The Four Impostors: Success, Failure, Knowledge Creation, and Learning

Some product developers observe that failures are almost always present on the path to economic success.  “Celebrate failures,” they say. Others argue that failures are irrelevant as long as we extract knowledge along the way. “Create knowledge,” they advise. Still others reason that, if our real goal is success, perhaps we should simply aim for success. “Prevent failures and do it right the first time,” they suggest. And others assert that we can move beyond the illusion of success and failure by learning from both. “Create learning,” they propose.  Unfortunately, by focusing on failure rates, or knowledge creation, or success rates, or even learning we miss the real issue in product development.

In product development, neither failure, nor success, nor knowledge creation, nor learning is intrinsically good. In product development our measure of “goodness” is economic: does the activity help us make money? In product development we create value by generating valuable information efficiently. Of course, it is true that success and failure affect the efficiency with which we generate information, but in a more complex way than you may realize. It is also true that learning and knowledge sometimes have economic value; but this value does not arise simply because learning and knowledge are intrinsically “good.” Creating information, resolving uncertainty, and generating new learning only improve economic outcomes when cost of creating this learning is less than its benefit.

In this note, I want to take a deeper look at how product development activities generate information of economic value. To begin, we need to be a little more precise on what we mean by information. The science of information is called information theory, and in information theory the word information has a very specific meaning. The information contained in a message is a measure of its ability to reduce uncertainty. If you learn that it snowed in Alaska in January this message contains close to zero information, because this event is almost certain. If you learned it snowed in Los Angeles in July this message contains a great deal of information because this event is very unlikely.

This relationship between information and uncertainty helps to quantify information. We quantify the information contained in an event that occurs with probability P as:

Information Equation

As we invest in product development, we generate information that resolves uncertainty. We will create economic value when the activities that generate information produce more benefit than cost. Since, our goal is to generate valuable information efficiently, we can decompose our problem into three issues:

  1. How do we maximize the amount of information we generate?
  2. How do we minimizing the cost of generating this information?
  3. How do we maximize the value of the information we generate?

Let’s start with the first issue. We can maximize information generation when we have an optimum failure rate. Information theory allows us to determine what this optimum failure rate is. Say, we perform an experiment which may fail with probability Pf and succeed with probability Ps. The information generated by our experiment is a function of the relative frequency with which we receive the message of success or failure, and the amount of information we obtain if failure or success occurs. We can express this mathematically as:

Information From Test
We can use this equation to graph information generation as a function of failure rate.

Informaton vs Failure rate

Note that this graph maximizes at a 50 percent failure rate. This is the optimum failure rate for a binary test. Thus, when we are trying to generate information it is inefficient to have failure rates that are either too high, or too low. Celebrating failure is a bad idea, since it drives us towards the point of zero information generation on the right side of the curve. Likewise, minimizing failure rates drives us towards the point of zero information generation on the left side of the curve. So, we address the first issue by seeking an optimum failure rate.

But, this is only part of the problem. Remember, we generate information to create economic value, and we create economic value when the benefit of the created information exceeds the cost of creating it. So, let’s look at the second issue: how do we minimize the cost of generating information?

This is done best by exploiting the value of feedback, as I can illustrate with an analogy. Consider a lottery that paid a $200 prize if you pick the correct 2 digit number. If it costs $2 to play, then this lottery is a break-even game.  But, what would happen if I permitted you to buy the first digit for $1, gave you feedback, and then permitted you to decide whether you wanted to buy the second digit for an additional $1? The second game is quite different. It still requires the same amount of information (6.64 bits) to identify the correct two digit number, but it will cost you an average of $1.10 to obtain this information instead of $2.00. Why? You are buying in information in two batches (of 3.32 bits each). However, because you will pick the wrong first digit 90 percent of the time, you can avoid buying the second digit 90 percent of the time, saving an average of $0.90 each time you play the game.  (In the language of options, buying one digit at a time, with feedback, creates an embedded option worth $0.90.) Thus, we address the second issue by breaking the acquisition process into smaller batches and providing feedback after each batch. (Lean Start-up fans will recognize this technique.)

The third issue is to maximize the value of the information that we acquire. The most useful way to assess value is to ask the question, “What is the maximum amount of money a rational person would pay for the answer to this question?” For example, if one of two envelopes has a $100 bill in it, what should you pay to know which envelope it is in? Certainly no more than $50, the expected value of picking a random envelope with no information. Thus, to get the $100 envelope you should pay no more than $50 for the 1 bit of information it takes to answer the question. If the amount in the envelope is $10, you should not pay more than $5 to know which envelope the money is in. In both cases, it takes one bit of information, but the value of this bit is different.

So we address the third issue by asking questions of genuine economic significance, and making sure the answer is worth the cost of getting it. In reality, we do not have unlimited resources to create either learning or knowledge; we must expend these resources to generate information of real value.

My guess is that your common sense has already reliably guided you to address these three issues. For example, when you play the game of 20 questions, you instinctively begin with questions that split the answer space into two equally likely halves. You clearly understand the concept of optimum failure rate. When you decide to try a new wine you buy a single bottle before you buy a case. You clearly recognize that while small batches and intermediate feedback will not change the probability that you will pick a bad wine, they will reduce the amount of money that you will spend on bad wine.  And, when your encounter two technical paths that have virtually identical costs, you don’t invest testing dollars to determine which one is best. You clearly recognize that information is only worth the value it creates. If you do these things, you should have little risk of blindly celebrating failure, success, knowledge creation, or learning. If your common sense has not protected you from these traps, an excellent substitute is to use careful reasoning. It will get you to the same place, and sometimes even further.

Don Reinertsen

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