This book is written by a psychologist but understanding how people think is a large part of decision science [1] - a particularly common application of data science. There are a lot of experiments described in the book to learn how people feel or make decisions in a certain context and to rationalize those seemingly irrational behaviors. I found this book to be useful for both business users and data scientists (as well as the self who is often a consumer). It reminds us to be more aware of the large extent our unconsciousness play in decision-making and biases.
For example,
- (Pg 117) "You will be willing to pay more to buy the same things compared to when you are not sad. ... we should beware of the underlying motivation-to-change state, triggered by sadness, that is driving the shopping behavior. There is evidence that compulsive shoppers tend to be depressed, and that shopping helps make them feel happier (or at least less sad). That sadness is at the root of much compulsive shopping is shown by the fact that antidepressant medications are effective in reducing such shopping." The author also writes that he'd noticed sad music being played at Walmart and a supermarket he frequented where these stores are actually trying to alter customers' moods to get more money out of them.
- (Pg 141) "At the beginning of the school year, Yale researchers gave at-risk students who were struggling in math a fictitious New York Times article about a student from another school who had won a major math award. There was a little "bio box" at the top of the article. In that box, for half of the students in the class, the birthday given for the award winner was made to be the same as for the student, although no mention was made of this fact. For the other students, the award winner's birthday was a different month and date from theirs. That was all the experimenters did, just a little tweak to create a link to the students'own identity. In May of the following year, at the end of that school term, the researchers looked at the final math grades for all the students in the study. And lo and behold, the students who had shared a birthday with the award winner had significantly higher final grades in math than the students who did not have the same birthday as the winner. Those who had the same birthday felt more similar to the award winner, and this carried over to their belief about their own math ability, with positive effects on their level of effort for the rest of the school year." This research done almost ten years ago showed unconscious affirmation of self-betterment.
- (Pg 201) "The more you are in touch with people who are happy, the happier you are; with people who are overweight, the heavier you will tend to be. When people in your network cooperate with others, you are more likely to as well, and when they seem very sad, you become a bit sadder, too. The moods and behaviors of people to whom we are connected by friendship, family, or the same workplace are likely to "infect" us. The contagion is usually at least three people deep - three degrees of virtual separation - so that people you don't even know are affecting your behavior and emotions, because they know somebody who knows somebody you know. Of course, it also works in the other direction. The average person has more than three hundred Facebook friends, so there is a great capacity for our own moods and behaviors to affect a lot of people in return. Researchers at Facebook measured how positive or negative the posts in a given Facebook user's newsfeed were and showed that the more positive or negative the posts they read, the more positive or negative the user's own posts became - up to three days later."
- (Pg 223) "One of the most important mental operations your goals influence is the evaluation of things and people as good or bad, depending not on your personal values or long experience with them as much as on whether they help or hinder that goal.Your current goal can even unconsciously change who you consider your best friends to be. ... Students at Northwestern University first had their goal for academic success, or for physical fitness, primed so that it was operating unconsciously in the background. If their academic achievement goal had been primed, the students wanted to be friend with people whom they could study with, but if their fitness goal had been primed instead, they wanted to be friends with others they could work with. They were not aware of the influence of their active goals on their friendship choices."
While those findings may appear to be obvious to some, people still fall into the same "trap" where they are nudged (or manipulated, for the lack of a better word) to perform certain actions because they just felt right at that moment. Hence this is what decision makers do; they create environments/ contexts to achieve their goals, be it generating sales or igniting certain sentiments, based on the data analysis done. But of course, knowing how humans act could help craft policies that instill good behaviors such as in the case of reducing smoking.
- (Pg 278) "Wendy Wood, of the University of Southern California, a leading expert on habits and self-control, told me (the author) that over the past twenty-five years, 'the successful campaign to reduce smoking was achieved mainly by changing the environments in which people live. Smoking has been reduced largely due to smoking bans, taxes, eliminating cigarette and tobacco ads from television and magazines, and removing cigarettes displays and ads in stores. These were environmental changes that made it more difficult to smoke and thus helped to break the habitual behavioral patterns.'"
[1] Dhiraj Rajaram, founder and CEO of Indian data analytics company Mu Sigma, provided definitions for data-based disciplines in Analytics Magazine.
• Data engineering applies technology to help collect, store, process, transform and structure data to enable it to be used for decision support.
• Data science applies math and technology to solve business problems. This involves analysis, visualization and algorithmic/mathematical computations to extract insights in response to clearly defined business problems, questions and hypotheses using clearly identified data elements. The field integrates and builds on data engineering by adding the discipline of math.
• Decision science is the interdisciplinary application of business, math, technology, design thinking and behavioral sciences. “It facilitates the design thinking paradigm: Taking business problems that start off as a hunch or as mysteries to becoming heuristic, rules and judgment based, to becoming algorithm as one starts to see patterns, to becoming codified and tool-ified in parts before being operationalized in systems,” Rajaram says. It enables data-driven insights to help organizations make better decisions. The decision science field integrates and builds upon data sciences by adding business context, design thinking and behavioral sciences.
Decision science incorporates an economic framework that is “a consistent, rational and objective system to ‘price’ each possible outcome, taking into account risks and rewards,” Rao adds. “It is simply a better way to make decisions.”