One recent trend in writing on applied behavioural science focuses on exceptions on the findings of behavioural science. Simply put, when what we know on Behavioural Science doesn’t work at all or as expected.
Here are some nice illustrations of this trend.
BE careful - A littleknowledge can be a dangerous thing By Crawford Hollingworth
Beyond WEIRD: Investigating Cultural Biases inBehavioural Science – webinar by Brainjuicer.
(Please Open them in new tabs and continue reading ;) )
In a nutshell, the idea is that sometimes the effects discovered by behavioural science are very small or non-existent.
This, however, is not a surprise if one knows the fundamentals of behavioural science, namely the role of ambiguity (fuzziness).
Most of the times, people rely on contextual cues only if there isn’t clarity regarding the issue at hand. For example, anchoring works because people don’t know the exact value that has to be estimated. If I ask you what the number of cat breeds is, I bet none of you know the exact answer and this is only natural. In this case presenting anchors such as 30 or 3000 will influence your estimation simply because there is incredible fuzziness regarding how many breeds of man-exploiting cute furry creatures (cats) are out there.
Remaining in the area of anchoring: if we ask an illiterate five-year-old child from East Africa in what year did WWII end, then providing anchors will strongly influence the child’s answer (that is unless he simply says “I don’t know”). However, if we ask WWII veterans the same question, then providing anchors will have zero effect. Moreover, the veterans will be offended by the lack of knowledge of the people asking the question.
Similarly to anchoring, other contextual influences work ONLY (mostly) when there is ambiguity. For example, using mental accounting and default opt-ins for re-routing some of the tax return money into savings (mentioned in the article BE careful - A little knowledge can be a dangerous thing By Crawford Hollingworth) did not work because for the ones most in need (low income – less than 50.000 USD/year/household) there wasn’t any fuzziness on what to do with the money. Low income people are very well skilled in managing tight budgets and any sum of money is allocated (budgeted) well in advance.
I believe that if households with a larger income would get some money back and not expect it, the default opt in on savings would work quite well simply because there is some ambiguity on what to do with the (sky fallen) money.
On the social influences avenue, the most powerful social influence works because there is ambiguity. Social Proof (which is distinct from Social Pressure – what Solomon Asch investigated) works because we infer that others know something we don’t know. If we choose to buy the most popular internet subscription plan, we do so simply because we don’t know which plan best fits our needs.
On the other side of things, I know what shoe size I have (43) and I will never buy shoes of a different size (e.g. 42) regardless of how many other people buy 42 size shoes.
Here’s a wonderful illustration of how the context strongly influences judgment. It works because there is ambiguity… lots of it.
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