Simulation Science, in our mental model, is the application of ‘linear science’ and ‘nonlinear science’ to solving problems in the real world.

’Linear science’ is located within the larger set of ‘nonlinear science’. We dislike using the phrase ‘nonlinear science’ as to us it’s like calling Zoology the ’study of nonelephant animals.’ (thanks go to the mathematician Stanislaw Ulam for this simile). Nor do we like the phrase ‘complexity science’ as that makes something easy sound unnecessarily hard. And, ‘chaos’ is even worse because it implies to the vast majority of people that the business will become frenzied or wild in appearance, when, in fact, management scientists are interested mostly in how a ‘chaotic’ business system can actually evolve in a way which appears smooth and ordered.

So, back at the start of the 21st century, we began calling it ‘Simulation Science’ instead. Or, just good science.

Simulation Science requires the use of computers and the application of nonlinear techniques (and ‘linear’ techniques when appropriate) for understanding our complex world so that people can solve real world problems more creatively and efficiently. By doing so, a new meta science, what we dub Simulation Science, emerged in the 1950’s that emphasizes multidisciplinary collaboration in pursuit of understanding the common themes that arise in natural, artificial, and social systems. This unique scientific enterprise attempts to uncover the mechanisms that underlie the deep simplicity present in our complex world.

Techniques used within Simulation Science might come from a variety of fields, e.g., regression analysis, agent-based modelling, cognitive neuroscience, network dynamics, brand dynamics, discrete-event simulations, system dynamics, strategy dynamics, fractal mathematics, dynamical systems, chaos, etc. ad nausea depending on the problem being studied.

So, illustratively, System Dynamics is a discipline within the larger set of Simulation Science. And, Strategy Dynamics is a sub-specialty within the discipline of System Dynamics. Additionally, we would argue that Biochemistry and Economics are also subsets within the larger Simulation Science set. That is, people need to understand the real-life problem and then choose the appropriate scientific discipline to analyze it and develop policy. Maybe it’s a System Dynamics model, maybe a discrete-event model or maybe some combination of a variety of techniques. Just make sure the scientific technique you are using embraces the facts of complex adaptive systems.

Greater than 90% of all business problems in the world currently being analyzed by the vast majority of consultants, economists, marketers, etc., are almost exclusively analyzed using techniques that paint patently false simulacrums of reality. Executives need to demand more from their analysts than just more black box spreadsheet models that create false security.

Many of these traditional simulacrums (often in the form of highly complex spreadsheet models) include assumptions that violate the most basic of physical laws. They are more of a hindrance to decision making than a benefit. We argue that analytical reductionism is more often used for improving perceived certainty and reducing perceived risk (leading to the all to often heard lament, “we did a bunch of analysis before it all went wrong, so its not our fault, rather something extrinsic to us caused our failure”).

Nothing wrong with doing what you’ve always done other than the technology for applying ‘nonlinear science’ is available (after decades of refinement) in the form of fast computers, excellent modelling software, solid management science, etc. Today, humans can build evermore robust simulacrums for improving their decision making.It is cheaper doing simulation science than traditional analysis. And, you get better results.

“Do I get my traditional consultants to answer this question or should I retain a simulation facilitator to help me understand what we need to change about our system to make it a more viable predator in the cut-throat business world?”

It’s a no-brainer decision . . . Hire the simulation facilitators and give your pricey consultants their walking papers.

Like a good book that has been relentlessly edited over time, simulation models often become simpler over time (unlike traditional analysis which gets more complex over time). Over time, the user interface may get more complex and robust, but the underlying simulation models (objects) should get simpler. Or, the more time we have, the simpler we can make the simulation objects. The complexity of the model arises when our modelers connect a bunch of very simple objects (molecules) together into a larger simulation.

It was nearly impossible before computers to truly understand the dynamics of businesses, because businesses are complex adaptive systems and it is impossible for any human being, no matter how clever, to solve high-order, nonlinear, dynamic systems other than in the most ‘gut-feel’ sort of way by using the most powerful computer on the planet (the human brains). Nothing wrong with that either — we just think it’s easier for people to follow their leaders if they make their mental models explicit in a computer simulation instead of requiring faith in the hidden mental models.

Or, even worse, are the consultants who are always forcing us to wade through a morass of complex, boring (and most likely completely wrong) spreadsheet models where the true structure remains hidden in the cells.

Simulation Science insights have diffused slowly over the past fifty years from the natural sciences to the social sciences. A 2002 citation search compared 5,400 social science journals against the 100 natural science disciplines covered by INSPEC (>4,000 journals) and Web of Science ( >5,700 journals) indexes. It shows keywords comput* and simulat* peak at around 18,500 in natural science, whereas they peak at 250 in economics and around 125 in sociology. For the keyword nonlinear citations peak at 18,000 in natural science, at roughly 180 in economics, and near 40 in sociology. In the words of the researchers who conducted this study:

“How can it be that sciences founded on the mathematical linear determinism of classical physics have moved more quickly toward the use of nonlinear computer models than economics and sociology—where those doing the science are no different from social actors—who are Brownian Motion?”

Our answer: It takes time.

So, one of the key challenges we are facing as Simudyne promulgates simulation science insights outside the academic world, is”

“How do I make senior executives realize that the reliability and accuracy of their decision-making can be improved by using simulation science?”

We believe that this involves educating executives that it is time to set aside their historical (and erroneous) preconceptions and prejudices of how to manage a business in the 21st century. Few executives today realize that the complexity of business is genuinely beyond them. And that’s not an easy sell. Humans can now profoundly deepen their understanding of the world they live in . . . thanks to computers and only with the help of computers. And, that’s an even harder sell.

Don’t agree? Pop down to your local antique store, buy an abacus or, better yet, grab some yarrow stalks so that you can consult the I Ching and get back to work!