Decision Sciences | Behavioral Economics

Blog Posts:Mu Sigma
Published On: 20 January 2012
Views: 2431

We at Mu Sigma believe that as organizations start maturing in Decision Sciences, behavioral economics will gain traction as a tool for improved decision making. There are two major reasons why this is more or less inevitable:

Companies are finally beginning to recognize that people (i.e. their customers) are not the utility-maximizing, rational agents but are usually driven by a complex set of intrinsic and extrinsic motivators. Any business decision that does not factor this fundamental fact about the customer is at best, sub-optimal.

While behavioral economics has been around for some time, we are reaching at an inflection point in our ability to apply the concepts – thanks to a combination of the explosion of data that can be used to better understand the customer (e.g. online behavior; data from video feeds) and countless data analytics platforms and analytics services.

Here are some of the key concepts that are going to be very important for Decision Sciences practitioners in the years to come.

  • Loss Aversion: This stems from the ‘Prospect Theory’ (developed by Daniel Kahnemann and Amos Tversky) – the paper (and the subsequent body of work) that can lay claim to have kick-started behavioral economics as a discipline. At the core of this theory is the concept of loss aversion – that is, people prefer to avoid losses as compared to acquiring gains. In other words, people tend to be risk averse when evaluating a possible gain and conversely, prefer risks that might mitigate a loss (called risk seeking behavior). In sporting terms, people would rather play defense. This has implications in different areas: one example being subscription-based services. People who sign up for a service at a heavily discounted rate show a higher propensity to opt-out if there is a steep increase in the rates at the end of the period, which is an example of risk seeking behavior. In a competitive scenario, the impact of a price decrease by one provider may not have the same impact on the defection rates as the increase in price by the competitor.

Loss Aversion

  • Choice Architecture: It has long been known that choices are never made in a vacuum. Behavioral economics has finally put the ‘rational agent’ who makes choices based on perfect information back in the realm of fiction. Business intelligence and analytics has clear that the conditions under which choices are made have a significant bearing on the way they are made. That opens up significant possibilities in terms of creating the conditions that could present the choices so as to influence the outcomes. This in turn has powerful implications on how businesses can offer options to their customers to influence the outcomes. Armed with a combination of experimentation techniques and the ability to rapidly process data from experiments, companies are increasingly adopting a culture of simulating multiple choice alternatives before narrowing down on a product or service offering.

Choice Architecture

  • Anchoring effect: This is probably the best known of behavioral concepts. Simply put, this recognizes the fact that individuals rely on a specific piece of information to drive their thought process. The classical assumption of an individual looking at all the available data with an unbiased lens before making a decision was never really true – and the anchoring effect is what challenges this view. A focusing illusion is one such cognitive bias where people tend to skew their emphasis on one specific event or data point, which ends up influencing their overall decision. This effect has been used by marketers for quite some time now to influence consumer decisions.

Anchoring Effect

  • Framing: When people are expected to choose between multiple options, their decisions can be influenced simply by how the options are expressed. Simple as it may sound, this has profound implications on how decisions can be influenced. For instance, the response to a disease prevention strategy (e.g. polio shots) can be strongly influenced by how the options are framed in a positive or negative manner.


  • Ambiguity Aversion: People show a remarkable tendency to prefer a known risk to an unknown risk, even if the impact of the known risk is high. This tendency has been shown to kick in when the choice allows for a comparison between multiple options – where the least vague option has a higher probability of selection. Once again, this is a clear departure from the classical utility theory. Choice models would do well to recognize this fact.

Ambiguity Aversion

  • Decoy effect: This is a phenomenon whereby consumers tend to change their preference between two options when a third option which complicates the decision making is presented. This is a clever trick that is routinely applied by companies to nudge their customers towards specific products. This is especially useful when product options are evaluated on multiple dimensions, often making it difficult for a customer to identify the best-fit product. In such cases, the introduction of a new ‘decoy’ option can be effectively used to influence the decision towards a particular choice.

Decoy Effect

  • Hyperbolic discounting: Humans have always shown a bias for instant gratification – i.e. given two similar rewards, people almost always show a preference for one that would materialize sooner than later as has been proved further using decision sciences. For instance, given a choice between $50 now or $100 a year from now, most people would prefer the $50 option. However, when asked for a choice between $50 in 10 years and $100 in 11 years, most people would switch to the $100 option, even though the choice is essentially the same. Such biased decisions are regularly made in the world of insurance using big data in banking – and insurance companies are taking note of this phenomenon.

Hyperbolic Discounting

  • The Allais Paradox: This is a subtle, yet powerful phenomenon that was first reported by Marice Allais (Nobel Prize 1988). It implies that in a situation of uncertainty vs uncertainty (i.e. different degrees of risk), people tend to maximize expected value, whereas in a situation of certainty vs uncertainty the same set of people tend to prefer certainty with an attempt to maximize expected utility (i.e. satisfaction) rather than value. In other words, depending on how the choices are made available, there is inconsistency in the decision making process.

Allais Paradox

Obviously, this list is neither complete nor comprehensive. Looking forward to inputs and in addition, any interesting example of applications from the real world would also be very valuable.

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