Skeptic priors and climate consensus

How much evidence would it take to convince climate skeptics that they are wrong? I explore this question within an empirical Bayesian framework. I consider a group of stylized skeptics and examine how these individuals rationally update their beliefs in the face of ongoing climate change. I find that available evidence in the form of instrumental climate data tends to overwhelm all but the most extreme priors. Most skeptics form updated beliefs about climate sensitivity that correspond closely to estimates from the scientific literature. However, belief convergence is a nonlinear function of prior strength and it becomes increasingly difficult to convince the remaining pool of dissenters. I discuss the necessary conditions for consensus formation under Bayesian learning and show that apparent deviations from the Bayesian ideal can still be accommodated within the same conceptual framework. I argue that a generalized Bayesian model provides a bridge between competing theories of climate skepticism as a social phenomenon.

Automated summary

The study shows that most climate skeptics tend to update their beliefs about climate sensitivity based on available evidence from instrumental climate data and scientific literature. However, belief convergence among skeptics is influenced by the strength of prior beliefs, making it increasingly difficult to convince remaining dissenters. Despite this, deviations from the Bayesian ideal can be accommodated within the same conceptual framework, providing insights into climate skepticism as a social phenomenon.