Swarm Intelligence, Ant Colony Optimizations – advances in analytic computing

Advances in computing have led to some new and interesting developments in the areas of new modeling techniques. This post is going to give some examples of these kinds of techniques. But before that, a small primer on basic modeling techniques. Most of the more commonly used models are generalized linear models. As the name suggests, these models try to establish a more-or-less linear relationships between what is tried to be predicted and what the inputs are. Ultimately the model fit problem is an optimization problem – an attempt to use a generalized curve to represent the data and while doing so, minimize the gap between the actual data and the approximate representation of the data produced by the model.

Of course, optimization problems present themselves in a number of areas. One is of course model fitting but other applications are in areas like planning and logistics – an example being the ever-popular traveling salesman problem. One of the more recent and interesting techniques in solving optimization problems is through a technique called Ant Colony Optimization (ACO). The optimization is a part of series of more generic AI/ machine learning tools called swarm intelligence. Wikipedia defines swarm intelligence as follows

Swarm intelligence (SI) is the collective behaviour of decentralized, self-organized systems, natural or artificial…. SI systems are typically made up of a population of simple agents or bodies interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents.

The ACO algorithm tries to mimic the behaviour of ants in search of food. When ants forage for food, every ant involved in the foraging process moves out of the colony in random ways to search for food. When a food source is located, the ant uses the scent trail of its own pheromones to bring the food back to the colony. Other ants begin to then use the trail left behind by the first ant to make further excursions to the food source and bring back food. Also, by the very nature of the pheromone trail (which is a volatile chemical and therefore evaporates after a certain point in time), the tendency of later ants is to follow more recent and fresher trails, which should also be the shortest ones logically speaking.

One of the more interesting business applications has been indeed in the area of material movement, i.e. logistics. The Italian pasta maker Barilla as well as Migros, the Swiss supermarket chain have been using these techniques to optimize their distribution networks and routes. A paper about this technique is available here. It is more technical. A more layman-friendly treatment of the technique appeared recently in the Economist and was also an interesting read.

The place of Systems Modeling in Analytics

When one talks about predictive analytics, the typical thought process goes in the direction of regression, neural nets, data mining techniques. Techniques that savvy marketers (consumer product companies, banks) have been using for close to two decades now in building insights about consumer behaviour. Systems modeling or Systems Dynamics is not something that immediately springs to mind.

So what is systems modeling all about? Systems modeling is creating a mathematical representation of a real-world phenomenon, trying to cover as wide range a set of inputs as feasible and the most valuable outputs. The systems model tries to explain how the inputs translate to outputs. How the systems model is different from a statistical predictive model is that the purpose of the systems model is not to try and explain variance in the output. The systems model instead tries to establish structural relationships between the input and the output. The model then further stresses the structural relationship by varying the inputs and looking at the impact on the output.

A good example of a subject that can be systems-modeled (my verb!) is the problem of terrorism. The problem has different inputs: unhappy people, territorial disputes, foreign power wanting to create trouble, funding, media coverage, etc. The immediate output is various actions of terrorism such as assassinations of leaders, suicide bombings, etc. It might be feasible to build a model that creates a structure on how these various inputs combine and interact with one another and cause the outputs. (If one goes back over the past 150 years, there should be plenty of data points.) Another way of looking at the output is a more holistic view that measures the damage done in terms of lives lost, economic damage incurred, etc.

What would be the purposes of this model? In my opinion, the value of such a model is less around where the next terrorist strike is going to be, or how big the next strike is going to be. (This is incidentally what a classic statistical model is going to try to do.) But rather, the model should try and explain what are the confluence of factors that produce a large output event (lives lost, economic damage) and how can some of the factors be controlled, ONCE an insurgency is already underway. The hypothetical model I am talking about does not try to predict, but rather to strengthen our understanding of the system dynamics. The model would have a PoV on what inputs can be controlled and to what extent are they controllable.

The model would then be used to understand how a large impact event can be prevented or its impact minimized. So if the federal government had a $100 billion to spend, how much should they spend on homeland security vs. promoting a positive image of the United States through foreign media? The model might tell that it is pointless to spend more than, say, $500 million on putting in a sophisticated software to block large untraced wire transfers as there are other ways in which the funding can be made available to the perpetrators of the terrorism act. So controlling the funding for an insurgency through sophisticated money laundering and layering detection algorithms may be pointless if the actual money gets exchanged through a non-electronic channel.

So an agency interested in curbing terrorism, might be better advised in, say, over-investing in trauma care health facilities and emergency services in vulnerable areas. This is so that when a strike does take place, medical help for the people who are affected is close at hand and casualties are minimized.

Why am I writing all this? Analytical problem solving is not just about fancy statistical algorithms or cool math, it is also about thinking hard about problems and creating their mathematical representations - and then being crystal clear about what those mathematical representations can and cannot do. This is where the systems modeling approach can be a very effective portion of the arsenal of a business modeler.

I'll close out with a couple of links, which prompted this wave of thinking on this subject. One is a paper in the Nature journal where the authors have presented a statistical model of insurgency events. The link is here. It's a gated article.

The following link has a very good critique on the article.