Santa Fe
Institute
  • Research
    • Themes
    • Projects
    • SFI Press
    • Researchers
    • Publications
    • Library
    • Sponsored Research
    • Fellowships
    • Miller Scholarships
  • News + Events
    • News
    • Newsletters
    • Podcasts
    • SFI in the Media
    • Media Center
    • Events
    • Community
    • Journalism Fellowship
  • Education
    • Programs
    • Projects
    • Alumni
    • Complexity Explorer
    • Education FAQ
    • Postdoctoral Research
    • Education Supporters
  • People
    • Researchers
    • Fractal Faculty
    • Staff
    • Miller Scholars
    • Trustees
    • Governance
    • Resident Artists
    • Research Supporters
  • Applied Complexity
    • Office
    • Applied Projects
    • ACtioN
    • Applied Fellows
    • Studios
    • Applied Events
    • Login
  • Give
    • Give Now
    • Ways to Give
    • Contact
  • About
    • About SFI
    • Engage
    • Complex Systems
    • FAQ
    • Campuses
    • Jobs
    • Contact
    • Library
    • Employee Portal

Science for a Complex World

Events

Here's what's happening

Give

You make SFI possible

Subscribe

Sign up for research news

Connect

Follow us on social media

© 2026 Santa Fe Institute. All rights reserved. This site is supported by the Miller Omega Program.

Home / News

Study: new model for predicting belief change

Fig. 2. Belief networks and development of interdependence over measurements.
August 19, 2022

A new kind of predictive network model could help determine which people will change their minds about contentious scientific issues when presented with evidence-based information. 

A new study in Science Advances presents a framework to accurately predict whether a person will change their opinion about a certain topic. The approach estimates the amount of dissonance, or mental discomfort, a person has from holding conflicting beliefs about a topic. 

Santa Fe Institute Postdoctoral Fellows Jonas Dalege and Tamara van der Does built on previous efforts to model belief change by integrating both moral and social beliefs into a statistical physics framework of 20 interacting beliefs. 

They then used this cognitive network model to predict how the beliefs of a group of nearly 1,000 people, who were at least somewhat skeptical about the efficacy of genetically modified foods and childhood vaccines, would change as the result of an educational intervention.

Study participants were shown a message about the scientific consensus on genetic modification and vaccines. Those who began the study with a lot of dissonance in their interwoven network of beliefs were more likely to change their beliefs after viewing the messaging, but not necessarily in accordance with the message. On the other hand, people with little dissonance showed little change following the intervention.

“For example, if you believe that scientists are inherently trustworthy, but your family and friends tell you that vaccines are unsafe, this is going to create some dissonance in your mind,” van der Does says. “We found that if you were already kind of anti-GM foods or vaccines to begin with, you would just move more towards that direction when presented with new information even if that wasn’t the intention of the intervention.” 

While still in an early stage, the research could ultimately have important implications for communicating scientific, evidence-based information to the public. 

“On the one hand you might want to target people who have some dissonance in their beliefs, but at the same time this also creates some danger that they will reduce their dissonance in a way that you didn’t want them to,” Dalege says. “Moving forward, we want to expand this research to see if we can learn more about why people take certain paths to reduce their dissonance.”

Read the paper, "Using a cognitive network model of moral and social beliefs to explain belief change," in Science Advances (August 19, 2022).





Share
  • Sign Up For SFI News
News Media Contact

Santa Fe Institute

Office of Communications
news@santafe.edu
505-984-8800



  • Tags
  • SFI News Release
  • Research


More SFI News

View All News

Upending assumptions about learning, inspired by an AI phenomenon

Looking at AGI through the lens of natural intelligence

A simple baseline for AI forecasting in machine learning

Constantino Tsallis to co-chair the 2027 Nobel Symposium on Statistical Mechanics

How novelty arrives: Review of “The Origins of the New”

Working group asks, what’s the benefit of a brain?

Measuring irreversibility in gene transcription

ACtioN Academy engages industry leaders on AI and complexity

Arguing for a complex adaptive power grid

Mark Newman Awarded 2026 SIAM John von Neumann Prize

Review: Nonesuch, by SFI Miller Scholar Francis Spufford

Laurent Hébert-Dufresne to receive Young Scientist Award

What does it mean to compute?

Reassessing the scientific method

SFI External Professor Santiago Elena elected to the American Academy of Microbiology

From cells to companies: Study shows how diversity scales within complex systems

SFI Press launches “The Economy as an Evolving Complex System IV”

New dataset reveals how U.S. law has grown more complex over the past century

Boldness is key to avoiding self-censorship, model shows

SFI welcomes Program Postdoctoral Fellow Jordan Kemp