Prospect theory and human decision-making

In their text Kahneman & Tversky introduce a decision-making theory called prospect theory as a critique and alternative for the previous utility theory (Kahneman & Tversky 1979). The prospect theory aims to explain human decision-making under uncertainty or risky situations. Such decision-making situations could be, for example investment or money related decisions but, also other decision situations. As a reference, Kahneman &

Tversky (1979) provide in their text multiple monetary examples, but also one regarding Russian roulette.

Prospect theory is a complex and relatively well adopted and extensively researched the-ory. For additional reading provided on prospect theory, one can look for texts by, for example, Barberis (2013), Kahneman (2003), Levy (1997), and Tversky & Kahneman (1992). However, the purpose of this chapter is to provide quick insight to the reader of the prospect theory to enable reader to comprehend the underlying fundamentals in hu-man decision-making for the further analysis in this thesis and future business model cre-ations.

There is a few important key assumptions in prospect theory, by which the prospect the-ory can be fairly rapidly summarized sufficiently for the purposes of this text. These are 1. Value is understood through relations and change, as gains or losses, not as a final

state of, for example, wealth

2. The assessment of change requires a reference point 3. Losses are weighed more heavily than gains

4. Probabilities are understood as subjective weights, that have a quantum nature and are biased from mathematical probabilities (Kahneman & Tversky 1979).

These are more intuitively illustrated in Figure 5 and Figure 6, which represent value function and weighting function, respectively.

Figure 5. Hypothetical value function (adapted from Kahneman & Tversky 1979, p.

297)

As can be seen from Figure 5, the losses are typically weighted more heavily than the gains. Thus, the curve on the negative side is steeper, as the value is presented as a func-tion of the losses and gains.

Figure 6. Hypothetical weighting function (adapted from Kahneman & Tversky 1979, p.

283)

Kahneman & Tversky (1979) argue, that the value is understood as relations or change, not as final state of the outcome. They provide an example, where change of 3°C is easier to distinguish from the change of 6°C rather than a change of 13°C from change of 16°C (Kahneman & Tversky 1979). This is due to the fact that the relative difference in change is bigger between 3°C and 6°C than between 13°C and 16°C, even though the absolute change is the same (Kahneman & Tversky 1979). They propose, that the same phenomena applies to monetary evaluation as well (Kahneman & Tversky 1979). This leads to the conclusion, that linear growth in outcome does not linearly increase the perceived value.

Instead, the growth in perceived value resembles a concave arc on the positive side and as a convex arc on the negative side, as illustrated in Figure 5 (Kahneman & Tversky 1979). However, as the losses are weighted more than the gains, due to the risk aversion nature, the arc on the negative side is steeper than on the positive side (Kahneman &

Tversky 1979).

To evaluate change, a reference point is needed (Kahneman & Tversky 1979). This ref-erence point is typically the current state, for example, current wealth (Kahneman &

Tversky 1979). Thus, the reference point is used to enable measure the realized change.

One particularly interesting factor considering the reference point is that, according to Kahneman & Tversky, its position can be affected by presenting the situation differently.

For example, by presenting the situation differently, a presenter can make the decision maker to understand how the things will go and this assumed result functions as a refer-ence point instead of the current situation. Kahneman & Tversky (1979) provide a

prehensive example of this, where a professional weathers a slump better than his com-petitors. In this situation, the professional might consider the smaller loss that he suffered, compared to his competitors, as a win, instead of a loss (Kahneman & Tversky 1979).

Another interesting dimension in decision-making that the prospect theory takes into ac-count is the subjectively experienced probability (Kahneman & Tversky 1979). Prospect theory includes a weighting function, illustrated in Figure 6 that is a subjectively experi-enced function to probabilities (Kahneman & Tversky 1979). This causes the experiexperi-enced probabilities to differ from the mathematical probabilities. Typically humans over exag-gerate small probabilities and a typical example of this is buying lottery tickets or insur-ances. On the other hand, Kahneman & Tversky (1979) introduce a term called subcer-tainty. By this term they mean, that other than for the probabilities that are not small and thus over exaggerated, typically the experienced subjective weight is lower than the math-ematical probability (Kahneman & Tversky 1979). This is shown in the Figure 6 as a weighing function appearing below the linear mathematical probability. On top of those two main phenomena considering the weighting function, the subjective weighing of probabilities has also a quantum nature (Kahneman & Tversky 1979). This means, that for extreme probabilities in both near 0 and 1, the probability is considered as impossible or certain, respectively. This is due to the humans’ lacking capability to understand ex-treme probabilities. This is shown in the Figure 6 as a discontinuity near values 0 and 1.

Human nature tries to also simplify the decision-making situations by both rounding and disregarding some alternatives. For example, if a probability of an outcome is near to 0.5, for example, 0.48, its probability might be understood as even. Same kind of rounding might happen for outcomes also. For example, if an outcome is near 1000, for example, 998, it might be understood as even 1000. Furthermore, very small probabilities and dom-inated alternatives are disregarded from the decision-making. (Kahneman & Tversky 1979)

When thinking intuitively, humans are also prone to use heuristic principles in their rapid decision-making (Tversky & Kahneman 1974). Heuristic principles are used to simplify the decision-making and they are usually quite important and beneficial. However, they can also lead to wrong conclusions and systematic errors. Tversky & Kahneman (1974) discuss three types of heuristics in their text. These heuristics are representativeness, availability, and adjustment and anchoring (Tversky & Kahneman 1974). In their text they prove that people use these kinds of heuristics in their decision-making and that the application of heuristics is not limited to laymen only, but that also trained scholars and professionals are subject to using such heuristics, when they think intuitively (Tversky &

Kahneman 1974).

As a summary for human decision-making, people do not always follow the mathemati-cally or objectively rational path in decision-making. Still, some general principles can

be identified. For example, a prospect theory argues that people weight losses more heav-ily than gains. Also, outcomes are understood more as a change, not as a final state. People do not also understand probabilities as their mathematical probability, but instead as a subjectively experienced weights that are biased from the mathematical probabilities.

Lastly, the chapter discussed briefly about the heuristic principles, which are used to sim-plify the decision-making process. It is important to understand these underlying factors considering normal human decision-making when creating a business model for new mar-ket entry and when the business model contains assumptions of customers making invest-ments under uncertainty.