People are strange. We all make decisions that are difficult to explain, sometimes even baffling. In particular, we make baffling decisions about how we spend. Why do we gamble, knowing that the casino is always in the green? Why do we hardly ever cancel subscriptions, even the ones we don’t use, and when we do, why does it feel like such a hassle? Surprisingly, there are certain answers to these questions. As you might infer, behavioural science holds those answers–and more particularly, the Prospect Theory does. To understand financial behaviour, we must first understand human behaviour. So, indeed, people are strange… when you’re a stranger to behavioural economics.
What is the Prospect Theory, and how does it explain the thought process behind our economic decisions? The Prospect Theory emerged as a more psychologically refined version of the Expected Utility Theory, which tries to explain the behaviour of rational individuals under uncertainty with the central notion that when faced with an uncertain outcome, a person will choose the option that maximizes their expected utility, a.k.a. the enjoyment or value we expect to receive from the realization of that choice. Unfortunately, humans aren’t innately rational, so that model isn’t the most helpful in predicting our behaviour. After a considerable amount of time, around 30 years, the Prospect Theory helped decipher economic decisions that were, at first glance, perfectly irrational and, in turn, entirely unpredictable. It turns out that these decisions, though they might not be rational, are still foreseeable. According to the PT, humans are predictably irrational, meaning that we follow consistent psychological patterns when making choices under uncertainty (Kahneman and Tversky, 1979). With this foundation in place, we can begin to answer some questions about our own behaviour using the main principles of the Prospect Theory.
Why does a €100 hoodie feel like a great deal when discounted from €200, but expensive when labelled €100 to begin with? The notion of Reference Dependence explains this phenomenon. We evaluate outcomes as gains or losses relative to a reference point, rather than based on our final wealth. Our expectations or past experiences usually form the reference point–generally, what we feel is normal (Kahneman and Tversky, 1979). So when we learn that this hoodie used to cost €200, that becomes our reference point, and now the new €100 price feels like a €100 gain. When the price is listed as €100, no discount, the reference point is our own expectations about what the price should be. So, it sparks no excitement and doesn’t create a sense of gain if, for example, the hoodies we already own cost below €100. While reference points help explain how we frame outcomes, they can’t explain why losses are experienced far more intensely than gains, which leads us to our next point.
Why is receiving a €10 parking ticket so annoying, while finding €10 in your jacket pocket that you’d forgotten about, not equally exciting? The answer is Loss Aversion. People are emotionally affected by losses about twice as much as by equivalent gains (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992). Loss Aversion has become a part of human nature as a mechanism of self-preservation. For a very long time in human history, losses were a lot more significant than gains. Losing shelter, food, water, and other such resources for survival could be fatal, so the brain evolved to overweigh losses to survive. Loss Aversion nowadays makes us more likely to avoid risk, stick to the status quo and potentially overreact to minor setbacks. It might explain the emotional weight of negative outcomes, but it doesn’t capture how we perceive price modifications of different magnitudes.
Why does €5 make a huge difference on a €15 cab ride, whereas no substantial difference on a €200 plane ticket? The amount is the same, but in the first occurrence, you will wait an additional 10 minutes for your taxi if it means you’ll get it for €5 cheaper, while if your preferred airline were charging €5 more for the plane trip, you’d purchase the more expensive ticket without batting an eye. This is the principle of Diminishing Sensitivity–the relative impact of changes becomes smaller the farther you get from the reference point. This is because relative differences feel smaller as amounts increase (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992). This is applicable even in contexts outside of finances–temperature changes, waiting time, volume changes, and even flavour changes are all perceived as according to the principle of Diminishing Sensitivity. Predicting behaviour under uncertainty, however, requires more than understanding value on its own; the way we interpret probabilities matters just as much.
Let’s get to the gambling question: Why do people gamble with the knowledge that the probability of winning big is all but zero? Why do we believe in the realization of an event with a near-zero probability enough to bet money on it? The explanation is in Probability Weighting. The Probability Weighting notion states that we distort probabilities in a peculiar way, namely, we overweigh small and underweigh medium and large probabilities (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992). This means we are very likely to overestimate the tiny chance of a life-changing casino or lottery win while underestimating the high likelihood that it will rain, for example. Even though the weather report claimed a 60% probability of rain in your area, you still decided to leave your umbrella at home. There’s still a 40% chance of no rain, right? As these probability distortions interact with the value function, an intriguing psychological reversal comes about.
Why do we prefer a certain win of €100 as opposed to a 50% chance of winning €200? The expected monetary value is the exact same in both cases, but still, we prefer certainty regarding gains. On that same note, why do we refuse a guaranteed €100 loss and instead opt for a 50% chance to lose €200? 50% is quite a large possibility of forfeiting a larger amount, but still, when choosing whether to play it safe or to gamble a loss, most people are going to select the gamble, i.e., we seek out risk in regard to losses (Kahneman and Tversky, 1979). This kind of behaviour, coined the Reflection Effect, can be explained by the fact that a certain loss is specifically painful, and therefore very difficult to accept from the get-go. At the same time, the idea of a sure win is extremely satisfying and very tough to refuse. So, we mirror our behaviour with respect to gains and losses. This justifies behaviours such as holding losing stocks for way too long, continuing to pursue projects that are obviously failing, and trying to “break even”, even though this leads to losses in the majority of cases. Altogether, these principles offer a glimpse of just how systematic we actually are in the way that our behaviour deviates from classical rationality.
All the provided examples illustrate basic principles of the Prospect Theory. Unfortunately, it is impossible to fit the entire theory with all its notions into a relaxed 2-page article. Still, the instances discussed here encompass the central notions that we observe in our day-to-day lives. So, the recurring theme in this piece and in the studies exploring choice under uncertainty with the Prospect Theory is that even though human economic behaviour cannot be predicted with a formula as simple as the one provided by the Expected Utility Theory, it certainly can be anticipated with the help of behavioural science.
Knowing what drives your decisions definitely won’t make you perfectly rational, but it might help you catch yourself in the act. Now, equipped with the knowledge of the Prospect Theory, are you going to think twice about your next purchase? Or perhaps, your next bet?
References
Barberis, N. (2013) ‘Thirty Years of Prospect Theory in Economics: A Review and Assessment’, Journal of Economic Perspectives, 27(1), pp. 173–196.
Kahneman, D. and Tversky, A. (1979) ‘Prospect Theory: An Analysis of Decision under Risk’, Econometrica, 47(2), pp. 263–292.
Tversky, A. and Kahneman, D. (1992) ‘Advances in Prospect Theory: Cumulative Representation of Uncertainty’, Journal of Risk and Uncertainty, 5, pp. 297–323.
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