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August 27, 2007
I'm biased. And so are you.
Earlier this year, I changed teams and moved offices within Microsoft. This interrupted a little habit I had developed: pinning up my “Cognitive Bias of the Week” outside my office.
Cognitive biases are somewhat like optical illusions, but they affect our thinking rather than our vision. A well known example is confirmation bias; we tend to give more weight to positive observations that confirm our beliefs rather than negative observations. Fortune-tellers may appear successful when people remember one or two correct predictions more readily than the many that were off the mark.
Of course, you wouldn’t make such an error, would you? Think again. Like an optical illusion, many biases are extremely difficult to shake even when you are aware of the effect. In fact, some biases are most effective when we try to think most logically.
I believe it’s important for those of in the BI world to understand these biases. We represent data and analytic conclusions in highly persuasive ways. We help our customers to get it right or to get it wrong - and at times our influence may be inadvertently malign. With that in mind, I’m going to translate my “Cognitive Bias of the Week” posters to occasional blog posts on particular biases. I hope you’ll find these interesting, and relevant. Let me know.
Here’s one to start with. It’s about risk, and it has some revealing insights into how we consider the impact of risk in our decisions. It’s often called “The Pseudocertainty Effect” and it was first examined by Tversky and Kahneman.
Imagine that the US is at risk from a new disease spreading from Asia. Without treatment, it will kill 600 people, but we have two treatments to choose from.
• With Program A, 200 people will certainly live.
• With Program B there is a 1/3 probability that all 600 people will leave. However, there is also a 2/3 probability that they will all die.
Program A is positive – you’re certainly going to save some people. Program B potentially has a better outcome, but it is way less than certain. What treatment program do you recommend?
In the original study, 72% recommended Program A, and only 28% preferred Program B.
Let’s flip the problem round.
• With Program A, 400 people will certainly die.
• With Program B there is a 1/3 probability that no-one will die. However, there is also a 2/3 probability that all 600 people will die.
Now, Program A is negative: 400 people will certainly die. Program B is still uncertain: there is a risk it will all go wrong. However, if you do nothing 600 will die anyway, and if you follow Program A, 400 will certainly die. With Program B you have a chance of saving everyone. In the original study, when presented in this way to a different sample, 78% chose Program B.
That’s pretty remarkable. Exactly the same choices, presented in a different way, led to a complete inversion of preferences.
From this example, you can perhaps see why I consider cognitive biases to be an important study for BI analysts and developers. We may think of ourselves, or our users, as super-rational objective analysts of complex data; but in reality we are subject to these same biases. Also, we will tend to fall back on these biases, shortcuts and heuristics when we are making decisions under stress.
As BI becomes ever more pervasive, emergency planners probably would use our tools and techniques to handle an epidemic. But we could also be discussing customer churn rather than a deadly disease. The specific KPIs we choose, the manner in which we present them – the ways in which they influence decisions may be subtle, but the impact can be dramatic.
I’ll try to keep up a regular posting of biases, with examples relevant to the BI world.
Posted by Donald Farmer at August 27, 2007 11:32 AM
Comments
Hello Donald, Great piece on cognitive bias! Can you send me an e-mail and let me know if you and Alison are going to attend the awards banquet on November 3rd? Thanks, we're putting our catering order in today. -Seumas Gagne, Slighe nan Gaidheal.
Posted by: Seumas Gagne at October 11, 2007 11:55 AM
Great post Don, and very true. However, this begs the question of whether a framework of sorts should be developed/applied to problems such as these to flesh out or identify cognitive biases. Since the fact/propisition in question is fairly constant, couldn't an MDX function be developed to provide both perspectives?
Posted by: David at May 13, 2008 12:46 PM
