Is it Reasonable to Study Decision-Making Quantitatively?

Scientists studying decision-making often provide a set of choices, each specified with values or distributions of values, and probabilities or distributions of probabilities. For example, "Would you prefer $100 with probability 1.0 or $1 with probability .9 and $1,000 with probability 0.1?" Other decision research examines choices made in the absence of most quantitative information; for example, "Would you prefer a Ford now or a Porsche a year from now?," "Which food would you prefer," but models the findings with precise quantitative assumptions. Yet other research does neither; for example, modeling verbally stated choices with verbally stated heuristics. This article asks about the relevance of the first two research approaches for much of the decision-making made in life. The use of quantitative research and modeling is unsurprising, given that this approach underlies most of science. In life, values and probabilities are almost always partly or wholly vague and qualitative rather than quantitative. For example, when deciding which house to buy, there are relevant features such as size, color, neighborhood schools, construction materials, attractiveness, and many more, but the decision-maker finds it difficult and of little use to assign these precise values or weights. Nonetheless, humans have evolved to make decisions in such vaguely specified settings. I provide an example showing how a very high degree of uncertainty can defeat the application of quantitative decision-making, but such a demonstration is not critical if quantitative research and modeling produce a good understanding of and a good approximation to decision-making in the natural environment. This perspective addresses these issues.