When it comes to predicting people's preferences, it pays to consider "the power of three"
MIT researchers found that the common way of predicting what people want, by comparing two options at a time, hides the real connections between people's choices. By asking large groups to rank three alternatives in order instead, the team showed those hidden links become measurable. The work upgrades random utility models, a framework nearly 100 years old, with direct uses in training AI systems from human feedback.
Key Takeaways
- An MIT team proved that it is impossible to get information about correlations from two-way comparisons alone.
Correlations can be discerned, however, when large numbers of people rate three alternatives in their order of preference.
- Credits : Image: iStock Previous image Next image In his 1927 paper, "A law of comparative judgment," the American psychologist 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.
- "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."
- What routes and modes of transport will they take?
Or, if a city suddenly receives a windfall of $20 million, how should those funds be disbursed to maximize the common good?
- The group's findings stem, in part, from a deficiency in the way RUMs are commonly estimated in practice, which has persisted since the days of Thurstone.
Stats & Key Facts
- #The work upgrades random utility models, a framework nearly 100 years old, with direct uses in training AI systems from human feedback.
- #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.
- #Or, if a city suddenly receives a windfall of $20 million, how should those funds be disbursed to maximize the common good?
- #Given that RUMs have been with us for almost 100 years, growing in sophistication over time, one might imagine that, at this stage, there would be little room for improvement.
An MIT team proved that it is impossible to get information about correlations from two-way comparisons alone. Correlations can be discerned, however, when large numbers of people rate three alternatives in their order of preference. MIT researchers provide a major upgrade to the nearly century-old idea of random utility models.
Steve Nadis | MIT Laboratory for Information and Decision Systems Publication Date : June 11, 2026 Press Inquiries Press Contact : Amanda Moore Email: amm@mit.edu Laboratory for Information and Decision Systems : An MIT team proved that it is impossible to get information about correlations from two-way comparisons alone. Correlations can be discerned, however, when large numbers of people rate three alternatives in their order of preference. Credits : Image: iStock Previous image Next image In his 1927 paper, "A law of comparative judgment," the American psychologist 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.
For more details please read the original article at MIT News AI.
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