Dan Goldstein notes the intuitiveness of using graphical representations of probability, especially in Bayesian settings. High-quality , sometimes even interactive, graphics and charts, have increased greatly in recent years as news and other information have migrated to the web. The New York Times has a dedicated staff of graphical artists well versed in information design, and many digital graphical artists are becoming better versed in statistics and displaying ideas and results. Even the NHS in in on the act, reporting my surgery’s performance with graphs and relevant comparison groups. (Kudos!) Enabling factors:
Digital graphics are inherently tinkerable for the designer, reducing turn-around times on trying new displays and ensuring the message is correct.
Probabilistic reasoning has become more important with the internet, more developed financial sectors, and more information. We thus have the need, and the circumstance, to use them more than our predecessors.
Our eyes and brain are much quicker at judging sizes and distances than translating numbers to an equivalent internal representation, so we can read (good) graphs quicker. With more exposure, we read them even quicker.
More people trained in statistics and related fields are moving into the public and private sector from academia, and trying to figure out how to communicate their ideas or conclusions to those without training.
Will this affect existing foundations of behavioural economics? Much of the extant literature is derived from numerical representations of probability. Both the papers in Dan’s link are from within the past 10 years, whereas core papers in the decision science field (Kahneman and Tversky etc) are from the 1970’s. Gerd Gigerenzer’s work actively works at finding when such biases are solely based on presentation effects, in effect eliminating them with graphical representations.
If graphical representations are consistenly found to enable superior probablistic reasoning, why aren’t they more widespread? I see far more cases where the producer (private business) loses from clear intuitive representation of charges, competition, and risk than benefits. A clear graph showing a specific business is the best only benefits one business.
The example used it clear for bayesian categorical reasoning - but what about probability distributions and raw numbers? We ran an experiment a while ago with discrete probability distributions which did pretty well, but I’m sure there are futher applications.
What’s next? Interactive versions such as Dan’s distribution builder is just one possibility.
For some truly fantastic graphics, which aren’t necessarily probablistic but do increase readability, see http://www.style.org/