What is behavioral economics?

Behavioral economics emerged around the 80s. It is a discipline that combines knowledge of psychology and sociology with tools and knowledge of economists.

Behavioral economics aims to describe optimal behavior and predict actual behavior. Thus, models of economic behavior can be developed that are applicable to real situations. In this sense, the discipline is experiencing a golden age, since its applications are reaching public policies, marketing and the world of work.

What topics does it address?

Behavioral economics attempts to understand and explain the motivations, decisions and influence of incentives. More specifically, its key topics include: behavioral analysis of incentives and motivations, social influence, heuristics and planning.

Incentives and motivations

Money has traditionally been considered the greatest incentive and, for its part, motivations can be intrinsic and extrinsic. Behavioral economics has also tried to study the influence of social motivations.

To do this, they use a well-known game, the ultimatum game (Güth et al., 1982). In it they indicate to participant “A” that he has an amount of money to distribute with participant “B”. On the other hand, participant B is given the opportunity to accept or reject the offer. In the event that he rejects her, neither of them would receive anything.

This game has shown that People are more generous than expected and that depends on the aversion to inequity. On the one hand, one may feel an aversion to disadvantageous inequality. In it, one feels that oneself is a victim of inequality. And on the other hand, the advantageous is when inequality is perceived with respect to those around us.

The social influences that can affect our decisions can be informational or normative. The informative ones are those in which, due to lack of information, We look at what others do and act accordingly.. The regulations, for their part, have to do with the pressure we feel from the group.

An example of these influences is when in doubt about which camera to buy, We review the opinions of other users. In this way, the decision is easier and faster, and the cognitive challenge is less.

Heuristics, risk and bias

Heuristics are a set of rules we use in decision making to simplify the process. Often they work well, but other times they create bias. Kahneman and Tversky identified three categories of rules:

Availability: use easily accessible information. This information may have emotionally vivid or recent content and distorts our perception of risk. Representativeness: a situation is judged by its similarity to others that we already know. Anchoring and adjustment: the decision is made based on a reference point.

According to these biases, it is understood that a person will make decisions in a mainly stable way. If on some occasion he makes a risky decision, it is understood, due to his biases, that he will act this way at other times..

Regarding time, behavioral economics studies our ability to make decisions that involve temporal planning. Just as economics understands that we develop stable risk patterns, stable preferences can also be developed.

Behavioral Economics has discovered that In the short term we act very impatiently (present bias). However, When it is a decision that will take place over a long period of time, we prefer to postpone it. For example, I would rather buy headphones today instead of tomorrow. But if I have to spend a lot of money on a car, I’d rather it be a year and a month from now rather than a year from now.

This same bias is related to overeating, difficulty giving up nicotine, or getting rid of other habits. Such is the interest of these findings from Behavioral Economics, which has even begun to generate economic benefits. An application called Beeminder takes advantage of this bias and charges you every time you postpone your goal.

Where it goes

As mentioned above, all these findings and simple instruments make this discipline booming. Its results are easily applicable to other areas.and even applied research groups are showing increasing interest in incorporating their methods, from neuroscientists to computer scientists.

On the other hand, the knowledge derived from this discipline may be relevant to financial markets and macroeconomics in general. For this reason, large-scale computational models and machine learning are already being developed with the aim of developing coherent and meaningful macroeconomic models and applicability. Without a doubt, these advances will come sooner rather than later.

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