Chris Scogings

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There has been some ambiguity about the growth of attractors in Kauffman networks with network size. Some recent work has linked this to the role and growth of circuits or loops of boolean variables. Using numerical methods we have investigated the growth of structural circuits in Kauffman networks and suggest that the exponential growth in the number of(More)
Agent-based models have been used to capture and analyze the essential behaviors of combat units although the number of agents used has been fairly low. We experiment with a microscopically detailed agent model in which over 20,000 soldiers are represented individually (one agent per soldier) in a simulation of the Battle of Isandlwana in 1879. We describe(More)
The area of computer-generated artificial life-forms is a relatively recent field of inter-disciplinary study that involves mathematical modelling, physical intuition and ideas from chemistry and biology and computational science. Although the attribution of “life” to non biological systems is still controversial, several groups agree that certain emergent(More)
We discuss algorithms and methods for classifying the clusters of model animals that emerge from simulations of collective behaviour in artificial life models. We show how important statistical properties for understanding scaling and universal growth can be measured from complex and chaotic model systems. We describe animal clustering algorithms and the(More)
Animat agents are usually formulated as spatially located agents that interact according to some microscopic behavioural rules. We use our predator-prey animat model to explore spatial segregation and other self-organising effects. We compare the emergent macroscopic behaviour with that of non-intelligence models such as those governed solely by microscopic(More)
Computer simulations of complex systems such as physical aggregation processes or swarming and collective behaviour of life-forms, often require order N-squared computational complexity for N microscopic components. This is a significant handicap to simulating systems large enough to compare with real-world experimental data. We discuss space partitioning(More)
The Kauffman N -K, or random boolean network, model is an important tool for exploring the properties of large scale complex systems. There are computational challenges in simulating large networks with high connectivities. We describe some high-performance data structures and algorithms for implementing large-scale simulations of the random boolean network(More)