Qualitative Modeling at CSIRO

From the CSIRO web site:

CSIRO, the Commonwealth Scientific and Industrial Research Organisation, is Australia’s national science agency and one of the largest and most diverse research agencies in the world.

They use some interesting Qualitative modeling techniques to predict the impact of ecological changes on biological systems.

In Qualitative Modelling and Bayesian Belief Networks ( big PDF, over 3 Meg ), the author give two practical examples of ecological risk assessment. The first is a simple model of Vegetation–Hare–Lynx interaction that correctly predicts non-linear behavior in the population of hares in an environmental perturbation of increased vegetation. The second is more complex example of interaction between shrimp, detritus, zooplankton, fish and benthic invertebrates, generally producing realistic results.

The reader can find an introduction to the modeling framework and mathematical methods in Qualitative Modeling of Complex Biological and Social Systems.

The document Flying Insect Agents in Complex Chemical Plumes uses a different approach to qualitative techniques, using agents as the fundamental organizing principle. The purpose is to build a realistic model of luring insects with pheromone attractors to control their numbers. The difference to the previous models is using simple rules for software agents to model the behavior of insects tracking the pheromone “plume”.

From the document:

We use a model agent (an insect ‘in the mood’) that obeys the following rules:

i) Fly at a constant speed;
ii) Fly upwind if it detects pheromone above a threshold;
iii) Fly in random crosswind directions when not detecting pheromone;
iv) Fly at a constant height (known source height).

This set of rules is perhaps the simplest set of instructions perating within the capability and processing power of an insect. The capability is not trivial and reflects utilisation of both optical signals (for sensing spatial location and determining wind direction) and chemical signals for decision making.

The result is a significant variation from the drunkards walk used by classical modeling techniques. The author notes that:

This process is a novel version of the drunkards walk – a particularly apt example because insect pheromone is often dominated by alcohol.

Hmmm. Not that different from human ecological systems, at least in urban environments. In the conclusion, they state:

It is a complex system, coupling the properties of a scalar field in turbulence (itself quantitatively complex) with an intelligent biological agent …

The archives of CSIRO contain a little classic of complex systems in Evaluating Team Performance at the Edge of Chaos. It makes a very lucid case for self-organization in complex systems, one of the clearest explanations for a counter-intuitive phenomena.

As pointed out in the literature [8, 5], emergent self-organisation or extropy may seem to contradict the second law of thermodynamics that captures the tendency of systems to disorder. The ‘paradox’ has been gracefully explained in terms of multiple coupled levels of dynamic activity (the Kugler-Turvey model [5]) self-organisation and the loss of entropy occurs at the macro level, while the system dynamics on the micro level generates increasing disorder.

One convincing example is described by Parunak and Brueckner [8] in context of pheromone-based coordination. Their work defines a way to measure entropy at the macro level (agents’ behaviours lead to orderly spatiotemporal patterns) and micro level (chaotic diffusion of pheromone molecules). In other words, the micro level serves as an entropy ‘sink’ – it permits the overall system entropy to increase, while allowing self-organisation to emerge and manifest itself as coordinated multi-agent activity on the macro level.

For more about the Kugler-Turvey model, See Entropy and Self-Organization in Multi-Agent Systems, which may link into Stuart Kauffman’s ideas about catalytic hypercycles and pre-biotic life.

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