Thinking skill, subject matter expertise, and information

Good thinking depends on all three, but they have different natures.

To illustrate, I'm going to use a forecasting tool called Scenario Planning to determine my chances of dating Milla Jovovich.

First we must figure out the causal structure of the scenario. The desired event, "Milla and I live happily ever after," we denote by $M$. Using my subject matter expertise on human relationships, I postulate that $M$  depends on a conjunction of two events:

• Event $P$  is "Paul Anderson – her husband – runs away with a starlet from one of his movies"
• Event $H$  is "I pick up the pieces of Milla's broken heart"

So the scenario can be described by $P \wedge H \Rightarrow M$. And probabilistically,

$\Pr(M) = \Pr(P) \times \Pr(H).$

Now we can use information from the philandering of movie directors and the knight-in-shining-armor behavior of engineering/business geeks [in Fantasyland, where Milla and I move in the same circles] to estimate $\Pr(P) =0.2$  (those movie directors are scoundrels) and $\Pr(H)=0.1$  (there are other chivalrous nerds willing to help Milla) for a final result of $\Pr(M)=0.02$, or 2% chance.

Of course, scenario planning allows for more granularity and for sensitivity analysis.

We could decompose event $P$  further into a conjunction of two events, $S$  for "attractive starlet in Paul's movies" and $I$  for "Paul goes insane and chooses starlet over Milla." We could now determine $\Pr(P)$  from these events instead of estimating it directly at 0.2 from the marital unreliability of movie directors in general, using $\Pr(P) = \Pr(S) \times \Pr(I).$

Or, going in another direction, we could do a sensitivity analysis. Instead of assuming a single value for $\Pr(P)$ and $\Pr(H)$, we could find upper and lower bounds, say $0.1 < \Pr(P) < 0.3$  and $0.05 < \Pr(H) < 0.15$. This would mean that  $0.005 < \Pr(M) < 0.045$.

Of course, if instead of the above interpretation we had

• Event $P$  is "contraction in the supply of carbon fiber"
• Event $H$  is "increase in the demand for lightweight camera tripods and monopods"
• Event $M$  is "precipitous increase in price and shortages of carbon fiber tennis rackets"

the same scenario planning would be used for logistics management of a sports retailer provisioning.

Which brings us to the three different competencies needed for scenario planning, and more generally, for thinking about something:

Thinking skill is, in this case, knowing how to use scenarios for planning. It includes knowing that the tool exists, knowing what its strengths and weaknesses are, how to compute the final probabilities, how to do sensitivity analysis, and other procedural matters. All the computations above, which don't depend on what the events mean are pure thinking skill.

Subject matter expertise is where the specific elements of the scenario and their chains of causality come from. It includes knowing what to include and what to ignore, understanding how the various events in a specific subject area are related, and understanding the meaning of the events (as opposed to just computing inferential chains like an algorithm). So knowing that movie directors tend to abandon their wives for starlets allows me to decompose the event $P$  into $S$  and $I$  in the Milla example. But only an expert in the carbon fiber market would know how to decompose $P$  when it becomes the event "contraction in the supply of carbon fiber."

Information, in this case, are the probabilities used as inputs for calculation, as long as those probabilities come from data. (Some of these, of course, could be parameters of the scenario, which would make them subject matter expertise. Also, instead of a strict implication we could have probabilistic causality.) For example, the $\Pr(P)=0.2$  could be a simple statistical count of how many directors married to fashion models leave their wives for movie starlets.

Of these three competencies, thinking skill is the most transferrable: knowing how to do the computations associated with scenario planning allows one to do them in military forecasting or in choice of dessert for dinner. It is also one that should be carefully learned and practiced in management programs but typically is not given the importance its real-world usefulness would imply.

Subject matter expertise is the hardest to acquire – and the most valuable – since it requires both acquiring knowledge and developing judgment. It is also very hard to transfer: understanding the reactions of retailers in a given area doesn't transfer easily to forecasting nuclear proliferation. 

Information is problem-specific and though it may cost money it doesn't require either training (like thinking skill) or real learning (like subject matter expertise). Knowing which information to get requires both thinking skill and subject matter expertise, of course.

Getting these three competencies confused leads to hilarious (or tragic) choices of decision-maker. For example, the idea that "smart is what matters" in recruiting for specific tasks ignores the importance of subject matter expertise.*

Conversely, sometimes a real subject matter expert makes a fool of himself when he tries to opine on matters beyond his expertise, even ones that are simple. That's because he may be very successful in his area due to the expertise making up for faulty reasoning skills, but in areas where he's not an expert those faults in reasoning skill become apparent.

Let's not pillory a deceased equine by pointing out the folly of making decisions without information; on the other hand, it's important to note the idiocy of mistaking someone who is well-informed (and just that) for a clear thinker or a knowledgeable expert.

Understanding the structure of good decisions requires separating these three competencies. It's a pity so few people do.

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* "Smart" is usually a misnomer: people identified as "smart" tend to be good thinkers, not necessarily those who score highly on intelligence tests. Think of intelligence as raw strength and thinking as olympic weightlifting: the first helps the second, but strength without skill is irrelevant. In fact, some intelligent people end up being poor thinkers because they use their intelligence to defend points of view that they adopted without thinking and turned out to be seriously flawed.

Note 1: This post was inspired by a discussion about thinking and forecasting with a real clear thinker and also a subject matter expert on thinking, Wharton professor Barbara Mellers.

Note 2: No, I don't believe I have a 2% chance of dating Milla Jovovich. I chose that example precisely because it's so far from reality that it will give a smile to any of my friends or students reading this.

The costly consequences of misunderstanding cost

Apparently there's growing scarcity of some important medicines. And why wouldn't there be?

Some of these medicines are off-patent, some are price-controlled (at least in most of the world), some are bought at "negotiated" prices where one of the parties negotiating (the government) has the power to expropriate the patent from the producer. In other words, their prices are usually set at variable cost plus a small markup.

Hey, says Reggie the regulator, they're making a profit on each pill, so they should produce it anyway.

(Did you spot the error?)

(Wait for it...)

(Got it yet?)

Dear Reggie: pills are made in these things called "laboratories," that are really factories. Factories, you may be interested to know, have something called "capacity constraints," which means that using a production line for making one type of pill precludes that production line from making a different kind of pill. Manufacturers are in luck, though, because most production lines can be repurposed from one medication to another with relatively small configuration cost.

Companies make their decisions based on opportunity costs, not just variable costs. If they have a margin of say 90 cents/pill for growing longer eyelashes (I'm not kidding, there's a "medication" for that) and say 5 cents/pill to cure TB, they are going to dedicate as much of their production capacity to the eyelash-elongating "medication" as they can.* (They won't stop making the TB medication altogether because that would be bad for public relations.)

Funny how these things work, huh?

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* Unless they can make more than eighteen times more TB pills than eyelash "medicine" pills with the same production facilities, of course.

Books on teaching and presentations

During a decluttering of my place, I had to make decisions about which books to keep; these are some that I found useful for teaching and presentations, and I'm therefore keeping:

They are stacked by book size (for stability), but I'll group them in four major topics: general presentation planning and design; teaching; speechwriting; and visuals design.

1. Presentation planning and design

Edward Tufte's Beautiful Evidence is not just about making presentations, rather it's about analyzing, presenting, and consuming evidence.

Lani Arredondo's How to Present Like a Pro is the only "general presentation" book I'm keeping (and I'm still pondering that, as most of what it says is captured in my 3500-word post on preparing presentations). It's not especially good (or bad), it's just the best of the "general presentation" books I have, and there's no need for more than one. Whether I need one given Beautiful Evidence is an open question.

Donald Norman's Living With Complexity and Things That Make Us Smart are not about presentations, rather about designing cognitive artifacts (of which presentations and teaching exercises are examples) for handling complex and new units of knowledge.

Chip and Dan Heath's Made to Stick is a good book on memorability; inasmuch as we expect our students and audiences to take something away from a speech, class, or exec-ed, making memorable cognitive artifacts is an important skill to have.

Steve Krug's Don't Make Me Think is about making the process of interactions with cognitive artifacts as simple as possible (the book is mostly about the web, but the principles therein apply to presentation design as well).

Alan Cooper's The Inmates Are Running The Asylum is similar to Living With Complexity, with the added benefit of explicitly addressing the use of personas for designing complex products (a very useful product design tool for classes, I think).

I had other books on the general topic of presentations that I am donating/recycling. Most of them spend a lot of space discussing the management of stage fright, a problem with which I am not afflicted.

If I had to pick just one to keep, I'd choose Beautiful Evidence. (The others, except How To Present Like a Pro, are research-related, so I'd keep them anyway.)


2. Teaching

As I've mentioned previously, preparing instruction is different from preparing presentations. The two books I recommended then are the two books I'm keeping:

Tools for teaching, by Barbara Gross Davis covers every element of course design, class design, class management, and evaluation. It is rather focussed on institutional learning (like university courses), but many of the issues, techniques, and checklists are applicable in other instruction environments.

Designing effective instruction, by Gary Morrison, Steven Ross, and Jerrold Kemp, complements Tools for teaching. While Tools for Teaching has the underlying model of a course, this book tackles the issues of training and instruction from a professional service point of view. (In short: TfT is geared towards university classes, DEI is geared towards firm-specific Exec-Ed.)

I had other books on the general topic of teaching (and a number of books on academic life) that I am donating/recycling.


3. Speechwriting and public speaking

Speak like Churchill, stand like Lincoln, by James Humes, should be mandatory reading for anyone who ever has to make a public speech. Of any kind. Humes is a speechwriter and public speaker by profession and his book gives out practical advice on both the writing and the delivery. I have read many books on public speaking and this one is in a class of its own.

I have a few books from the Toastmasters series; I'm keeping (for now at least) Writing Great Speeches and Choosing Powerful Words, though their content overlaps a lot with Virginia Tufte's Beautiful Sentences, a book I'm definitely keeping as part of my writing set.

I'm probably keeping Richard Dowis's The Lost Art of The Great Speech as a good reference for styles and as motivation reading. (Every so often one needs to be reminded of why one does these things.)

I have other books on writing, in general, but the ones in the pile above are specific to speechwriting. I'm throwing out a few books on the business of speechwriting; they are so bad that I thought of keeping them as satire. Donating them would be an act of cruelty towards the recipients.

If I had to pick just one book on speechwriting, I'd go with Speak like Churchill, Stand like Lincoln. Hands down the best in the category, and I've read many.

4. Visuals design

Yes, the design of visuals for presentations or teaching, not Visual Design the discipline.

Edward Tufte's books are the alpha and the omega in this category. Anyone with any interest in information design should read these books carefully and reread them often.

The Non-Designer Design Book, by Robin Williams lets us in on the secrets behind what works visually and what doesn't. It really makes one appreciate the importance of what appears at first to be over-fussy unimportant details. I complement this with The Non-Designer Type Book and Robin Williams Design Workshop, the first specifically for type, the second as an elaboration of the Non-Designer Design Book.

Universal principles of design, by William Lidwell, Kristina Holden, and Jill Butler is a quick reference for design issues. I also like to peruse it regularly to get some reminders of design principles. It's organized alphabetically and each principle has a page or two, with examples.

Perhaps I'm a bit focussed on typography (a common symptom of reading design books, I'm told), but Robert Bringhurst's The Elements of Typographic Style is a really good and deeply interesting book on the subject. Much more technical than The Non-Designer Type Book, obviously, and the reason why I hesitate to switch from Adobe CS to iWork for my handouts.

Zakia and Page's Photographic Composition: A visual guide is very useful as a guide to laying out materials for impact. Designing the visual flow of a slide (or a handout) -- when there are options, of course, this is not about "reshaping" statistical charts -- helps tell a story even without narration or animation.

I had some other books on the general topic of slide design, which I am donating. I also have a collection of books on art, photography, and design in general, which affords me a reference library. (That collection I'm keeping.)

If I had to pare down the set further, the last ones I'd give up are the four Tufte books. If forced to pick just one (in addition to Beautiful Evidence, which fills the presentation category above), I'd choose The Visual Display of Quantitative Information, because that's the most germane to the material I cover.


CODA: A smaller set

Not that I'm getting rid of the books in the larger set above (that's the set that I'm keeping), but I think there's a core set of books I should reread at least once a year. Unsurprisingly, those are the same books I'd pick if I really could have only one per category (or one set for the last category):

Note that the Norman, Heath Bros, Krug, Cooper books and my collection of art, photography, and design books are exempted from this choice, as they fall into separate categories: research-related or art. I also have several books on writing (some of them here).

And the books that didn't make the pile at the beginning of the post? Those, which I'm donating or recycling, make up a much larger pile (about 50% larger: 31 books on their way out).

Somewhat related posts:

Posts on presentations in my personal blog.

Posts on teaching in my personal blog.

Posts on presentations in this blog.

My 3500-word post on preparing presentations.

What to do about psychological biases? The answer tells a lot... about you.

There are many documented cases of behavior deviating from the normative "rational" prescription of decision sciences and economics. For example, in the book Predictably Irrational, Dan Ariely tells us how he got a large number of Sloan School MBA students to change their choices using an irrelevant alternative.

The Ariely example has two groups of students choose a subscription type for The Economist. The first group was given three options to choose from: (online only, $\$60$); (paper only, $\$120$); or (paper+online, $\$120$). Overwhelmingly they chose the last option. The second group was given two options : (online only, $\$60$) or (paper+online $\$120$). Overwhelmingly they chose the first option.

Since no one chooses the (paper only, $\$120$) option, it should be irrelevant to the choices. However, removing it makes a large number of respondents change their minds. This is what is called a behavioral bias: an actual behavior that deviates from "rational" choice. (Technically these choices violate the Strong Axiom of Revealed Preference.)

(If you're not convinced that the behavior described is irrational, consider the following isomorphic problem: a waiter offers a group of people three desserts: ice cream, chocolate mousse, and fruit salad; most people choose the fruit salad, no one chooses the mousse. Then the waiter apologizes: it turns out there's no mousse. At that point most of the people who had ordered fruit salad switch to ice cream. This behavior is the same -- use some letters to represent options to remove any doubt -- as the one in Ariely's example. And few people would consider the fruit salad to ice-cream switchers rational.)

Ok, so people do, in some cases (perhaps in a majority of cases) behave in "irrational" ways, as described by the decision science and economics models. This is not entirely surprising, as those models are abstractions of idealized behavior and people are concrete physical entities with limitations and -- some argue -- faulty software.

What is really enlightening is how people who know about this feel about the biases.

IGNORE. Many academic economists and others who use economics models try to ignore these biases. Inasmuch as these biases can be more or less important depending on the decision, the persons involved, and the context, this ignorance might work for the economists, for a while. However, pretending that reality is not real is not a good foundation for Science, or even life.

ATTACK. A number of people use the existence of biases as an attack on established economics. This is how science evolves, with theories being challenged by evidence and eventually changing to incorporate the new phenomena. Some people, however, may be motivated by personal animosity towards economics and decision sciences; this creates a bad environment for knowledge evolution -- it becomes a political game, never good news for Science.

EXPLOIT. Books like Nudge make this explicit, but many people think of these biases as a way to manipulate others' behavior. Manipulate is the appropriate verb here, since these people (maybe with what they think is the best of intentions -- I understand these pave the way to someplace...) want to change others' behavior without actually telling these others what they are doing. In addition to the underhandedness that, were this a commercial application, the Nudgers would be trying to outlaw, this type of attitude reeks of "I know better than others, but they are too stupid to agree." Underhanded manipulation presented as a virtue; the world certainly has changed a lot.

ADDRESS AND MANAGE. A more productive attitude is to design decisions and information systems to minimize the effect of these biases. For example, in the decision above, both scenarios could be presented, the inconsistency pointed out, and then a separate part-worth decision could be addressed (i.e. what are each of the two elements -- print and online -- worth separately?). Note that this is the one attitude that treats behavioral biases as damage and finds way to route decisions around them, unlike the other three attitudes.


In case it's not obvious, my attitude towards these biases is to address and manage them.

Probability interlude: from discrete events to continuous time

Lunchtime fun: the relationship between Bernoulli and Exponential distributions.

Let's say the probability of Joe getting a coupon for Pepsi in any given time interval $\Delta t$, say a month, is given by $p$. This probability depends on a number of things, such as intensity of couponing activity, quality of targeting, Joe not throwing away all junk mail, etc.

For a given integer number of months, $n$, we can easily compute the probability, $P$, of Joe getting at least one coupon during the period, which we'll call $t$, as

$P(n) = 1 - (1-p)^n$.

Since the period $t$  is $t= n \times \Delta t$, we can write that as

$P(t) = 1 - (1-p)^{\frac{t}{\Delta t}}.$

Or, with a bunch of assumptions that we'll assume away,

$P(t) = 1- \exp\left(t \times \frac{\log (1-p)}{\Delta t}\right).$

Note that $\log (1-p)<0$. Defining $r = - \log (1-p) /\Delta t$, we get

$P(t) = 1 - \exp (- r t)$.

And that is the relationship between the Bernoulli distribution and the Exponential distribution.

We can now build continuous-time analyses of couponing activity. Continuous analysis is much easier to do than discrete analysis. Also, though most simulators are, by computational necessity, discrete, building them based on continuous time models is usually simpler and easier to explain to managers using them.

Small probabilities, big trouble.

After a long – work-related – hiatus, I'm back to blogging with a downer: the troublesome nature of small probability estimation.

The idea for this post came from a speech by Nassim Nicholas Taleb at Penn. Though the video is a bit rambling, it contains several important points. One that is particularly interesting to me is the difficulty of estimating the probability of rare events.

For illustration, let's consider a Normally distributed random variable $P$, and see what happens when small model errors are introduced. In particular we want to how the probability density $f_{P}(\cdot)$ predicted by four different models changes as a function of distance to zero, $x$. The higher the $x$ the  more infrequently the event $P = x$ happens.

The densities are computed in the following table (click for larger):



The first column gives $f_{P}(x)$ for $P \sim \mathcal{N}(0,1)$, the base case. The next column is similar except that there's a 0.1% increase in the variance (10 basis points*). The third column is the ratio of these densities. (These are not probabilities, since $P$  is a continuous variable.)

Two observations jump at us:

1. Near the mean, where most events happen, it's very difficult to separate the two cases: the ratio of the densities up to two standard deviations ($x=2$) is very close to 1.

2. Away from the mean, where events are infrequent (but potentially with high impact), the small error of 10 basis points is multiplied: at highly infrequent events ($x>7$) the density is off by over 500 basis points.

So: it's very difficult to tell the models apart with most data, but they make very different predictions for uncommon events. If these events are important when they happen, say a stock market crash, this means trouble.

Moving on, the fourth column uses $P \sim \mathcal{N}(0.001,1)$, the same 10 basis points error, but in the mean rather than the variance. Column five is the ratio of these densities to the base case.

Comparing column five with column three we see that similarly sized errors in mean estimation have less impact than errors in variance estimation. Unfortunately variance is harder to estimate accurately than the mean (it uses the mean estimate as an input, for one), so this only tells us that problems are likely to happen where they are more damaging to model predictive abilities.

Column six shows the effect of a larger variance (100 basis points off the standard, instead of 10); column seven shows the ratio of this density to the base case.

With an error of 1% in the estimate of the variance it's still hard to separate the models within two standard deviations (for a Normal distribution about 95% of all events fall within two standard deviations of the mean), but the error in density estimates at $x=7$ is 62%.

Small probability events are very hard to predict because most of the times all the information available is not enough to choose between models that have very close parameters but these models predict very different things for infrequent cases.

Told you it was a downer.

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* Some time ago I read a criticism of this nomenclature by someone who couldn't see its purpose. The purpose is good communication design: when there's a lot of 0.01% and 0.1% being spoken in a noisy environment it's a good idea to say "one basis point" or "ten basis points" instead of "point zero one" or "zero point zero one" or "point zero zero one." It's the same reason we say "Foxtrot Universe Bravo Alpha Romeo" instead of "eff u bee a arr" in audio communication.

NOTE for probabilists appalled at my use of $P$  in $f_{P}(x)$ instead of more traditional nomenclature $f_{X}(x)$ where the uppercase $X$ would mean the variable and the lowercase $x$ the value: most people get confused when they see something like $p=\Pr(x=X)$.

Decline and fall of Western Manufacturing - a pessimistic reading of Pisano and Shih (2009)

Those who don't know history are condemned to repeat it.

Unfortunately those of us who do know history get dragged right along with the others, because we live in a world where everything is connected to everything else.



Above is my visualization of Pisano and Shih's 2009 Harvard Business Review article "Restoring American Competitiveness." This is a stylized version of a story that has happened in several industries.

Step 1: Companies start outsourcing their manufacturing operations to companies (or countries) which can perform them in a more cost-effective manner. Perhaps these companies/countries have cheaper labor, fewer costly regulations, or less overhead.

Step 2: Isolated from their manufacture, companies lose the skills for process engineering. After all, improving manufacturing processes is a task that depends on continuous experimentation and feedback from the manufacturing process. If the manufacturing process is outsourced, the necessary interaction between manufacturing and process engineers happens progressively more inside the contractor, not the original manufacturer.

Step 3: Without process engineering to motivate it, the original manufacturer (and the companies supporting it in the original country, in the diagram the US) stops investing in process technology development. For example, the companies that developed machine tools for US manufacturers in conjunction with US process engineers now have to so do with Taiwanese engineers in Taiwan, which leads to relocation of these companies and eventually of the skilled professionals.

Step 4: Because of spillovers in technological development between process technologies and product technologies (including the development of an engineering class and engineering support infrastructure), more and more product technology development is outsourced. For example, as fewer engineering jobs are available in the original country, fewer people go to engineering school; the opposite happens in the outsourced-to country, where an engineering class grows. That growth is a spillover that is seldom accounted for.

Step 5: As more and more technology development happens in the outsourced-to country, it captures more and more of the product innovation process, eventually substituting for the innovators in the original manufacturer's country. Part of this innovation may still be under contract with the original manufacturer, but the development of innovation skills in the outsourced-to country means that at some point it will have its own independent manufacturers (who will compete with the original manufacturer).

Pisano and Shih are optimists, as their article proposes solutions to slow, stop, and reverse this process of technological decline of the West (in their case, the US). It's worth a read (it's not free but it's cheaper than a day worth of lattes, m'kay?) and ends in an upbeat note.

I'm less optimistic than Pisano and Shih. Behold:

Problem 1: Too many people and too much effort dedicated to non-wealth-creating activities and too many people and too much effort aimed at stopping wealth-creating activities.

Problem 2: Lack of emphasis in useful skills (particularly STEM, entrepreneurship, and "maker" culture) in education. Sadly accompanied by a sense of entitlement and self-confidence which is inversely proportional to the actual skills.

Problem 3: Too much public discourse (politicians of both parties, news media, entertainment) which vilifies the creation of wealth and applauds the forcible redistribution of whatever wealth is created.

Problem 4: A generalized confusion between wealth and pieces of fancy green paper with pictures of dead presidents (or Ben Franklin) on them.

Problem 5: A lack of priorities or perspective beyond the immediate sectorial interests.

We are doomed!