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The flocking algorithm
When Newton published his laws of motion in 1687, he fired the starting gun for the scientific revolution. In demonstrating the mathematics behind gravity and the movement of planets, he showed that the natural world could be viewed as a vast machine that ran like clockwork. Other scientists, like Kelvin and Carnot in the 1840s, used this approach to analyze steam engines and developed the laws of thermodynamics and entropy.
These two strands of physical science were then fused together by Boltzmann and Maxwell in the 1890s with the development of statistical thermodynamics. This modeled gasses as a collection of tiny billiard balls, which when statistically aggregated together created the pressure in a steam engine.
Most of the tools of aggregate risk modelling in insurance today, like the Monte Carlo method, stem from this work.
The analytical approach behind these breakthroughs was fundamentally reductionist; breaking things down into their component parts to see how they worked. But then came quantum mechanics, which showed that, at small scales, particles resemble waves rather than tiny billiard balls. This led later in the 20th century to a view that a holistic approach to science could prove more fruitful than a reductionist one. A complex system with many interactive feedback loops exhibits emergent behavior that can only be understood at a macro level. Such complex adaptive systems are biological not physical in nature, and a murmuration of starlings is a good example.
Complex adaptive systems cannot be analyzed by isolating the individual agents or by aggregating them into a single statistical entity, because both those processes eliminate the interactions between them.
The dynamic unpredictability of the flock derives from these three rational fundamentals. A simple computer simulation based on this algorithm creates uncannily lifelike flocking behavior. Prediction of the future may be impossible, but understanding is not. This suggests that those who wish to model cyber risk should look to incorporate analytical tools from biology or behavioral economics, rather than just relying on those borrowed from the store cupboard of physics.
Attraction Try to move to a position near the center of the flock. Separation Avoid bumping into any other bird by aligning yourself in the same direction as others around you.
Alignment Align yourself in the same direction as others around you.
The dynamic unpredictability of the flock derives from these three rational fundamentals. A simple computer simulation based on this algorithm creates uncannily lifelike flocking behavior. Prediction of the future may be impossible, but understanding is not. This suggests that those who wish to model cyber risk should look to incorporate analytical tools from biology or behavioral economics, rather than just relying on those borrowed from the store cupboard of physics.
