Risk and Impact
By: Daniel Nystrom
Tags: Decision Making, Small Business, Racial Justice, Machine Learning, Loans

Why do people make bad decisions?

I asked my statistics professor this question during the second term of grad school. A week before, he had given a class lecture on behavioral economics, and related topics in probability theory, to emphasize the importance of understanding common errors of managerial judgment. I was captivated, and desperately craved for more.

When I walked into office hours, I thought that I would receive at least a basic answer to this unnervingly simple question. After I asked it, however, he just shifted in his office chair, chuckling to himself from the other side of the desk, and asked: “How much time do you have?”

Although our paths never really crossed again after that quarter, his answer drove me to spend the rest of my time as an MBA student studying decision science: the mutant offspring of several fields — with the largest contributions hailing from economics, psychology, and statistics.

As the name implies, decision science is the study of…well…decisions. But, on a more fundamental level, it is the formal study of how a decision-maker (e.g. an individual, manager, or firm), makes sense of an uncertain environment by:

Framing current and past events; Weighing possible future outcomes; and Contextualizing these judgments in a network of culture, beliefs and emotions.

Decision science is very closely related to the field of data science, an equally quantitative discipline, but decision science tends to place higher emphasis on the decision-making process whereas data science places greater emphasis on the statistical techniques and analytical methodologies themselves[1].

So what does decision science have to say about bad decisions?

First, it is worth emphasizing that decision-making, on a biological level, is almost unfathomably complex; and there much about this process that we simply do not yet understand. Much of the aforementioned process occurs within seconds, and often quite subconsciously (I say, as if we really understand what consciousness is or how it arises). The brain — really just a three-pound lump of fat and water — is quite a marvel of processing power. It can handle numerous tasks at once. It can recall information on the fly and make rapid judgments based on said memories. It is such an understatement to say that “there is a lot going on in the brain”, that you would be forgiven for thinking that bad decisions are just an unfortunate byproduct of the complex workings of this truly amazing organ. But there is even more to it than that.

Perhaps it is easiest to think of the brain as a computer — you have hardware (basic capabilities), software (learned experiences), and a finite quantity of processing power available (what is sometimes referred to as cognitive load). And, just like any computer system, glitches are quite commonplace.

Due to limited processing power, for example, our brain tends to take “shortcuts” using various rules-of-thumb and approximations: what researchers refer to as heuristics[2]. These tricks help us process the thousands of snap decisions we make every day, and are generally quite helpful as approximates for in-depth reasoning, though they can occasionally mislead us — e.g. believing brand A is better than brand B simply because more customers bought A than B (what is referred to as the bandwagon effect).

Sometimes, these shortcuts can result in systemic errors in judgment (the perceived probability of an event occurring) — what researchers refer to as cognitive bias. A great example of this is the widespread fear of flying: there is, in fact, very little risk of dying while flying (much less risk than other, more commonplace, tasks such as driving a car), but, because it is easy to recall plane crashes (as they tend to receive global news coverage), individuals tend to believe the risk of such disasters is much higher than it actually is (a phenomenon referred to as an availability bias).

But the brain-computer analogy goes much deeper than this: just like a computer algorithm, the brain’s cognitive function is heavily reliant on the way a problem is framed. For example, if a public health decision is framed in terms of “saving lives”, rather than “avoiding deaths”, decision-makers tend to be more willing to take risks (such as a approving uncertain medical treatment) than they would otherwise: an effect referred to as loss aversion[3].

These three examples are, of course, just the tip of the iceberg. The study of decision science (and another related field: behavioral economics) has been bustling with activity over the past fifty or so years to better understand the fundamental basis of why people behave the way they do, and how they came to make the decisions they have.

And the reason why this study is worth the effort, and why I am beginning with a discussion about decision science at all, is because of how significant these principles are for a wide range of fields. In marketing, these principles are already being put into practice in hundreds of ways: designing advertising that can shift individuals’ purchasing behavior, optimizing the quantity produced to foster a sense of exclusivity, or developing products that meet fundamental needs. In public health, these principles can, and are, being used to help improve the overall health and happiness of the population: through smoking cessation programs, vaccination campaigns, and efforts to improve healthcare equity and inclusion. And, in finance, these principles have been leveraged to promote financial responsibility: through retirement savings, or debt reduction, for example.

Just as important, however, is the role that the field of decision science, and the brain-computer analogy, can have for applications in machine learning.

Machine learning (far from the world-dominating AI that Hollywood often portrays it as) is already ubiquitous in our daily lives — actually, you have probably already used machine learning algorithms today. Every time you receive a music or movie recommendation from a platform, or see a digital advertisement, or use a search engine, or apply for a credit card, you are leveraging a technology that uses machine learning algorithms underneath. And, on the whole, machine learning has made our lives better.

However, while machine learning may have had a positive impact on modern life, it is subject to many of the same errors that our own brains fall prey to.

In fact, many (dare I say: most!) of the errors in our own thinking, and the “thinking” of machine learning, can be distilled down to two issues: