Dr John Cartlidge
Department of Computer
Merchant Venturers Building, Woodland Road, Bristol, BS8-1UB,
I have a first class BSc in Artificial Intelligence & Mathematics
(University of Leeds, 2000) and a PhD in Evolutionary Computation
(University of Leeds, 2004, supervised by
Seth Bullock). Between 2004-08, I spent four years
in industry working on a variety of commercial research projects.
As an employee of HP Labs and the London Stock Exchange, I worked with
on multi-agent simulation models of the stock market (2004-05).
For Ripple, an Irish start-up offering eBay analytics and services,
I developed software to optimize pricing strategies of sellers
by modelling market supply and demand (2006). In partnership with
Steve Phelps, I co-founded Victria.net, a private
software company specialising in financial services (2007-2012).
Contracts include the design of a proprietary dark-liquidity
exchange for a London finance firm. In 2008 I returned to academia
as a Research Associate in Evolutionary Computation and Finance
at the University of Central Lancashire (2008-10). In October 2010 I became
a Research Associate at the University of Bristol, working
with Prof Dave Cliff on the development of simulation
models of next-generation large-scale data centres for delivery
and pricing of cloud services. I am funded by EPSRC grant
EP/H042644/1, part of Phase II of
the UK's national research and training
initiative in the science and engineering of Large-Scale Complex
IT Systems. In 2012, I founded
Electric Lamb Ltd.,
a consulting and software development company specialising in intelligent
Over the last 10 years I have been engaged in multiple avenues
of research in both academia and industry. In general, my research
interests fall into three broad categories: Cloud Computing;
Automated Trading, Auctions and Financial Systems;
and Evolutionary Computing. I describe each in
more detail, below.
Keywords: Adaptation, Agents, Auctions, Automated
Trading, Cloud Computing, Coevolution, Complex Systems, Evolutionary
Computing, Genetic Algorithms, Markets, Modelling, Optimisation,
abstracts and downloads available here.
The notion of “cloud computing”, where computing infrastructure,
platforms, and software application services are offered at low
cost from remote very-large-scale data centres accessed over the
internet, is one that has recently received large amounts of attention
in the IT industry. There have been predictions that this ‘utility
computing’ will predominate in future with organisations discarding
their internal servers in favour of applications accessible “in
the cloud”. To service users, clouds clearly offer advantages
in scalability, may reduce the costs of application management,
and may reduce overall hardware costs. To service providers, they
offer the opportunity to leverage existing data-centre infrastructure
and to take advantage of the economies of scale available exclusively
to purchasers of extremely large volumes of hardware and network
However, designing, testing and pricing the cloud is problematic.
While many engineering domains have robust industry-standard
simulation tools for system design and optimisation—e.g.,
SPICE for integrated circuit design—a
well established realistic simulation framework of cloud computing
facilities is lacking. Hence, massively parallel, tightly-coupled,
complex data centres are put to service having undergone insufficient
pre-testing, while services are sold using simple pricing models
that are likely to be inefficient.
At the University of Bristol, we are attempting to build realistic
simulation models of cloud computing infrastructure and services to help
solve these problems. An open-source software release of
Cloud Research Simulation Toolkit, a java-based cloud computing
simulation framework, is now available for download.
- J. Cartlidge & P. Clamp, (2014),
a financial brokerage model for cloud computing: closing the
window of opportunity for commercialisation,”
Journal of Cloud Computing: Advances, Systems and Applications, vol. 3, no. 2, Apr 2014.
- J. Cartlidge, (2014),
using financial agents in a simulated cloud computing commodity
market,” in Proc. 6th Int. Conf. Agents and
Artif. Intelligence, Vol. 2 - Agents (ICAART-2014).
B. Duval, J. van den Herik, S. Loiseau & J. Filipe, Eds.
Angers, France: SciTePress, Mar. 2014, pp. 311-317.
- P. Clamp & J. Cartlidge, (2013),
“Pricing the cloud:
An adaptive brokerage for cloud computing,”
in Proc. 5th Int. Conf. Advances in System Simulation (SIMUL-2013),
M. Bauer & P. Lorenz, Eds.
Venice, Italy: IARIA XPS Press, Oct 2013, pp. 113-121.
- J. Cartlidge & D. Cliff, (2013),
Cloud Middleware Protocols and Subscription Network Topologies using CReST,
the Cloud Research Simulation Toolkit,” in Proc. 3rd Int. Conf.
Cloud Computing & Services Science (CLOSER-2013),
F. Desprez et al., Eds. Aachen, Germany:
SciTePress, May 2013, pp. 58-68.
- J. Cartlidge & I. Sriram, (2011),
in cloud-scale data centres,” in Proc. 23rd European Modeling
& Simulation Symposium (EMSS-2011), A. G. Bruzzone et al., Eds.
Rome, Italy: University of Genoa Press, Sep. 2011, pp. 299-307.
- J. Cartlidge & S. Phelps, (2011),
for dynamic pricing in electronic markets,” GSTF Int. Journal
on Computing (JoC), vol. 1, no. 2, pp. 128–133, Feb. 2011.
Automated Trading, Auctions, and Financial Systems:
For many of the world's major financial markets, the proportion
of market activity that is due to the actions of “automated
trading” software agents is rising: in Europe and the USA,
major exchanges are reporting that 30%—75% of all transactions
currently involve automated traders. However, there have been very
few controlled laboratory experiments studying the interactions
between human and software-agent traders. At the University of Bristol
we are attempting to fill the knowledge-gap between Agent-based
Computational Economics (agent vs agent traders) and Experimental
Economics (human vs human traders) by performing a series of
human-agent experiments. Results continue to give interesting and
often counter-intuitive insights into the efficiency of heterogeneous
human-agent financial markets. To enable and encourage further
research in this area, during the summer of 2011 I led a
team of undergraduate interns in building Exchange Portal (ExPo), a web-based financial
trading platform for conducting agent-human trading experiments,
soon to be released open-source.
S. Stotter, J. Cartlidge, & D. Cliff, (In press),
investigations of financial trading agents using Exchange Portal
(ExPo),” To appear in Springer's LNCS Transactions on
Computational Collective Intelligence (TCCI), special edition
selected papers from ICAART-2013.
G. Baxter & J. Cartlidge, (2013),
the seat of their pants: What can High Frequency Trading learn from
aviation?,” in Proc. 3rd Int. Conf. Application and Theory
of Automation in Command and Control Systems
(ATACCS-2013). G. Brat et al., Eds.
Naples, Italy: IRIT Press, May 2013, pp. 64-73.
J. Cartlidge & D. Cliff, (2013),
“robot phase transition” in experimental
human-algorithmic markets,” in Proc. 5th Int. Conf. Agents and
Artif. Intelligence, Vol. 1 - Agents (ICAART-2013). J. Filipe
& A. Fred, Eds. Barcelona, Spain: SciTePress, Feb. 2013, pp. 345-352.
S. Stotter, J. Cartlidge, & D. Cliff, (2013),
assignment-adaptive (ASAD) trading agents in financial
market experiments,” in Proc. 5th Int. Conf. Agents and
Artif. Intelligence, Vol. 1 - Agents (ICAART-2013). J. Filipe
& A. Fred, Eds. Barcelona, Spain: SciTePress, Feb. 2013, pp. 77-88.
J. Cartlidge, & D. Cliff, (2012),
the “robot phase transition” in experimental
human-algorithmic markets.” Foresight, The Future of
Computer Trading in Financial Markets,
Driver Review DR 25, Crown Copyright, Apr. 2012.
J. Cartlidge, C. Szostek, M. De Luca, & D. Cliff, (2012),
fast too furious: faster financial-market trading agents
can give less efficient markets,” in Proc. 4th Int. Conf.
Agents and Artif. Intelligence, Vol. 2 - Agents (ICAART-2012), J. Filipe
and A. Fred, Eds. Vilamoura, Portugal: SciTePress - Science and
Technology Publications, 6-8 Feb. 2012, pp. 126-135.
M. De Luca, C. Szostek, J. Cartlidge, & D. Cliff, (2011),
Interactions Between Human Traders and Algorithmic Trading Systems.”
Foresight, The Future of Computer Trading in Financial Markets,
Driver Review DR 13, Crown Copyright, Sep. 2011.
The burgeoning predominance of “algorithmic” trading is
altering the dynamics of the global financial markets. It is now generally
agreed that the “flash crash” of May 6th 2010—where more
than $1 trillion was temporarily wiped off the value of US equity markets
before recovering only 20 minutes later—was execerbated by the activity of
high frequency trading algorithms. On April 22nd 2010, I gave a short
presentation at TradeTech—Europe's premier industry-only
financial trading technology conference—warning
of the risks that algorithmic trading strategies can have on global
market dynamics. Two weeks later the flash crash happened!
Economic theory suggests sellers can increase revenue through dynamic
pricing; selling identical goods or services at different prices.
However, such discrimination requires knowledge of the maximum price
that each consumer is willing to pay; information that is often unavailable.
Fortunately, electronic markets offer a solution; generating vast quantities
of transaction data that, if used intelligently, enable consumer behaviour
to be modelled and predicted.
- J. Cartlidge & S. Phelps, (2010),
demand from high-frequency data,” in Proc. Ann. Int. Academic
Conf. Business Intelligence and Data Warehousing (BIDW-2010), K.
Kumar, Ed. Singapore: Global Science and Technology Forum (GSTF),
Jul. 2010, pp. 132–138
Much of the research undertaken during my time in industry is protected by
non-disclosure agreements. In general my work centred around modelling
behaviour and dynamics in electronic auction trading systems, including
financial exchanges, alternative trading venues such as dark liquidity
pools, and online electronic auctions such as eBay.
Evolutionary computing, including genetic algorithms (GAs), offers a
population-based method of automated optimisation and design that has been
successfully applied in countless application areas. Coevolutionary computing is
a sub-set of evolutionary computing where multiple populations evolve via
a process analogous to competitive self-play. While standard evolutionary
algorithms employ a static, absolute fitness metric, coevolutionary
algorithms assess individuals by their performance relative to
populations of opponents that are themselves evolving.
Although this arrangement offers the possibility of avoiding long-standing
difficulties such as premature convergence, it suffers from its own
unique problems of cycling, over-focusing and disengagement.
I have been engaged in research in coevolutionary systems since 2000.
My primary contributions to the field include the “Reduced Virulence”
(RV) technique, developed in 2002 to counteract the problem of disengagement,
and its extension Autonomous Virulence Adaptation (AVA). Details of AVA
were first published in IEEE Trans. on Evolutionary Computation (2011), the
world's premier journal specialising in Evolutionary Computing.
For an interactive demonstration of RV and AVA, refer to
EStA, the Evolutionary Steering App.
For related reading, see:
- J. Cartlidge & D. Ait-Boudaoud, (2011),
adaptation improves coevolutionary optimization,” IEEE Trans.
Evol. Comput., vol. 15, no. 2, pp. 215–229, Apr. 2011.
- J. Cartlidge, (2008),
adapting parasite virulence to combat coevolutionary
disengagement (abstract),” in Proc. 11th Int. Conf.
Simulation and Synthesis Living Systems (Alife-11), S. Bullock et al.,
Eds. Winchester, UK: MIT Press, Aug. 2008, p. 757.
- J. Cartlidge & S. Bullock, (2004),
disengagement by reducing parasite virulence,” Evol. Comput.,
vol. 12, no. 2, pp. 193–222, Summer 2004.
- J. Cartlidge & S. Bullock, (2004),
CIAO plots: Understanding irregular coevolutionary cycling,”
Adaptive Behaviour, vol. 12, no. 2, pp. 69–92, Jun. 2004.
- J. Cartlidge, (2004),
Engagement: Competitive Coevolutionary Dynamics in Computational
Systems,” PhD thesis, Sch. Comput., Univ. Leeds, UK.
- J. Cartlidge & S. Bullock, (2003),
“Caring versus sharing:
How to maintain engagement and diversity in coevolving populations,”
in Proc. 7th Eur. Conf. Artif. Life (ECAL’03), W. Banzhaf
et al., Eds. Dortmund, Germany: Springer Verlag, Sep. 2003, pp.
- S. Bullock, J. Cartlidge, & M. Thompson, (2002),
for computational steering of evolutionary computation,” in
Workshop Proc. 8th Int. Conf. Artif. Life, E. Bilotta et al., Eds.
Sydney, Australia: MIT Press, Dec. 2002, pp. 131–137.
- J. Cartlidge & S. Bullock, (2002),
from the common cold: How reducing parasite virulence improves coevolutionary
optimization,” in Proc. Congr. Evol. Comput. (CEC’02),
D. Fogel et al., Eds. Honolulu, HI: IEEE Press, Jun. 2002,
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