Tag: Mental Models

The Mind’s Search Algorithm: Sorting Mental Models

Mental models are tools for the mind.

In his talk: Academic Economics: Strengths and Weaknesses, after Considering Interdisciplinary Needs, at the University of California at Santa Barbara, in 2003, Charlie Munger honed in on why we like to specialize.

The big general objection to economics was the one early described by Alfred North Whitehead when he spoke of the fatal unconnectedness of academic disciplines, wherein each professor didn’t even know of the models of the other disciplines, much less try to synthesize those disciplines with his own … The nature of this failure is that it creates what I always call ‘man with a hammer’ syndrome. To a man with only a hammer, every problem looks pretty much like a nail. And that works marvellously to gum up all professions, and all departments of academia, and indeed most practical life. So, what do we do, Charlie? The only antidote for being an absolute klutz due to the presence of a man with a hammer syndrome is to have a full kit of tools. You don’t have just a hammer. You’ve got all the tools.

The more models you have from outside your discipline and the more you iterate through them when faced with a challenge in a checklist sort of fashion, the better you’ll be able to solve problems.

Models are additive. Like LEGO. The more you have the more things you can build, the more connections you can make between them and the more likely you are to be able to determine the relevant variables that govern the situation.

And when you learn these models you need to ask yourself under what conditions will this tool fail? That way you’re not only looking for situations where the tool is useful but also situations where something interesting is happening that might warrant further attention.

The Mind’s Search Engine

In Diaminds: Decoding the Mental Habits of Successful Thinkers, Roger Martin looks at our mental search engine.

Now for the final step in the design of the mentally choiceful stance: the search engine, as in ‘How did I solve these problems?’ ‘Obviously,’ you will answer yourself, ‘I was using a simple search engine in my mind to go through checklist style, and I was using some rough algorithms that work pretty well in many complex systems.’ What does a search engine do? It searches. And how do you organize an efficient search? Well, algorithm designers tell us you have to have an efficient organization of the contents of whatever it is you are searching. And a tree structure allows you to search more efficiently than most alternative structures.

How a tree structure helps simplify search: A detection algorithm for ‘Fox.’
How a tree structure helps simplify search: A detection algorithm for ‘Fox.’

So what’s Munger’s search algorithm?

(from an interview with Munger via Diaminds: Decoding the Mental Habits of Successful Thinkers:)

Extreme success is likely to be caused by some combination of the following factors: a) Extreme maximization or minimization of one or two variables. Example[:] Costco, or, [Berkshire Hathaway’s] furniture and appliance store. b) Adding success factors so that a bigger combination drives success, often in nonlinear fashion, as one is reminded of the concept of breakpoint or the concept of critical mass in physics. You get more mass, and you get a lollapalooza result. And of course I’ve been searching for lollapalooza results all my life, so I’m very interested in models that explain their occurrence. [Remember the Black Swan?] c) an extreme of good performance over many factors. Examples: Toyota or Les Schwab. d) Catching and riding some big wave.

Charlie Munger’s lollapalooza detection algorithm, represented as a tree search.
Charlie Munger’s lollapalooza detection algorithm, represented as a tree search.

(via Diaminds: Decoding the Mental Habits of Successful Thinkers)

A good search algorithm allows you to make your mental choices clear. It makes it easier for you to be mentally choiceful and to understand the reasons why you’re making these mental choices.

Now, what should go on the branches of your tree of mental models? Well, how about basic mental models from a whole bunch of different disciplines? Such as: physics (non-linearity, criticality), economics (what Munger calls the ‘super-power’ of incentives), the multiplicative effects of several interacting causes (biophysics), and collective phenomena – or ‘catching the wave’ (plasma physics). How’s that for a science that rocks, by placing at the disposal of the mind a large library of forms created by thinkers across hundreds of years and marshalling them for the purpose of detecting, building, and profiting from Black Swans?

The ‘tree trick’ has one more advantage – a big one: it lets you quickly visualize interactions among the various models and identify cumulative effects. Go northwest in your search, starting from the ’0’ node, and the interactions double with every step. Go southwest, on the other hand, and the interactions decrease in number at the same rate. Seen in this rather sketchy way, Black Swan hunting is no longer as daunting a sport as it might seem at first sight.

Developing a Mental Framework for Effective Thinking

mental framework effective thinking

Becoming a better thinker means understanding the way you think and developing a way of approaching problems that allows you to see things from multiple lenses. These lenses, or mental models, are built on the foundations of physics, biology, math, psychology, as well as history and economics. The more tools you have in your mental toolbox the better able you will be to make an incrementally better decision.

These tools also allow you to better understand when to follow and when to reject conventional wisdom. Ideally you want to go through them checklist style — just run right through them — asking what applies.

Consilient Thinker
John Snow was a doctor based in London during the acute cholera outbreak of the summer of 1854. He represents a powerful example of the impact a lollapalooza effect can have. A lollapalooza is when several ideas combine to produce an unusually powerful result. Snow developed systems to ease the pain of surgery with ether and chloroform.

In the book The Ghost Map, author Steven Johnson explains:

Snow was a truly consilient thinker, in the sense of the term as it was originally formulated by the Cambridge philosopher William Whewell in the 1840s (and recently popularized by Harvard biologist E. O. Wilson). “The Consilience of Inductions,” Whewell wrote, “takes place when an Induction, obtained from one class of facts, coincides with an Induction obtained from another different class. This Consilience is a test of the truth of the Theory in which it occurs.” Snow’s work was constantly building bridges between different disciplines, some which barely existed as functional sciences in his day, using data on one scale of investigation to make predictions about behavior on other scales. In studying ether and chloroform, he had moved from the molecular properties of the gas itself, to its circulation of those properties throughout the body’s overall system, to the psychological effects produced by these biological changes. He even ventured beyond the natural world into the design of technology that would best reflect our understanding of the anesthetics. Snow was not interested in individual, isolated phenomena; he was interested in chains and networks in the movement from scale to scale. His mind tripped happily from molecules to cells to brains to machines, and it was precisely that consilient study that helped Snow uncover so much about this nascent field in such a shockingly short amount of time.

Suspending belief in the common theory at the time on how diseases were spread, Snow ended up rejecting miasma theory, which said the disease was spread via “bad air.” He did this through science. He conducted interviews with residents and traced the majority of cases back to a single water source. His willingness to challenge conventional thinking, along with approaching the problem through multiple lenses, resulted in finding the deadly source and changes in municipal water systems from that day forward.

***

Elements of the mental framework

Charlie Munger is a strong advocate of a mental framework. In Damn Right: Behind the Scenes with Berkshire Hathaway Billionaire Charlie Munger, he offered five-simple notions that help solve complex problems.

In The Focused Few: Taking a Multidisciplinary Approach to Focus Investing, Richard Rockwood explores the concepts from many disciplines. Adding them together can yield a useful mental checklist.

Element 1: Invert

In The Focused Few, Rockwood writes:

Inverting, or thinking problems through backward, is a great way to understand information. Charlie Munger provides the best illustration I have ever seen of this type of thinking.

During a speech he offered an example of how a situation could be examined using the inversion process. He discussed the development process of Coca-Cola from the perspective of a person creating a soda company from scratch and examining the key issues that would need to be resolved to make it a reality.

He listed some of the issues the entrepreneur would need to address:

  • What kind of properties should the new drink strive for, and what are those it should avoid? One property the drink should not have is an aftertaste. Consumers should be able to consume large quantities over a period of time and not be deterred by an unpleasant aftertaste.
  • The soda should be developed in such a manner that it can be shipped in large quantities at minimal costs. This makes it easier to develop an efficient, large-scale distribution system.
  • Keeping the soda formulation a secret will help alleviate competition and create a certain aura of mystique around the product.
  • The company also can deter competition by expanding the business as quickly as possible. For example, the distribution system could be expanded until it reaches a critical mass that competitors would find hard to duplicate without massive capital expenditures.

Element 2: First- and second-level thinking

In The Focused Few, Rockwood writes:

Let’s examine the decision-making process by breaking it down into two components. The first component, first-level thinking, generally occurs when you make decisions quickly based on a simple theme or common sense. For example, a person may decide to invest in a company simply because its products are trendy. Making decisions based on first-level reasoning has significant problems, however. Common sense “is wonderful at making sense of the world, but not necessarily at understanding it.” (Duncan Watts Everything Is Obvious: How Common Sense Fails Us)

The danger is that you may think you understand a particular situation when in fact you have only developed a likely story.

Second-level thinkers, in contrast, approach decisions differently. What kinds of questions should a second-level thinker ask?

In his book, The Most Important Thing: Uncommon Sense for the Thoughtful Investor, Howard Marks provides a useful list of questions to ask.

  1. What is the range of likely future outcomes?
  2. Which outcome do I think will occur?
  3. What is the probability that I’m right?
  4. What is the prevailing consensus?
  5. How does my expectation differ from the consensus?
  6. How does the current price for the asset comport with the consensus view of the future— and with mine?
  7. Is the consensus psychology that is incorporated into the price too bullish or bearish?
  8. What will happen to the asset’s price if the consensus turns out to be right, and what if I’m right?

Element 3: Use decision trees

decision trees

In The Focused Few, Rockwood writes:

Decision trees are excellent tools for helping you decide on a course of action. They enable you to lay out several possible scenarios, investigate their possible outcomes, and create a balanced picture of the risks and rewards associated with each.

[…]

Let’s examine the decision-tree process in greater detail. First, identify the decision and the outcome alternatives available at each point. After you lay out each course of action, determine which option has the greatest value to you. Start by assigning a cash value to each possible outcome (i.e., what the expected value would be if that particular outcome were to occur). Next, look at each break, or point of uncertainty, in the tree and estimate the probability of each outcome occurring. If you use percentages, the combined total must equal 100% at each break point. If you use fractions, these must add up to 1.

After these two steps have been taken (i.e., the values of the outcomes have been entered and the probabilities have been estimated), it is time to begin calculating the expected values of the various branches in the decision tree.

Element 4: The multidisciplinary approach

When trying to resolve a difficult situation or determining exactly why a product has been, and may continue to be, successful, it helps to think about the problem by creating a checklist that incorporates the vital components of other disciplines.

The Focused Few goes on to explore more of the elements of multidisciplinary thinking.

Thinking About Thinking

I wrote a response on quora recently to the question ‘how do I become a better thinker’ that generated a lot of attention and feedback so I thought I’d build on that a little and post it here too.

(c) Shane Parrish fs.blog

Thinking is not IQ. When people talk about thinking they make the mistake of thinking that people with high IQs think better. That’s not what I’m talking about. I hate to break it to you but unless you’re trying to get into Mensa, IQ tests don’t matter as much as we think they do. After a certain point, that’s not the type of knowledge or brainpower that makes you better at life, happier, or more successful. It’s a measure sure, but a relatively useless one.

If you want to outsmart people who are smarter than you, temperament and life-long learning are more important than IQ.

Two of the guiding principles that I follow on my path towards seeking wisdom are: (1) Go to bed smarter than when you woke up; and (2) I’m not smart enough to figure everything out myself, so I want to ‘master the best of what other people have already figured out.’

Acquiring wisdom is hard. Learning how to think is hard. It means sifting through information, filtering the bunk, and connecting it to a framework that you can use. A lot of people want to get their opinions from someone else. I know this because whenever anyone blurts out an opinion and I ask why, I get some hastily re-phrased sound-byte that doesn’t contextualize the problem, identify the forces at play, demonstrate differences or similarities with previous situations, consider base rates, or … anything else that would demonstrate some level of thinking. (One of my favorite questions to probe thinking is to ask what information would cause someone to change their mind. Immediately stop listening and leave if they say ‘I can’t think of anything.’)

Thinking is hard work. I get it. You don’t have time to think but that doesn’t mean you get a pass from me. I want to think for myself, thank you.

***

So one effective thing you can do if you want to think better is to become better at probing other people’s thinking. Ask questions. Simple ones are better. “Why” is the best. If you ask that three or four times you get to a place where you’re going to understand more and you’ll be able to tell who really knows what they are talking about. Shortcuts in thinking are easy, and this is how you tease them out. Not to make the other person look bad – don’t do this maliciously – but to avoid mistakes, air assumptions, and discuss conclusions.

Another thing you can do is to slow down. Make sure you give yourself time to think. I know, it’s a fast-paced internet world where we get some cultural machoism points for answering on the spot but unless it has to be decided at that very moment, simply say “let me think about that for a bit and get back to you.” The world will not end while you think about it.

You should also probe yourself. Try and understand if you’re talking about something you really know something about or if you’re just regurgitating some talking head you heard on the news last night. Your life will become instantly better and your mind clearer if you simply stop the latter. You’re only fooling yourself and if you don’t understand the limits of what you know, you’re going to get in trouble.

***

Learning how to think really means continuously learning.

How can we do that?

First we need a framework to put things on so we can remember, integrate, and make them available for use.

A Latticework of Mental Models, if you will.

Acquiring knowledge may seem like a daunting task. There is so much to know and time is precious. Luckily, we don’t have to master everything. To get the biggest bang for the buck we can study the big ideas from physics, biology, psychology, philosophy, literature, and sociology.

Our aim is not to remember facts and try to repeat them when asked. We’re going to try and hang these ideas on a latticework of mental models. Doing this puts them in a useable form and enables us to make better decisions.

A mental model is simply a representation of an external reality inside your head. Mental models are concerned with understanding knowledge about the world.

Decisions are more likely to be correct when ideas from multiple disciplines all point towards the same conclusion.

It’s like the old saying, “To the man with only a hammer, every problem looks like a nail.” Let’s make every attempt not to be the man with only a hammer.

Charlie Munger further elaborates:

And the models have to come from multiple disciplines because all the wisdom of the world is not to be found in one little academic department. That’s why poetry professors, by and large, are so unwise in a worldly sense. They don’t have enough models in their heads. So you’ve got to have models across a fair array of disciplines.

You may say, “My God, this is already getting way too tough.” But, fortunately, it isn’t that tough because 80 or 90 important models will carry about 90% of the freight in making you a worldly wise person. And, of those, only a mere handful really carry very heavy freight.

These models generally fall into two categories: (1) ones that help us simulate time (and predict the future) and better understand how the world works (e.g. understanding a useful idea from like autocatalysis), and (2) ones that help us better understand how our mental processes lead us astray (e.g., availability bias).

When our mental models line up with reality they help us avoid problems. However, they also cause problems when they don’t line up with reality as we think something that isn’t true. So Beware.

In Peter Bevelin’s masterful book Seeking Wisdom, he highlights Munger talking about autocatalysis:

If you get a certain kind of process going in chemistry, it speeds up on its own. So you get this marvellous boost in what you’re trying to do that runs on and on. Now, the laws of physics are such that it doesn’t run on forever. But it runs on for a goodly while. So you get a huge boost. You accomplish A – and, all of a sudden, you’re getting A + B + C for awhile.

But knowing is not enough. You need to know how to apply this to other problems outside of the domain in which you learned it.

Munger continues:

Disney is an amazing example of autocatalysis … They had those movies in the can. They owned the copyright. And just as Coke could prosper when refrigeration came, when the videocassette was invented, Disney didn’t have to invent anything or do anything except take the thing out of the can and stick it on the cassette.

***

What models do we need?

I keep a running list that I’m filling in over time, but really how we store and sort these are individual preferences. The framework is not a one-stop-shop, it’s how it fits into your brain.

How can we acquire these models?

There are several ways to acquire the models, the first and probably best source is reading. Even Warren Buffett says reading is one of the best ways to get wiser.

But sadly if your goal is wisdom acquisition, you can’t just pick up a book and read it. You need to Learn How To Read A Book all over again. Most people look at my reading habits (What I’m Reading) and think that I speed read. I don’t. I think that’s a bunch of hot air. If you think you can pick up a book on a subject you’re unfamiliar with and in 30 minutes become an expert … well, good luck to you. Please go back to getting your opinions from twitter.

Focus on the big, simple ideas.

Focus on deeply understanding the simple ideas (see Five Elements of Effective Thinking). These simple ideas, not the cutting-edge ones are the ones you want to hang on your latticework. The latticework is important because it makes the knowledge useable – you not only recall but you internalize.

But the world is always changing … what should we learn first?

One of the biggest mistakes I see people making is to try and learn the cutting-edge research first. The way we prioritize learning has huge implications beyond the day-to-day. When we chase the latest thing, we’re really jumping into an arms race (see: The Red Queen Effect). We have to spend more and more of our time and energy to stay in the same place.

Despite our intentions, learning in this way fails to take advantage of cumulative knowledge. We’re not adding, we’re only maintaining.

If we are to prioritize learning, we should focus on ideas that change slowly – these tend to be the ones from the hard sciences. (see Adding Mental Models to Your Toolbox)

The models that come from hard science and engineering are the most reliable models on this Earth. And engineering quality control – at least the guts of it that matters to you and me and people who are not professional engineers – is very much based on the elementary mathematics of Fermat and Pascal: It costs so much and you get so much less likelihood of it breaking if you spend this much… And, of course, the engineering idea of a backup system is a very powerful idea. The engineering idea of breakpoints – that’s a very powerful model, too. The notion of a critical mass – that comes out of physics – is a very powerful model.

To help further prioritize learning

From : What Should I Read?

Knowledge has a half-life. The most useful knowledge is a broad-based multidisciplinary education of the basics. These ideas are ones that have lasted, and thus will last, for a long time. And by last, I mean mathematical expectation; I know what will happen in general but not each individual case.

Integrating Knowledge

(Source: Adding Mental Models to Your Toolbox)

Our world is mutli-dimensional and our problems are complicated. Most problems cannot be solved using one model alone. The more models we have the better able we are to rationally solve problems. But if we don’t have the models we become the proverbial man with a hammer.

To the man with a hammer everything looks like a nail. If you only have one model you will fit whatever problem you face to the model you have. If you have more than one model, however, you can look at the problem from a variety of perspectives and increase the odds you come to a better solution.

No one discipline has all the answers, only by looking at them all can we come to grow worldly wisdom.

Charles Munger illustrates the importance of this:

Suppose you want to be good at declarer play in contract bridge. Well, you know the contract – you know what you have to achieve. And you can count up the sure winners you have by laying down your high cards and your invincible trumps.

But if you’re a trick or two short, how are you going to get the other needed tricks? Well, there are only six or so different, standard methods: You’ve got long-suit establishment. You’ve got finesses. You’ve got throw-in plays.

You’ve got cross-ruffs. You’ve got squeezes. And you’ve got various ways of misleading the defense into making errors. So it’s a very limited number of models. But if you only know one or two of those models, then you’re going to be a horse’s patoot in declarer play…

If you don’t have the full repertoire, I guarantee you that you’ll over-utilize the limited repertoire you have – including use of models that are inappropriate just because they’re available to you in the limited stock you have in mind.

As for how we can use different ideas, Munger again shows the way …

Have a full kit of tools … go through them in your mind checklist-style. … [Y]ou can never make any explanation that can be made in a more fundamental way in any other way than the most fundamental way.

When you combine things you get lollapalooza effects — the integration of more than one effect to create a non-linear response.

A two-step process for making effective decisions

There is no point in being wiser unless you use it for good. You know, as Aunt May put it to Peter Parker, “with great power comes great responsibility.”

(Source: A Two-step Process for Making Effective Decisions)

Personally, I’ve gotten so that I now use a kind of two-track analysis. First, what are the factors that really govern the interests involved, rationally considered? And second, what are the subconscious influences where the brain at a subconscious level is automatically doing these things-which by and large are useful, but which often misfunction.

One approach is rationality-the way you’d work out a bridge problem: by evaluating the real interests, the real probabilities and so forth. And the other is to evaluate the psychological factors that cause subconscious conclusions-many of which are wrong.

This is the path, the rest is up to you.

Charlie Munger: Adding Mental Models to Your Mind’s Toolbox

In The Art of War Sun Tzu said “The general who wins a battle makes many calculations in his temple before the battle is fought.”

Those ‘calculations’ are the tools we have available to think better. One of the best questions you can ask is how we can make our mental processes work better.

Charlie Munger says that “developing the habit of mastering the multiple models which underlie reality is the best thing you can do.”

Those models are mental models.

They fall into two categories: (1) ones that help us simulate time (and predict the future) and better understand how the world works (e.g. understanding a useful idea  autocatalysis), and (2) ones that help us better understand how our mental processes lead us astray (e.g., availability bias).

When our mental models line up with reality they help us avoid problems. However, they also cause problems when they don’t line up with reality as we think something that isn’t true.

Your Mind’s Toolbox

In Peter Bevelin’s Seeking Wisdom, he highlights Munger talking about autocatalysis:

If you get a certain kind of process going in chemistry, it speeds up on its own. So you get this marvellous boost in what you’re trying to do that runs on and on. Now, the laws of physics are such that it doesn’t run on forever. But it runs on for a goodly while. So you get a huge boost. You accomplish A – and, all of a sudden, you’re getting A + B + C for awhile.

He continues telling us how this idea can be applied:

Disney is an amazing example of autocatalysis … They had those movies in the can. They owned the copyright. And just as Coke could prosper when refrigeration came, when the videocassette was invented, Disney didn’t have to invent anything or do anything except take the thing out of the can and stick it on the cassette.

***

This leads us to an interesting problem. The world is always changing so which models should we prioritize learning?

How we prioritize our learning has implications beyond the day-to-day. Often we focus on things that change quickly. We chase the latest study, the latest findings, the most recent best-sellers. We do this to keep up-to-date with the latest-and-greatest.

Despite our intentions, learning in this way fails to account for cumulative knowledge. Instead, we consume all of our time keeping up to date.

If we are prioritize learning, we should focus on things that change slowly.

The models that come from hard science and engineering are the most reliable models on this Earth. And engineering quality control – at least the guts of it that matters to you and me and people who are not professional engineers – is very much based on the elementary mathematics of Fermat and Pascal: It costs so much and you get so much less likelihood of it breaking if you spend this much…

And, of course, the engineering idea of a backup system is a very powerful idea. The engineering idea of breakpoints – that’s a very powerful model, too. The notion of a critical mass – that comes out of physics – is a very powerful model.

After we learn a model we have to make it useful. We have to integrate it into our existing knowledge.

Our world is mutli-dimensional and our problems are complicated. Most problems cannot be solved using one model alone. The more models we have the better able we are to rationally solve problems.

But if we don’t have the models we become the proverbial man with a hammer. To the man with a hammer, everything looks like a nail. If you only have one model you will fit whatever problem you face to the model you have. If you have more than one model, however, you can look at the problem from a variety of perspectives and increase the odds you come to a better solution.

“Since no single discipline has all the answers,” Peter Bevelin writes in Seeking Wisdom, “we need to understand and use the big ideas from all the important disciplines: Mathematics, physics, chemistry, engineering, biology, psychology, and rank and use them in order of reliability.”

Charles Munger illustrates the importance of this:

Suppose you want to be good at declarer play in contract bridge. Well, you know the contract – you know what you have to achieve. And you can count up the sure winners you have by laying down your high cards and your invincible trumps.

But if you’re a trick or two short, how are you going to get the other needed tricks? Well, there are only six or so different, standard methods: You’ve got long-suit establishment. You’ve got finesses. You’ve got throw-in plays. You’ve got cross-ruffs. You’ve got squeezes. And you’ve got various ways of misleading the defense into making errors. So it’s a very limited number of models. But if you only know one or two of those models, then you’re going to be a horse’s patoot in declarer play…

If you don’t have the full repertoire, I guarantee you that you’ll overutilize the limited repertoire you have – including use of models that are inappropriate just because they’re available to you in the limited stock you have in mind.

As for how we can use different ideas, Munger again shows the way …

Have a full kit of tools … go through them in your mind checklist-style.. .you can never make any explanation that can be made in a more fundamental way in any other way than the most fundamental way. And you always take with full attribution to the most fundamental ideas that you are required to use. When you’re using physics, you say you’re using physics. When you’re using biology, you say you’re using biology.

But ideas alone are not enough. We need to understand how they interact and combine. This leads to lollapalooza effects.

You get lollapalooza effects when two, three or four forces are all operating in the same direction. And, frequently, you don’t get simple addition. It’s often like a critical mass in physics where you get a nuclear explosion if you get to a certain point of mass – and you don’t get anything much worth seeing if you don’t reach the mass.

Sometimes the forces just add like ordinary quantities and sometimes they combine on a break-point or critical-mass basis … More commonly, the forces coming out of … models are conflicting to some extent. And you get huge, miserable trade-offs … So you [must] have the models and you [must] see the relatedness and the effects from the relatedness.

John Holland: The Building Blocks of Innovation

“Most innovation comes from combining well-known, well-established, building blocks in new ways.”

***

light_bulb

John Holland, a professor of two vastly different fields—psychology and engineering—at the University of Michigan, lectures frequently on innovative thinking.

According to Holland, there are two steps to innovation. The first step is to try and find the right building blocks—the basic knowledge. Second, is the use of metaphors to relate understanding.

The first step is to try and find the right building blocks—the basic knowledge. Second, is the use of metaphors to relate understanding.

Holland is focused on innovation, but many readers of Farnam Street will recognize the first step—acquiring the building blocks—as the process for acquiring worldly wisdom.

Like us, Holland wants to connect well-known and well-established ideas from multiple disciplines in new ways to solve problems.

In our case, the basic building blocks are the big ideas in each major discipline. While this seems daunting, luckily, according to Charlie Munger “80 or 90 important models will carry about 90% of the freight in making you a worldly-wise person.”

Domain Dependence and Linking

It’s important that you can link these models together and recognize them outside of the domain they are presented. Many people, for instance, don’t link the supply and demand from economics and equilibrium from physics. Yet in many ways, they are the same thing. If you can’t recognize the forces at play outside of the system in which you learned about them, you are domain dependent.

In Antifragile, Nassim Taleb writes:

We are all, in a way, similarly handicapped, unable to recognize the same idea when it is presented in a different context. It is as if we are doomed to be deceived by the most superficial part of things, the packaging, the gift wrapping.

If you don’t have a basic understanding of each of the major models you won’t be able to link them together. And if you can’t link them together, you’re going to go through life like a one-legged man in an ass-kicking contest.

Construction of a Model

According to Holland, “the construction of a mental model … closely resembles the construction of a metaphor:”

  1. There is a source system with an established aura of facts, interpretation and practice.
  2. There is a target system with a collection of observed phenomena that are difficult to interpret or explain.
  3. There is a translation from source to target that suggests a means of transferring inferences for the source into inferences for the target.

Seeing new connections requires models and metaphors. Holland continues:

For most who are heavily engaged in creative activities, be it in literature or the sciences, metaphor and model lie at the center of their activities. In the sciences, both the source and the target are best characterized as systems rather than isolated objects. … In the sciences, decisions about which properties of the source system are central for understanding the target, and which are incidental, are resolved by careful testing against the world. As a result of testing and deduction, a well-established model in the sciences accumulates a complicated aura of technique, interpretation, and consequences, much of it unwritten. One physicist will say to another “this is a conservation of mass problem” and immediately both will have in mind a whole array of knowledge associated with problems modeled in this way.

“The essence of metaphor,” write Mark Johnson and George Lakoff in their book Metaphors We Live By, “is understanding and experiencing one kind of thing in terms of another.”

Holland posits that metaphors help us translate ideas into models, which form the building blocks of innovative thinking.

“In the same way that a metaphor helps communicate one concept by comparing it to another concept that is widely understood,” Robert Hagstrom writes in Investing: The Last Liberal Art, “using a simple model to describe one idea can help us grasp the complexities of a similar idea. In both cases, we are using one concept (the source) to better understand another (the target). Used this way, metaphors not only express existing ideas, they stimulate new ones.”

Metaphor and Innovation

From the Credit Suisse Thought Leader Forum:

To understand the problems in any discipline, it is necessary to have deep knowledge in that discipline. To resolve those problems, it is often necessary to look at the problem through the filters of a different and often distant discipline. The simplest analogy to the phenomenon is simply perspective. It is very difficult to understand, say, the traffic patterns of a city if you are stuck in your car at rush hour. With the distance provided by a traffic helicopter, however, it is much easier to see the major thoroughfares, the bottlenecks and the overall dynamics of the traffic system. The shift in vantage point offers better understanding and new insights for your strategy. For more abstract challenges, the use of metaphor serves the same purpose as distance in the traffic example. If we are stuck on the challenge of distribution in a global manufacturing company, for example, it may be useful to apply models from other disciplines as metaphors. The model that we have developed and used for our distribution system has been quite effective over the years, but we just cannot seem to resolve some particular challenge. Perhaps we can learn something from other kinds of distribution systems. How does an ant colony collect and distribute its resources? How does the human body manage its circulation and processing of nutrients and wastes?

Three Keys to effectively applying metaphors

There are three keys to effectively applying metaphors to achieve insight and innovation. First, you must develop a deep understanding of the metaphorical system. You will gain no new insights if you look at the human circulatory system and say, “Aha! The brain tells the rest of the body where to send things!” If you draw conclusions too quickly, then more than likely you have only recreated your existing model of distribution systems – you are seeing the human body’s circulation system as if it were the distribution system of a global manufacturing firm. If you invest the time and effort to understand this complex new system on its own merits, then you might discover something interesting about your own discipline. For instance, the human circulatory system is one of several overlapping hierarchical systems that allow the human body to grow, heal, change and yet maintain homeostatic balance. How do those systems overlap? How are the priorities of those different systems balanced? What overlapping systems exist in our global manufacturing firm, and how do they interact? An in-depth study of a new complex system should force you to ask new questions about your own, seemingly familiar system.

The second key to applying metaphors is to recognize the value of the cognitive leap. As you map the models from one system onto another, the fit will never be perfect. Our global manufacturing firm is not a human body. Therefore the solution to our challenge will not lie in our suddenly believing this to be true. Instead, the solution will lie one or two steps away. As we look at the human circulatory system, we will encounter questions and tangential thoughts. “What performs the function of ‘hormones’ in our organization?” We will not, of course, implement a system of complex chemical exchanges in our organization, but this might lead us to think about our communication systems or decision-making metrics in a new way.

The final key is often the most frustrating for managers — only a very few of the ideas that this process produces will be highly valuable. Some of the ideas will be useless. Many of the ideas will be interesting but impractical or irrelevant. Other ideas will serve as useful, incremental improvements to your system. But only a rare few of these ideas will be truly revolutionary. The secret is the same in any game of statistics – you have to try large numbers of these metaphors for the big ideas to hit. These ideas are the outliers, not the norm, and while metaphor can push your thinking towards the innovative, no process can guarantee that your new ideas will be both different and effective. Many managers are willing to try this approach once or twice, and give up when it does not immediately return impressive results. … In order to discover great innovations, you must engage regularly in the search and recognize that most of your discoveries will have either marginal or moderate value. Creative combination is a process that increases your odds of discovering breakthrough innovations, but it cannot guarantee success. This is a tool, not a silver bullet.