Why we generally prefer to keep situations as they currently are

The 

Default Bias

, explained.

What is Default Bias?

In understanding how individuals make decisions, it is important to note that for most decisions, one option is to do nothing – to keep the status quo – this is also called Default Bias.

Why does it happen?

When considering these options, people will find the default more appealing than they ought to in most situations. The essential reason for this is a core tenant of behavioral economics: loss aversion. Loss aversion is the principle that losses are more painful than gains (about twice as painful). As a result, consider, for example, considering moving to a new city. Say it cost five units of happiness in terms of friendships lost, but offered 7 units of happiness in terms of a new job. While it would be beneficial to move, most people wouldn’t, because they would weight the losses more than they should.

Example

Students were asked to make hypothetical decisions for a fictional company inherited from a fictional uncle. They were given the choice of investing in a variety of portfolios, with differing amounts of risk. One portfolio, selected at random, was given as the default option. Regardless of the portfolio’s contents, its selection rate increased as the default.

Sources

[1] The Behavioural Insights Team. (2017). The Behavioural Insights Team Update Report 2016-17. Retrieved from: http://38r8om2xjhhl25mw24492dir.wpengine.netdna-cdn.com/wp-content/uploads/2017/10/BIT_Update-16-17_E_.pdf

[2] R. B. Cialdini, A. Levy, C. P. Herman, L. T. Kozlowski & R. E. Petty. (1976). Elastic shifts of opinion: Determinants of direction and durability. Journal of Personality and Social Psychology, 34(4), 663-672. Retrieved from: http://dx.doi.org/10.1037/0022-3514.34.4.663

[3] J. Guszcza. (2015). The last-mile problem: How data science and behavioral science can work together. Deloitte Review, 16. Retrieved from: https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-16/behavioral-economics-predictive-analytics.html

[4] M. Kosinski, D. Stillwel & T. Graepel. (2013). Private traits and attributes are predictable from digital records of human behavior. PNAS, 110(1), 5802-5805. Retrieved from: http://www.pnas.org/content/110/15/5802.full.pdf

[5] Y. Wang & M. Kosinski. (2017). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Retrieved from: osf.io/zn79k