Publications and Finished Manuscripts
Unconvinced Yet Influenced: Vaccine Decisions in the Shadow of Misinformation (with Mihaela Popa-Wyatt and Ed Pertwee)(Forthcoming in Misinformation and Other Epistemic Pathologies, Cambridge University Press, Draft): This paper examines how misinformation shapes vaccine refusal, even while maintaining dubious towards false claims. Exposure to misinformation can alter one's perceptions of vaccine's rare side-effects and their perceived credibility of authoritative sources. When individuals are uncertain about vaccine safety, these changes can lead them to refuse vaccination. We use the expected utility framework to show that such refusal, while shaped by misinformation, can still be an instrumentally rational response under uncertainty rather than a cognitive failure. We identify three mechanisms through which misinformation influences decision-making: (1) erosion of trust in authoritative sources, (2) heightened emotional responses to anecdotal risks, and (3) a combination of relatively modest shifts in both trust and side-effect perception. Our model highlights the irreversible impact of misinformation and underscores the need for proactive interventions, such as pre-bunking and media literacy, to counter its effects.
A paper on the rationality of not engaging with statistical demographic evidence (Title withheld for anonymous review): This paper introduces a framework for deciding whether to engage with newly salient information, developed for a bounded, inquisitive Bayesian who is about to make a decision under uncertainty. It then uses this framework to argue that such an agent is rational in ignoring demographic statistical information if (i) they are statistically sophisticated and they judge the study to be aligned with social stereotypes OR (ii) they are socially aware and they judge the study to be politically relevant .
Does Political Relevance Make a Difference When Reading Science: I will argue that the mere fact that a claim is politically salient, even though it is well-supported within a peer-reviewed literature, can be enough to justify suspension of judgement on it. I will model a bounded Bayesian who cannot engage with every relevant possibility and show why they should engage with the possibility of an article being biased when its claim is politically salient. Then, I will show why such engagement destabilizes their credence towards the article's claim. Finally, I will use a probabilistic account of belief to show why this destabilization is enough to justify excluding politically salient claims from their set of beliefs.
Mind Your Probability Language: The notion of probability has multiple interpretations, and one of these interpretations is the propensity interpretation. Given this possible interpretation and the opaque causal structure of the social world, it can be argued that when probabilistic statements aligned with social stereotypes are used about social groups, the propensity interpretation is implicated. Moreover, it can be argued that the propensity interpretation of conditional probabilities suggests a partially stable causal relation. So, reporting a probabilistic correlation that is aligned with social stereotypes implicates a partially stable causation about a social group. This implicature is dangerous, as in some policy-making conversations, uttering probabilistic correlations can conversationally suggest interventions that are aligned with oppressive social practices. These implicatures render statistical generalizations about social groups vulnerable to exploitation and misinterpretation, potentially perpetuating social injustice. This paper scrutinizes the pragmatics of probabilistic statements in relation to oppressive social practices. It also outlines some strategies to minimize the chance of exploitation and misunderstanding.
In Preparation
Rational Resistance to Correction: A Bayesian Analysis of the Continued Influence Effect (with Ed Pertwee and Mihaela Popa-Wyatt): This work demonstrates why receiving correct information cannot nullify the effect of exposure to misinformation on people's epistemic attitudes, even when they are ideally rational. It models a Bayesian agent whose credence in a proposition changes through exposure to misinformation, then models corrective information as testimony that the experience of learning misinformation is defeated. The paper outlines plausible scenarios where receiving such testimony fails to nullify misinformation's effect on the agent's credence function and belief set. To establish this result, the paper provides an in-depth analysis of defeater propositions within the Bayesian framework, distinguishing three distinct types of defeaters and exploring how Bayesian agents should update their credences after a learning experience when uncertain whether that experience has been defeated. This updating procedure proves not to be straightforward conditionalization: when certain defeater propositions are involved, the rigidity condition fails to hold, necessitating an alternative method for estimating the posterior.
