unguarded view on efficient markets is also illuminating33:

Warren Buffett proves that theres not an efficient market, and yet Warren Buffett is whatmakes the market efficient, and both statements are right. If the market were 100% efficient,nobody could make any money making it efficient, and then it wouldnt be efficient again. Soin a way its self-contradictory to suppose that there really is an efficient market.

Professor Farmer concluded that we need a broader style of model that can assumedeviations from efficiency: inefficiencies need to be understood because deviations can bedramatic.

4. What work could the project commission?

Participants were tasked in the final plenary with recommending to the project what furtherwork should be commissioned by the project. In particular, they were challenged to definewhat data would be most useful and for what purpose. Table 1 summarises this plenarysession and also includes the other suggestions that were made during the workshop.


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Appendix A: Workshop Agenda

FUTURE OF COMPUTER TRADING IN FINANCIAL MARKETS PROJECT:

COMPLEXITY MATTERS WORKSHOP

DATE: 26TH OCTOBER 2011

VENUE: 30 PAVILION ROAD, LONDON SW1X 0HJ (map overleaf)

CONTACT: CHRIS GRIFFIN ([email protected], +44 (0)20 7215 4223

PROJECT WEBSITE: http://www.bis.gov.uk/foresight/our-work/projects/current-projects/computer-trading

WORKSHOP AGENDA

Key Workshop Objectives:

Understand better how complexity manifests itself in the computer-generated tradingsystem

Learn lessons from other complex systems on how risk can be managed and

regulation can be effective

Sketch out work that the project could commission to embed complexity in its analysisand findings

0900 Coffee

0930 Introduction by Professor Sir John Beddington, UK Government Chief ScientificAdviser

0940 Introduction by Professor J. Doyne Farmer, Santa Fe Institute, Workshop Chair


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1100 Table discussion and plenary

What are the parallels between the systems described in the presentations and thecomputer-based trading system?

In what ways is the computer-based trading system complex?

What risks are associated with this complexity? What opportunities are associated with

this complexity?

Are there useful analogies to other complex systems?

1150 Presentation 3 - Meltdown in the markets a nuclear perspectiveProfessor Robin Bloomfield, City University London

Presentation 4 - Using complexity to identify and predict financial bubbles andcrashes Dr Ryan Woodard, Swiss Federal Institute of Technology, Zurich

1230 Table discussion

What are the parallels between the systems described in the presentations and the

computer-based trading system?

In what ways is the computer-based trading system complex?

What risks are associated with this complexity? What opportunities are associated with

this complexity?

Are there useful analogies to other complex systems?

1300 Lunch

1345 Plenary

1410 Workshop Chairs perspective

1430 Table discussion

What have we learnt?

What tools are available to analyse complexity in the computer-based trading system?


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Appendix B: Workshop Participants

Participant Institution \ Organisation Additional Title

Professor Sir John Beddington UK Govt Chief Scientific Adviser Project Director

Professor J. Doyne Farmer Santa Fe Institute, US Workshop Chair

Professor Lord Robert May University of Oxford, UK Workshop Speaker

Professor Philip Bond University of Oxford, UK Lead Expert Group

Edgar Peters First Quadrant, US

Dr Jon Danielsson London School of Economics, UK

Professor Jean-Pierre Zigrand London School of Economics Lead Expert Group

Professor Dave Cliff University of Bristol, UK Lead Expert Group

Professor Oliver Linton University of Cambridge, UK Lead Expert Group

Professor Iain Main University of Edinburgh, UK

Professor Seth Bullock University of Southampton, UK

Professor Spyros Skouras Athens University of Economics and Business, Greece

Professor Robin Bloomfield City University London, UK Workshop Speaker


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Dr Alfonso Dufour University of Reading, UK

Kevin Houstoun Chairman, Rapid Addition Lead Expert Group

Andrew Bowley Managing Director, Nomura

Professor Neil Johnson University of Miami, US Workshop Speaker

Dr Anne Wetherilt Bank of England, UK

Professor Peter Allen Cranfield University, UK

Professor Rosario Mantegna University of Palermo, Italy

Dr Ryan Woodard ETH, Zurich, Switzerland Workshop Speaker

Chris Sims Ignis Asset Management

Dr Enrico Gerding University of Southampton, UK

Dr Graham Fletcher Cranfield University, UK

Professor Sheri Markose University of Essex, UK

Dr Claire Craig Government Office for Science, UK

Professor Sandy Thomas Foresight, UK

Dr Miles Elsden Government Office for Science, UK


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Zubin Siganporia University of Oxford, UK

D e r e k F l y n n Foresight, UK

L u c a s P e d a c e Foresight, UK Project Leader

M i c h a e l R e i l l y Foresight, UK

A l u n R h y d d e r c h Foresight, UK

Y a s m i n H o s s a i n Foresight, UK

C h r i s t o p h e r G r i f f i n Foresight, UK

I s a b e l H a c c h e

Foresight, UK

complexity matters

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