Complex '09 Workshop on Causality in Complex Systems
How should we think about causality in complex systems ??
One of the reasons people have difficulty in dealing with complex systems is that the linear causal chain way of thinking - A causes B causes C causes D ... etc - breaks down in the presence of feedback and multiple interactions between causal and influence pathways. One could say that complex systems are characterised by networked rather than linear causal relationships.
Moreover, the open-ended nature of complex adaptive systems implies that their structure, properties and behaviour can change dynamically as a result of interactions with the system's environment (e.g. through adaptation) and as a result of internal interactions (through self-organisation), so traditional notions of causality are even further stretched by these adaptive, self-organising and autopoietic behaviours.
Nevertheless it is important to be able to reason about complex systems, make inferences about factors that contribute to current and alternative states of complex systems and explore their possible future trajectories, especially if we wish to influence them towards more favourable futures, and away from more dangerous possibilities.
Large scale examples include ecosystems, economic systems, coupled biophysical-socioeconomic systems, integrated supply chains/industrial systems and social systems, but these remarks also apply for example to attempts to understand a physical organism as a complex system.
Workshop announcement and CFP - Causality in Complex Systems
Under the auspices of Complex '09 which is being held in Shanghai from the 23rd to 25th of February, we are organising a workshop to review the state-of-the-art in thinking about Causality in Complex Systems, and to develop and discuss the key research questions the complex systems community most needs addressed. The workshop aims to leverage relevant experience and knowledge by bringing together people who have expertise in particular domain-specific approaches to dealing with causal networks in different fields. We seek to stimulate cross-disciplinary synthesis and cooperation in methodological research.
1030-1040 Anne-Marie Grisogono “Introduction to Workshop”
1040-1110 David Batten "Causality and Complexity in Adaptive Neural Systems"
1110-1140 Peter Goodison, Peter Johnson, Joanne Thoms "Establishing Causality in Complex Human Interactions: Identifying Breakdowns of Intentionality"
1140-1210 Patrick Beautement, Christine Broenner "Complex Multi-modal Multi-level Influence Networks - Affordable Housing Case Study"
1210-1300 Lunch Break
1300-1330 Qin Zhang "The Difference between Single-Valued and Multi-Valued Cases in the Compact Representation of CPD in Bayesian Networks"
1330-1400 Qiang Luo, Xu Liu, and Dongyun Yi "Reconstructing Gene Networks from Microarray Time-Series Data via Granger Causality"
1400-1430 Frank Emmert-Streib, Matthias Dehmer "Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection using Bagging"
1430-1500 Discussion on the Papers Presented
This workshop has been concluded, however other workshops on this topic are being run throughout 2009. If you would like to get involved contact the chair (Dr Anne-Marie Grisogono) or myself (Vanja Radenovic).
Papers should address one or more of these questions:
- What kinds of causal and influence networks are there and how should we classifiy them? Can we use a set of model systems, such as from graph theory, as a basis for exploring these relationships?
- What are the important characteristics that define them?
- What techniques are, or could be, used to:
- Learn about the causal and influence networks operating?
- Represent and interrogate our models of causal and influence networks?
- Understand sources of uncertainty and the decision-relevant limits of these representations?
- Test our hypotheses on causation in complex systems?
- What is the relationship between causality and topology? How to explore the topology of the system's phase space and its relationship to causality?
- Reason about what possible trajectories the system can take?
- Identify what possible attractors exist?
- What are the strengths, weaknesses and domains of validity of the different techniques?
- What promising ideas are there for new approaches and techniques?
- What are the most critical deficiencies in our capabilities?
- What decision-making and organizational approaches are aligned with the complex nature of the systems we seek to influence, and environments within which we must manage?
Papers can be illustrated with insights or examples drawn from various domains including:
- Philosophical and logical aspects of causation in complex systems
- Causation processes, abductive reasoning, indeterministic causation, causal preemption, etc.
- Gene regulatory networks and random boolean networks
- Agent-based modelling techniques
- Exploratory modeling and exploratory analysis
- Mathematical techniques
- Interactions between adaptive processes
- Threshold analysis and regime shifts
- System dynamics
- Bayesian techniques
- Ecosystem and environment modelling
- Participative modelling techniques
- Decision making under conditions of complex-adaptive-systems-related "deep uncertainty"
- Socio-cognitive-emotional aspects of interacting with complex systems
Chair: Dr Anne-Marie Grisogono, DSTO, Australia
Professor Dietrich Dorner, University of Bamberg, Germany
Dr Axel Bender, DSTO, Australia
Dr Alex Ryan, DSTO, Australia
Dr Jimmie McEver, Evidence Based Research, USA
Dr David Batten, CSIRO, Australia
Bohdan Durnota, Tjurunga Research Pty Ltd, China & Australia
Dr Matthew Berryman, DSTO, Australia
Vanja Radenovic, DSTO, Australia