The world changes; some people don't get that

I read something by a smart person that basically said: "90% of attempted X have failed, therefore X won't be viable."

The problem is that what that person really should have said was: "90% of the attempted X up to now have failed, therefore unless the underlying conditions have changed, X won't be viable."

Here's an example, adapted from a real situation in the past, with disguised (and simplified) numbers; smart person would have spoken up in year 20 and said: "people have been trying that for 20 years now, and trying in ever increasing numbers of attempts, and over 90% of the attempts have failed."

(click for bigger)

And while that's true, by the year 25, sixty-eight percent of all attempts made that year succeed! The secret is a change in underlying conditions (could be infrastructure, social acceptance, technology, economics, or other change).

The details of the model are the following:

1. Number of attempts in year $N$ is $A[N] = 2^{N-1}$.

2. Given a probability of success $p[N]$ in year $N$, the number of successes is the expected number (so we get rid of any stochasticity, for simplicity only):

$S[N] = p[N] \, A[N]$.

3. The probability of success in year $N$ is given by a deterministic difference equation,

$p[N+1] - p[N] = (a + b\, p[N]) (1 - p[N])$,

where the parameters $a$ (innovation rate) and $b$ (imitation rate) are between zero and one, with the specific values for the model above being $a=0.00001, b=0.6$, and $p[0] = 0$.

In other words, very slow innovation, but a large imitation rate (which leads to slow adoption at first then a cascading effect at some point, before saturation sets in). 

[The general form of this process is called a Bass model of evolution (used to analyze product diffusion in the past); I don't like it for data analysis or decision support (for a variety of technical reasons), but it's a convenient model for simulation when we take the parameters as given.]

Here's a real-world example of things changing, possibly one of the reasons of the current interest (institutional as well as individual) in cryptocurrencies:

But some people have this blindness: that just because something has been tried before and hasn't worked then, it must never work. And that something could be SpaceX landing its boosters, or the feasibility of Bitcoin, electric vehicles, small nuclear reactors, etc.

Fear is the mind killer. And not just for scions of House Atreides.

Fun with numbers for November 9, 2019

Science illustrations made by people without quantitative sensibility

From a tweet I saw retweeted by someone I follow (lost the reference), this is supposed to be a depiction of the Chicxulub impact:

My first impression (soon confirmed by minor geometry) was that that impact was too big; yes, the meteor was big for a meteor (ask the dinosaurs…), but the Earth is really really big compared to meteors. Something that created such a large explosion on impact wouldn't just kill 75% of the species on Earth, it would probably kill everything on the planet down to the last replicating protein structure, boil the oceans, and poison the atmosphere for millions of years.

Think Vorlon planet-killer, not Centauri mass driver. 🤓

Using a graphical estimation method (fit a circle over that segment of the Earth to get the radius in pixels, so that we can translate pixels into kilometers), we can see that this is an overestimation of at least 6-fold in linear dimensions (the actual crater diameter is ~150km):

6-fold increase in linear dimensions implies 216-fold increase in volume (and therefore mass); using the estimated energy of the actual impact from the Wikipedia, the energy of the impact above would be between $2.81 \times 10^{26}$ and $1.25 \times 10^{28}$ J or up to around 22 billion times the explosive power of the largest H-bomb ever detonated, the Tsar Bomba.

The area of the Earth is 510.1 million square kilometers, so that's 43 Tsar Bombas per square kilometer --- which is a lot, considering that the one Tsar Bomba that was detonated had a complete destruction radius in excess of 60 km (or an area of 11,310 square kilometers) and partial destruction (of weaker structures) at distances beyond 100 km (or an area of 31,416 square kilometers). And, again, that's 43 of those per square kilometer; so, yeah, that would probably have been the end of all life as we know it on Earth, and I wouldn't be here blogging about it.

A more accurate measurement, using a bit of trigonometry (though still using Eye 1.0 for the tangents):

Because of the eye-based estimation, it's a good idea to do some sensitivity analysis:

(Results are slightly different for the measured case because of full-precision calculation as opposed to dropped digits in the original, hand-calculator and sticky notes-based calculation.)

It gets worse. In some depictions we see the meteor, and it's rendered at the size of a planetoid (using the graphical method here too, because it's quick and accurate enough):

To be clear on the scale, that image is 442 pixels wide, the actual Chicxulub meteor at the same scale as the Earth would be 1-7 pixels wide, which is smaller than the dots in the dotted lines.

For additional context, the diameter of the Moon is 3,474 km, so the meteor in the image above is almost 1/3 the diameter of the Moon (28% to be more accurate) and that impact crater is over 1/2 the diameter of the Moon (60% to be more accurate).

Solar energy density in context

2 square kilometers for 100 MW nameplate capacity… and they're in the shade in that photo, so not producing anything at the moment.

Capacity factor for solar is [for obvious reasons] hard bound at 50%. For California, our solar CF is 26%; let's give Peter Mayle's Provence slightly better CF at 30%, and those 2 square km of non-dispatchable capacity become about 1/20 of a single Siemens SGT-9000H (fits in 1200 square meters with a lot of space to spare for admin offices and break room, works 24/7).

Nano-review of R Programming Compiler for the iPad

Basics: Available on the iOS app store; uses a remote server to run the code, so must have a net connection. Free for the baseline but seven dollars for plots and to use packages, which I paid. The extended keyboard is very helpful considering the limitations of the iPad keyboard. (Also runs on the iPhone and the iPod touch, though I haven't used it on them yet.)

I wouldn't use it to develop code or even to run serious models, but if there's a need to do a quick simulation or analysis (or even as a matrix calculator), it's better than Numbers. Can also be used offline to browse (and edit) code, though not to run it.

The programmer-joke code snippet in the above screen capture run instantly over a free lobby internet in a hotel conference center, so the service is pretty efficient for these small tasks, which are the things I'd be using this for.

Some retailers plan to eat the losses from tariffs

From Bain and Company on Twitter:

My comment (on twitter): Yeah, these are well-behaved cost and demand functions so when a tariff is added to the cost, typically the quantity drops and the price rises, unless there's some specific strategic reason to incur short-term opportunity costs.

Rationale (from any Econ 101 course, but I felt like drawing my own, just for fun):

Note that Bain's breakpoint at 50% of the tariff is the solution to the problem under linear demand with constant marginal cost, but other shapes of demand can make that number much bigger, for example, this exponential leads to 74% (numbers rounded in the diagram but not in the computation):

The demand function is nothing awkward or surprising, just a nice decreasing exponential:

On the other hand, if the marginal cost decreases with quantity, particularly if marginal cost is strongly convex, there's a chance the actual price increase from a tariff is higher than the tariff, even with linear demand:

Note that this is different from lazy markup pricing. Lazy markup pricing always raises the price by more than the tariff, so in places where such outdated pricing practices [cough Portugal /cough] are common, tariffs have a disproportionate negative impact on the economy and general welfare.

Late non-numerical entry: Another news item based on not understanding the life cycle of technologies

From Bloomberg (among many others) we learn that there's a new solar energy accumulator technology, and as usual the news write it up as if product deployment at scale is right around the corner, whereas what we have here is a lab testing rig… that's a lot of steps before there's a product at scale. And many of those steps are covered with oubliettes.

Some thoughts on quant interviews

Being a curmudgeonly quant, I started reacting to people who "love" science and math with simple Post-It questions like this:

(This is not a gotcha question, all you need is to apply Pythagorean theorem twice. I even picked numbers that work out well. Yes, $9 \sqrt{2}$ is a number that works out well.)

Which reminds me of quant interviews and their shortcomings.

I already wrote about what I think is the most important problem in quantitative thinking for the general public, in Innumeracy, Acalculia, or Numerophobia, which was inspired by this Sprezzaturian's post (Sprezzaturian was writing about quant interviews).


In search of quants

That was for the general public. This post is specifically about interviewing to determine quality of quantitative thinking. Which is more than just mathematical and statistical knowledge.

One way to test mathematical knowledge is to ask the same type of questions one gets in an exam, such as:

$\qquad$ Compute $\frac{\partial }{\partial x} \frac{\partial }{\partial y} \frac{2 \sin(x) - 3 \sin(y)}{\sin(x)\sin(y)}$.

Having interacted with self-appointed "analytics experts" who had trouble with basic calculus (sometimes even basic algebra), this kind of test sounds very appealing at first. But its focus in on the wrong side of the skill set.

Physicist Eric Mazur has the best example of the disconnect between being able to answer a technical question and understanding the material:

TL; DR: students can't apply Newton's third law of motion (for every action there's an equal and opposite reaction) to a simple problem (car collision), though they can all recite that selfsame third law. I wrote a post about this before.

Testing what matters

Knowledge tests should at the very least be complemented with (if not superseded by) "facility with quantitative thinking"-type questions. For example, let's say Bob is interviewing for a job and is given the following graph (and formula):

Nina, the interviewer, asks Bob to explain what the formula means and to grok the parameters.

Bob Who Recites Knowledge will say something like "it's a sine with argument $2 \pi \rho x$ multiplied by an exponential of $- \kappa x$; if you give me the data points I can use Excel Solver to fit a model to get estimates of $\rho$ and $\kappa$."

Bob Who Understands will start by calling the graph what it is: a dampened oscillation over $x$. Treating $x$ as time for exposition purposes, that makes $\rho$ a frequency in Hertz and $\kappa$ the dampening factor.

Next, Bob Who Understands says that there appear to be 5 1/4 cycles between 0 and 1, so $\hat \rho = 5.25$. Estimating $\kappa$ is a little harder, but since the first 3/4 cycle maps to an amplitude of $-0.75$, all we need is to solve two equations, first translating 3/4 cycle to the $x$ scale,

$\qquad$ $ 10.5 \,  \pi x = 1.5 \,  \pi$ or  $x= 0.14$

and then computing a dampening of $0.75$ at that point, since $\sin(3/2 \, \pi) = - 1$,

$\qquad$  $\exp(-\hat\kappa \times 0.14) = 0.75$, or $\hat \kappa = - \log(0.75)/0.14 = 2.3$

Bob Who Understands then says, "of course, these are only approximations; given the data points I can quickly fit a model in #rstats that gets better estimates, plus quality measures of those estimates."

(Nerd note: If instead of $e^{-\kappa x}$ the dampening had been $2^{-\kappa x}$, then $1/\kappa$ would be the half-life of the process; but the numbers aren't as clean with base $e$.)

This facility with approximate reasoning (and use of #rstats :-) signal something important about Bob Who Understands: he understands what the numbers mean in terms of their effects on the function; he groks the function.

Nina hires Bob Who Understands. Bonuses galore follow.

Bob Who Recites Knowledge joins a government agency, funding research based on "objective, quantitative" metrics, where he excels at memorizing the 264,482 pages of regulation defining rules for awarding grants.

Intellectual counterfeit fashionistas and the corruption of STEM and analytics

I have acquaintances who say they like classical music but never listen to it and can't tell Bach from Brahms. While this is entertaining to classical music aficionados, a similar disconnect happens in STEM and business analytics, where it has serious consequences.

I've observed many people who are always saying how important science is, who can name several recent Nobel laureates in the sciences, but can't compute the kinetic energy of a 2-ton SUV going 65MPH (766kJ), or, ironically, can't explain what the research of those Nobel laureates was about.

I know people who are always talking about Big Data™ and "the" Management Information Revolution™ (yes, they think the current one is the only one), but cannot write Bayes's formula and think that standard deviation is the same as standard error.

These are the signs of the rise of the intellectual counterfeit fashionista (ICF). The ICF wants others to consider him or her an intellectual (that's the I), up to date on the latest hottest intellectual topic (that's the F), but is not willing to do the work and the learning necessary to understand that topic (that's the C).

No matter how infuriating or entertaining an ICF can be on a personal level, their rise is a problem -- chiefly because of their effect on education, the practice of technical professions, and the general perception of STEM and analytics in society.

Education: by trying to recruit proto-ICFs into STEM/analytics, teaching institutions end up having to water down their courses, since the ICFs don't want to do the work needed for real learning. This leads to lower quality education for every student, even the non-ICFs.

In the mid-to-long term, this creates a number of credentialed ignoramuses and gives rise to the strange situation where people who hire engineers say there's a dearth of them, while engineering associations say there's a glut. I guess it depends on how you define engineer, by skills or by credentials.

Professions: the obvious effect of ICFs is the rise in average incompetence. The more pernicious effect is the destructive nature of internal politics, which always increase in organizations with large numbers of people for which appearances and narratives are more important than observable realities and hard work.

I wish nerdiness became unfashionable again, so that the ICFs moved on to corrupt something else and left STEM and analytics alone.

Twitter valuation is a bet on network value

No, I don't think Twitter is prima facie over-valued.

The following graphic from the WSJ (reproduced here because deep linking is discouraged) has been making the rounds, generally in support of the idea that Twitter's valuation is yet another finance mistake:

But here's the funny thing. Note how both LinkedIn and Twitter are apparently over-priced, and suddenly an alternative explanation appears: the market understands that, while right now the revenue models of these companies are not good, there is value in their networks that, either directly through advertising and other attention-monetizing strategies, or indirectly via the information value of the network, will eventually be captured. (Even if that requires a change of management, which sometimes it does.)

It's a bet on the future value of networks and their associated preference and communication data.*

As I mentioned in my post about the Skype acquisition, these companies are not just some black-box generators of revenue. In particular Twitter's resources include:

• Knowledge of the network to a level of detail that can be closed off to outsiders.
• Personnel and technology that allow for exploitation of the knowledge in the network; inasmuch as the data and technology have unique features, the personnel and the resources are partially locked into the company and are assets to be taken into account in valuation.
• An installed base that serves as a barrier to entry to competitors trying to build their network.

So, not being privy to the financial details, I cannot say whether the valuation is right or wrong, but I can certainly say that people who pass judgment on that valuation based on last year's revenue are terminally myopic. Sadly even people whom I respect seem to fall for this trap.

There's gold in those networks and the nerds who can analyze them.

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* A bet not dissimilar to that of Google trying to build their own social network with Google Plus and all the actions they take in other properties like YouTube trying to nudge people into using the social media affordances of Google Plus instead of the older comments and video responses (now discontinued, get your linkage on G+).

Math in business courses: derivating + grokking

I used to start my Product Management class with a couple of business math problems like the following: let's say we use a given market research technique to measure the value of a product; call the product $i$ and the value $v(i)$. We know -- by choice of the technique -- that the probability that the customer will buy $i$ is given by

$\Pr(i) = \frac{\exp(v(i))}{1 + \exp(v(i))}$.

My question: is this an increasing or a decreasing function of the $v(i)$?

Typically this exercise divided students in three groups:

First, students who were afraid of math, were looking for easy credits, or otherwise unprepared for the work in the class. These math problems made sure students knew what they were getting into.

Second, students who could do the math, either by plug-and-chug (take derivative, check the sign) or by noticing that the formula may be written as

$\Pr(i) = \frac{1}{1 + \exp(-v(i))}$

and working the increasing/decreasing chain rule.

Third, students who had a quasi-intuitive understanding ("grok" in Heinlein's word) that probability of purchase must be an increasing function of value, otherwise these words are being misused.

Ideally we should be training business students to mix the skills of the last two groups: a fluency in basic mathematical thinking and grokking business implications.

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Administrative note: Since I keep writing 4000+ word drafts for "important" posts that never see the light of blog (may see the light of Kindle single), I've decided to start posting these bite-sized thoughts.

Is Pete Fader right that Big Data doesn't imply big money?

He's right, in that Big Data doesn't necessarily lead to big money, but I think he exaggerates for pedagogical effect. Why he feels the need to do so is instructive, especially for Big Data acolytes.


Some days ago there was agitation in the Big Data sociosphere when an interview by Wharton marketing professor Peter Fader questioned the value of Big Data. In The Tech, Fader says

[The hype around Big Data] reminds me a lot of what was going on 15 years ago with CRM (customer relationship management). Back then, the idea was "Wow, we can start collecting all these different transactions and data, and then, boy, think of all the predictions we will be able to make." But ask anyone today what comes to mind when you say "CRM," and you'll hear "frustration," "disaster," "expensive," and "out of control." It turned out to be a great big IT wild-goose chase. And I'm afraid we're heading down the same road with Big Data. [Emphasis added.]

I think Pete's big point is correct, that Big Data by itself (to be understood as: including the computer science and the data analysis tools, not just the data -- hence the capitalization of "Big Data") is not sufficient for Big Money. I think that he's underestimating, for pedagogical effect, the role that Big Data with the application of appropriate business knowledge can have in changing the way we do marketing and the sources of value for customers (that is both the job of marketer and the foundations of business).

This is something I've blogged about before.

So, why make a point that seems fairly obvious (domain knowledge is important, not just data processing skills), and especially why make it so pointedly in a field that is full of strong personalities?

First, since a lot of people working in Big Data don't know technical marketing, they keep reinventing and rediscovering old techniques. Not only is this a duplication of work, it also ignores all knowledge of these techniques' limitations, which has been developed by marketers.

As an example of marketing knowledge that keeps being reinvented, Pete talks about the discovery of Recency-Frequency-Money in direct marketing,

The "R" part is the most interesting, because it wasn't obvious that recency, or the time of the last transaction, should even belong in the triumvirate of key measures, much less be first on the list.*    [...]

Some of those old models are really phenomenal, even today. Ask anyone in direct marketing about RFM, and they'll say, "Tell me something I don't know." But ask anyone in e-commerce, and they probably won't know what you're talking about. Or they will use a lot of Big Data and end up rediscovering the RFM wheel—and that wheel might not run quite as smoothly as the original one.

Second, some of the more famous applications of machine learning, for example the Netflix prize and computers beating humans at chess, in fact corroborate the importance of field-specific knowledge. (In other words, that which many Big Data advocates seem to believe is not important, at least as far as marketing is concerned.)

Deep Blue, the specialized chess-playing computer that defeated Kasparov, had large chess-specific pattern-matching and evaluation modules; and as for the Netflix prize, I think Isomorphismes's comment says all:

The winning BellKor/Pragmatic Chaos teams implemented ensemble methods with something like 112 techniques smushed together. You know how many of those the Netflix team implemented? Exactly two: RBM’s and SVD.    [...] 

Domain knowledge trumps statistical sophistication. This has always been the case in the recommendation engines I’ve done for clients. We spend most of our time trying to understand the space of your customers’ preferences — the cells, the topology, the metric, common-sense bounds, and so on.

Third, many people who don't know any technical marketing tools continuously disparage marketing (and its professionals), and some do so from positions of authority and leadership. That disparagement, repeated and amplified by me-too retweets and Quora upvotes, is what makes reasonable people feel the need for pointedly making their points.

Here are two paraphrased tweets by people in the Big Data sociosphere; I paraphrased them so that the authors cannot be identified with a simple search, because my objective is not to attack them but rather illustrate a more widespread attitude:

It's time marketing stopped being based on ZIP codes. (Tweeted by a principal in an analytics firm.)

Someone should write a paper on how what matters to marketing is behavior not demographics. (Tweeted by someone who writes good posts on other topics.)

To anyone who knows basic marketing, these tweets are like a kid telling a professional pianist that "we need to start playing piano with all fingers, not just the index fingers" and "it's possible to play things other than 'chopsticks' on the piano." (Both demographics and ZIP codes have been superseded by better targeting approaches many decades ago.)

These tweets reflect a sadly common attitude of Big Data people trained in computer science or statistics: that the field of marketing cannot possibly be serious, since it's not computer science or statistics. This attitude in turn extends to each of these fields: many computer scientists dismiss statistics as something irrelevant given enough data and many statisticians dismiss computer scientists as just programmers.

That's a pernicious attitude: that what has been known by others isn't worth of consideration, because we have a shiny new tool. That attitude needs deflating and that's what Pete's piece does.

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* An explanation of the importance of recency is that it's a proxy  for "this client is still in a relationship with our firm." There's a paper by Schmittlein, Morrison, and Colombo, "Counting your customers," Management Science, v33n1 (1987), that develops a model of market activity using a two-state model:  the purchases are Poisson with unknown $\lambda$ in one of the states (active) and there's an unobserved probability of switching to the other state (inactive), which is absorbing and has no purchases. Under some reasonable assumptions, they show that recency increases the probability that the consumer is in the active state. BTW, I'm pretty sure that it was Pete Fader who told me about this paper, about ten years or so ago.

A tale of two long tails

Power law (Zipf) long tails versus exponential (Poisson) long tails: mathematical musings with important real-world implications.

There's a lot of talk about long tails, both in finance (where fat tails, a/k/a kurtosis, turn hedging strategies into a false sense of safety) and in retail (where some people think they just invented niche marketing). I leave finance for people with better salaries brainpower, and focus only on retail for my examples.

A lot of money can be made serving the customers on the long tail; that much we already knew from decades of niche marketing. The question is how much, and for this there are quite a few considerations; I will focus on the difference between exponential decay (Poisson) long tails and hyperbolic decay (power law) long tails and how that difference would impact different emphasis on long tail targeting (that is, how much to invest going after these niche customers), say for a bookstore.

A Poisson distribution over $N\ge 0$ with parameter $\lambda$ has pdf:

$ \Pr(N=n|\lambda) =\frac{\lambda^{n}\, e^{-\lambda}}{n!}$.

A discrete power law (Zipf) distribution for $N\ge 1$ with parameter $s$ is given by:

$ \Pr(N=n|s) =\frac{n^{-s}}{\zeta(s)},$

where $\zeta(s)$ is the Riemann zeta function; note that it's only a scaling factor given $s$.

A couple of observations:

1. Because the power law has $\Pr(N=0|s)=0$, I'll actually use a Poisson + 1 process for the exponential long tail. This essentially means that the analysis would be restricted to people who buy at least one book. This assumption is not as bad as it might seem: (a) for brick-and-mortar retailers, this data is only collected when there's an actual purchase; (b) the process of buying a book at all -- which includes going to the store -- may be different from the process of deciding whether to buy a given book or the number of books to buy.

2. Since I'm not calibrating the parameters of these distributions on client data (which is confidential), I'm going to set these parameters to equalize the means of the two long tails. There are other approaches, for example setting them to minimize a measure of distance, say the Kullback-Leibler divergence or the mean square error, but the equal means is simpler.

The following diagram compares a Zipf distribution with $s=3$ (which makes $\mu=1.37$) and a 1 + Poisson process with $\lambda=0.37$ (click for larger):

The important data is the grey line, which maps into the right-side logarithmic scale: for all the visually impressive differences in the small numbers $N$ on the left, the really large ratios happen in the long tail. This is one of the issues a lot of probabilists point out to practitioners: it's really important to understand the behavior at the small probability areas of the distribution support, especially if they represent -- say -- the possibility of catastrophic losses in finance or the potential for the customers who buy large numbers of books.

An aside, from Seth Godin, about the importance of the heavy user segment in bookstores:

Amazon and the Kindle have killed the bookstore. Why? Because people who buy 100 or 300 books a year are gone forever. The typical American buys just one book a year for pleasure. Those people are meaningless to a bookstore. It's the heavy users that matter, and now officially, as 2009 ends, they have abandoned the bookstore. It's over.

To illustrate the importance of even the relatively small ratios for a few books, this diagram shows the percentage of purchases categorized by size of purchase:

Yes, the large number of customers who buy a small number of books still gets a large percent of the total, but each of these is not a good customer to have: elaborating on Seth's post, these one-book customers are costly to serve, typically will buy a heavily-discounted best-seller and are unlikely to buy the high-margin specialized books, and tend to be followers, not influencers of what other customers will spend money on (so there are no spillovers from their purchase).

The small probabilities have been ignored long enough; finance is now becoming weary of kurtosis, marketing should go back to its roots and merge niche marketing with big data, instead of trying to reinvent the well-know wheel.

Lunchtime addendum: The differences between the exponential and the power law long tail are reproduced, to a smaller extent, across different power law regimes:

Note that the logarithmic scale implies that the increasing vertical distances with $N$ are in fact increasing probability ratios.

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Well, that plan to make this blog more popular really panned out, didn't it? :-)

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.

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!

Thoughts on the Kenan-Flagler $89,000 online MBA (and the MBA degree in general)

 I saw the news this morning that the Kenan-Flagler Business School at the University of North Carolina—Chapel Hill is starting a new online MBA and charging the same as for its in-class MBA, $\$89,000$.

Online MBAs have so far been mostly consigned to the low end of the MBA spectrum; Kenan-Flagler is a serious school with serious quality, so this is a game-changer. Which raises the important question:

Can a online MBA be worth the same as a regular, in-class MBA?

Futurology is a field fraught with error, so let's do what smart managers do at the beginning of a category lifecycle: think carefully about the likely path of the value proposition and the revenue models. The revenue model here is tuition plus alumni donations, same as for a in-class MBA; let's analyze the four components of the value proposition:

1. Technical business material. Things like how to value a put option; how to measure consumer preferences; how to brief an advertising agency; how to organize a value chain; how to analyze the potential of a market. These technical materials are learned the same way every other technical material is (math, science, engineering): mostly by practice. Practice comes from preparing for in-class discussions or homework. So, this part of the MBA value proposition is easily transferred to a online (or even textbook-based) education.

We may like to think that students are learning the technical material in class – because we are so good at explaining it, of course – but the students only learn the material for real when they put it into practice. More often than not, when they are gearing up to solve problems or analyze cases they have to review their notes or read the textbook. All this can work in a self-guided study, be it from the textbook or from online materials. It all depends on how the motivation (aka the assessment) is executed.

2. Managerial skills. Things like leadership, decision-making on-the-fly, presentation, consensus-building, teamwork. These are essential parts of Participant-Centered Learning, and very hard to do online. There's a point in a manager's training where she has to stop analyzing, raise her hand and – in front of a group of people who are trying to find fault with it – present her view of the case. The ability to convince others and get them to execute your decisions is fundamental to the job of manager and this type of experience does require the presence in a classroom.

In fact many critics of the MBA degree as preparation for management jobs state that the in-class experience is not enough to develop these skills and should be complemented with specific soft-skill development exercises, which – needless to say – have to be done in-class.

This might sound trivial, but many students have told me that, before doing it, they had no idea what it felt like to be called upon to defend a decision that they were 51.5% sure of and make it sound convincing (otherwise it would be dead on arrival). These are the kind of skills that make the difference between a back-office analyst and a line manager.

3. Networking and the broader community. Some non-MBAs, who tend to believe in conspiracies to explain their personal shortcomings, dismiss the MBA degree as just a network-building exercise. That is incorrect, but the network one creates as part of one's MBA is a valuable part of the program. A consultant might look good "on paper," but you save your firm a lot of grief (and money) because your old Strategy teammate told you that the guy can't tell a experience effect from a sound effect. This kind of networking will only develop with continued physical proximity.

But the community doesn't end there: there are the other cohorts (older and younger) and even the contacts with faculty (which in a business school may be more useful than, say, in a humanities school). There's also the broader campus community; some MBA students at a school that shall remain nameless, located close to the Longfellow Bridge on Memorial Drive in Cambridge, MA, use their affiliation with the larger Institute to contact faculty in areas that they might find useful; engineering comes to mind. This broader community might be available to online students, especially in this age of email, instant messaging, and videoconferencing.

4. Screening and signaling. Getting accepted into the program, completing the coursework, and paying a high tuition on the expectation that future earnings will more than make up for it are all signals a student sends to the market. The more selective the school is with its incoming class, the more informative that signal is. Since the same criteria can be applied to online and in-class students, the signal should be the same. (Whether the market accepts that the criteria are the same, that's a different story.)

At first I was flabbergasted that K-F was going to charge the same for the online as for the in-class MBA, but now I think that's actually smart on two levels: one, by having the same price for both, K-F signals that both programs are in fact two variants of the same degree; two, by keeping it expensive, they feed the third component of the signal, that those enrolling expect to make a lot of money, which means work hard.


So, what is the verdict?

On one hand, there are a few serious impediments to delivering the full value proposition of an MBA through a online environment. On the other hand, some of those issues can be mitigated with short "in campus" events. And, some people point out, many MBA students never really get the "soft-skill" parts that would be missing from an online MBA; even at K-F. (Even at Halberd, come to think of it.)

The market will decide, but a-priori there's no reason why, at least for technical jobs in business (including most of the consulting and finance jobs that MBAs crave) this wouldn't be a useful program.

Selection effects, Buffett's rebuttal, and the causality question

Some thoughts on causality based on a story I recall from Alice Schroeder's  The Snowball, Warren Buffett's biography. (I read the book over two years ago, and it was a library copy, so I can't be sure of the details, but I'm sure of the logic.)

Warren Buffett attended a conference on money management where he made a big splash against a group of efficient market advocates. Efficient financial markets imply that, in the long term, it's impossible to have returns above market average, something that Buffett had been doing for several years by then.

The efficient markets hypothesis advocates present at this conference made the predictable argument against reading too much in the outsized returns of a few money managers: if there's a lot of people trading securities, then some will do better than the median, while others will do worse than the median, just as an artifact of the randomness. To over-interpret this is to imagine clusters where none exists.

Buffett then told a parable along the following lines: "Imagine that you look at all the money managers in the market last year, say 20,000, and see that there are 24 that did much better than the rest of the 20,000. So far it could be the case of a random cluster, yes. Then you find those 24 traders, and discover that 23 came from a very small town, [Buffett gave it the name of a mentor, but I can't recall it] Buffettville. Now, most people would think that there's something in Buffettville that makes for good managers; but you are telling us that it's all a coincidence."

Buffett's argument carries some weight in the sense that the second variable (i.e. being from Buffettville) is not a-priori related to having higher returns, so it must be related by a hitherto unknown causality relationship.

But there's a problem here. Even if a large proportion of the successful managers are from Buffetville, that doesn't mean that being from Buffettville makes people better managers; it might be the case that there were many other Buffettville managers in the 20,000 and those were at the very bottom. That would mean that managers from Bufettville have a much higher variance in returns than the market, and that the results, once again were the result of randomness.

My argument here is that the story as I recall it being told in Schroeder's book is an incomplete rebuttal of the efficient markets hypothesis, not a defense of that hypothesis. I'm not a finance theorist; I'm in marketing, where we do believe that some marketers are much better than others, so I have no bone to pick either with the theory or its critics.

I'm just a big fan of clear thinking in matters managerial or business.

Two quick thoughts about Microsoft's purchase of Skype

1. Valuation of a property like Skype is a lot more than just some multiple of earnings.

Quite a few bloggers, twitterers, and forum participants jumped on Facebook, Google, and Microsoft for their billion-dollar valuations of Skype. Usually the criticism was based on Skype's lackluster earnings. This is a massively myopic point of view.

One can acquire a company for many reasons beyond its current revenue stream: the company may own resources that it is not adequately exploiting, such as technology or highly valuable personnel; it may have a valuable brand or a large user base (which is certainly true for Skype); it may have valuable information about its customers (again true for Skype as the communication graph -- not just the link graph -- is valuable); and finally, the company may have untapped revenue potential, just not with their current revenue model.

As a general rule, just because one cannot think of a way to monetize something, it doesn't mean that there is no way to monetize that thing.

Another possible reason to buy a company is strategy at a corporate level: to stop it from developing into a competitor for some of our products, to stop competitors from buying it (and therefore becoming better competitors), and to signal commitment to a specific market.


2. Perhaps there's a little Winner's Curse going on here, or perhaps not

When three companies (Google, Facebook, and Microsoft) compete for the same company, there's always the possibility of a little Winner's Curse effect:

 Assume that the value of Skype to these companies includes a big fraction that is common, meaning that it will be realized independent of the owner. Call that true common value $v$. To simplify, for now, assume that there are no synergies or strategic advantages for any of the buying companies; so the whole value is $v$.

Using all the information available, Google, Facebook, and Microsoft estimate $v$, each coming up with a number: $\tilde v_G$, $\tilde v_F$, and $\tilde v_M$. Note that these are estimates of the same $v$, not a representation of different actual value that Skype might have for these three companies. The estimates are different because each company uses different financial models and has access to different information or weighs it differently.

In a competitive market the winner will be the company who has the highest estimate, so we can assume that $\tilde v_M > \tilde v_G$ and $\tilde v_M > \tilde v_F$. The question now becomes: is what Microsoft paid for Skype higher than $v$ (the true $v$)?

Probabilistically the winning $\tilde v$ is likely to be higher than $v$,* since it's the maximum of three unbiased estimates -- one hopes these three companies have good financial advisers -- of the true $v$. Microsoft knows this and may shade its offer down a little from $\tilde v_M$. But even so, there's a chance that it paid too much.

Except that we're ignoring all the non-common value: synergies, strategic fit with Microsoft's other properties, and signaling to the market that Microsoft isn't yet a zombie like IBM was in the '90s.

There's a lot going on between Skype and Microsoft that the online comentariat missed. Then again, that's the fun of reading it.

(Hey, I finally wrote a business post in this blog that I repositioned as a business blog over a month ago!)

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* If the distribution of the errors in estimates of $v$ is symmetrical around zero (ergo the median of $\tilde v$ is $v$), the probability that the maximum of three observations $\tilde v$ is higher than $v$ is $7/8$.

In defense of BS (Business Speak)

Let's leverage some synergies, the comedian said and all laughed.*

This happened in the middle of a technology podcast, the sentence unrelated to anything and off-topic. Such is the state of comedy: make a reference to a disliked group (businesspeople) and all laugh, no need for actual comedic content.

Business-Speak, or BS for short, does have its ridiculous moments. Take the following mission statement:

HumongousCorp's mission is to increase shareholder value by designing and manufacturing products to the utmost standards of excellence, while providing a nurturing environment for our employees to grow and being a responsible member of the communities in which we exist.

There are two big problems with it: First, it wants to be all things to all people; this is not credible. Second, it is completely generic; there's no inkling of what business HumongousCorp is in. Sadly, many companies have mission statements like this nowadays.

Back when we were writing mission statements that were practical business documents, we used them to define the clients, technologies/resources, products, and geographical areas of the business.

FocussedCorp's mission is to to design and manufacture medical and industrial sensors, using our proprietary opto-electronic technology, for inclusion in OEM products, in Germany, the US, and the UK.

This mission statement is about the actual business of FocussedCorp. Mission statements like this were useful: you could understand the business by reading its mission statement. It communicated the strategy of the company to its middle management and contextualized their actions.

FocussedCorp's mission statement is what was then called a strategic square (should be a strategic tesseract): it has four dimensions, client, product, technology/resources, and geography. Which brings up the next point:

Most BS is professional jargon for highly technical material, just like the jargon of other professions and the sciences. So why is it mocked much more often than these others?

Pomposity is a good candidate. Oftentimes managers take simple instructions and drape them in BS to sound more important than they are. In some cases this might even be a form of intimidation, along the lines of "if you question my authority, I'm going to quiz you in this language that you barely speak and I'm fluent in."

Fair enough, but there's much technical jargon in work interactions and only BS gets chosen for mockery. Professionals and scientists do use their long words to the same pompous or intimidating effect as managers, and the comedian in the podcast is as unlikely to know the meaning of "diffeomorphism," "GABA agonist," or "adiabatic process" as that of "leveraging synergies."

I suspect the mockery of BS rather than other professional jargon has to do with the social and financial success of the people who work in business, and therefore are conversant in BS. The mockers are just expressing that old feeling, envy.

They can't play the game, so they hate the players.

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* Leveraging synergies means to use economies of scope, spillovers, experience effects, network externalities, shared knowledge bases, and other sources of synergy (increasing returns to scope broadly speaking) across different business opportunities.

Quants make good scapegoats

Inspired by this post by Eric Falkenstein, here's some advice to managers:

You need a quant. If there's any risk you'll make a mistake, and if your boss, board, or stockholders are dumb enough to accept a pass-the-bucket excuse, you need a quant!

Quants make good scapegoats. Nobody likes smart people, nobody understands their elaborate models, and everybody wants to beat up the kids whose success is based on being smart and knowing difficult technical stuff.

You may be thinking finance is the only field blessed with such great flak-catcher posts as "Chief Economist" and "Head of Analytics," but if you're in marketing or strategy, quants are now available to you as the whipping boys for the ignorant to feed upon.

Forgot that marketing is about creating and delivering value to customers, first and foremost? (Oh, you were texting during that MBA class?) No problem, for only a zillion of your stockholders' dollars you can buy a CRM system that will support your multiple decisions to force churn the bottom 10% of customers -- until there's no one left. Then you don't need to bother with the pesky customers and can blame SAP/SAS/Accenture/Whomever. Never mind that these CRM purveyors tried hard to explain what you were doing wrong; they'll take the blame because they can't succeed by attacking their clients. At least they understand this.

No time for strategic thought? Why bother with complicated things like understanding the sources of differential advantage or identifying potential threats? You can get always a quadruple-PhD's macro-economic model to take the blame when you miss out subtle indicators, such as your competitor buying your only distribution channel. Odds are that your golfing buddies... I mean your board will side with you over the kid who can't tell a mashie from a niblick.

Don't like your quants' recommendations? Ignore them. Got in trouble? Point the finger at the nearest quant. Odds are that when quants start explaining nobody will listen, anyway. Nobody ever wants to listen to knowledgeable smart people. And the quants will be on the defensive, with only the truth on their side... and truth is so overrated in these post-modern times.

Get a quant! They're cheap insurance against your incompetence.

Because not everyone may notice this is sarcasm, my position on the above is summarized by the chyron with which I finish all my modeling classes:

Unlike the managers who blindly trust them, computer models cannot be fired.