Expert Voice: Icosystem's Eric Bonabeau on Agent-Based Modeling - ' Eric Bonabeau, continued '

Do you test a lot of interventions before knowing you have the right solution?
Yes, you're building the model to solve a problem. So you have an objective. You want to maximize profitability by creating a pricing strategy that will do that, for example. Or you want to create a set of regulations for the stock market that will prevent collusion. Based on your research and knowledge gathered from people in the field or experts in the organization, you try out a series of interventions. And you select those 3 or 4 or 10 percent of interventions that seem to produce the best outcome with respect to your objective. Then you breed them and you mutate them, and you have a new generation of solutions and so on and so forth.

By the way, in many projects that we're working on, the objective is not to find an optimal solution; the objective is to find a robust solution. So a robust intervention, one that will work fine no matter what, is what you want, not one that will work optimally under a very specific set of conditions. Because if there is a change in the environment, if there is something that you forgot to include in the model that is actually key, then you end up with something that is optimal but just doesn't work in the real world; it's fragile and brittle. So optimality is another slippery concept.

This is becoming a big issue in IT areas like supply chains, where the notion of an optimal chain has been the ideal for many years. But now it's becoming clear that it's the most adaptive supply chain that is desirable, not the most optimized, because that just ends up becoming inflexible.

A related concept is one that people don't think about enough: evolvability. Instead of building a system that is optimal or even robust, you may want to build it to prepare for future generations of products. If you're a software company that builds a routing algorithm, you don't want to make it too dependent on Cisco routers the way they are built today because in two years they may be very different, and then you're going to be stuck with software that you're going to have to redo from scratch. So what you want to do is make it evolvable, easy to transform for the next generation.

Can an agent-based model do that? Can it identify whether a system has the emergent property of evolvability?
Right, you could use an agent-based model to build scenarios for evolvability by determining the flexibility of different components in the system and how this affects the overall performance of the system.

What are some of the applications that you're working on?
We have clients that are interested in pricing strategies or in marketing strategies—we've worked on predicting the performance of new-product launches and on defining the right mix of marketing channels. We do portfolio management work for the pharmaceutical industry where we use agent-based modeling to simulate the operations of an R&D facility, and the intervention is how to implement a profitability strategy. Right now, we're predicting how consumers will choose new healthcare plans. We have a lot of data on how they made choices in the past and we're actually very, very good at predicting what they'll choose in the future. We're between 95 percent and 99 percent accurate. Consumer behavior is going to be one of the most successful applications of agent-based modeling in the next few years. That's because with agent-based modeling, you can go way beyond traditional techniques like econometrics. Using agent-based modeling you actually model the decision-making behavior, so you have access to a deeper layer inside the consumer.

What is a typical interaction between you and a potential client? How much do you have to educate potential clients about agent-based modeling?
The executives we work with are usually familiar with complexity science and agent-based modeling, otherwise it's an uphill battle that we can't afford. We're only working with people we know are going to be responsive and understand the strengths and weaknesses of our approach. That's number one. Number two is, depending on the kind of issue they want us to address, we have to assess whether or not we can do it, and we have to evaluate what kind of value we can bring to the client. Is it going to be prediction, or is it going to be insight? Usually, if it's insight, it's because we don't have enough data to do forecasting, but we can at least identify and describe how their system operates. I'm much more comfortable with prediction, because when it comes to insight, I really don't know how to measure the value that we're bringing to our clients. So when clients ask for insight modeling, I have to be very candid and say, 'Please don't make any decisions based on this model. It's a model that is aimed at helping you think about your problem, not a model that is aimed at solving your problem.'"

Let's assume we're building a predictive model for our client. Usually we do a small pilot experiment in which we take the model's results and try it out in the real world. Hopefully, what occurs in the real world is consistent with the predictions the model made. Or if it's not completely consistent, we may discover certain things that we failed to take into account. But usually we come out of the pilot comfortable with what we've done, and then it's a matter of the client making a decision to use the model on a large scale in the real world.

But implementing a full-scale application like that requires a lot of faith by these executives. Failure isn't easily accepted in corporations. And faith in agent-based modeling will only spread after some high-profile company achieves quantifiable results with it. Real financial results. When a company says we saved or we made $200 million thanks to agent-based modeling, there will be a big difference in the popularity of this approach. I think that is what is needed. We don't have that yet.

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