Social groups share not just beliefs but how they interpret evidence
Contagious Misinterpretations: Social Learning with Shared Interpretations
Social and Information Networks
Summary
People don’t just share what they believe about different ideas, but also how they understand the evidence behind those ideas. The authors study what happens when everyone talks about both their beliefs and their ideas about what evidence means. They find that over time, people tend to agree on a shared way of understanding the evidence, shaped by the most influential people in the group. This shared understanding then affects which idea the group believes in most strongly, but it can sometimes lead the group to accept the wrong idea if some people misunderstand their own data.
What this means in practice
- •For social media platform designers: Improve algorithms that model how groups form consensus by including shared interpretations, not just shared beliefs, to better predict viral misinformation dynamics.
- •For organizational decision makers: Design communication protocols that monitor how interpretation of data shifts in teams to prevent collective adoption of incorrect conclusions.
Tested on simulated data.
Authors
Pol Rodríguez Farrés, Jesus Cerquides
Abstract
When people discuss which of several hypotheses is true, they share not only their beliefs but also their interpretation of each one, that is, the distribution of signals each hypothesis is expected to produce. This interpretation is an opinion about what each hypothesis means and what evidence it predicts, and it is revised only through conversation, not by confronting it with the observed data. Standard non-Bayesian social learning captures only belief-sharing and keeps the likelihoods private. We study what happens when agents share both beliefs and likelihoods under static networks. We show that interpretations converge geometrically to a common reading shaped by the most influential agents. Then, beliefs concentrate, almost surely and at an exponential rate, on the hypothesis that, under this consensus interpretation, best explains the signals privately observed by all agents taken together. As a result, sharing interpretations can lead a population to adopt, unanimously and confidently, a hypothesis it would have rejected if it had shared only beliefs. Whether this helps or hurts when a true hypothesis exists depends on how accurately agents initially read the data they observe. When every agent initially interprets the true hypothesis exactly as the data it would observe, sharing interpretations cannot strictly improve learning and can strictly harm it when agents observe different data distributions. When interpretations are logically coherent and some agents misread their own data, sharing can either rescue the population, leading it to learn the true hypothesis, if an accurate reading dominates the interpretation network, or corrupt it, leading it to a wrong hypothesis, if a misreading one does.