What is Behavioral Economics? ? Part 2

By Akhil Raj Gupta

(An overview of the biases that govern decision-making and why we should learn about them) 

In my first article of this series, I established the need for an alternative mechanism for understanding human nature. More simply, it was an attempt to convince us of the possibility that people don’t always behave in the calculative manner that economists assume, and whether it is an issue worth considering more deeply or not. I will now describe exactly how we depart from the rational model of decision making (known as “bias”), whether there is a uniformity or pattern in these departures, and how can we potentially manipulate people’s decisions on the premise that they are susceptible to bias. In popular science, this is known as ‘choice architecture’, a term coined by Richard Thaler to describe the potential for influencing people’s decision by slightly tweaking the context in which they have made decisions. I hope that I will be able to simplify and condense an entire field of thought into a few hundred words that will pique your curiosity enough to pursue these ideas further.

What is the genesis of the argument for bounded rationality? Think about it. You probably consider yourself outside this set of deficiency-prone individuals (an irrationality unto itself, but more on that later) Before we proceed however, I would like you to consider the following three questions – (The answer might be easier than you think.

1. A bat costs $1 more than a ball. Together the bat and the ball cost $1 10c. How much do they cost individually?
2. A lily pad occupies a certain portion of a pond. Each day, the pad doubles in size. If it covers the entire pond in 48 days, how much time will it take to cover half the pond?
3. If 5 machines make 5 widgets in 5 days, how much time will 100 machines take to make 100 widgets?

If you are like most people, the answers that came to your mind instinctively were $1 and 10 cents, 24 days, and 100 days. Now read the questions again, a little more slowly. The correct answers are – $1.05 and 5 cents, 47 days, and 5 days.  The arithmetic is clearly not very difficult. So then why do we get these wrong? And does it really carry any implication for understanding decision-making?

Psychologists (most notably Daniel Kahneman and Amos Tversky, the founding fathers of behavioral economics) contend that our brain is divided into two parts. They label these System 1 and System 2, for no particular reason but a severe lack of imagination. System 1 corresponds to intuition; it is fast, unabashed, and spectacularly wrong (on most occasions.) System 2 is slow, deliberate, and thoughtful. It is capable of making comparisons based on certain parameters, evaluating each argument independently, and arriving at an aggregate total. It corresponds to cognition. While this nomenclature tends to oversimplify, biologists have confirmed the existence of such a divide in our brain, by segregating it into the limbic brain (System 1) and neo-cortex (System 2). What does this distinction imply?

Kahneman and Tversky state that we make a majority of our decisions, whether financial or otherwise, on the basis of System I. This purportedly saves us the mental effort of invoking the ‘lazy’ System 2 but also leads to systematic mistakes. This act of making decisions offhand or via ‘rule of thumb’ is known as a ‘heuristic.’ It is worthwhile to remember that heuristics manifest themselves in the form of biases, of which I would like to discuss a few that are the most popular.

1. Prospect theory

The study of gambling behavior has intrigued many a social scientist, mostly because it provides a succinct diagnosis of people’s risk appetite. The theory ubiquitous amongst academic circles is Bernoulli’s expected utility theory. Bernoulli suggests that we evaluate gambles through an expected utility computation by taking a weighted average of the utilities of different states of wealth. This utility function is concave (mostly logarithmic), implying that the incremental utility from additional wealth increases at a decreasing rate (diminishing marginal utility.) An extension of concavity implies that individuals are risk-averse i.e. they prefer to play it safe rather than to take their chances, allowing for foregone gains. When you flip the argument, our risk appetite increases i.e. we are risk-seeking in the domain of losses. You would prefer to toss a coin for a loss of 150 or nothing rather than incurring a sure loss of 75, even though the expected value of the gamble is the same. Risk seeking implies loss-aversion, even for minor changes in wealth. More concisely, we dislike losses more than we like gains. This theory has some remarkable applications because it describes gambles as prospects (gains or losses) rather than different states of wealth.

1. Framing

Prospect theory teaches us that a transaction can be framed as either a      gain or a loss, evoking different psychological reactions. This has strong insights for salesmen in particular. For example – If I give you 50 with the intention of letting only 20 remain with you, that can be framed as either a gain of 20 or a loss of 30, both affecting you differently while remaining quantitatively equal. Or consider the cost-savings that an energy-efficient generator provides; it can be represented as money earned (not very evocative) versus money lost by not replacing your obsolete model (more sensitive).

Another bias that can contort decision making is our (mis)understanding of probabilities and subsequent reactions to uncertain events. Consider a shift in the probability of an event from 0 to 10% v/s 50 to 60%. Mathematically it shifts the odds by the same quantum, but evokes very different reactions. For a more comprehensive discussion, I highly recommend Daniel Kahneman’s “Thinking, Fast and Slow”.

Understanding probability is fundamental to making decisions about insurance or stocks. The availability bias creeps in when we are asked to estimate the probability of a terrorist attack. Because terrorist attacks receive higher media coverage and evoke images of widespread destruction, we over-estimate and overweight their impact on our lives. That might motivate us to take insurance against a terror attack or a flight hijacking versus mad cow disease which is, mathematically speaking, far more likely to kill you. Vividness of a fact distorts decisions negatively. For example, saying that one out of a thousand people die due to noise pollution every year is more vivid than stating that the probability of death by noise pollution is 0.001.

 2. Anchoring and adjustment

Another seminal idea in behavioral economics is anchoring, which states that once we attach an initial value to a product, our estimate of its true value hovers around the same figure. Consider the case of charitable donations. If you receive a pamphlet stating that the average donation is 10, you would probably donate around the same amount. But if you received a pamphlet stating that the highest donation was 1000, your own interpretation gets shaken up and you are certainly likely to donate more than 10. Of course, all these ideas have undergone empirical rigour.

For the sake of brevity, I will conclude this here, having skimmed only the surface of a vast pool of knowledge. The key takeaway is to remember that mapping psychology has applications far beyond intellectual stamp collecting and manipulating people’s behavior slightly can yield markedly different outcomes. These shall be discussed much more heavily in the piece on “Applications of behavioral economics.”

Akhil is currently in his second year at college, pursuing a Bachelor of Arts degree in Economics (Hons) at Sri Ram College of Commerce, University of Delhi. He has been passionate about writing since an early age and is currently involved with the official College magazine and Economics Department magazine at SRCC. His areas of interest include behavioural economics / finance, econometric analysis, macroeconomic policy, and political theory. He spends his free time reading extensively, watching interesting videos on YouTube, and trying to convince everybody around him that he really does know a thing or two about economics in the midst of all the pontification.