Habit #1: Challenge Unexamined Assumptions

Let’s go back to Kuhn’s classic study of scientific innovation. Having reviewed decades of scientific progress, he concluded that:

Individuals who break through by inventing a new paradigm are almost always either very young … or very new to the field whose paradigm they change. These are [individuals] who, being little committed by prior practice to the traditional rules of normal science, are particularly likely to see that those rules no longer define a playable game and conceive another set that can replace them.

If the world were like Sudoku, decision making could be tackled in an equally direct way. The characteristics of Sudoku that make such an approach possible are:

• There is one and only one solution, and when it is identified we know that we have found it. Objectives are clear and constant.

• The play is not influenced by the responses of others to moves that are made. Interactions with others, if they are relevant at all, are limited and controlled or predictable.

• There is a complete list of possible actions and we know that all the potential actions we consider are in fact available to us. Even if we do not know what will happen in future, we know the range of possibilities and can sensibly attach probabilities to them. The problem is closed.

• The number of alternative ways of filling in the boxes, although running into many millions, is nevertheless sufficiently small that all can, at least in principle, be evaluated. Complexity, even if extensive is bounded.

The game of Sudoku is closed, determinate, tractable and has a clear-cut objective.

But IBM failed to commercialize Tesauro’s project—why would a vendor of business servers sell commercial backgammon software to a few hundred customers? Why, indeed.

This adorable nook in the marketplace was filled in 1994 by the Norwegian researcher Fredrik Dahl. Dahl was an unusual man who enjoyed backgammon, chess, simulated tank battles, jiu-jitsu, and foraging in the woods for edible fungi. He worked for Norway’s defense establishment, where he simulated outcomes from a hypothetical Soviet invasion. His work drew inspiration from the 1983 movie WarGames, starring Matthew Broderick. In that movie, an AI attempts to start a nuclear war.

Dahl worked on this problem for many years. The obstacle was that he had almost no idea how his poker bot actually worked. The structure of its neural net was no easier to interpret than the nervous system of an invertebrate, and trying to tease out game-playing strategies by examining the individual weights of the grid was like trying to unravel consciousness

by looking at brain cells through a microscope. This was the criticism of neural nets and the thing that so biased the academic community against them. Once a neural net hit a training plateau—and they almost always did—there was rarely an obvious way to make it better. Classical programming was orderly and logical, but tinkering with neural nets required a different cast of mind. Dahl compared it to running a biology experiment: outcomes were unpredictable, and altering seemingly minor variables could have all manner of unanticipated results. Dahl tried everything he could think of to improve his no-limit poker bot. He fiddled with its evaluation function, he futzed with his computer’s memory, he replaced the activation trigger for the neurons, he even synthesized a simpler data universe for the bot to explore—but he never got it to play at an expert level.

Sophie was experiencing what behavioral economist Dan Ariely calls “loss aversion,” our human tendency to want to keep all options open. In a famous series of experiments profiled in the New York Times, Ariely showed just how far we’ll go to keep from closing doors. Students in the experiment played a computer game that paid real cash to look for money behind three doors on the screen. After they opened a door by clicking on it, each subsequent click earned a little money, with the sum varying each time. As players went through the 100 allotted clicks, they could switch rooms to search for higher payoffs, but each switch used up a click to open the new door. The best strategy, players learned, was to quickly check out the three rooms and settle in the one with the highest rewards. But if they stayed out of any room, its door would start shrinking visually and eventually disappear. Ignoring those disappearing doors turned out to be impossible. In fact, participants wasted so many clicks rushing back to reopen doors that their earnings dropped 15 percent. And they frenetically continued keeping all their doors open even as penalties for switching got stiffer, costing not just clicks but cash fees.