Anchored and over-optimistic: why quick and dirty estimates are almost always incorrect
Some time ago, a sales manager barged into my office. “I’m sorry for the short notice,” she said, “but you’ll need to make some modifications to the consolidated sales report by tomorrow evening.”
I could see she was stressed and I wanted to help, but there was an obvious question that needed to be asked. “What do you need done? I’ll have to get some details before I can tell you if it can be done within the time,” I replied.
She pulled up a chair and proceeded to explain what was needed. Within a minute or two I knew there was no way I could get it finished by the next day. I told her so.
“Oh no…this is really important. How long will it take?”
I thought about it for a minute or so. “OK how about I try to get it to you by day after?”
“Tomorrow would be better, but I can wait till day after.” She didn’t look very happy about it though. “Thanks,” she said and rushed away, not giving me a chance to reconsider my off-the-cuff estimate.
After she left, I had a closer look at what needed to be done. Soon I realised it would take me at least twice as long if I wanted to do it right. As it was, I’d have to work late to get it done in the agreed time, and may even have to cut a corner or two ( or three) in the process.
So why was I so wide off the mark?
I had been railroaded into giving the manager an unrealistic estimate without even realising it. When the manager quoted her timeline, my subconscious latched on to it as an initial value for my estimate. Although I revised the initial estimate upwards, I was “pressured” – albeit unknowingly – into quoting an estimate that was biased towards the timeline she’d mentioned. I was a victim of what psychologists call anchoring bias – a human tendency to base judgements on a single piece of information or data, ignoring all other relevant factors. In arriving at my estimate, I had focused on one piece of data (her timeline) to the exclusion of all other potentially significant information (the complexity of the task, other things on my plate etc.).
Anchoring bias was first described by Amos Tversky and Daniel Kahnemann in their pioneering paper entitled, Judgement under Uncertainty: Heuristics and Biases. Tversky and Kahnemann found that people often make quick judgements based on initial (or anchor) values that are suggested to them. As the incident above illustrates, the anchor value (the manager’s timeline) may have nothing to do with the point in question (how long it would actually take me to do the work). To be sure, folks generally adjust the anchor values based on other information. These adjustments, however, are generally inadequate. The final estimates arrived at are incorrect because they remain biased towards the initial value. As Tversky and Kahnemann state in their paper:
In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient. That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring.
Although the above quote may sound somewhat academic, be assured that anchoring is very real. It affects even day-to-day decisions that people make. For example, in this paper Neil Stewart presents evidence that credit card holders repay their debt more slowly when their statements suggest a minimum payment. In other words the minimum payment works as an anchor, causing the card holder to pay a smaller amount than they would have been prepared to (in the absence of an anchor).
Anchoring, however, is only part of the story. Things get much worse for complex tasks because another bias comes into play. Tversky and Kahnemann found that subjects tended to be over optimistic when asked to make predictions regarding complex matters. Again, quoting from their paper:
Biases in the evaluation of compound events are particularly significant in the context of planning. The successful completion of an undertaking, such as the development of a new product, typically has a conjunctive character: for the undertaking to succeed, each of a series of events must occur. Even when each of these events is very likely, the overall probability of success can be quite low if the number of events is large. The general tendency to overestimate the probability of conjunctive events leads to unwarranted optimism in the evaluation of the likelihood that a plan will succeed or that a project will be completed on time.
Of course, as discussed by Kahnemann and Fredrick in this paper, biases such as anchoring and the planning fallacy can be avoided by a careful, reflective approach to estimation – as opposed to a “quick and dirty” or intuitive one. Basically, a reflective approach seeks to eliminate bias by reducing the effect of individual judgements. This is why project management texts advise us (among other things) to:
- Base estimates on historical data for similar tasks. This is the basis of reference class forecasting which I have written about in an earlier post.
- Draft independent experts to do the estimation.
- Use multipoint estimates (best and worst case scenarios)
In big-bang approaches to project management, one has to make a conscious effort to eliminate bias because there are fewer chances to get it right. On the other hand, iterative / incremental methodologies have bias elimination built-in because one starts with initial estimates, which include inaccuracies due to bias, and subsequently refine these as one progresses. The estimates get better as one goes along because every refinement is based on an improved knowledge of the task.
Anchoring and the planning fallacy are examples cognitive biases – patterns of deviations of judgement that humans display in a variety of situations. Since the pioneering work of Tversky and Kahnemann, these biases have been widely studied by psychologists. It is important to note that these biases come into play whenever quick and dirty judgements are involved. They occur even when subjects are motivated to make accurate judgements. As Tversky and Kahnemann state towards the end of their paper:
These biases are not attributable to motivational effects such as wishful thinking or the distortion of judgments by payoffs and penalties. Indeed, several of the severe errors of judgment reported earlier (in the paper) occurred despite the fact that subjects were encouraged to be accurate and were rewarded for the correct answers.
The only way to avoid cognitive biases in estimating is to proceed with care and consideration. Yes, that’s a time consuming, effort-laden process, but that’s the price one pays for doing it right. To paraphrase Euclid, there is no royal road to estimation.