The problem is that, often, the models of human functioning on which policies are based do not correctly represent how people's minds actually work. The consequence is the creation of policies that, although well-intentioned, are not adjusted to the real agent and therefore are not as effective as they could be. Consider the following case: in 2013, the United States government launched an online platform to help people choose a health insurance plan. On this platform, people could compare the prices and characteristics of all insurance plans in their area of residence. They had all the information needed to choose the best health insurance. However, a study with a simulated version of the platform showed that most people probably made, in fact, the wrong health insurance choice. In that study, many people chose a plan, on average, $888 more expensive than probably needed! This is an example that shows that having knowledge and information does not always lead to better choices, and can even cause an overload of information that hinders the decision-making process.
When policies do not work as expected, and when this happens in a systematic way, we need to re-evaluate how we think about them. Virtually all public and corporate policies have a behavioral dimension, and their efficacy depends largely on the extent to which people behave as intended. So, in order to create more effective policies, we need to apply scientific knowledge about how people really think and make decisions. Policies based on this knowledge can be referred to as behavioral science-based policies. It is precisely in the development of this type of policies that we, at Cloo, work every day.
Policies based on the knowledge of the behavioral sciences represent a new approach to how policies are thought and created: policies are created around people (i.e. how people actually are) rather than on the expectation that people will readjust their lives around the created policies. Under this approach, policies are drawn on the basis of scientific and realistic assumptions about how people decide, not on the expectation that people will rearrange their lives around policies created from idealistic and unrealistic assumptions about human behavior.
Evidence around the world has revealed that shaping policies, products and services based on the insights of the behavioral sciences produces great results, often saving money to populations, governments and companies. The importance of this evidence has been strongly recognized internationally, namely in reports from multilateral institutions such as the World Bank, the OECD and the European Commission.
The results of these interventions have also shown that small changes in the context can have great effects. We will give several examples of this in our next publications, but we want to leave you with a well-known example: the program Save More Tomorrow (SMT).
The SMT is a program designed by Professors Richard Thaler (winner of the Nobel Prize for Economics in 2017) and Shlomo Benartzi (professor of behavioral decision making at the UCLA Anderson School of Management), that aims to increase retirement savings rates of US citizens. The program consists of three key elements:
1. Asking people to commit themselves in the present moment to save more in the future for their retirement. This commitment helps people avoid the human tendency to value the benefits and costs of the present much more than the future;
2. When the salary is raised, their savings rates are raised as well. This reduces the influence of loss aversion  because the amount of money people receive at the end of the month never decreases;
3. When people are enrolled in the program, they only get out if they actively decide to do so (i.e., if they opt-out). This element applies the power of inertia, for the benefit of people’s savings.
Because the SMT incorporates knowledge about how people think and behave, it helps them to make better financial decisions for themselves. According to recent data, the program has helped approximately 15 million US citizens to save more for their retirement. The SMT is an example of how we can create effective policies for social and political problems by looking at how people think and make decisions. The results of the SMT program clearly show that we can promote desired behaviors by changing behavioral variables, without necessarily changing the structure of the economic incentives (for example, the SMT did not attributed any type of bonuses for program membership). This approach produces very positive results, often at comparatively lower costs, thus representing a very competitive policy option for decision-makers.
 Loss aversion is the tendency people have to avoid losses rather than acquire equivalent gains.