Complex System Science

A fundamental concept underpinning Complex System Science (CSS) is that local interactions between relatively simple components can lead to considerably more complex non-local behaviours. A fundamental conjecture CSS attempts to study is that these local interactions are the drivers for processes like self-organisation and emergence and, in turn, are responsible for the immense variety of structures, patterns and phenomena we see in Nature. These two ideas can be found, in slightly different form, in most 'manifestos' of CSS, which, implicitly or explicitly, associate CSS with an attempt to go beyond the constraints of the reductionist framework which has guided most past and current scientific successes. A natural question arising from this observation is how much of the current scientific arsenal can be saved by giving up reductionism; is the scientific method as we know it today suitable to study processes like self-organisation, emergence and adaptation? We ask what it means to carry out CSS by computer modelling and what kind of complexity can we study with this approach. We believe these are important questions, given that so much work in CSS is done with the aid of computer modelling and since much of the development of CSS was made possible by the availability of fast computing. In particular we discuss a) if and how computer modelling differs when it is applied to a complex rather than to a non-complex problem, b) what is the role of the modeller in overall complex-problem solving and c) whether common measures of process and model complexity match human perception of the difficulty of solving a 'complex' problem by employing a computer model.

My research in Complex System Science focuses on the following areas:

Definition and measurement of complexity in ecological models and data sets

I am trying to answer questions like:

• what is the best way to understand and model an ecological system?
• can a complex system be modelled?
• what is the difference between a complex and a non-complex system?
• can the same criterion be applied to computer models?

Papers:

• Boschetti F., 2007, Mapping the complexity of ecological models, Ecological Complexity, Vol 5/1, pp 37-47, doi:10.1016/j.ecocom.2007.09.002. Grigg N and F.Boschetti, Characterising model behaviour using nonlinear time series analysis, 'Complex Systems Science for Australia', under revision.
• Prokopenko M, F.Boschetti, and Ryan A, 2009, An Information-Theoretic Primer On Complexity, Self-Organisation And Emergence, Complexity, DOI: 10.1002/cplx.20249.F.Boschetti, McDonald D., & Gray, R., 2008, Complexity of a modelling exercise: a discussion of the role of computer simulation in Complex System Science, Complexity, 13, 6, pp 21-28.
• Lavoue, Boschetti, Ryan & Grisogono, A framework for studying the increase of complexity of interacting finite state machines, submitted.Bertello G. , Arduin P, F.Boschetti., Weatherley D., 2009, First Experiments in the application of Computational Mechanics to the analysis of seismic time series, New Generation Computing, 27, 1, pp 1-23. 10.1007/s00354-008-0052-x.
• Boschetti F.& Gray R. 2007, A Turing test for Emergence, in M. Prokopenko (ed.), Advances in Applied Self-organizing Systems, Springer-Verlag, London, UK, 2007 , pp 349-364.
• Batten D, Salthe S & F.Boschetti, 2008, Visions of Evolution: Self-organization proposes what natural selection disposes, Biological Theory, Vol. 3, No. 1, Pages 17-29.

Emergence: understanding, modelling and formal description

Emergence is seen as the most significant feature discriminating 'complex' from 'non complex' systems. Nevertheless, no standard definition of emergence is currently available in the literature. This lack of a shared view affects both scientific and engineering applications. In our work we review some definitions of emergence with the aim to describe how they can be implemented to detect and understand emergent processes.

Papers:

• Boschetti F. & Gray R., 2007, Emergence and Computability, Emergence: Complexity and Organization, Volume 9 Issues 1-2, 120-130 .
• Prokopenko M, F.Boschetti, and Ryan A, 2009, An Information-Theoretic Primer On Complexity, Self-Organisation And Emergence, Complexity, DOI: 10.1002/cplx.20249.
• Boschetti F.& Gray R. 2007, A Turing test for Emergence, in M. Prokopenko (ed.), Advances in Applied Self-organizing Systems, Springer-Verlag, London, UK, 2007 , pp 349-364.

Monitoring and managing sustainable eco-systems

How can our understanding of complex systems, our modelling skills and our understanding of human systems be used to monitor and manage sustainable systems and renewable resources?

Papers:

• Boschetti F., 2007, Improving resource exploitation via Collective Intelligence by assessing agents' impact on the community outcome, Ecological Economics, 63, pages 553-562.
• Boschetti & Brede, 2009, An information-based adaptive strategy for resource exploitation in competitive scenarios, Technological Forecasting & Social Change, 76(4), 525-532, doi:10.1016/j.techfore.2008.05.005
• Boschetti, McDonald and Brede, A rapid assessment agent based model for natural resource management, ModSim07, ChristChurch, NZ, December 2007.
• Brede, Boschetti & McDonald, 2008, Strategies for Resource Exploitation, Ecological Complexity, Volume 5, Issue 1, Pages 22-29.

CSS in the Geosciences

This is my previous 'life' and the CSS link is less strong.

Papers:

• Durrleman, S., Boschetti F, A. Ord, and K. Regenauer-Lieb (2006), "Automatic detection of particle aggregation in particle code simulations of rock deformation", Geochem. Geophys. Geosyst., 7, Q05006, doi:10.1029/2005GC001063.
• F.Boschetti, M. Dentith, R. List, A fractal based algorithm for detecting first arrivals on seismic traces, 1996, Geophysics, 61, 1095-1102.(see my thesis)
• Ord A., Boschetti F, and Hobbs B., 2004, "3D Imaging of Jointed Rock Masses", in Fractals in Geotechnical Engineering, D. Kolymbas (editor), Logos, Berlin, (in print)
• Boschetti, F.,2004, Controlling and investigating Cellular Automata behaviour via interactive inversion and visualization of search space, New Generation Computing, Special Issues on Intertactive Evolutionary Computation, Vol.23, No.2, February 2005.
• Kaltwasser P., Boschetti F., and Hornby P., 2004, Measure of similarity between geological sections accounting for subjective criteria, Computer & Geosciences, Vol 31/1 pp 29-34 .