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There’s A Mental Model In Your Head
There’s A Mental Model In Your Head
In this article: Our author outlines gaining competitive advantage by recognizing, challenging, and refining our mental models and the computer models based on them.

There’s a mental model in your head. There’s a mental model in my head too. There’s one in the head of your colleague down the hall, and in the head of your company’s CEO, and in the head of your favorite business-school professor.

This article is about the reflexive, self-evidently true mental models that you, I, and every other businessperson on Earth use to make business decisions. It is about gaining competitive advantage by recognizing, challenging, and refining our mental models and the computer models based on them.

It matters because there is always a model when we make decisions. Always. That’s one of the five rules of models. We’ll meet the other four later.

Mental models
Mental models are the stories and algorithms in our heads that say if we do this then the result will be that. They reflect our knowledge, beliefs, and interpreted experience about how the world works. We often are unaware of them and their details. We may call them judgment or even instinct. 

Mental models differ dramatically and contentiously when people’s experience is varied, complex, or ambiguous. As with competitive strategy. 

Imagine you and I run a manufacturing business. Your mental model for what makes it tick may go like this:
  • We have heavy fixed costs from our factory and machines.
  • To be profitable, we must cover those fixed costs and all other costs.
  • Costs per unit go down if we sell more units.
  • Customers buy more when the price is low.
I’m not saying your mental model is right or wrong. I’m saying only that it is a mental model.

Your mental model focuses on internal operations (costs), recognizes the effects of volume (keeping the factory full reduces total costs per unit), and favors price cuts or promotions because those actions help build volume.

I’ve encountered that mental model in my career, and I’m sure you have too. It’s common and it makes sense.

Let’s get back to our manufacturing business. Here’s my mental model:
  • We have heavy fixed costs from our factory and machines.
  • To be profitable, we must cover those fixed costs and all other costs.
  • It’s easier to cover our total costs if our price is high.
  • Excellent quality and a strong brand make customers willing to pay a premium price.
I’m not saying my mental model is right or wrong. I’m saying only that it is a mental model, and it differs from your mental model.

My mental model focuses on customer perceptions (quality and brand), recognizes that a superior product can command a higher price, and favors price increases or product differentiation because those actions help build margin.

I’ve encountered that mental model in my career, and I’m sure you have too. It’s common and it makes sense.

Our manufacturing business is hurting. What should we do? Your mental model says cut price to build volume, mine says raise price to build margin. We debate, we spreadsheet, we PowerPoint, we cite anecdotes and trends that make our cases. But we may not even wonder why we reach different conclusions when we start with the same information about our business; that is, we may not ask whether our mental models differ.
“We may not even wonder why we reach different conclusions when we start with the same information...we may not ask whether our mental models differ.”

The point is not that any given mental model is right or wrong. The point isn’t even that we must eventually choose one mental model or another.

The point is that:
  • We all have mental models
  • They’re unconsciously, self-evidently true to us
  • They’re invisible to others
  • They often differ from others’ mental models
Mental models have benefits:
  • They’re as flexible and creative as the human brain
  • They can integrate new information in real time
  • They’re always with us, they’re always switched on, and we don’t need help from IT to use them
Alas, mental models also have drawbacks:
  • They’re as slippery and imprecise as the human brain
  • They are personal, and egos get involved
  • They are prone to confirmation, availability, and other cognitive biases

You and I might debate our grow-volume and raise-margin mental models “in theory”. But the bottom line is that numbers matter. The bottom line is a number.

So we face the challenge of calculating mental models in our heads. Try this simple problem. Our manufacturing business sold 1,000 units this year, at a price of $50 per unit. Fixed costs were $40,000 and variable costs per unit were $12. We expect unit demand to grow at 3% per year, and we think prices and costs will hold still. What will be our annual gross margin three years from now? You’re right, it’s $1,224. I was just checking to be sure you knew too.

What will happen if we cut our price 10%? Raise it 7%? Consider results three years from now or five? Use costlier or cheaper raw materials? Etc.? That’s why we turn to computer models.

Computer Models
All computer models start life as mental models.

Remember that our mental models tell us if we do this then the result will be that. We transfer our mental models into computers — that is, we program computers, even if it’s with simple spreadsheets — so they can do the arithmetic for us. Computers also let us share our models with our colleagues, so we can all work with the same methodology.

The colossally important point: You tell your computer how to think. (Philosophical note: If you replicate your thinking in your computer, then your computer thinks, and it thinks like you. Ditto with machine learning. That’s not a colossally important point, but we can discuss it over a nice single malt.)

You tell your computer how to think. If you believe you should forecast sales by extrapolating trend lines into the future, you will type trend-line-extrapolation equations into Excel. If you believe you should forecast sales with a statistical model, you will commission a statistical model. If you ask someone else to figure it out, your computer will think like the person to whom you delegated thinking. And if you believe your mental model is good enough, you won’t even turn on your computer. Let it sit there.

Because they think like humans — that is, because they are electronic clones of mental models — computer models are neither better nor worse than humans, qualitatively speaking. Quantitatively, though, is another matter.

  • Turning a mental model into a computer model forces us to be explicit and transparent about how the model works. Humans can see, debate, enhance, revise, and share the thinking embodied in the model.
  • Computers compute many orders of magnitude faster than humans. That’s more than a convenience. It means also that it’s practical to conduct serious what-if tests with many scenarios. (See A Tournament Pits Strategies Against Each Other to See What Works. That tournament calculates about 200,000 scenarios per second, which is handy when I ask it to simulate billions.)
  • Computers can track many more variables than humans. A strategy simulator I created for a company could handle hundreds of actions for them and their competitors in multiple countries. Not a job for a human brain, which is why the company wanted the simulator.
  • Computers compute much more accurately than humans. Remember the gross-margin arithmetic problem I posed earlier? The real answer is $1,524. The $1,224 I mentioned was a sly test. You couldn’t tell it was wrong? That’s the point.

Because computer models embody mental models, they are vulnerable to lousy mental model in, garbage out. Garbage out would be the fault of the lousy mental model in, not of the computer or programmer.

Is the model valid?
No matter what goes in, no one wants garbage out. That’s why sooner or later someone will ask whether your model is valid. For many people, “valid” means the model’s output fits known data.

It is always possible to build a model that fits known data. If you expect the future to look just like the past, such a model might even be useful. But the time you really need a good model is when the future will not look like the past. Unfortunately, there are no data about the future, so the does-it-fit-the-data test isn’t an option.

I think it’s at least as important to ask about conceptual validity as numerical accuracy. In other words: Does a model make sense? Ask its author how it works and what variables it considers. Remember that making strategy decisions is not about accounting, trend lines, or forecasting. Strategy decisions are about strategy, and the models you select for strategy decisions should work with strategy concepts. (See With All This Intelligence, Why Don’t We Have Better Strategies?)

The ultimate point about validating models, and about selecting and using models, is this: You are going to make decisions no matter what, and you are going to use a model (mental or computer) to make those decisions. You are going to use a model because it takes a model to say if we do this then the result will be that. The relevant question is not whether a model is perfect. It isn’t. The relevant question is whether you can make a better decision with one model than with a different model.
“Improving the odds of success is all you can hope to get by using better models.”

Improving the odds of success is all you can hope to get by using better models. Fortunately, improving the odds by even a few percentage points can pay off big. Only a few percentage points separate the gambler and the casino.

Change the model, change the game
Using better models improves the odds that we will make good strategy decisions. Conversely, using worse models reduces the odds of good strategy decisions.

Therefore, we make an immensely important decision when we choose our model.

Remember the two models for our manufacturing business? Both acknowledge that our business has high fixed costs and that we must cover our costs to be profitable. They diverge from that point.

  • One model said total costs per unit go down as we sell more units, and that selling more units is easier when our price is lower. We’ll call that Model V, for volume.
  • The other model said it’s easier to cover our total costs if our prices are high, and that excellent quality and a strong brand give us pricing power. We’ll call that Model M, for margin.

Neither Model V nor Model M is right for our manufacturing business. They’re not right because each model misses something important: what’s in the other model!

And that’s not all. Neither V nor M analyzes changes in capacity, streamlining operations, or outsourcing. Both V and M focus only on our business, and ignore competitors’ actions and reactions. Our unit sales may go up if we cut our price, but what if competitors cut their prices too? Will market demand grow enough to compensate for the lower prices? Maybe yes, maybe no. The point is that we won’t even ask that question if we use Model V or M because both of them ignore competitive dynamics.

The situation isn’t necessarily as dire as it sounds. We can take "I feel lucky today" comfort because our competitors may choose the same flawed models we do. If a whole industry adopts a flawed way of thinking, no single business suffers competitive disadvantage. On the other hand, a business could enjoy the competitive advantage of a better model — i.e., of better decisions — if it finds one. That’s what those disruptive upstarts coming out of left field do. That’s what we call breaking the rules and changing the game.

Changing our model changes our game, and changing our model starts by recognizing and challenging our current model. See, for example, the real-life story that opens “Putting the Lesson Before the Test”, a chapter I co-authored in Wharton on Dynamic Competitive Strategy (Day and Reibstein, editors, 1997). A new technology would allow a company to delight customers and expand rapidly, and it passed all internal reviews. The strategy would work beautifully. Motivated by curiosity and strategic due diligence, the company tried one more review: a business war game. The war game showed competitors would feel they had no choice but to counterattack. The company decided to shelf the strategy. By doffing their green eyeshades and seeing the market through uncolored competitive-dynamics eyes — that is, simply by experimenting with a different model — the company avoided a very, very expensive disaster.

When you challenge your current model and experiment with others, you re-frame the game you choose to play and you discover new strategy options:

  • If customers care about product or service quality, you need a model that goes beyond price.
  • If you want to estimate changes in market share, you need a model that considers differentiation, customer loyalty, and switching costs.
  • If competitors’ behavior affects your performance, you need a model that goes beyond your business’ actions.
  • If you expect (or want to cause) the future to look different from the past, you need a model that doesn’t extrapolate the past into the future.

Three things to notice about that brief “a model that” list
  • It’s not controversial.
  • It’s not complicated or arcane.
  • Many models fail the "does it make sense?" criterion.

The five rules of models

There is always a model. Computer or mental. Model V or Model M or Model XYZ. Your model or mine. You never decide whether to use a model; you only decide which model to use. Choose a model based on its suitability for the decision at hand. Don’t ask a spreadsheet budget for advice about competitive strategy. Don’t bow to mere assertions of “experience,” “judgment,” and “gut instinct”. They are models too, and they should be subject to the same does-it-make-sense scrutiny as other models.

Computer models are people too. Your computer knows only what you have told it and it thinks only as you have taught it. Intel might or might not be inside, but you certainly are.

Models stimulate creativity. People see results from models — their mental models, others’ mental models, computer models — and those results can lead to new ideas. People even get ideas by designing models, because they think things through and discover the wide range of variables at play. You build your creativity by pondering “how would I model that?” when you come across interesting problems or decisions.

Surprise is good. If you can always predict what a model will say, the model doesn’t add any value. The model adds value when it tells you something you didn’t already know. Corollary: If your model and you disagree, that doesn’t determine who was wrong. (See Don’t Let Your Mistakes Go to Waste.)

There is no such thing as the future. There are many, many possible futures. (See The How-Likely Case.) Game theory and sheer complexity mean no model, mental or computer, can tell us which competitive-strategy future will happen. (See William Poundstone’s brilliant book and Rob Reiner’s brilliant movie scene about game theory.) Making wise decisions requires that we explore uncertainty, not deny it.

If you want to outperform the crowd, you have to do something the crowd isn’t doing. (See How the Very Best Strategists Decide.) The quickest, cheapest, easiest, most-effective, and most-fun way to do that is to out-think the crowd. Try a new model on for size. If you don’t like how it fits, you can always go back to your current model.

Competitive Intelligence Magazine
CIM is the premier voice of the Strategic, Market, and Competitive Intelligence community, published continuously since 1998. By curating original, high quality content featuring progressive ideas and best practices, we empower intelligence-driven growth and strategic decisions. Members can click above to browse the full archive. 

This article was originally published in January 2020 (CIM Volume 25, Issue 1).  



“The relevant question is not whether a model is perfect. It isn’t. The relevant question is whether you can make a better decision with one model than with a different model.”
Mark Chussil
Advanced Competitive Strategies, Inc.



Author - Mark Chussil
Mark Chussil is Founder of Advanced Competitive Strategies, Inc., an Adjunct Instructor at the University of Portland, and a SCIP Fellow. Contact him at or

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