Don’t Listen to Jack Welch (Only His Best Part)

Jack Welch advises executives to leave their rooms and find out more about organizations they manage.

I’m afraid, this is what executives will do. Why? Welch makes two points. First, he shows the problem, which is real for sure. Knowing your organization is important. Second, he suggests to solve it by visiting “stores, trading floors, regional offices, factories.” It’s also a good point, but not the best solution.

By taking Welch’s advice literally, executives will find no more than a mess of emotions, stories, suggestions, and demands. It’s like reading a morning newspaper: you really need a lot of prejudices to make sense out of this flow of information, when this flow doesn’t have any sense. It’s best at confirming existing prejudices. If you really want to know something about the world, you should do a comprehensive study on topic.

How does it look in management? If you want knowledge, organize it. Build an IT system that let your people talk freely (even if anonymously), send requests to supervisors, get feedbacks, and discuss ideas in a single place. Not face-to-face meetings of the king and His Majesty’s subjects (it always looks this way). It must be a distant platform. A person must know it’s for real and feel no pressure.

Computers are stupid but extraordinarily good at handling whatever comes out of this. Can a human delegate 8.5 million problems to 3.7 million solvers in milliseconds? StackOverflow does this routinely and arguably saved more working hours than YouTube wasted. It’s a matter of minutes to find popular problems, topics, and experts. It’s easy to find where your help is needed. This system shows what matters.

You can spend time traveling around “stores, trading floors, regional offices, factories” to declare, like Jonny Cash, “I’ve been everywhere.” Or you can systematically improve the system that delivers real information from real people right to your armchair. An IT system is better at everything that travels can do: moods, relevant problems, upcoming disasters, and best ideas. Exciting travels, as Welch noted, show that you’re not alone. But they are not for decision making.

Computer-driven operations at Amazon and Walmart have beaten flesh-and-blood shops around the corner. These systems know what customers want, unlike shopkeepers who talk to their customers for hours each day. There must be some sense of modesty regarding own abilities to admit this, but it would be one level up in business management. The creators of Amazon and Walmart could improve because they recognized their limitations and let machines do their work.

This transformation is slow in management because of the email reputation IT systems have. They’re something delivering tons of letters you have no time to read. It’s a failure of design. Emails came from the 70s and haven’t changed since then. ERM and other “management” systems often copy emails in asking too much irrelevant information. They lack human input and the sense of importance. But that’s how public web services looked in the 1990s. Since then they’ve changed tremendously; and so will B2B systems. Don’t miss this moment traveling.

Stack Exchange and reward for being on the top

As mentioned in the previous posts, Stack Exchange has a very interpretable structure. It’s a market in which demand for answering a question meets supply, and supply is paid with upvotes. Such a rude interpretation is necessary for learning how knowledge exchange works.

I once looked into a demand side of Stack Exchange, but now a few points on the supply side. In general, we are interested in efficient allocation of resources. Given the fact that sometimes one answer is enough (especially for software development questions), many answers may be a waste.

And that’s the distribution of answers per question:

Well, it’s a peak at 2 with a long tail. The details:

number of answers Freq. Percent Cum.
1 2,123 18.35 18.35
2 2,601 22.48 40.83
3 2,138 18.48 59.3
4 1,458 12.6 71.9
5 967 8.36 80.26
6 674 5.82 86.09
7 461 3.98 90.07
8 325 2.81 92.88
9 190 1.64 94.52
10 135 1.17 95.69

About 80 percent of questions end with five answers or less.

The Reward for Being on the Top

But what’s the reward for having your answer on the top of the others? These are the means of fractions of total upvotes by the position a given answer occupies:

It says that the answer on the top have an stable advantage over all answers to a given question. You can see that after the fifth answer, adding more answers does not decrease total upvotes given to the existing answers. And the first answer gets no less that half of all upvotes.

That’s a huge bonus, since multiple other answers have to split the remaining half of upvotes. That may be discouraging for participants, as competition is high and the winner takes all.

Sample summary statistics

Variable Obs Mean Std. Dev. Min Max
upvotes 45463 5.390141 24.11624 0 1553
downvotes 45463 0.1868992 0.9164532 0 82
net (up – down) 45463 5.203242 23.94713 -19 1552
position 45463 4.449794 6.709625 1 114
total_answers 45463 7.899589 10.94102 1 114
relative position 45463 0.6272573 0.2922457 0.0087719 1
total_uv_b~q 45463 67.75934 221.0013 0 2488
frac_uv 44188 0.2440255 0.3074606 0 1

Nontrivial Economics

In his biography Surely, You’re Joking, Mr. Feynman, Richard Feynman recalled a story about mathematicians at Princeton challenging him with counterintuitive possibilities. One example they gave him was an orange:

It often went like this: They would explain to me, “You’ve got an orange, OK? Now you cut the orange into a finite number of pieces, put it back together, and it’s as big as the sun. True or false?”

And that was true for the special orange that we can keep cutting indefinitely. A mathematical orange. After some time spent listening such examples, Feynman just responded to each such case with “It’s trivial!”—parodying his interlocutors.

It happens in economics, too. Mathematician Stanislaw Ulam famously asked Paul Samuelson to name an economic idea that is true and nontrivial. Samuelson took a long pause and suggested Ricardian comparative advantage as an example: a country with absolute technical advantage in producing any goods still benefits from trading with less efficient nations. Like the US buying vegetables from Liberia, in which agriculture still contributes more than a half to the GDP.

The definition of “nontrivial” is itself a trivial question: you can define it as you like and get the desired property. But generally, “nontrivial” is something that goes against popular opinion. Mathematicians hold an advantage here because in math you define your own rules, and other people remain too far from this world to make educated guesses. Economists pursue a different task: finding nontrivial results in the everyday world. If the results don’t go against popular opinion, then what’s the point?

And these nontrivial results happen to be more numerous than Ricardo’s logic exercise in the 19th century. Each field offers its own examples:

Growth theory. Geography is a long-standing favorite in explaining the income gap between countries. The impact of bad weather on work is so natural, and who needs more? Still, the geographical hypothesis is far more advanced than, say, the assertion that countries are poor because of natural intellectual limitations of their populations.

In contrast to both these stories, the institutional hypothesis suggests endogenous causes of growth. It warns against the China hysteria: the opinion that this model is viable for economically advanced societies, and we soon may see the demise of democracy. Third, it suggests that direct financial aid to developing countries not necessarily improves these countries’ growth prospects.

Macroeconomics. Anything that we find out about relations between inflation and employment, or the absence of thereof, is non-trivial. What about the efficiency of fiscal policy in economic downturns? Important arguments here cannot be discovered with just common sense.

Labor economics. In the well-known 2000 study of New Jersey and Pennsylvania fast food restaurants, David Card and Alan Krueger discovered that the minimum wage increases employment. The result was so counterintuitive for economists themselves that David Card had to clarify their position to explain political attacks that followed.

The wage example shows that most interesting findings basically inform us about our gaps in understanding. We had a too-simple model of labor markets: here is one reason to look deeper. Unlike mathematics, economics has little a priori knowledge. Whether the first derivative of a labor demand function is greater or less than zero depends on our ability to discover this function’s parameters. When we discover these parameters, they necessarily surprise us compared to our previous experience. Non-triviality in science is just new facts uncovering old mistakes.

Unethical Economics

Introduction to The Oxford Handbook of International Relations edited by Reus-Smit and Snidal (2008):

Instead of a proper engagement between normative and scientific positions, we typically see either mutual neglect or mutual critiques that fall on deaf ears. The result is a divide, with “science” on one side and “normative” on the other. This separation severely impairs the ability of international relations to speak to practical concerns. On the one hand, the unwillingness of “scientists” to tackle ethical and seemingly unscientific problems means it often has little to say on the important problems of the day; on the other hand, insofar as normative international relations is insufficiently well grounded in empirical knowledge, it is not competent to say what we should do in specific cases.

Christian Reus-Smit and Duncan Snidal’s concerns about normative and positive studies in international relations remind those in economics.

In economics, ethical issues about allocation of resources appear mostly in heterodox works or lobbying. Mainstream economics resolved the issue by referring to preferences, Pareto efficiency, equilibrium, and descriptive research in general. It does have inquiries in fairness, but fairness there is a factor affecting decisions, not recommendations about “fair” distributions. For instance, have a look at Matthew Rabin’s paper on incorporation of fairness in game theory or Ernst Fehr’s theory of fairness, competition, and cooperation. They are like, “Yes, we study economics ignoring a significant factor, and let’s move closer to a more realistic description of reality.”

Economists leave decision making to decision makers. That’s the division of labor. Economists do research, people and their selected representatives make decisions, which are, after all, about their own lives. No philosopher kings involved.

Why scholars in international relations concern about themselves making decisions? Maybe the field is much closer to practical policymaking than economics is to business and government.

Economics separated from policymaking not so long ago. While Malthus and both Mills still were advisers on practical matters, Alfred Marshall is already academic economics. Well, Adam Smith was in the ivory tower as well and didn’t hesitate to make recommendations, but the tower itself was different by that time. The century that followed after Smith had transformed the approach to economics.

Gaps happen to be not in normative judgments, but in positive understanding. Say, governments can redistribute income, but generally the consequences are too foggy. We barely understand the tradeoffs. Taxes distort incentives, but inequality leads to unstable economy. We would like to increase social welfare, but only started to understand behavioral foundations of utility functions. We can subsidize education for some, but why does tuition increase?

Taxes, social welfare, and subsidies are ethical questions because we know too little about their impact. And our beliefs about “right” things not necessary lead to the outcomes we would like to have. Economics tries to connect our desires, sometimes ethical, to actions necessary to achieve that desires. Again, this definition of economics is an invention of the 19th century.

Scientists can make normative judgments as humans, not as scientists. Science itself is about discovering facts and explaining them. If some scientific field is struggling with normative judgments, it’s either not scientific or does someone else’s job.

No-knowledge Land

The previous post discussed detrimental impacts of false knowledge. Now, it’s time for the absence of knowledge.

Only a blind man can say that he sees everything. If not for vision, it’s true for knowledge. Anyone who dealt with knowledge carefully would confirm that we know almost nothing about anything. The quest never ends. The no-knowledge land is everywhere else, so it makes natural for humans recognition of our limitations in understanding the world.

And this acceptance is the first step to finding truth. We say, “Let’s assume we are not sure how this thing works and will try to find out.” This start doesn’t guarantee success. You still need to look into the thing right, or you’ll arrive to something like miracles of bloodletting. This acceptance just helps to start looking.

What’s so interesting about it? First, recognition of own limitations is a painful procedure, and this first step rarely occurs at all. Second, fierce enemies of this blank state are both truthful knowledge and false knowledge.

If I know how to make a wheel, I don’t feel much need to reinvent it. For that, I’m unlikely to invent the car wheel or caterpillar. That’s the place to restate the observation saying that scientific theories disappear as their authors die. Authors and supporters are committed way too much to their old theories than to anything what comes next. When you learn about things working one way, you become less perceptive to alternatives. One famous research says that scientists get Nobel Prizes for research made mainly in their thirties. Clearly, many factors matter here, but one is that mature researchers are less likely to risk for new business.

True knowledge has its own dead ends. That just means there must always be some space for alternative ways. Pluralism in the sense of Paul Feyerabend: let those scientists abandon the rules, since their most important strength is in inventing new rules. Rigid scientific methodology prevents new discoveries. And prolific researchers violated rules. They developed their fields in terms of methodology and criteria of truth. Physics and economics, for instance, are still very different in delivering concepts about the world, despite their rapid convergence over the last 50 years.

As for false knowledge, Cartesian doubts are up to this. You question everything to bring things back onto no-knowledge land. You even question things that came after rigorous research. It’s not a method. It’s a principle.

Science is kind of famous for dealing with no-knowledge lands. The issue is actually more pronounced in business and governance, where doubts are a sign of weakness and recognizing knowledge limits is something to get fired for. A boss can’t tell about her doubts because that may harm her subordinates’ confidence. And a subordinate can’t do either, because his competence would be questioned.

The areas are institutionally protected from doubts. Business and governments proceed in a Darwinist fashion, hoping that confident actors with mistaken beliefs disappear. But they don’t, while all the major decisions affecting humans still happen here. And it’s a great challenge for social sciences to understand how humans make decisions and how to improve these decisions.

Knowledge, Witches, and the Church

There are three kinds of knowledge: true knowledge, no knowledge, and false knowledge.

False knowledge is the most dangerous of the three. It gives assurance about things, and we start acting on extremes. Ancient doctors were sure that bloodletting was good for patients. But it was the opposite. It required two thousand years of mistakes to start questioning the practice, and another one hundred years for the doubts to eliminate the practice. Habits die hard, bad habits kill.

These bad habits have their own categories: unintentional delusions and fallacies by design. The latter again deserves more attention. Take one example. The invention of witchcraft by the Church was a tool for eliminating political opponents. Witches appeared pagan competitors, mostly unchecked by official religious authorities, who, in turn, had close connections with political leaders. Joan of Arc happened to be just the most famous victim of this political tool.

Meanwhile, in the medieval society witches served as doctors, more liberal than Church officials. One of the most brutal attacks on their practices occurred in the 15th century. After the Black Death had killed about half of European population, feudal lords sought ways of increasing their rents. Since the lords had their own fallacies about economic wealth coming from the quantity of workers around, they needed to increase birth rates.

And here, as historian John Riddle suggests, witches became an obstacle. You see, they were pro-choice and helped women with abortions and contraception. Feudal leaders couldn’t like the idea of families making independent decisions about the number of children. Still, you can’t attack the entire population that wants fewer children. What you can do is to destroy professionals who knew how to control births.

Witches did remain miles away from what are now clinical trials. But the Church did worse: it declared their practices illegal saying that some of them actually work. Clergy couldn’t just say that witches had skills people needed and elites didn’t want. Clergy had to invent the image of a dangerous woman with spells and black magic. That was false knowledge about demons on one side and angels on another: neither existed, but the illusion kept living thanks to the strong political support.

Carefully designed fallacies bear hundreds of years of attacks and still persist. They are protected because they are important. And overcoming these fallacies is decisive for human survival. It’s easy to see what false knowledge persists now and how it threatens humans. Say, one relates to the average temperature on the globe. It’s far from being clear how to dissolve this false knowledge.