The Nudge Theory
People do not always choose what is best for them (Loewenstein, 1996; Thaler & Sunstein, 2003) in the long run (Thaler & Shefrin, 1981), therefore policymakers are interested in assisting people to improve their decisions and, consequently, social welfare (Lynch Jr & Zauberman, 2006). Individuals face limitations on their ability to process information due to mental capacity (Simon, 1955).
Our cognitive system can be split into two types, System 1 is fast and intuitive, and System 2 is slow and thoughtful. However, it can conduct us to some mistakes due to mental shortcuts when using system one instead of two in some circumstances, and vice-versa (Kahneman, 2011).
Each of them has its own advantages and disadvantages, System 1 takes information and reaches correct conclusions almost effortlessly by using intuition and rules of thumb (Kahneman & Frederick, 2002). We trust in System 2 to warn us when our intuition is wrong or judgment is more difficult due to emotional charge (Kahneman & Frederick, 2002). For the purpose of illustration, the intuitive and prompt answer (System 1) for the question retrieved from CRT: “A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?” (Shane, 2005) is 10 cents. Although, when you employ System 2, I mean, you stop to think about the solution, you will probably arrive on the right conclusion, the ball costs 5 cents.
Following this, Nudge Theory can be applied in certain decision making processes to solve this issue. Nudges are imperceptibly modifications in the environment or how the problem is framed to deal with our cognitive systems in order to help people elect wiser choices (Thaler & Sunstein, 2008), without limiting or excluding the other options (Thaler & Sunstein, 2003), neither change the financial incentives linked to the alternatives in a significant way (Beshears & Kosowsky, 2020). Moreover, they often present a low cost to be implemented (Benartzi et al, 2018). In a nutshell, Nudge theory is an assortment of “choice architecture” strategies that gently guides people to better outcomes by psychology of decision making (Beshears & Kosowsky, 2020).
Nudges can be categorised in two main aspects: if they use automaticity or do not. Automaticity implies that a choice was already made to the individuals unless they explicitly pick another option (opt-out) (Beshears & Gino, 2015). For instance, U.S. companies used to offer retirement plans in which their employees needed to express their will to adhere (opt-in), they saw their retirement plans rate to rocket to 90% of adherence after modifying to an opt-out response (Thaler & Benartzi, 2004). Opposed to automaticity, the other category needs to trigger System 1 due to its biases and emotions, to engage System 2 or bypass both systems. First, there are plenty of ways to trigger System 1 like arousing emotions; harness biases, it means, policymakers and executives can use cognitive biases on their favour; simplify processes i.e. organizational and governmental processes often involve unnecessary steps that lower motivation or increase potential for cognitive biases, streamlining process can improve it (Beshears & Gino, 2015).
Nonetheless, engaging system 2 can be a great chance to enhance a considered decision. It can be reached out by several techniques such as use joint evaluations, rather than separate; create opportunities to reflection; use planning prompts; inspire broader thinking; increase accountability; encourage the consideration of disconfirming evidence and last, but not least, use reminders (Beshears & Gino, 2015).
Furthermore, another substitute to improve individuals’ decision making by avoiding cognitive biases or lack of motivation is bypass both systems. Set the default, it transforms the outcome when no option was actively selected; and automatic adjustments, this changes the outcome when an active selection was made. For instance, Microsoft automatically adds buffer time to projects proposed by their project owners, accordingly to the project complexity.
Social scientists have already proven that nudge significantly impacts individual outcomes (Benartzi et al, 2018) such as substantial increase on retirement saving plans (Thaler & Benartzi, 2004; Carrol et al, 2009; McKenzie & Liersch, 2011; ), raising in college enrolment rate for recent high school graduates (Bettinger et al, 2012), pushing up energy conservation (Allcott, 2011; Asensio & Delmas, 2015), enhancing influenza vaccination rates (Milkman et al, 2011; Chapman et al, 2010), reusing bathroom towels in hotel (Baca-Motes et al, 2013) and food consumption (Sparkman & Walton, 2017).
Beshears, J., & Gino, F. (2015). Leaders as Decision Architects. Harvard Business Review, 93(5), 52–62.
Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118. https://www.jstor.org/stable/1884852?seq=1
Thaler, R. H., & Benartzi, S. (2004). Save more tomorrow: Using behavioral economics to increase employee saving. Journal of Political Economy, 112(1). https://doi.org/10.1086/380085
Milkman, K. L., Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2011). Using implementation intentions prompts to enhance influenza vaccination rates. Proceedings of the National Academy of Sciences of the United States of America, 108(26), 10415–10420. https://doi.org/10.1073/pnas.1103170108
McKenzie, C. R. M., & Liersch, M. J. (2011). Misunderstanding savings growth: Implications for retirement savings behavior. Journal of Marketing Research, 48, S1–S13.
Baca-Motes, K., Brown, A., Gneezy, A., Keenan, E. A., & Nelson, L. D. (2013). Commitment and behavior change: Evidence from the field. Journal of Consumer Research, 39(5), 1070–1084. https://doi.org/10.1086/667226
Thaler, R. H., & Sunstein, C. R. (2003). Libertarian Paternalism. American Economic Review, 93(2), 175–179.
Benartzi, S., Beshears, J., Milkman, K. L., Sunstein, C. R., Thaler, R. H., Shankar, M., Tucker-Ray, W., Congdon, W. J., & Galing, S. (2017). Should Governments Invest More in Nudging? Psychological Science, 28(8), 1041–1055. https://doi.org/10.1177/0956797617702501
Bettinger, E.P., & Long, B., Oreopoulos, P., Sanbonmatsu, L. (2012). The Role of Application Assistance and Information in College Decisions: Results from the H&R Block Fafsa Experiment. Quarterly Journal of Economics, 127(3), 1205-1242.
Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9–10), 1082–1095. https://doi.org/10.1016/j.jpubeco.2011.03.003
Asensio, O. I., & Delmas, M. A. (2015). Nonprice incentives and energy conservation. Proceedings of the National Academy of Sciences of the United States of America, 112(6), E510–E515. https://doi.org/10.1073/pnas.1401880112
Thaler, R. H., & Shefrin, H. M. (1981). An Economic Theory of Self-Control. Journal of Political Economy, 89(2), 392–406. https://www.jstor.org/stable/1833317
Beshears, J., & Kosowsky, H. (2020). Nudging: Progress to date and future directions. Organizational Behavior and Human Decision Processes, 161, 3–19. https://doi.org/10.1016/j.obhdp.2020.09.001
Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49–81). Cambridge University Press.
Loewenstein, G. (1996). Out of Control: Visceral Influences on Behavior. Organizational Behavior and Human Decision Processes, 65(3), 272–292.
Lynch Jr, J. G., & Zauberman, G. (2006). When Do You Want It? Time, Decisions, and Public Policy. Journal of Public Policy and Marketing, 25(1), 1547–7207.
Frederick, S. (2005). Cognitive Reflection and Decision Making. Journal of Economic Perspectives, 19(4), 25–42.
Carroll, G. D., Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2009). Optimal Defaults and Active Decisions. The Quarterly Journal of Economics, 124(4), 1639–1674. https://doi.org/10.1162/qjec.2009.124.4.1639
Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux