Stories, Data and Decision Making

3 Dec

Stories certainly set a scene. Stories enable the listener or reader to locate themselves within a context, and then see that context through the eyes of the storyteller. A story allows the listener and reader to shadow the thinking, problem solving and emotional recollections of the storyteller. In other words, a story allows two or more people to attach someone else’s experience onto their own experiences.

Stories transfer knowledge by triggering the associative memory. You could be listening how someone tackled a situation you are familiar with in a completely different way. Or you could be listening to a situation you are totally unfamiliar with and wondering how your own experiences and qualities would handle this unfamiliar situation. A story can change the way you think by providing you with a fresh perspective. But it also worth being cautious how much weight you place on a single story, as very often you are dealing with a very small sample.

Because stories are frequently about a human experience, we can relate to them (because the listener is frequently human too) and find them compelling. Yet this can also be a problem. Kahneman (2011) observed how science based investigations can be found very interesting, and we can agree with the results from investigations, but we very rarely apply these results to our lives. For example, you might hear that 90% of public health research projects (a fictitious figure) go over deadline. However, when you are asked to design and deliver a public health project, you and your team confidently predict a deadline based on your own resources, motivations and personal qualities. Your research project is your own story, this is the inside perspective. The outside perspective is the 90% fail rate. The 90% is a number, a statistic, and fails to attach and modify your own inside perspective populated with your high levels of motivation, excellent team and generous resources. The 90% can be explained away, it doesn’t get any emotional traction. In other words, there is no connection between the inside and outside perspective.

The outside perspective, the 90%, is the base rate. Simply basing predictions on a singular story, is base rate neglect. Stories need to interact with base rates and vice versa. Over confident predictions based on personal or local merits need to be adjusted based on the results of far larger numbers attempting the same objectives. Likewise, base rates need local context to make them applicable to the current situation. In the example of our overly confident public health research project, hearing a story from the experiences of a team from within the 90% failure rate (the outside view), would enable the statistic to be related (at least more easily) to personal experience. The research bears this out.  Gilbert (2014) suggests that forecasting can be improved by consulting someone who has lived your future- talk to someone who tried to bring in a research project to deadline, but failed. I’ve made an emphasis here, by suggesting that the “someone who has lived the future” is from the base rate.

Tetlock and Gardner (2015) in their research have found that the best forecasters are people who are able to integrate the inside and outside perspective. The current story is contrasted with the existing base rate to produce an accurate forecast. This leads to critical questions regarding the current story; is the current situation unique? How does it differ from the dominant features present in the base rate? Do these features make it more or less likely to out-perform\under-perform the base rate?

Stories can not only help us relate to and learn from another person’s experiences. Stories can also help us use base rates more effectively. But what is essential when making decisions which extend into the future is the need to integrate both the inside and outside perspectives.


Wilson, Timothy D.; Daniel T. Gilbert (2003). “Affective Forecasting”. Advances in Experimental Social Psychology 35: 345–411

Kahneman, D (2011) Thinking Fast and Slow. Penguin

Gilbert, D (2013) (p.45) AFFECTIVE FORECASTING…OR…THE BIG WOMBASSA: WHAT YOU THINK YOU’RE GOING TO GET, AND WHAT YOU DON’T GET, WHEN YOU GET WHAT YOU WANT in John Brockman (Editor) (2013) Thinking: The New Science of Decision-Making, Problem-Solving, and Prediction. Harper Collins

Tetlock, P. E. & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown




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