Why plans are useless but planning indispensable

“Plans are worthless, but planning is everything”
-Dwight D. Eisenhower

“Failure to plan is planning to fail”
-Benjamin Franklin

We all have the fantasy of the perfect plan that goes without a hitch. Heist movies, like Ocean’s Eleven (Twelve and Thirteen), The Italian Job, The Bank Job, etc. all fuel that fantasy that you can be a mastermind capable of seeing all the angles, predicting everyone’s behavior several moves ahead, getting the timing right down to the second and achieving your goal exactly as you planned. In the real world however this is rarely the case. Why?

We live in a complex, interconnected world. Every action we take can cause ripples of unpredictability in the system. Complex systems are by their very nature unpredictable because there are no universal laws that govern them. Even if every agent in the system were to have simple rules by which they make decisions, the overall system behavior that emerges is unpredictable.

To account for all the possible scenarios quickly exceeds the capacity of even the most powerful of today’s computers. Just look at the weather patterns. Despite all the advances in computational power and simulation capabilities, we still can only forecast the weather with any level of accuracy a few days in advance. The complex behavior of the water molecules, the air temperature, atmospheric pressure, initial conditions and other factors make it nearly impossible to analyze and predict what will happen.

There are however systems which are highly predictable even if they seem very complex. A computer program’s behavior for example is very predictable (most of the time anyway) A car’s various systems: the engine, transmission, brakes, electrical systems, etc. are also very predictable even if they are interconnected and interdependent.

So what’s the difference?

David Snowden’s Cynefin framework (pronounced kun-ev-in) recognizes three types of systems: Ordered, Complex and Chaotic. The difference between them is the level of constraint in each system.

Ordered systems are highly constrained and as such their behavior is very deterministic and predictable. You can easily determine cause and effect and the patterns you find are very likely to repeat in the future. Ordered systems are further divided into Simple and Complicated. A highly structured business process for example (like getting a loan) is a Simple system. It’s highly constrained and relatively easy to fix or optimize. Cause and effect relationships are clearly visible and you can predict with very high accuracy what will happen.

A car is an example of a Complicated system. It’s still Ordered because it’s highly constrained (there’s little to no variation beyond what’s been specified by the system designer) but the level of detail in the design makes it much harder to understand and notice cause and effect relationships. This is why you need highly trained professionals (experts) to analyze the system and figure out cause and effect relationships.

Complex systems on the other hand are only partially constrained. Complexity science is still actively being studied and discovered but we do know a few things that can help us understand how these systems work. Complex systems are made up of agents that interact with each other and with the system based on their own rules and strategies and the constraints imposed by the system.

In the example above we saw that cars were Ordered systems because of the high level of constraint in every aspect of their design; traffic on the other hand is only partially constrained and as such it’s a Complex system. There are rules in the form of laws and guidelines such as speed limits, traffic signs, traffic lights, highways, ramps, paved roads, direction of driving, etc. but these rules do not fully constrain driving. You can choose to dive fast or slow, change lanes frequently or not at all, slow down or speed up, turn left or right, etc.

This creates unpredictable emergent patterns such as accidents, traffic jams, traffic congestion or sparsity, etc. On top of that, the traffic patterns from moment to moment, from day to day are completely novel and unique. There’s no way to know for sure when an accident will occur or when the traffic will become congested. Even though you may know exactly why an accident happened, it doesn’t help you fully predict future accidents.

Chaotic systems are highly unconstrained. Imagine for a second that one day none of the rules of driving applied. You could drive in the middle of the road if you wanted, drive backwards, go through red lights and stop signs, drive on the opposite side of the road, cut through lanes at will, make sudden u-turns, break and accelerate as you wished, etc. What would happen? Complete and utter chaos. It would be impossible to predict anything.

Side Note: Temporarily removing constrains in a system is an excellent way to unclog bureaucratic gridlock in an organization and spur innovation. Dave Snowden calls this “shallow dive into chaos” but that’s a topic for another day.

So how does this relate to planning?

Most planning is done under the assumption of Ordered systems. We assume that the future is predictable from past events so making plans is easy. Planning comes naturally to us as our brains function like cybernetic (goal seeking) systems. We set a goal and immediately our brain provides ways to achieve it.

Now if the system you’re dealing with is highly constrained, these plans are very likely to succeed. For example if you wanted to buy a house you’d need a bank loan and since getting a loan is an Ordered system, given certain criteria, you can predict with very high accuracy if you will succeed or fail.

If we’re dealing with a complex system however, or a chaotic system, we would be unable to account for all the possible future scenarios and contingencies and our plans would be at best incomplete. Before the advent of GPS and turn-by-turn navigation systems with up the the minute traffic data, it was impossible to plan a route down to the minute and be very confident you would arrive at a particular time.

So the reason why plans are useless is that more often than not they are incomplete and don’t account for all the possible contingencies in the complexity of today’s systems.

Why then is planning indispensable?

The process of planning gets us to think through many of the possible futures and scenarios that can unfold and help us be better prepared if any of those futures scenarios were to happen by creating contingencies. Of course we can’t cover every single scenario and we need to be agile and capable of course correction. The measure of true agility is the ability to ditch your plans halfway through when the situation has changed and made your plans obsolete even if the sunk cost might be high.

Always have multiple theories for explaining and understanding things

When trying to understand or explain something that’s happening, like a certain behavior pattern in your friends or significant other or a trend in fashion, technology, etc, it helps to have more than one hypothesis (theory), (even better if it’s more than two) and assign each one a probability of being right.

Then as you get more evidence for any one of your multiple theories, you adjust the probabilities of what the correct explanation could be. You might also run multiple experiments to cover all your theories. This will lead you to a more accurate understanding of people or the world around you which then leads to more accurate forecasts, better decisions, more confidence and decreased levels of stress.

I believe that there’s always more than one way to explain things, there’s always more than one theory that fits a situation and I’m not attached to any one of them at first. This doesn’t mean that I like being wrong, in fact this means that I want to be even more accurate so I want to cover all my bases. As I gather more data, I eventually converge on a single theory, while still keeping an open mind that it could still change in the future.

As humans we’re addicted to being right, it’s a compulsion that threatens to derail our friendships and our relationships. We want our intuition to be the correct one. It’s very easy to get emotionally attached to certain explanations that benefit us, make us feel smarter, more confident and more proud, or that ensure that we keep our jobs.

When you have multiple competing theories for why something is happening you keep yourself open to possibility, and as a result you understand the world better. You might not look as smart or as confident or as self assured as the person with a single theory, but more often than not, you will end up having more accurate predictions and be more confident than them in the end.

The Dangers of Optimization

“We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil” -Donald Knuth

In the field of Computer Science (CS) optimization is the process of changing the algorithm (the logic) of a program in order to improve its efficiency (i.e. make it run faster or consume fewer resources.) In order to do this, certain assumptions need to be put in place and my argument is that these assumptions make optimization dangerous and not just in the context of programming.

First of all in order to optimize a process you need to understand it very well. You need to know how it behaves in different circumstances or under various boundary conditions. If we were to look at an organically (necessity) grown business process that we want to optimize, it’s usually necessary to see where the inefficiencies or bottlenecks are in the process and remove them.

Second, when you optimize you may need to restrict boundaries, make certain assumptions, use special cases, tricks and complex trade-offs which will achieve the required result but at the expense of potentially over-complicating a process or over-specializing it. This causes a loss of agility in the long run or over-adaptation (as may be the case in over-optimized diets).

Let’s use a simple example from manufacturing to explain this. Let’s assume that you have a retool-able machine that produces widgets at 95% defect rate. This means that 5 out of every 100 widgets are defective. When you’re looking to optimize the defect rate, you’re looking to produce fewer defective widgets without slowing down the machine or the manufacturing process. Suppose also that you know the process of making this widget very well, since you make millions of them every year.

Since you know the process well you know that there are just a few ways to optimize the defect rate, you can for example utilize the machine better by redesigning the overall process or you can install much more specialized machines that make just this one widget. Now you have an optimized defect rate but it has come at the expense of over-specialization and a huge loss of flexibility or agility. Imagine what can happen after you’ve replaced your retoolable machines with specialized machines and the market changes to where it no longer needs those widgets. You’re practically out of business.

The danger is that not all processes are fully known. Even in a precise field such as computer science there’s always some level of unknown, certain unexpected conditions or special circumstances where the program will fail. If you were to optimize the process without knowing all these cases you run the risk of having an incorrect program that no longer solves the original problem.

Nowhere is this more prevalent than in digital marketing. First let’s get one obvious thing out of the way. SEO or search engine optimization is not really optimization per se. You’re not optimizing your website to work better or faster, you’re adapting it to fit the search engine’s constraints.

The whole idea behind the concept of targeting is really about optimization. When you’re trying to target a certain segment of your audience, you’re trying to optimize the marketing dollars so you increase the efficiency. After all why would you want to spend money on leads that will not respond to your offer?

There is nothing wrong with targeting (although Roy H. Williams aka The Wizard of Ads would beg to differ) The problems arise when you start to over-optimize while assuming that you really understand your audience and what offers they actually respond to. The truth is you really don’t, so all this optimization will really hurt sales. There are many other things in a business you can optimize and improve, such as your close ratio.

A good example of this is a recent story I read in Roy H. William’s Monday Morning Memo (a highly recommended business newsletter by the way) The story is about two lawyers who take different approaches to marketing. One believes in targeting, and converts about 10% of inquiries while the other believes in casting a wider net via radio advertising and ends up converting 60% of inquiries proving once again that marketing is primarily about the messaging and the offer.

Implicit vs. Explicit Mental Models

There are two kinds of mental models, implicit and explicit. They are categorized based on the acquisition method (i.e. how did they end up in our mind)

Explicit Mental Models

Explicit models are the ones you learn from studying various disciplines such as math, physics, economics, etc. In my last post I talked about Charlie Munger and his mental models he uses to evaluate deals and make investment decisions.

He draws them out of various disciplines and then uses them in contexts where they weren’t necessarily built to be used. For example, my background is in computer science which teaches the principles of computing.

Taking that model and applying it to any electronics device has allowed me to fix a lot of non-computer gadgets. It’s a great model to use for that purpose, but it fails terribly when applied to human interactions. You’ll need another model for that.

Another good example is the supply and demand model from economics. It’s a wonderful model for understanding many facets of human behavior. It can be applied on a micro level – like one-to-one daily transactions between humans – and on a macro level – like the economy of a country.

Note: Both the above examples illustrate the limits and failure of models in general, something that was discussed previously.

These are both examples of explicit models, where you learn the model from an outside source and then you apply it to a situation where it works.

Implicit models are the ones that your mind creates out of various patterns it notices around it through the five senses. The mind is a pattern matching machine. It seeks out patterns in the randomness and tries to make sense of it by creating models. These are also known as generalizations or beliefs.

Implicit Mental Models

Implicit mental models are harder to detect because they work essentially behind the scenes, filtering and distorting reality to fit what we believe. Yes, in case you didn’t know it, when presented with evidence, humans don’t change their minds. Instead, they interpret the facts through their internal mental models, but this is a discussion for another day.

How do you pick up these implicit mental models? There are several ways. First it’s through our culture. Culture indoctrinates us without us even being aware of it. You don’t know it’s there, you don’t know why it’s there, you just assume that’s how things are supposed to be. In fact, many people are unaware of indoctrination effect their culture has until they leave their country and live abroad for a while.

Second it’s through media. This is impossible to escape; every show you watch, every magazine or newspaper article, every movie, every song has built in assumptions and ends up reinforcing the same mental models about reality over and over.

For example, it’s impossible to watch a romantic comedy nowadays without implicitly believing that you’re supposed to have some spark or chemistry with someone right off the bat in order to fall in love, which is then a prerequisite for a successful relationship and marriage. It’s only when you study the history of society that you understand that marriages in the past were often arranged for economical or political reasons.

Third it’s through your peer group. Even if you don’t try, if you hang out with a group of people long enough, you’ll eventually start to change and adapt your mental models to fit those of the leader of the group. This is done completely outside of your awareness, but the processes that occur in your mind (such as reframing and the change of meaning) are very powerful and can be utilized on purpose to upgrade your mind.

How do these models compare?

Of the two, implicit models are the ones that seem to be more deeply entrenched and more likely to be outside of awareness. I believe this is due to the nature of the acquisition method. If the model was installed outside of our awareness, it will tend to operate outside of our awareness and control (or regulate) our life as it on autopilot.

There are benefits to this of course. Since the brain can rely on a predetermined pattern, it doesn’t need to expend energy again to solve the same problem in the future. It writes neurological software and then sets it on autopilot. Unless you explicitly go in and look at the code (by becoming aware of the underlying model) and refactoring it.

Experiments performed on mice in a maze show that brain activity is very high the first time that the mouse runs through the maze to find the hidden piece of cheese. After that, subsequent trials show brain activity leveling off as mice learn the path to the cheese. (see The Power of Habit by Charles Duhig)

On the other hand, being deeply entrenched, implicit models are very difficult to modify when you’re trying to rid yourself of some unwanted pattern of thought or behavior. Explicit models on the other hand, can also get deeply entrenched – this depends a great deal on the emotional charge during the “installation” process – but in general tend to be easily updated, upgraded or removed.

If you’ve learned Newtonian physics and then you delve into general relativity, it’s easy to update your mental model which now becomes more enriched. The only trouble seems to be having the model you’ve learned from a book available to you in the moment when you need it to make a decision or solve a problem.

The power of context

One of the properties of mental models is the concept of a context or situation when or where a model is appropriate. A context can be something like work or home or with friends” You could have the most amazing set of explicit models “installed” in your mind but if they don’t permeate through to the right context, you’ll find yourself using sub-optimal response and behavior patterns.

For example, you could have a set of useful mental models that you use at work, with your colleagues, bosses, underlings, etc. You could be the best manager in the company; your employees could love you, your colleagues could be asking you for advice, but when you go home you find yourself yelling at your spouse or your children. In fact you could be a completely different person.

It’s all in the power of implicit and explicit models. You’re not a different person, you just have a different set of models you could be using implicitly for family life and the work life models don’t seem to permeate there. You’d have to first become aware of them and then put in some effort in order to get them “copied” over.

The Dangers of Mental Models – Intro to Mental Models Continued

In the previous post, I talked about what mental models are and how important they are to your thinking. As we delve  deeper into refactored thinking, mental models are going to become crucial in understanding and implementing the process of refactoring your thoughts.

Mental Model Pitfalls:

First I want to talk about a few pitfalls that are common with mental models of any kind.

There is a tendency of humans to want to simplify things in order to understand them better, but sometimes this simplification is over the top and we end up with a dumbed down model. There are two fallacies that are direct descendants of this tendency.

The first one I call the Single Model Fallacy, and it’s something that plagued me for a long time. The single model fallacy is very simply the tendency for wanting to explain everything with the same model. This is not really anything new, as science has been pushing the idea that there is a single unifying theory that explains everything.

We see the same thing in areas like psychology, where different models of therapy from Freud to Skinner tried to explain human behavior and every single one of them claimed that their model was the right model. I subscribed to this view for way too long, trying desperately to come up with a single unifying theory for why we act the way we do.

It wasn’t until I read this quote from Charlie Munger (Warrant Buffett’s partner and a millionaire in his own right) that I started to see my own faulty thinking. Mr. Munger claims that all you really need to make a decision is a “latticework of mental models” from various disciplines:

“You’ve got to have models in your head. And you’ve got to array your experience—both vicarious and direct—on this latticework of models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and in life. You’ve got to hang experience on a latticework of models in your head”.  –Charlie Munger (Wordly Wisdom)

The second one, I call Model Reduction and Mapping, and it’s the idea of reducing something new that you don’t know so that it maps into an existing set of concepts in your mind which you already know and understand.

When you’re learning new concepts and ideas, you tend to try and make sense of them from a frame of reference that you already know. For example if you’re studying physics, you will try and map the concepts you learn onto their math counterparts (speed is the a derivative of distance and acceleration is a derivative of speed) This helps you integrate your learning and refactor your thoughts so you understand things better.

There’s an inherent danger to this reduction. It prevents you from learning new things. If you’re always trying to map new concepts into existing concepts, you never learn new things and your view of the world tends to collapse rather than expand.

Ideologies, cults and religions, have an inherent (and secret I might add) interest in teaching you how to reduce and map new concepts into its existing set of beliefs. They use techniques such as relabeling, and reframing to make it seem like every new idea is something that you already know about if you study their stuff. The collapsing effect is absolutely necessary in order to keep people mentally “chained” to them.

How do you prevent this from happening?

The first step is to allow any new material to fit into its own box in your mind and let it simmer there until you’ve had the time to look it over and refactor it into either an existing model, or under its own category. 

As far as the single model fallacy, it’s important to understand that the world as we know it is a far more complicated system that we make it out to be. It might be decades before theoretical physicists even agree on a unifying model of the world if they even get there. Human behavior is another very complex process to fully comprehend. Until then, we have plenty of available models to explain it and to influence it. Don’t stick to just one!

An Introduction to Mental Models

What are mental models and how are they useful?

By definition, a model is a simplified representation of reality. The real world is a very complex system and our minds have a limited capacity to store everything that we perceive through our senses. In order for us to understand and function in this complex world, we use mental models of how the world works. These are constructs that simplify reality enough that we can act and think accordingly.

Despite being incorrect, based on their definition, mental models are very useful: Here just a handful of examples that make models useful:

  1. You can use models to understand the world better. This is what science helps us do. Think about Newton’s gravity model. While it’s not correct (as anyone who’s taken quantum mechanics will tell you) it is extremely useful.
  2. You use models every day to shortcut decision-making by using proven methods, best practices and guidelines. For example in direct marketing there’s a model called RFM (recency, frequency, monetary) This model allows a business to optimize their mailing list or email list and prioritize it by how recently a customer responded, how often they respond and how much did they spend. This is a simple model that can greatly influence the growth of your business even if you don’t know a lot about your customers.
  3. Understand how another person thinks and why they think the way they think and influence them. Models don’t just apply to reality, they also apply to human behavior. Psychology has created many different categorization systems for people, such as systems based on personality, information processing, etc. A good example of this is the Myers-Briggs (MBTI) profile that categorizes people based on Carl Jung’s ideas on types and archetypes. Once you understand where someone falls in those categories, it allows you to understand them, accept them and even influence them.
  4. Predict the future. One of the most powerful uses of  models is their ability to predict with a relative accuracy what will happen next. As you know, humans are creatures of habit and unless we refactor our thinking we will keep using the same models over and over again, which makes us predictable to a certain extent. Predicting what someone will say or do next helps you stay a few moves ahead of them and influence them in powerful ways. The effects of this are even bigger when it’s done within a closed system or context where the rules are well-defined (such as in a game of chess or at work)
  5. Influence and improve yourself. We’ll talk more about this below.

The dark side of models

Because models are essentially simplifications, by default they have limitations. It’s very important to understand the limitations of a model when it comes to using them. You have to start thinking in terms of probabilities and be keenly aware of the models you’re using. If you keep getting undesirable results in a certain context in your life, it’s likely that you’re using an implicit mental model.

By refactoring your thoughts, you can make these models explicit and then change them to expand your thinking. I have a personal example of the kind of refactoring that can happen inadvertently when you read a book or article.

About 4-5 years ago, I heard about a book called The 4-Hour Workweek by Tim Ferriss. The title sounded very intriguing so I picked it up and started to read it. Within the first chapter, I was not only hooked, but my jaw had dropped. My entire life’s mental model of “study hard, get good grades, go to college, get a good job, save money, retire, enjoy life” had been completely shattered to pieces and replaced with a new one called “lifestyle design”

Without getting too much into detail about what “lifestyle design” is (you should really pick up the book and read it. I highly recommend it), I can tell you that this book changed everything about how I think about life, work, retirement, savings, etc. Books like that are rare, but the do come along.

In conclusion, before this turns into a book, mental models are very powerful and as such they can be extremely useful but also severely limiting. Understanding how they work, and how you can refactor them into more useful patterns, will go a long way towards making you intelligent, influential and make life a bliss. .