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Abstracts and Presentations
This page lists the abstracts of all presentations at Complex'07.Where authorisation has been given, the presentations are also available, usually in Adobe Acrobat (pdf) and Apple Quicktime movie format.
The abstracts can also be obtained in Adobe Acrobat (pdf) format by downloading the Handbook & Abstracts.
Permission is granted for this material, presented at the 8th Asia-Pacific Complex Systems Conference to be shared for non-commercial, educational purposes. To disseminate otherwise or to republish requires written permission from the author(s).
Contents
Plenaries
Anomalous Diffusion
Business and Economics
Complex Systems Engineering
Complex Systems in the Earth Sciences
Complexity in Energy, Water, and Urban Development
Computational Modelling for Biology and Chemistry
Defence and Security
Social Networks and Epidemiology
Social Science and Management
Turbulence
General Track
Poster Session
Software Demonstrations
Plenaries
Anomalous Diffusion
Business and Economics
| Using Kauffman
Ian Wilkinson (The University of New South Wales), James Wiley (Victoria University of Wellington, New Zealand)
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Complex Systems Engineering
| Towards A Complex Systems Model Of Australian Air Traffic Management
Ariel Liebman (ARC Centre for Complex Systems, The University of Queensland), Peter Lindsay (ARC Centre for Complex Systems, The University of Queensland), Colin Ramsay (ARC Centre for Complex Systems, The University of Queensland), Martijn Mooij (The Key Centre for Human Factors, The University of Queensland)
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Complex Systems in the Earth Sciences
Complexity in Energy, Water, and Urban Development
 | The Intelligent Grid Project [pdf]
Simon Dunstall (CSIRO Mathematical and Information Sciences), Rodolfo Garcia-Flores (CSIRO Mathematical and Information Sciences), Nectarios Kontoleon (CSIRO Mathematical and Information Sciences), Bill Lilley (CSIRO Energy Technology), Rene Weiskircher (CSIRO Mathematical and Information Sciences)
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| Tuning the cognitive complexity in participatory modelling
Nils Ferrand (Cemagref), Géraldine Abrami (Cemagref - Unite Mixte de Recherche Gestion de l'Eau, Acteurs et Usages), Nicolas Becu, Daniell Katherine, Natalie Jones, Pascal Perez (CIRAD; and The Australian National University), Jean-Emmanuel Rougier
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Computational Modelling for Biology and Chemistry
 | Importance sampling strategies for forwards and backwards processes in population genetics [pdf]
Martin O'Hely (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems; and The University of Queensland), Mark Beaumont (University of Reading, United Kingdom), Lounes Chikhi (CNRS Toulouse), Robert Cope (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems; and The University of Queensland), Jean-Marie Cornuet (INRA Montpellier), Leesa Wockner (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems; and The University of Queensland)
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 | Modeling the Import of Nuclear Proteins [mov] [pdf]
John Hawkins (ARC Centre for Complex Systems, The University of Queensland), Mikael Boden (School of Information Technology & Electrical Engineering, The University of Queensland)
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 | Modelling population processes with random initial conditions [pdf]
Philip Pollett (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, University of Queensland), Anthony Dooley (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems; and The University of New South Wales), Joshua Ross (University of Warwick, United Kingdom)
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Defence and Security
| Examining 'Federation Learning': Assisting Development of Australia's National Security Systems
Rick Nunes-Vaz (Land Operations Division, Defence Sciences and Technology Organisation), Dawn Hayter (IGOR Human Science Consultancy, Urban Providore Pty Ltd, Adelaide, Australia), Dion Grieger (Land Operations Division, Defence Sciences and Technology Organisation), Gary Hanly (Land Operations Division, Defence Sciences and Technology Organisation), Prior Chad, Monique Kardos (Land Operations Division, Defence Sciences and Technology Organisation)
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| Improving wargames using complex system practices
Anthonie van Lieburg (Netherlands Organisation for Applied Scientific Research), Peter Petiet (Netherlands Organisation for Applied Scientific Research), Nanne le Grand (Netherlands Organisation for Applied Scientific Research)
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Social Networks and Epidemiology
| Complexity and the obesity epidemic
Matthew Beaty (CSIRO Sustainable Ecosystems), Cathy Baker (ACT Health), Cathy Banwell (National Centre for Epidemiology and Population Health, The Australian National University), Guy Barnett (CSIRO Sustainable Ecosystems), Helen Berry (National Centre for Epidemiology and Population Health, The Australian National University), Jane Dixon (National Centre for Epidemiology and Population Health, The Australian National University), Rob Dyball (The Australian National University), Sharon Friel (National Centre for Epidemiology and Population Health, The Australian National University), Amy Griffin (The University of New South Wales; and Australian Defence Force Academy), Katrina Proust (The Australian National University)
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 | Social influence models [mov] [pdf]
Galina Daraganova (School of Behavioural Science, University of Melbourne), Philippa Pattison (School of Behavioural Science, University of Melbourne), Garry Robins (School of Behavioural Science, University of Melbourne), Peng Wang (School of Behavioural Science, University of Melbourne)
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Social Science and Management
Turbulence
| Eddy structure in the Roughness Sub-Layer
John Finnigan (CSIRO Centre for Complex Systems Science), Roger Shaw (University of California, United States of America), Ned Patton (National Center for Atmospheric Research, USA), Ian Harman (CSIRO Centre for Complex Systems Science)
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 | Statistical Models of a Tracer Plume in the Complex Urban Canopies [pdf]
Alex Skvortsov (HPP Division, Defence Science and Technology Organisation), Ralph Gailis (HPP Division, Defence Science and Technology Organisation), Michael Borgas (CSIRO Marine and Atmospheric Research), Peter Dawson (HPP Division, Defence Science and Technology Organisation), Michael Roberts (HPP Division, Defence Science and Technology Organisation)
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General Track
 | Mapping model complexity [pdf]
Fabio Boschetti (CSIRO Marine and Atmospheric Research), Nicky Grigg (CSIRO Land & Water), David McDonald (CSIRO Marine and Atmospheric Research)
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 | Self-avoiding walk enumeration via the lace expansion [pdf]
Nathan Clisby (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, The University of Melbourne), Richard Liang (University of California, Berkeley, United States of America), Gordon Slade (University of British Columbia, Canada)
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Poster Session
| Computational Models for Studying Signalling Control Mechanisms behind Legume Autoregulation of Nodulation
Liqi Han (ARC Centre of Excellence for Integrative Legume Research and ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, The University of Queensland), Peter M. Gresshoff (ARC Centre of Excellence for Integrative Legume Research), Jim Hanan (ARC Centre of Excellence for Integrative Legume Research, ARC Centre for Complex Systems and Advanced Computational Modelling Centre, The University of Queensland)
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| Approximating extinction times and probabilities for absorbing birth-death processes
David Sirl (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, University of Queensland), Hanjun Zhang (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, The University of Queensland), Philip Pollett (ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, University of Queensland)
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| Computational techniques for modeling complex biological systems
James Watson (ARC Centre for Complex Systems and ARC Centre in Bioinformatics, The University of Queensland), Mark Wakabayashi (Thinking Systems, Queensland Brain Institute and School of Information Technology and Electrical Engineering, The University of Queensland), Jared Moore (The University of Queensland), Andres Sanin Montoya (The University of Queensland), Kai Willadsen, Nic Geard (ECS, University of Southampton), Daniel Bradley (ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, The University of Queensland), Janet Wiles (The University of Queensland)
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| Reinforcement learning in complex computer game environments
Michelle McPartland (ARC Centre for Complex Systems, School of Information Technology & Electrical Engineering, The University of Queensland), Marcus Gallagher (ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, The University of Queensland)
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| Robots and the Evolution of Spatial Language
Ruth Schulz (School of Information Technology & Electrical Engineering, The University of Queensland), David Prasser (School of Information Technology & Electrical Engineering, The University of Queensland), Mark Wakabayashi (Thinking Systems, Queensland Brain Institute and School of Information Technology and Electrical Engineering, The University of Queensland), Janet Wiles (The University of Queensland)
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| Searching Concept Spaces using Physical Navigation Strategies
Paul Stockwell (Thinking Systems, School of Information Technology and Electrical Engineering, The University of Queensland), Andrew Smith (The Institute for Social Science Research, The University of Queensland), Janet Wiles (The University of Queensland)
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| Virtual Kiwifruit: Modelling Annual Growth Cycle
Mikolaj Cieslak (The University of Queensland), Alla N. Seleznyova (HortResearch, New Zealand), Jim Hanan (ARC Centre of Excellence for Integrative Legume Research, ARC Centre for Complex Systems and Advanced Computational Modelling Centre, The University of Queensland)
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Software Demonstrations
| EcoLab 5
Russell Standish (Mathematics and Statistics, The University of New South Wales)
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Plenaries
Are fractal skeletons the explanation for the plankton paradox and narrowing of arteries due to cell trapping in a disturbed blood flow?
Celso Grebogi (Institute for Complex Systems, King's College, University of Aberdeen, Scotland, United Kingdom)
Nature is permeated by phenomena in which active processes, such as chemical reactions and biological interactions, take place in environmental flows. They include the dynamics of growing populations of plankton in the oceans and the evolving distribution of ozone in the polar stratosphere. I will show that if the dynamics of active particles in flows is chaotic, then necessarily the concentration of particles has the observed fractal filamentary structures. These structures, in turn, are the skeletons and the dynamic catalysts of active processes, yielding an unusual, singularly enhanced productivity. I will argue that this singular productivity could be the hydrodynamic explanation for the plankton paradox, in which an extremely large number of species are able to coexist, negating the competitive exclusion principle that asserts the survival of only the most perfectly adapted to each limiting resource. I will then suggest that the presence of such fractal skeletons in arterial flow could be the explanation for the eventual restenosis (recurrence of narrowing) of arteries after a stent-assisted angioplasty.
References
Chemical and biological activity in open flows: A dynamical system approach, T. Tél, A. Moura, C. Grebogi and G. Károlyi, Phys. Reports 413, Issues 2-3, pages 91-196 (2005).
Carbon-climate-human interactions as a complex system
Mike Raupach (CSIRO)
Dr Michael Raupach
Carbon-climate-human interactions can be modelled as a low-dimensional dynamical system. This system is now exhibiting two kinds of disturbance-amplifying feedback.
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Biophysical vulnerabilities: Land-air and ocean-air exchanges of carbon (both as CO2 and CH4) are being modified by changes in climate and land use. Together, effects create a potential climate change impact which is not negligible in comparison with projected direct consequences of anthropogenic emissions. New observational evidence shows that the land and ocean CO2 sinks (which now take up over half the total annual CO2 emission flux from human activities) are already weakening.
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Vulnerabilities arising from human behaviour: CO2 emissions from fossil-fuel burning and industrial processes have been accelerating at global scale. The emissions growth rate since 2000 was greater than that for the most fossil-fuel-intensive of the IPCC emissions scenarios developed in the late 1990s. Growth since 2000 was driven by a reversal of earlier declining trends the carbon intensity of the economy, coupled with continuing increases in population and per-capita GDP.
These interacting feedbacks place the carbon-climate-human system at three crossroads: the Gaian crossroads (the coevolution of climate, biogeochemical cycling and the living biosphere); the Kyoto crossroads (representing choices about how to deal with human impacts on the planet); and the crossroads of Robert Johnson, who according to legend sold his soul to the Devil in exchange for musical genius. Our challenge here is to reshape the bargain that, through energy derived from detrital biospheric carbon, has fuelled the genius of the human race.
Complex systems challenges in health care
Michael Ward (Central Clinical Division, School of Medicine, The University of Queensland)
Research based advances in healthcare over the past 40-50 years have been substantial and unequivocal. This has resulted in an extensive and ever increasing catalogue of powerful diagnostic, therapeutic and procedural interventions. These have led to significant decreases in the mortality and morbidity of many diseases. However there is increasing concern amongst clinicians and health service managers about the quality and safety of healthcare delivery. This is based upon evidence of: variations of several orders of magnitude in the outcomes of treating the same condition; of little correlation between these outcomes and the relevant costs; of a failure to deliver interventions of proven efficacy to around half of those in need, and of a 10% chance of harm caused by the processes of healthcare. These problems have resisted conventional service improvement initiatives for over a decade. It seems clear that some of this intractability may be due to the complex non-linear interactions amongst the many specialised component parts of healthcare that contribute to the benefits. These interactions generate complex adaptive systems that are difficult to both understand and manage. This presentation will focus upon those components of this complexity where the techniques of non-linear data analysis seem to have potential value both for understanding key human dynamics, and for improving systems management. The presentation will be followed by a panel discussion of researchers and clinicians with relevant experience and expertise with the objective of providing a framework for future collaborative research.
Complex Systems Perspective on the Revolution in Human Performance Optimization
Kenneth Boff (Air Force Research Laboratory)
The revolutions in Info, Bio and Nano Technologies have spawned a revolution in human enhancement technologies that fundamentally challenge the notion of what it means to be human. Human performance optimization has potential to bestow unprecedented competitive and military advantages in future human-technology systems. Over the past decade, classical human factors engineering (Gen 1) has rapidly given way to 'cognitive systems engineering' (Gen 2) which, in turn has spawned a growing investment in Cognitive Systems Integration (Gen 3) wherein silicon-based technologies are neurally-coupled to symbiotically enhance cognitive performance. The biological optimization of human performance capabilities (Gen 4) is emergent and indicates that the means to redesign our basic human factors - that is, how we think, how we feel, how we look, how we age, and how we communicate with one another - is close at hand. Taken together, the revolution in human performance optimization has profound implications for design consideration of the human as a variable in complex systems where traditional boundary constraints may no longer be valid. This presentation will review and explore the options and implications of this revolutionary paradigm shift on the design and deployment of complex systems.
Controlling complex resources over different timeframes in process control
Penelope Sanderson (Cognitive Engineering Research Group, The University of Queensland)
Monitoring and controlling complex systems becomes particularly challenging when human controllers must satisfy constraints operating in multiple timeframes. Examples of systems with such temporal complexity are air traffic control, chemical process control, and power generation. We outline how the problem of temporal complexity emerges in different domains and imposes challenges to effective control. To illustrate our work we focus on hydropower system control. A hydropower company generates power according to targets handed down by the central market organisation and responds to market opportunities, but at the same time the company must preserve its ability to meet corporate strategic and tactical objectives. The human controller at the 'sharp end' of hydropower system operations must coordinate water, generation, and transmission resources consistently with those objectives. In this presentation we describe the information needed by hydropower system human controllers for effective control. Specifically, we outline the general form of analyses that identify the purposes, priorities and functions of the hydropower domain, and that identify the timeframes in which different subsystems function. From such analyses we develop the general form of interface displays that help human controllers solve the problem of coordinating resources over different timeframes. In particular, such displays make the temporal boundaries of safe operating regions visible. Finally, we draw conclusions for analysis and design of human-system interfaces for complex systems in which resources must be coordinated by different parties across multiple timeframes.
Evolution Toward Enterprise Systems Engineering (If You don't Have a Billion Years, Will a Billion Dollars Do?)
Joseph DeRosa (Director Systems Engineering, MITRE Corporation, United States of America)
Corporations are like living organisms - they evolve and adapt. They can start out simple, and go through successive stages of increased complexity and organization. They have their own niche in the business ecosystem. Individual companies, as well as the entire business segment to which they belong, face dangers of extinction from competitors and the environment. At the same time some adapt and thrive. This is the story about the on-going evolution of a company known for its expertise in traditional systems engineering toward the murky waters of complex systems and enterprise systems engineering.
Exponential random graph models for social networks
Philippa Pattison (School of Behavioural Science, University of Melbourne)
In this talk I describe a program of work whose aim is to develop
statistical models for social networks. Global network structure is
hypothesised to arise as the outcome of interactive processes occurring
within local neighbourhoods of a network. Each neighbourhood is
conceived as a possible site of interaction and is associated with a
subset of possible network ties. An illustration of the approach is
presented using a model specification that relies on Markovian
neighbourhoods (Frank & Strauss, 1986) as well as generalized
realisation-dependent neighbourhoods that are generated, in part, by
interactive network processes themselves (Snijders, Pattison, Robins &
Handcock, 2006). The model is estimated from an observation of a
complete network. I then consider conditional maximum likelihood
estimation of models of this form from partial network data obtained
from multi-wave snowball sampling schemes. Snowball sampling schemes
are those in which nodes adjacent to one or more "seed" nodes are
identified, as are nodes adjacent to each of those nodes, and so on. I
describe a separation condition for the case of multiple seed nodes,
report some simulations assessing the estimation approach, and discuss
potential applications.
References
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842. Snijders, T., Pattison, P., Robins, G., and Handcock, M. (2006). New specifications for exponential random graph models. Sociological Methodology, 36, 99-153.
From words to meanings - Human knowledge as a complex system
Andrew Smith (Leximancer)
Researchers have characterised human knowledge in terms of semantic networks and concept maps for several decades. Concept Maps, or Mind Maps, are often created directly by the humans who possess the knowledge in question. However, the systematic generation of sematic networks is generally based on language. Natural language data - text and speech - is regarded as the principal diagnostic output of the human cognitive system.
Natural language shows several key indicators of a complex system. Humans habitually use language creatively. People's knowledge representations of a given system are known to depend on their current goal, context and background. Key components of the human knowledge representation are: the concept, or category, such as a chair as opposed to a table, and the relationship, such as the observation that the chair is under the table. Concepts could also be referred to as classes, or as agents, and have multiple slowly changing attributes. Relationships between concepts, on the other hand, are much more dynamic. Object Role Modelling is one systematic formalism for expressing knowledge as a flexible network of more primitive conceptual agents.
Our Leximancer system employs a two-level process to automatically extract knowledge networks from text. The first level is a recursive algorithm for identifying concept agents and discovering their various lexical attributes - this is the extracted Thesaurus. The second level identifies the co-occurrence network of concept agents in the text, then uses these relational observations to find the emergent conceptual phase space via a recursive and dissipative nonlinear algorithm. This phase space is the resulting concept map. A key part of our work is to test the reproducibility of the emergent patterns using parallel data sets.
Structure and dynamics of complex networks
Hawoong Jeong (Department of Physics, Korea Advanced Institute of Science and Technology, Republic of Korea)
Complex systems as diverse as the Internet or the cell can be described by networks with complex topology. Traditionally it has been assumed that these networks are random. However, recent studies indicate that such complex systems emerge as a result of self-organizing processes governed by simple but generic laws, resulting in inhomogeneous scale-free topologies strikingly different from those predicted by random networks. Such studies also lead to a paradigm shift regarding our approach to complex systems, allowing us to view them as dynamical systems rather than static graphs. I will review historical development of complex network studies, and discuss the implications of these findings on the error and attack tolerance of the Internet and the robustness of the cells. Also recent research activities especially on dynamical aspect of complex network will be presented, including large-scale data analysis of social networking service (SNS) and price of anarchy of transportation networks.
Synchronisation and emergent intelligence in networked agents
Akira Namatame (Department of Computer Science, National Defense Academy of Japan)
The understanding of emergent collective phenomena in natural and social systems has driven the interest of scientists from different disciplines. Among emergent collective phenomena, the synchronization of a set of interacting individuals occupies a privileged position because its ubiquity in the natural systems. For instance, flocking behavior of fish school, birds and many animals are good examples. The dynamics of each flocking member is controlled by the local interactions between each member and its neighbors that control the spatial and velocity coherence of the system. Although a substantial element of this research has successfully concentrated on building mathematical and simulation models that simulate flocking behavior, the control of flocking dynamics in order to achieve a specific object is difficult. Previous research on synchronization only measures the stability of a system's dynamics and does not consider the level of coherence or desirability in the behaviors exhibited by a system.
The fact that selfish behavior may not achieve full efficiency at the aggregate level has been well known in the literature. As the complexity of networked systems has increased, the presence of undesirable behaviors resulting from strategic interactions among agents with the different interests of these networked systems has grown. These collective behaviors can have catastrophic consequences, as in the sudden collapse. Therefore we need to cope with networked agents systems by attempting to stack the deck in such a way that agents with selfish incentives have to do what is the desirable thing. Of particular interests is the question how interactions and couplings among agents can be restructured so that they are free to choose their actions while avoiding outcomes that none would have chosen.
In this talk, we discus the study that provides a new perspective and tools to control emerging collective behavior. We discuss how local patterns of synchronization emerge differently in networked agents, driving the process towards a certain global synchronization degree following desirable paths. The dependence of synchronization dynamics on the coupling rules among agents and on the interaction topology among agents is discussed. We discuss the relationship between synchronization in networked agents and emergence of desirable outcomes as collective intelligence. We can observe the wisdom of collective agents at the microscopic level as evolved local rules that constitute constraints on agents' behaviors to achieve desirable outcomes.
Systems-level metabolic engineering of bacteria using genome-scale in silico models
Sang Yup Lee (Dept of Chemical & Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea)
Recent availability of the complete genome sequences of numerous organisms is making it possible to reconstruct the in silico genome-scale metabolic models. Metabolic engineering aims at purposeful modification of cellular and metabolic network in order to achieve several goals including enhanced production of desired products, production of novel bioproducts, broadening the substrate utilization range, among others. In this lecture, I will present the results obtained in my group regarding the development of new metabolic engineering strategies for the production of primary and secondary metabolites using the in silico genome-scale metabolic simulation coupled with omics studies.
The colourful geometry of nature
Michael Barnsley (The Australian National University)
Arrays of data from biology, astronomy, physics, meteorology, etc. often display complex geometries of form and colour which are hard to quantify. This lecture will showcase a new fractal-based method for identifying regularities in such data: a coupled chaos game is used to construct mappings between intricate structures which appear to be different but are inherently related. The method is simple to apply in diverse situations.
Anomalous Diffusion
A Fractional Cable Equation for Anomalous Electrodiffusion in Nerve Cells
Bruce Henry (The University of New South Wales), Trevor Langlands (The University of New South Wales) and Susan Wearne (Mount Sinai School of Medicine)
We describe a fractional cable equation to model anomalously slow electrodiffusion of ions in nerve cells. Fundamental solutions are presented and results for firing rates and voltage attenuation are obtained in terms of the anomalous diffusion parameters. A particluar application to model the passive propagation of a postsynaptic potential along a spiny dendrite is described.
Anomalous diffusion with linear reaction dynamics
Trevor Langlands (The University of New South Wales), Bruce Henry (The University of New South Wales) and Susan Wearne (Mount Sinai School of Medicine)
We consider the problem of anomalously sub-diffusing species, modelled at the mesoscopic level using continuous time random walks, to include linear reaction dynamics. If a constant proportion of walkers are added or removed instantaneously at the start of each step then the long time asymptotic limit yields a fractional reaction-diffusion equation with a fractional order temporal derivative operating on both the standard diffusion term and a linear reaction kinetics term. If the walkers are added or removed at a constant per capita rate during the waiting time between steps then the long time asymptotic limit has a standard linear reaction kinetics term but a fractional order temporal derivative operating on a non-standard diffusion term.
We also consider a two-particle variant of the latter model where the first particle type is transformed into the second particle type when it is removed. Both particle types still undergo anomalous sub-diffusion. Here the equation for the first particle type remains the same as in
the single particle case but the equation for the second particle type differs in form to that
of the first.
Results from the single particle cases are compared with a phenomenological model with standard linear reaction kinetics and a fractional order temporal derivative operating on a standard diffusion term. We also compare the results with Monte Carlo simulations.
Fractional reaction diffusion along flow lines
Boris Baeumer (The University of Otago, New Zealand)
We model anomalous dispersion using power-law jump distributions. In the scaling limit this leads to a fractional diffusion equation. This allows for the added feature of reaction, modeling invasion of species/spread of diseases. We then take the dispersion model and generalise it to account for regional features, using an old idea by Bochner, namely subordination. This leads to a PDE involving fractional powers of the generator of the underlying flow group.
Heat Conduction in Nonlinear Systems
Bambi Hu (Hong Kong Baptist University and University of Houston)
Heat conduction is an old yet important problem. Since Fourier introduced the law bearing his name 200 years ago, a first-principle derivation of this law from statistical mechanics is still lacking. Worse still, the validity of this law in low dimensions and the necessary and sufficient conditions for its validity are still far from clear. In this talk I'll review recent works done on this subject. I'll also report our recent work on asymmetric heat conduction in nonlinear systems. The study of heat conduction is not only of theoretical interest by also of practical interest. I'll discuss various designs of thermal rectifiers and thermal diodes.
Nonlocal heat transport in laser-produced plasmas
Frank Detering (The Australian National University)
Deviations from classical, local heat diffusion are typical in laser-produced plasmas due to the sharp temperature gradients and the large collisional mean free paths. The heating process in itself leads to non-Maxwellian particle velocity distributions which lead to further differences to the classical collisional transport.
A practical approximation is often needed to account for these deviations in large-scale hydrodynamical simulation codes which are commonly used to study the entire experiments.
In the present study we attempt to study and justify different nonlocal transport approximations for these applications.
Using Fokker-Planck and particle-in-cell (PIC) simulations of the evolution of the electron distribution a single hot spot has been studied in a laser produced plasmas. The practical formula for nonlocal heat flux has been derived as a generalized expression of nonlocal linear approach [V. Yu. Bychenkov et al., Phys. Rev. Lett. 75, 4405 (1995)] and tested in simulations. The Electron distribution function is studied at different spatial locations with respect to localized heating source. It is found to be dependent on the
interplay between the effects of collisional heating and nonlocal transport. Significant non-Maxwellian high-energy tails of electron
distribution function are found which may have strong impact on
the behaviour of other important processes in non-uniformly heated
laser plasmas [O. V. Batishchev, V. Yu. Bychenkov, F. Detering, W. Rozmus, R. Sydora, C. E. Capjack, and V. N. Novikov, Phys. Plasmas 9, 2302 (2002)].
Simulation study on heat conduction and beyond
Nobuyasu Ito (The University of Tokyo)
Relation between microscopic dynamics and macroscopic heat conduction is one of the
most fundamental problems in nonequilibrium statistical physics and nonlinear sciences.
Recent computer simulation finally succeeded to reproduce Fourier law in three-dimensional
systems[1-3], and this made theoretical and simulational studies of nonlinear nonequilibrium
structures and phenomena possible[4]. On the other hands, some nonlinear lattices turn
out to show anomalous heat conduction in mesoscopic scale even if they are three-dimensional[5].
In this talk, such heat-conduction and transport simulations are to be given together with
a future perspective.
References
[1] T. Shimada, T. Murakami, S. Yukawa, K. Saito and N. Ito, J. Phys. Soc. Jpn. vol.69 (2000) p.3150
[2] T. Murakami, T. Shimada, S. Yukawa and N. Ito, J. Phys. Soc. Jpn. vol.72 (2003) p.1049
[3] F. Ogushi, S. Yukawa and N. Ito, J. Phys. Soc. Jpn. vol.74 (2005) p.827.
[4] F. Ogushi, S. Yukawa and N. Ito, J. Phys. Soc. Jpn. Vol.75 (2006) 073001.
[5] H. Shiba, S. Yukawa and N. Ito, J. Phys. Soc. Jpn. vol.75 (2006) 103001.
Business and Economics
'Discovering' Small Worlds in Potentially Biased Networks: A Methodological Critique
Sam MacAulay (ARC Centre for Complex Systems, The University of Queensland), John Steen (ARC Centre for Complex Systems, The University of Queensland) and Tim Kastelle (ARC Centre for Complex Systems, The University of Queensland)
This paper argues that much of the empirical work that identifies small world properties within social and economic systems is, unlike that studying physical networks, potentially misestimating the degree of 'small world-ness' within these systems. It is conjectured that misestimation largely stems from a systematic bias in methodological design, which in turn results from the disparate complexity and cost between collecting systemic network data (e.g. both strong and weak ties) for socio-economic (e.g. interpersonal collaboration) and physical networks (e.g. the internet). This disparity exists because the socio-economic literature is primarily aimed at examining the structural properties of socially embedded interdependencies between agents, instead of physical interdependencies (e.g. neural, electricity, electronic communication and transportation networks) whose physical nature in many ways makes them inherently less complex/costly to identify and analyse.
Within socio-economic networks the interdependencies that are least costly to identify and measure are strong ties (e.g. members of boards, alliance members, research collaborations) due to their relatively stable, structured and systemic nature (Montgomery, 1994). Conversely, weak interdependencies within a network (weak ties within sociological parlance) are more problematic to identify due to their inherently dynamic, quasi-random and subtle nature (Granovetter, 1973). Therefore, when carrying out methodological design researchers are much more likely to 'satisfice' by selecting a data collection model biased towards the collection of 'strong tie' data at the expense of 'weak tie' data. Whilst the ultimate effect of this bias on the small world statistic is likely to be contingent on a range of factors (e.g. the distribution of agent-degree centrality within the sample and the population), as weak ties are known to be more likely to bridge local clusters within networks of diverse agents (e.g. Burt, 2004; Uzzie and Spiro, 2005) it can be hypothesised that the identified bias will be systematic. This argument is supported by a meta-analysis of methodology within the field and reference to a number of recent articles that examine the sensitivity of the small world statistic to missing nodes (Deng, Zhoa, Li, 2007), weight randomization of network ties (Li, Fan, Wang, Li, Wu and Di, 2007) and the changes in the proportion of weak-to-strong ties within a network (Shi, Adamic and Strauss, 2007). The implications for the study of socio-economic networks are then discussed.
A Review of Design Approaches Within Schumpeterian Economic Simulations
Craig Lynch (Macquarie University)
Schumpeter's Theory of Economic Development has been an object of attention and in-depth analysis since it was introduced nearly a century ago. Despite it being a representation of Schumpeter's reason and logic, supported by some empirical research, it is in essence a clearly defined dynamic system that ties together almost all components and behaviours within a typical economy. Over the past few decades the means to simulate and explore this theory have emerged through the availability of several computing methodologies and tools. As a consequence a number of models have been constructed that focus on some aspect of the theory, and the term 'Schumpeterian' has become a much-used adjective within this arena.
The question of whether these models adequately simulate the complete theory, given its breadth and reliance upon the combinations of innovation, entrepreneurial action, the supply of credit, and interactions amongst all economic agents, represents the foundation of this paper. A range of models, with varying methodological approaches, are analysed to determine how closely they encompass all aspects of Schumpeter's theory. The implicit design within each model is reviewed to the degree to which transactions generated from individual behaviour can be aggregated at an industry or economy level, and a first approximation of an overall design framework is consequently distilled from this exercise. Of particular interest is the ability for entrepreneurs and firms to make strategic choices and succeed in improving or sustaining profit accordingly. The paper also provides a contrast between agent-based modeling and a recent adaptation of computable general equilibrium approaches in the simulation of an economy operating within Schumpeter's behavioural rules.
ACE modelling: does size matter?
Paul Davis (Macquarie University)
Agent-based Computational Economics (ACE) is a relative new comer to the field of economic research. ACE modelling involves the creation of computer based economic models comprised of heterogeneous agents and environmental rules. Models are executed enabling the modeller to study the emergent (macroeconomic) behaviour resulting from complex inter-agent dynamics (microeconomics).
This presentation is part of a program of work within a PHD the focus of which is research into Schumpeterian dynamics and in particular economic development and the emergence of cyclicality. The question being considered here is whether the size of a model, reflected in the number of agents, impacts on the modelling processes of design, execution and analysis, or stated colloquially, ACE modelling: does size matter?
The methodological analysis is performed against two models, a model with no fixed size and an extant model. Questions asked are aligned with the following model development life-cycle process: Design, Execution and Analysis, and include: Is model transparency influenced by Agent Quantity? What is the impact of parallel or sequential processing? How does the modeller determine whether a behaviour is emergent or pre-programmed?
Findings show that the ACE modelling capability to embrace and understand complexity enables models with large agent populations to be effectively utilised. This has the potential to re-establish the linkages between microeconomic behaviour and macroeconomic observation as well as connect the results from ACE modelling with real-world economic analysis.
Agent-based design considerations to ensure behaviour is emergent: A Labour market simulation using RePast
Paul Davis (Macquarie University)
Joseph Schumpeter in his 1912 book The Theory of Economic Development argued Labour and Land were the fundamental building blocks of economies; entrepreneurial utilisation of capital to fund innovations that drove economic growth in a "creative destruction" style manner; new business lines destroying the old resulting in the appearance of business cycles. A question has arisen during a program of work within a PHD whose focus is researching Schumpeterian dynamics, economic development and the emergence of cyclicality; how does one model a Schumpeterian economy and ensure if the model generates cycles, they are emergent (not pre-programmed) phenomena?
This presentation is a PHD progress report examining the impact ensuring emergence has on the design and implementation of an agent based model under construction. The model is being developed using RePast (Java). Currently it is comprised of two types of agents, Persons and Firms and a market mechanism enabling agents to trade Labour. As production represented as a function of the consumption of labour has not yet been introduced the model does not display cyclicality however the work to date has demonstrated to the researcher:
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object orientated methodologies provide atomic modelling at the cost of added complexity
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sequential "event-based" modelling requires effective use of randomisation
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hard-coded limits (e.g. population size) increases the risk that emergent behaviour may not appear
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careful design of factors influencing decision variables is required to ensure that the interaction of the factors does not result in the emergence of results that can be predicted
Automatic Extraction and Modelling of Human Knowledge Networks from Natural Language using a Complex Systems Approach
Andrew Smith (Leximancer), Michael Humphreys (School of Psychology, The University of Queensland) and Bettina Cornwell (School of Business, The University of Queensland)
Some researchers have recognised that the knowledge contained in
unstructured natural language is complex. Humans appear to be capable of:
maintaining contradictory beliefs and definitions, reinterpreting
identities and roles subject to context and goal, using vocabulary and
metaphor creatively, and modifying entities and relationships with almost
infinite degrees of variation. Since language reflects to some degree our
knowledge of the complex world around us, and combining this with the
dynamic requirements of the human organism which has its own internal
cognitive complexity, the conclusion that knowledge expressed as natural
language is a complex system might appear unavoidable.
And yet, in disciplines such as Knowledge Management, Cataloging, Semantic
Web, and Intelligence Extraction, it is common for fixed ontologies to be
designed, and subsequent language data to be forced into alignment with the
specified ontological rules. This mechanistic approach is analogous to
clear-felling a rain forest without understanding what we are destroying.
This paper will present two case studies which will use the new text
analysis system called Leximancer to extract knowledge networks from
natural language collections in an emergent fashion. Leximancer's recursive
algorithms fall into the definition of complexity, and while the
algorithmic rules which define the interactions between words and between
concepts are fairly simple, the resulting models capture a high degree of
the complexity of the data. Cross validation of emergent patterns is an
important aspect of these studies.
The first study will present a new method for Brand Concept Mapping. This
method involves prompting individual subjects with minimal stimulus
material which is designed to maximise the thoroughness of their response.
The premise for this study is to discover the internal cognitive map of
potential consumers which would operate when they are making a product
choice on their own. For this reason, and to avoid the experiment being
confounded by social interaction effects, knowledge elicitation was
conducted as an isolated essay writing task rather than as an interview or
focus group. Data was collected using several different stimulus materials
so that the sensitivity of the method to manipulation of the experimental
conditions could be assessed.
The data was analysed using Leximancer to automatically extract aggregate
concept maps from the material. Maps were extracted from different samples
of the data both within group and between group to test for sampling
validity and sensitivity.
The second case study will employ the dynamic (temporal) mapping feature of
Leximancer to examine the dynamics of complexity in a news feed. The
dynamic map presents as an animated concept map which shows concepts and
themes emerging and fading. The map shows periods of equilibrium where
there is a stable attractor and also periods of instability. It is
hypothesized that there may be both areas of chaos and of transitional
complexity in the unstable regions, but this is the subject of ongoing
research.
Dynamic conceptual mapping will be useful for visually monitoring the
shared knowledge state in several key situations: trading and financial
services (using news wire, email, and corporate reporting data);
organisational and project team communications (with email, chat text, and
meeting minutes); extended and distributed planning meetings (using
multi-channel text transcription); ongoing investigations (using
statements, interview transcripts, and investigator notes); and market or
opinion research.
Breaching Walras's Law: a first step to modelling endogenous money
Steve Keen (School of Economics & Finance, The University of Western Sydney)
Walras's Law has been a mainstay of general equilibrium mathematical modelling in economics for over a century, and much of the post-WWII development of macroeconomics was undertaken to try to make Keynes's arguments about an unemployment equilibrium consistent with this Law.
In this paper I argue that Walras's Law is invalidated in a truly monetary framework, and present a simple linear model of a monetary exchange economy in which Walras's "Law" is shown only to apply in a situation of zero growth. From a complex systems perspective, this means that the full nonlinear dynamics of a monetary production economy are dissipative, not conservative.
Combining System Dynamics and Choice Modelling to Simulate Demand Effects of Integrated Customer-Centric Marketing and Revenue Management
Christine Mathies (The University of New South Wales)
Stated preference choice experiments were conducted in the airline and hotel industry to examine how the current practice of simultaneous yet unintegrated customer-centric marketing and revenue management impacts customers' choices. An unintegrated approach was found to create perceived conflicts and unfairness for customers, which manifest in their purchase choices. The choice analyses form the basis for a series of simulations of how an integrated approach to customer-centric marketing (CCM) and revenue management (RM) can avoid conflicts and alter customers' choices. Firstly, the preference estimates from the choice experiments were used for basic what-if predictions of choice probabilities if one or more attributes are changed. The findings suggest that service firms can achieve substantial revenue increases. However, a more sophisticated approach to predicting demand from a basic integrated CCM-RM system combines system dynamics with discrete choice modelling to help airlines and hotels decide on the best strategy of how to incorporate CCM and RM.
The simulation models for each industry include the market, represented by three different market segments with distinct preference structures, and the providers, represented by the focal airline, and hotel respectively, and three competitors.
Preference estimates are employed to specify the decision rules of the simulation model. While some existing research brings together conjoint analysis and simulation models with the objective to add precision to the model formulation, this paper expands this idea to stated choice analysis and compares the simulation outcomes from two different techniques.
Introduction to an Agent-Based Model of Development Processes in Tanzania
Brett Parris (Dept of Econometrics & Business Statistics, Monash University; and World Vision Australia)
Policy makers increasingly require models that can integrate the socio-economic, political, epidemiological and environmental dimensions of development. Agent-based models can achieve this integration using interacting agents in computer simulations to represent individuals, households, firms, governments and land types. This paper presents progress on the development of an agent-based model of a Tanzanian region developed using RepastJ. It uses a range of data sources, including the national household survey, World Bank development data and a Social Accounting Matrix (SAM).
People are modelled as individuals, who are born, grow up and eventually die. They must remain healthy, and need to be educated as children. They may marry, have children, divorce, get sick and recover. They are related to other specific individuals and are members of households. These households may be rural or urban, very poor, poor or non-poor and may have access to transport, and other household amenities such as sanitation, electricity, and telecommunications. The characteristics of the households determine their relative power in bargaining over the prices of the goods and services. Richer, healthier households, with higher levels of education, and good access to fertile land, transport and information, have greater bargaining power than poorer, more isolated households on marginal land. The model enables some of the dynamics of poverty and wealth to be explored.
Mean Bad Birds versus Kind Friendly Chickens: Group Selection and the Evolution of Cooperation
Ian Wilkinson (The University of New South Wales), Dan Ladley (The University of Leeds, United Kingdom) and Louise Young (The University of Technology, Sydney)
The development of collaborative relations and networks among and within organisations is an important source of competitive advantage for firms, industries, regions and nations. The problem is that firms often begin from a state of adversarial relations and it is difficult to turn them into more collaborative forms. Various theories of the emergence of cooperation have been proposed based on kinship ties and repeated interaction and reciprocity but they cannot account for the emergence of large scale cooperation among people and organisations that are ' strangers,' which characterises many types of business cooperation. Group selection mechanisms offer ways forward.
A renewed focus on group selection mechanisms has been championed by leading evolutionary biologists such as Edward O. Wilson at Harvard and David Sloan Wilson, who see it as opening up 'a whole new ballgame' in research. Group selection mechanism can be used to provide another type of explanation for and a means for facilitating the emergence of cooperative behaviour in social and economic systems, including business relations and networks. We examine the impact of group selection on the outcomes of mixed motive game simulations, revisiting research done in the 80s and 90s. We demonstrate that group selection evolves fitter (better performing) strategies than does individual selection, because individual strategies as well the mixes of interacting strategies in groups co-evolve.
Lastly, given the focus on producing high performing researchers in university departments as a result of the RQF funding scheme, the relevance of this research to that context has not gone unnoticed!
Minimalism and model-building: an assured model of the exchanges between consumers, retailers and manufacturers
David Midgley (INSEAD, France), Robert Marks (The Australian Graduate School of Management), Daniel Klapper (The University of Frankfurt, Germany) and Dinesh Kunchamwar (Barclays Capital, Singapore)
Model assurance combines ideas from software proof, destructive testing and empirical validation. Previously we raised the philosophical issue of whether social scientists should take a traditional scientific approach to building agent-based models or whether they should prefer a minimalist approach. Taking our own advice we developed a minimalist version of our model. We present the results of assuring this new model. The specific steps taken to assure the model include: 1. Verification a. Two external experts have inspected the RePast code to discover whether it follows the model specification correctly. b. We use the Genetic Algorithm as an optimizer to test the bounds of the model by seeking implausible results. 2. Validation a. A real supermarket chain has provided two databases which we use to validate the model. b. Here we follow a hybrid approach where we use one database to calibrate consumer agents at the micro-level and then we fit the retailer and manufacturer models to the other database at the macro-level, again using the Genetic Algorithm. We report the results of this model assurance exercise and use it to define 'minimalism' more tightly, arguing that it is more restrictive than 'parsimony.' We also extend this debate by discussing the practical barriers that currently prevent ABMs reaching their full potential in the social sciences. These include the costs of software proof and the lack of data to validate many aspects of the agents.
Modeling innovation changes in business networks
Sharon Purchase (The University of Western Australia), Doina Olaru (The University of Western Australia) and Sara Denize (The University of Western Sydney)
Actors' choices concerning their communication processes (intensity and richness), cooperation, and knowledge sharing with other business actors change existing network structures resulting in improved overall 'network innovation' (Möller and Svahn, 2003). The extant literature establishes need to elaborate the interplay between these actor choices and performance, in particular via longitudinal studies (Ferguson et al., 2005, Walter et al., 2006). However, it is difficult to secure cooperation for such research and real world cases do not allow the researcher to exact the necessary detail to include most complex interactions. This research investigates how changes in communication processes produce variations in the innovative performance of the network using simulation.
The 'outcome' variable in this conceptualisation is the 'network innovation' that results from the communication processes developing cooperation, knowledge sharing, learning and adaptation in business networks (Hummond, 2000). Specifically, actors modify the strength of their connections with the other actors by altering the amount and type of information they share with other network actors. By changing the input variables such as, communication intensity and richness, within similar network structures, emerging patterns of network innovation are examined. Numerous simulations of network structures are assessed for a particular size of network (parameter of the model).
The complexity of the problem, the nonlinear effects arising from structure asymmetry, the nature of the communication, and the self-organising features of social networks make fuzzy sets an attractive paradigm to use within the simulation process (Robinson 2003, Bonabeau 2002). The model uses seven inputs with 3-5 adjectives to capture flexibility in learning and adaptation, background/environment conditions, communication, network position and the fuzzy-rule system models ways in which dynamics in network structure and relations can trigger the development of new ideas within the network.
Both academic and managerial implications emerge from the modelling exercise - understanding how individuals take advantage of the clustering and 'short-cuts' in the network in order to improve their central or actor-in-the-middle/broker position is required to influence/moderate both radical and marginal changes in innovation in business networks.
References
Bonabeau, E. (2002) Agent-based modeling: Methods and techniques for simulating human systems, PNAS 99 (3), 7280-7287.
Ferguson, R. J., Paulin, M., Möslien, K. and Müller, C. (2005) Relational governance, communication and the performance of biotechnology partnerships, Journal of Small Business and Enterprise Development, 12:3 395-408.
Hummond (2000) Utility and Dynamic Social Networks, Social Networks 22, 221-249. Möller, K. and Svahn, S. (2003) Managing Strategic Nets: A capability perspective, Marketing Theory, 3(2), 209-234.
Robinson, V.B. (2003) A Perspective on the Fundamentals of Fuzzy Sets and their Use in Geographic Information Systems, Transactions in GIS 7(1), 3-30. Walter, A., Auer, M. and Ritter, T. (2006) The impact of network capabilities and entrepreneurial orientation on university spin-off performance, Journal of Business Venturing, 21: 541- 567.
Performance metrics: Towards an uncertainty principle for organizations
Bill Lawless (Paine College)
I'm currently working on two projects, the first more conceptual and applied to seven
military Medical Department Research Centers and once Central Business University;
and the second more mathematical.
A new organizational metrics: A quantum approach
A future goal of robot teams and agent-based models (ABM's) is to field systems
of organizations based on first principles derived from human counterparts. However, the
failure of traditional organizational theory has forestalled that opportunity but at the same
time opened the way to innovative theories of organizations and change. Inspired by Bohr
and Heisenberg's ideas about the application of interdependent uncertainty in the
interaction between action and observation, making organizations bistable, we have
begun to construct a theory of organizations based on the uncertainty of energy level
(resources) and belief/action consensus, leading to preliminary metrics of organizational
performance that we have applied in field studies. Our goal in this project is to address
the problem posed by organizations with: the development of new theory; field tests of
new metrics for organizations; and the development of quantum ABM's set within a
social circuit as a building block for an organization. Should we be successful, our
research would represent a fundamental departure from traditional observational methods
of social science by forming the basis of a predictive science of organizations. We expect
that replacing the traditional method of observation with a predictive science must
account for when cognitive observations work and when they do not ("illusions").
Examples with mergers are provided and discussed.
An evolvable game theory: A bistable or quantum approach
A new approach to game theory has been designed to resolve two of its major
problems: The arbitrariness of valuing cooperation greater than competition in
determining social welfare; and the lack of interdependent uncertainty. The approach is to
develop quantum or bistable agents and relationships. The quantum approach means that
agents in relationships are more likely to be found in bistable states that correspond to
their energy levels - the more complex, competitive, or conflictual the state, the greater
the energy required. In our view, games are initialized, evolved to a state that solves a
target problem, then measured, consequently creating a measurement problem. In past
research, we have resolved the measurement problem. The measurement problem led to
the development of metrics that have been applied to organizations in the field (we
briefly illustrate an application to military Medical Department Research Centers). In this
paper, we focus on modeling control in bistable close and market relationships to produce
evolvable systems.
The Cost of Information Acquisition in a Supply Network of Rationally Bounded Negotiating Agents
Rodolfo Garcia-Flores (CSIRO Mathematical and Information Sciences), Nectarios Kontoleon (CSIRO Mathematical and Information Sciences), Rene Weiskircher (CSIRO Mathematical and Information Sciences) and Simon Dunstall (CSIRO Mathematical and Information Sciences)
Industrial problems that arise
in situations where responsibility is shared and knowledge is
incomplete often do not yield clear-cut optimisation problems which
can be solved "monolithically". A new paradigm for distributed
problem solving is needed that requires problem decomposition by
independent entities that are able to optimise with local models
and/or data which are concealed, inaccessible or incompatible, and
who must learn or infer about their environment using the incomplete
information available to them. Learning is often made through
negotiation. In this process, two parties with some apparent
conflict, interact in a potentially opportunistic way to arrive at a
mutually beneficial solution. Learning through negotiation implies
bounded rationality, a limitation that is often disregarded in
optimisation literature. Thus, the actual cost of negotiation as a
search process is seldom considered in optimisation studies. The
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