We investigate the epistemic role of coherence in scientific reasoning, focusing on its use as a heuristic for filtering evidence. Using a novel computational model based on Bayesian networks, we simulate agents who update their beliefs under varying levels of noise and bias. Some agents treat reductions in coherence as higher-order evidence and interpret such drops as signals that something has gone epistemically awry, even when the source of error is unclear. Our results show that this strategy can improve belief accuracy in noisy environments but tends to mislead when evidence is systematically biased. We explore the implications for the rationality of coherence-based reasoning in science.

Why Demagogues Lie Big

Climate change is one of the most pressing challenges of our time. Addressing it requires an unprecedented level of collaboration and engagement between ...

The Department of Philosophy (Faculty of Arts, University of Maribor), its students, and The Slovenian Society for Analytic Philosophy (DAF) are honoured to invite any graduate and PhD students to the...

More Hope for Conciliationism