The Predictive Math of Strategy
Thanks to the Simpsons

The Predictive Math of Strategy

So, historically, this would be a discussion on either statistical modeling, or game theory, depending on the writer. Now then, I have bad habits left over from majoring in Astrophysics, and have made a career working outside the box, so here is a different take - that even those of you who hate math can use to your advantage. 

Today is a simple if abstract discussion. The math of strategy. Now the majority of my peers at ASP specifically avoid the quantitative, and most strategists I’ve encountered are MBA’s, historians, or soldiers that are not that keen on math, and don’t have much use for it beyond the simple arithmetic of “our big number beats their little number.” (which isn’t even always true).

TED, FRY, Complexity, Systems and Strategy 

By now, many of us are familiar with TED Talks (Technology, Entertainment, Design). Personally, I was chagrined when I found out Ted.com was taken. But at least it’s being put to good use.

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The TED talks are about “Ideas worth Sharing.” They started out very cool, and have gotten more commercialized over time, but more often than not, are fascinating; if typically focused on technology (due to their silicon valley roots). There are also independently organized TEDX talks that really embody the “diamond in the rough” meme that started TED. It’s a great place to see well communicated ideas.

The other day on my lunch hour I happened upon a TEDx Talk from Britain that well, hit my sensibilities as a physicist, mathematician, and strategist squarely.

FRY?

Dr. Hannah Fry, according to her website, is a “Dr. of Fluid Dynamics, researcher of Complexity Theory and all round bad-ass.” If you take a look at her Ph.D. Thesis, it’s 200 pages of graduate level mathematics that pretty well qualify her self description (As do her blogs posts on Python scripting).

OK, enough background. SO what?

Point is, Dr. Fry did a really well developed TEDx Talk that demonstrates in layman’s terms the power of modern mathematics. Specifically, they created a mathematical model given available information and science that accurately recreates what happened in the 2011 London Riots, and appears to predict the behavior of riots.

Complexity

OK, I’m biased. I’m an astrophysicist that does quantitative analysis of business intelligence and project planning and execution for a living. My pile of books to read at home includes math texts on catastrophe theory, game theory, and complexity theory. But I confess that it was John Mauldin that first showed me the value of applying complexity theory to strategic thinking.

Here’s how Math works. Your classical physics and math are great for directly predicting simple things. This is the majority of everyday engineering and construction. We can accurately mathematically predict how well engineered machines and processes will work before we build them (e.g. machines, electronics, factories). We can use math to measure and control the consumption of measurable quantities, be is Gas in our cars, grams of protein for athletes, sales revenue, market share or money in our bank accounts. But you are limited by what you can measure, and it has to be “simple” mathematically speaking.

2 Problems.

1 – There are many things you can’t measure.

2 – Some problems require you to measure too many things.

Now in the 20th century, science got around some of those problems by using statistics (i.e. simple pattern recognition). We can mathematically model and predict how huge systems work if their behavior is simple when treating them as a whole. This works for things like galaxies, stars, gasses, explosions, fluids, water, etc. I don’t need to track every molecule of water in a pipe, just the sum of its parts and everything works out.

The problem is the middle ground. A complex system is something that has too many variables to measure and solve simply; and not consistent enough to treat as a single simple whole (i.e. a business, a war, the economy).

Now in the 21st century (with the help of computers), complexity theory is starting to get good. Researchers like Dr. Fry are starting to create mathematical models that accurately reproduce and potentially predict the behavior of real life complex systems, using rationality and complex math. This is possible by basing the model by adapting existing models that work in analogous scientific systems.

For example, assume:

How viruses spread = How information spreads

Shopping travel habits = Rioting Travel habits

Predator Prey movement = Rioter / Police movement

Adapt those three models into a single predictive mathematical model, and they actually do model and predict how fast, how long, and where riots will spread; based on input conditions and changing variables. The model is roughly as accurate as the input data and does a good job of recreating riots that have happened. But even here, the precision and accuracy of the model are based on how many measurements you can put in. Historical data plus known science becomes a constraint on future prediction. That’s mathematical modeling 101, at least it was when I was in college.

Notice business kids, they use the known science of shopper behavior to accurately predict how rioters behave. Personally, I had forgotten there was a science to shopper behavior. Point – there is tons of data and science out there if you take the time to find it (there nothing new under the sun, odds are somebody else already knows the solution to your problem).

Systems

So at this point, ya’ll are saying, cool Ted. So some math geeks can do some math that models a riot. So what?

Ok kids. Any organization; a family, an army, an NGO, government or business is a system of interrelated parts that exchange information and resources to accomplish a variety of activities to accomplish organizational goals while also doing activities it needs to do to survive. E.G. A Family may go on vacation – focusing on logistics, travel, and entertainment, but still have to deal with tactical needs of food, shelter, sleep, and family politics plus the long term requirements like bills, insurance, and a home.

A business may be focused on selling its product, with the normal cycle of R&D, product development, marketing, supply chain, manufacturing its product – but also has to have HR, Legal, Accounting, Facilities, IT, Taxes, and possibly PR and Lobbying to support the simple goal of building and selling a product.

Point is – all the functions of the business form a system of relationships that exchange information and resources in a constant and definable way. Time sheets feed accounting so they can do payroll, but need charge numbers on the timesheets to feed financial cost controls and bucket costs for filing taxes. These are mathematically speaking, complex systems like what Dr. Fry works with.

Hopefully, the system thing is making sense. System, ecosystem – simply a collection of processes that flow resources and information through the organization to accomplish what needs to happen. When working in an Engineering firm, I saw plenty of technical process flow diagrams for things like refineries and power plants – and the methods are the same as when we are doing business processes. There is a reason why there is a whole “systems thinking” movement in management consulting. It’s accurate to the real world and works when done well. I was amazed when I started studying business strategy, all these cyclical strategy frameworks were almost identical to standard engineering control theory.

Funny to me at least. When I bring up that point at an ASP meeting fully of strategy consultants that developed these business strategy frameworks, they are honestly ignorant of their parallel development of the existing engineering science that is many decades old. 

Well, kids, there is a math to your organization’s system. And an easier paradigm than complexity theory. Google the term “topology”. It’s a branch of mathematics developed to study shapes, but what topology really does is map the relationships of different groups with different functions. Which means two relevant things. 1 – there is a math for quantitatively modeling the relationships; i.e. the flows of information, resources, and processes within your organization. And that math is also the math of databases; so if you can find someone willing to learn the theory of a homeomorphism (one to one vs. one to many relationships),  you can have a model or framework for understanding exactly how to map the system of processes of your organization; and even use it to streamline the series of databases that IT already maintains to try and manage that existing system via IT based business intelligence (ERP). This allows you to data mine your big data intelligently if you understand the topology, the relationships between how different sets of data interact. (I actually got to do this, the results are VERY cool). This is very relevant to application data science and data analytics.

Then to miss Fry’s point. If you can find some graduate students in complexity theory, you can model the behavior of your organization’s processes and accurately and precisely predict how they will react under different scenarios. You can take what good managers do instinctively, and improve on it with hard numbers.

Now, let’s be honest. The vast majority of us have not studied graduate level mathematics like complexity or topology. But you don’t really need to do the formal math to be effective. Math is just the science of patterns and relationships. It’s built on rules and numbers. But the rules work without the numbers (accurately if not precisely).

If you can understand the difference between a one to one and a one to many relationship, and notice that a pattern of a one week delay in supply chain results in a two week delay and cost in manufacturing – You can sketch out some simple process flow diagrams to model the systems of your organization, and see the patterns of what goes right, what goes wrong, and will have a legitimate engineered if simple approach to troubleshooting and understanding what goes right and what goes wrong. It works for plants filled with a billion dollars of machinery, it can work for your organization filled with people doing various interdependent functions working towards team production.

Learn the concepts, and it can paradigm shift your ability to understand and solve problems. If you can define and recognize the consistent patterns of your organization, you can predict how changes will affect those patterns.

Strategy

So what?

We covered, well, a lot in a small space. I obviously have too much applied math to get off my chest.

Dr. Fry can’t tell you when a riot will happen. But she can tell you how a riot will happen. That allows for some very precise scenario planning. Be ready for an event or change before it happens. And honestly, scenario planning has been around for decades. Even going through the act of inaccurate scenario planning prepares the participants for change – orients them to better understanding the patterns of an organization and how they react to change.

If you understand the patterns, you have a chance at anticipating, managing and changing them.

Example

Here’s the math free example of applying the math of understanding patterns of behavior and relationships. Charles Duhigg wrote a very useful book called the Power of Habit.  He thinks in terms of habits and the behavioral triggers that cause them. Which is simple math: Trigger = Habitual Behavior; only gets more complex as you see how a system of habits as a process in a population.

So what? Well, how’s this for problem solving. Some years back, Mr. Duhigg spent some time in Iraq during the war. In the city he was in, the Army had a problem that every evening people would gather in the town square, protest, and there were some riots.

Over a few several days, Army leadership observed the habits – people didn’t show up to riot. The town square was also a market. People arrived to shop, ate dinner, and then were already gathered in the evening, in the town square, looking for something to do. Trouble simply ensued after dinner.

Solution? The Army Major tasked to solve the problem simply banned kebab stands from the town square.

So at the end of the day, people in the town square got hungry, went to find food, and left the square. Ended violent protests and riots with zero force, no violence, no death.

Charles Duhigg rightly calls that the "Power of Habit". Someone like Dr. Fry would likely call that an elegant solution by simply understanding the equation, and changing a variable to zero. I simply call it good strategy.

Dry Fry may be able to predict the pattern of a riot. In one instance, Charles Duhigg witnessed an Army officer able to identify the pattern and disrupt it and prevented the riots from happening. In other words, he successfully predicted the conditions needed for a riot. They both can do that because, in their own way, they do the math (pattern recognition) to understand why things happen the way they do.

The lessons here are simple:

  • You can’t predict the future (unless you are very, very well informed and good at game theory, then you can a little – but we’ll leave that for next time.)
  • But if you can identify predictable patterns of behavior for a system of relationships ( e.g. an organization), you can predict how that behavior will happen; e.g. Dry Fry and her complexity models that show how riots happen.
  • The better the math (pattern recognition), the more precise the model. But even simple models can yield accurate and usable predictive results for patterns of behavior.
  • You can use a system map or process diagram to identify the relationships and patterns of your organization (i.e. its structure). That will give you a framework for understanding, visualizing, and ideally predicting the patterns of your organization.
  • If you can predict how your organization will respond to change, you can prepare for and manage that change. Whether a change in the market or any deliberate strategic change to the organization driven by leadership. If you know how the organization works as a system of processes, you can deliberately engineer the desired results.

And the big points

  • You know change is coming. You know the world is not standing still. This is a tool that can help you be ready for change, and respond and change your organization and its system of processes in a faster and more appropriate way. Simply put, an appreciation of math combined with some leg work, and you can adapt faster and better than your competition.
  • Yes kids, this is another approach to process improvement.
  • Even better – If you can do that internally with your organization, you can probably also map the system of processes for your value chain, supply chain, market, competition, etc. And get the same sort of feel for predicting and appreciating changes external to your company. Allowing you to adapt your external view and relationships just like the internal above.
  • Even better, if you take the time to understand how things work in the world, you may be able to manipulate (influence) the process to get results more in your favor. I.E. it may be possible to alter a riot in progress, or a market shift to change the outcome predicted by the model, and achieve a more desirable outcome.

You can’t predict when events will happen, but you can be ready with a good response for when they do. Yeah, it’s basically the boy scout motto. But there is legitimate math that can help you manage the problems that are too big to measure, and too unpredictable to treat simply.

By rigorous study of the math of the patterns, we can learn them, and that allows us to predict, adapt to, and potentially disrupt, prevent or improve those patterns.

If you understand the patterns of conflict, you have the potential to predict, adapt and manipulate it to a more desirable result.

And in my mind, that is what strategy is all about.

If you made it this far, thanks for reading my pedantic rant on math.

-Ted S. Galpin SPP, CCP

First Published at: https://strategicscience.org/ted-fry-complexity-systems-and-strategy/

Mihai Ionescu

Strategy Management technician. 20,000+ smart followers. For an example of a strong nation, look where European cities are bombed every day by Dark Ages savages. Slava Ukraini! ????

7 年

Very nice, Ted S Galpin SPP, CCP, PMP, to my liking (a left-brain engineer :-) But I think that the problem with the Strategy [formulation] is not the failure to recognize patterns and act accordingly, by extrapolating the information available into the future. The problem is the choice between many patterns, often overlapping or conflicting, and the handling of [strategic] HYPOTHESIS that allow us to favor one pattern against another or select those that seem the most valid for estimating what future will bring to us, should we take one set of decisions or another, based on [some of] the patterns' interpretation. .

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Mihai Ionescu

Strategy Management technician. 20,000+ smart followers. For an example of a strong nation, look where European cities are bombed every day by Dark Ages savages. Slava Ukraini! ????

8 年

Applied Strategy technicians like me (mostly, engineers turned to business) are probably reading this article with a grim perspective ... something along the lines of "we can somehow understand what he says, but does he somehow understand what we do?" .

Mihai Ionescu

Strategy Management technician. 20,000+ smart followers. For an example of a strong nation, look where European cities are bombed every day by Dark Ages savages. Slava Ukraini! ????

8 年

Applied Strategy technicians like me (mostly, engineers turned to business) are probably reading this article with a grim perspective ... something along the lines of "we can somehow understand what he says, but does he somehow understand what we do?". .

Joseph Ezenwa

Managing Consultant

8 年

Ted,you are really great.I can see now that you play with numbers.I am at home with figures too.I taught mathematics and i did additional maths at secondary school level.I know that you are very much at home with calculus.Now i teach structured and corporate finance together with mathematics of finance.

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