Applying machine learning in behavioral economics

Applying machine learning in behavioral economics helps better understand human choices, enabling businesses to make better-informed decisions based on an individual’s preferences.

Behavioral economics incorporates the study of psychology into the analysis of the decision-making behind an economic outcome, such as the factors leading to a consumer buying one product instead of another. It examines how emotional, cognitive, and psychological factors affect human decisions and how these decisions deviate from rational thinking. This helps to better understand human behaviour which allows designing better nudge policies. These nudges are also called precision nudges as it specifically targets individuals based on their likes and choices.

But, what exactly is a nudge?

In terms of using machine learning in behavioral economics, a nudge is an approach to encourage individuals to perform certain tasks or buy certain products. One can think of the push notifications e-commerce apps sends its customers at regular intervals based on their search and shopping history or the items the individual has in the cart or the wish list. These notifications are aimed to influence the customers to buy the products they are interested in or show similar recommendations so that the user spends more time on their platform, potentially resulting in a purchase from the customer.

What are the applications of machine learning in behavioral economics?

While the most obvious application for nudges seems to be the e-commerce sector, there are other potential industries that can leverage precision nudges and machine learning in general. Some of the areas in which the combination of machine learning and behavioral economics can work wonders include:

Nutrition

Personalized nutrition is gaining prominence over universal diets. People are becoming aware of the benefits of maintaining a healthy lifestyle. Technology has made it easier to track the fitness levels and routines of individuals, and, hence, a personalized approach has replaced a general one. Similarly, in the case of diets, a personalized diet has replaced the general approach previously adopted. Doctors can recommend customized diets based on an individual’s genetics, physiology, and habits. These nudges can alert the user to consume particular food items at a particular time of the day. These nudges can be delivered to the user through an app on their mobile device or any other smart device. The combination of computer vision and machine learning in behavioral economics makes it possible to easily exchange data with a dietician. Thus, dieticians can constantly monitor the diet of the individual and ascertain whether the diet is being followed or not. General nudging has already proven beneficial in targeting a general set of population, and personalized nudges might prove even more effective.

Healthcare

In addition to monitoring the diet of an individual, the use of machine learning in behavioral economics can help monitor the overall health of an individual. This can prove significantly useful in monitoring the health of individuals who have undergone a serious medical procedure recently. For example, a majority of readmissions in hospitals after a major surgery results due to non-adherence to guidelines prescribed by the doctor. If a patient doesn’t follow the prescribed treatment after being discharged, it can result in the medical procedure being compromised. This also results in increased healthcare costs.

Applying machine learning in behavioral economics can help reduce the additional healthcare costs incurred. Machine learning models can be used to predict which nudges will prove the most effective for such patients. Each person can be nudged in a particular way to adopt a set of habits that can help in faster recovery. These nudges can be customized according to their health conditions and medical history, place of residence, and personal likings among other variables. The only challenge that remains is to figure out which data will prove to be the most useful for a strong predictive model. Machine learning already has a wide range of applications in healthcare and the combination of behavioral economics is only going to add to the benefits.

e-Commerce

E-commerce websites have taken over traditional brick and mortar stores. People usually spend hours checking out various products, even if they don’t want to buy them or if they plan to buy them in the future. By applying machine learning in behavioral economics, customized nudges can be sent to individuals providing better recommendations. The machine learning algorithm can analyze the search and purchase history of the user, the time of the purchase, and the duration between subsequent purchases. The data can also include the amount spent per purchase or the items that were canceled or returned in a short period. With such variables, precision nudges can be specifically targeted which can result in the individual purchasing the items. The global e-commerce sales splurged to a whopping USD 3 trillion in 2019. With precision nudges, the number can be expected to go higher as targeted nudges can increase the probability of purchase by the customer. Hence, e-commerce websites must implement measures to incorporate precision nudging in their business framework.

What are the challenges faced by precision nudging?

Although the combination of machine learning and behavioral economics has wide applications with potential benefits, it is limited by its challenges. The two major issues faced by precision nudging include the availability of the data for the machine learning algorithm. The second issue concerns the ethical use of the data to produce unbiased results.

Data availability

The success or failure of machine learning applications depends on data availability. If the data is insufficient, to begin with, the machine learning algorithm may not function properly. It may take longer for the algorithm to perform to its potential. Additionally, the data used for training the machine learning algorithm must be reliable and bias-free. Typically, to be assured that the outcome of the machine learning algorithm can be relied upon, a large scale of data having a high resolution is required. Shortcomings in even one factor can compromise the machine learning algorithm significantly. However, data augmentation can help negate the problem through data manipulation to some extent. But, it can’t be completely relied upon. It may be not clear which dataset can be manipulated to what extent to provide results that can be relied on.

Ethics

A possible negative impact of nudging can be the unethical influencing of behaviors by applying individual nudges. The nudges can be targeted towards a particular set of populations. This can lead to swarm behavior. For example, the data can be manipulated so that individuals purchasing items online on an e-commerce platform can be influenced to buy a particular product. This can create bias and lead to financial losses for competitors. Data interference and manipulation by any third party must be checked to avoid such bias. In the case of personalized nudges, it is better if the individuals receive no nudge at all instead of incorrect nudges.

The use of machine learning in behavioral economics, i.e. precision nudging, if implemented properly, without bias and within ethical limits has the potential to transform the way individuals make decisions. It can be applied to a wide range of problems currently faced today. If proven to be widely effective with future research, machine learning in behavioral economics will be implemented to augment human decisions in healthcare, finance, education and a plethora of unexplored sectors.