Big Data

An Odd Couple: Pairing Big Data and Behavioral Science?

An Odd Couple: Pairing Big Data and Behavioral Science?

Posted by: Sahana Rajan
Category :Big Data


A piece, “What Big Data Means for Psychological Science” from Observer of the Association for Psychological Science researches into the contribution and role of Big Data for psychology. In this regard, Michael N Jones (from Indiana University Bloomington) remarks, “Every little piece of data is a trace of human behavior and offers us a potential clue to understanding basic psychological principles.” Patterns of behavior can be inferred on analysis from copious data generated daily. Moreover, eventually behavioral information could be collected without the need for sample human participants at all. This is possible by using products like wearables and smartphones which can gather data on social and physical interactions.


Big Data analytics is the art of using available information to come up with economically friendly decisions in public sector, healthcare and others. Behavioral science explores how human beings make judgments and decisions in varying conditions. Big Data analytics and behavioral science are similar to each other, in the sense of sharing this dimension of decision making. While analytics is primarily known with regard to computer technology including Big Data, behavioral science explores human psychology. How is Big Data related to behavioral science?


The starting point for both Big Data analytics and behavioral science is that human beings are not completely predictable and do take irrational decisions. The two disciplines come up with different responses for this core assumption. Big Data analytics aims to build tools which can manage mental biases while behavioral science intends to create an environment which would comply with psychology of making decisions. When I collect a series of data on how people are using ABC website, I can observe patterns of behavior on it – for instance, that customers tend to leave after double clicking into a particular page, those visitors who bought an item follow a certain chain of navigation.


A psychological insight can be backed or challenged by Big Data. In a moment of agreement between them, alterations could be made to webpage, product or service to extract greater customer interest. In case of disagreement, further data can be collected to reach a point of Big Data-Behavioral science intersection. For instance, it is a psychological observation that people do not like changes. For this reason, the default option in a certain situation would be one which people would generally select in a calm and composed state of mind. You can save paper in your company by setting the printer to two-side printing by default. Psychology also states that people are affected by what and how others lead their lives. This observation can be harnessed to use peer contrast and social proof for influencing behavior. Examples of social proof include Starbucks endorsing through celebrities, user ratings that back up Google Play Store and best-selling products column on e-commerce sites.


This method of designing programs based on principles of decision-making psychology is called “choice architecture”. The mission is not to constrain the users’ choices but to create an atmosphere where they can make daily decisions that would sync with long-term objectives. The behavior could also be calculated to bring about intended behavioral change. Behavioral science and Big Data can complement each other through the common objective of predicting and altering the users’ behavior while also symbiotically assisting one another. For instance, by collecting Big Data on how search engines are used, it was found that taking into account words or acronyms independent of the context is an inadequate way of answering to the users’ needs. Web search can be improvised by designing searches that would include the context. What would the context include? As a minimum condition, the context would include the geographical location, time of search and socio-cultural profile of the user. Thus, if there are ten users who enter the same word into the engine, each of them will get different results based on customization.


Big Data provides information that can be used to reach psychological insights while the pre-existent psychological principles can be used for interpreting available data. Once we understand the intricate and potentially powerful relation between these two disciplines, we can imagine their application in a variety of cases.


Consider using technology for treating anxiety attacks and depression: Earlier, the prime method was to send “How are you today?” or “Did you feel upset today?” survey every week to the patient. With this method, there are many challenges: the patient can often not precisely report what one feels and such a survey oversimplifies the patient’s mental states. Today, we can extract data from social media, wearables and smart phones to access the patients’ minds. This is the merit of Big Data age. The patient undergoes different moods regularly and social media becomes a platform where such moods can be instantly updated. How the patient expresses himself on social media shows the chronology of how his moods progressed and how he expresses it through certain set of terms.  How do we move from the gathered data to psychological insight?


A series of basic algorithms can be created to account for different sources of data (‘about me’ message, status updates). This algorithm can continue evolving to include within itself more and more intricate sources. We can look at the list of friends and form associations between them. One can see the density of relationship of the patient with different friends to gauge the strength of social network. This data can be analyzed for forming a person’s character sketch.


In this way, Big Data and behavioral science are related to one another through their common objective of understanding the process of decision making and can assist one another to infer and alter consumer psychology.

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