In his 1927 paper, “A law of comparative judgment,” the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice. 

Thurstone was a pioneer of “psychometrics” — a field built upon the premise that mental processes, which we cannot see, can nevertheless be measured and quantified. His 1927 paper laid the groundwork for what are now called random utility models, which provide a mathematical framework for describing human preferences — information that can be relied upon, in turn, to make predictions about various hypothetical situations.

Random utility models (RUMs) are so named because they assess the “utility,” or benefit, that can be obtained from a given choice — such as deciding which book to read first among the stack of novels you brought back from the library. “These models are inherently random,” explains Gabriele Farina, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and principal investigator at the Laboratory for Information and Decision Systems (LIDS), “because people are different. Everyone has their own preferences, and even those preferences can vary from time to time.” For example, someone who normally picks coffee over tea in the morning, and prefers tea after dinner, may, upon occasion, mix up that order entirely.

RUMs, to be sure, are frequently used within government and industry in situations of far greater consequence than the selection of a hot (or iced) beverage.

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