People Care More About Being Right Than Avoiding Mistakes, New Study Finds

Research suggests people judge predictions differently than the experts and algorithms that produce them

BALTIMORE, June 23, 2026 — Experts who build forecasting models often focus on minimizing mistakes. New research suggests that may not be how most people think.

A study published in the INFORMS journal Management Science found that when people make or evaluate predictions, they place greater value on the possibility of being exactly right than on reducing the size of potential errors. In many cases, they prefer predictions that increase the chance of a perfect outcome, even if doing so creates a greater risk of larger mistakes.

The findings challenge a core assumption underlying many forecasting systems, predictive analytics tools and artificial intelligence models, which are typically designed to minimize average error rather than maximize the likelihood of being precisely correct.

The study, “Understanding People’s Preferences for Predictions: People Prioritize Being Right Over Minimizing How Wrong They Are in Expectation,” was authored by Berkeley J. Dietvorst of the University of Chicago Booth School of Business.

“For many people, the goal of prediction is to be right, not just to minimize average error,” said Dietvorst. “This is news to those who build models for predictive analysis who often work to minimize gross inaccuracies instead of simply working to make the predictions perfect.”

The research suggests that people evaluate prediction errors in a fundamentally different way than many experts assume.

Rather than responding proportionally to every increase in error, people appear to be highly sensitive to the difference between being exactly right and being slightly wrong. Once a prediction is already wrong, additional errors matter much less than many forecasting models assume.

Dietvorst analyzed 16 studies involving a variety of prediction tasks and experimental designs. Across the studies, participants consistently demonstrated what researchers describe as “diminishing sensitivity to prediction error.”

In multiple experiments, participants were presented with probability distributions and asked to make predictions. They were significantly more likely to choose outcomes that maximized their chances of being exactly correct rather than outcomes that would reduce expected error overall.

Additional studies examined emotional reactions to prediction errors. Satisfaction dropped sharply when participants moved from being perfectly correct to slightly wrong. By comparison, larger increases in error produced much smaller declines in satisfaction.

The findings have implications far beyond forecasting.

Many predictive systems, including AI tools, recommendation engines, business forecasting models and decision-support technologies, are designed around statistical measures that prioritize minimizing average error. The research suggests that users may judge those systems using a very different standard.

“The results highlight the importance of understanding human objectives before designing predictive systems,” Dietvorst said. “If models are optimized for goals that differ from what people value, adoption and trust may suffer.”

As organizations increasingly rely on predictive technologies to guide decisions, the study raises a broader question: Are today's forecasting systems optimized for statistical performance, or for what people actually want?

The findings suggest those two goals may not always be the same.

Read the full study here. 

About INFORMS and Management Science 

INFORMS is the world’s largest association for professionals and students in operations research, AI, analytics, data science and related disciplines, serving as a global authority in advancing cutting-edge practices and fostering an interdisciplinary community of innovation. Management Science, a leading journal published by INFORMS, publishes research on decision sciences, behavioral economics, analytics, and quantitative methods that inform managerial and policy decisions. INFORMS empowers its community to improve organizational performance and drive data-driven decision-making through its journals, conferences and resources. Learn more at www.informs.org or @informs.

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