Sceptic priors and climate consensus

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