I want to be safe: understanding the main drivers behind vaccination choice throughout the pandemic

BACKGROUND

Despite being a major advancement in modern medicine, vaccines face widespread hesitancy and refusal, posing challenges to immunization campaigns. The COVID-19 pandemic accentuated vaccine hesitancy, emphasizing the pivotal role of beliefs in efficacy and safety on vaccine acceptance rates. This study explores the influence of efficacy and safety perceptions on vaccine uptake in Italy during the pandemic.

METHODS

We administered a 70-item questionnaire to a representative sample of 600 Italian speakers. Participants were tasked with assessing the perceived effectiveness and safety of each vaccine dose, along with providing reasons influencing their vaccination choices. Additionally, we conducted an experimental manipulation, exploring the effects of four framing messages that emphasized safety and/or efficacy on participants' willingness to receive a hypothetical fourth vaccine dose. Furthermore, participants were asked about their level of trust in the scientific community and public authorities, as well as their use of different information channels for obtaining COVID-19-related information.

RESULTS

Our study reveals a dynamic shift in vaccine efficacy and safety perceptions throughout the COVID-19 pandemic, potentially influencing vaccination compliance. Initially perceived as more effective than safe, this assessment reversed by the time of the third dose. Beliefs regarding safety, rather than efficacy, played a significant role in anticipating future vaccinations (e.g., the booster dose). Safety-focused messages positively affected vaccination intent, while efficacy-focused messages showed limited impact. We also observed a changing trend in reasons for vaccination, with a decline in infection-related reasons and an increase in social related ones. Furthermore, trust dynamics evolved differently for public authorities and the scientific community.

CONCLUSIONS

Vaccine perception is a dynamic process shaped by evolving factors like efficacy and safety perceptions, trust levels, and individual motivations. Our study sheds light on the complex dynamics that underlie the perception of vaccine safety and efficacy, and their impact on willingness to vaccinate. We discuss these results in light of bounded rationality, loss aversion and classic utility theory.

Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory

The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We explore these ideas in the context of supervised machine learning, namely the branch of artificial intelligence that studies algorithms which balance simplicity and accuracy in order to effectively learn about the features of the underlying domain. Focusing on statistical learning theory, we show that situations exist for which a preference for simpler models (as modeled through the addition of a regularization term in the learning problem) provably slows down, instead of favoring, the supervised learning process. Our results shed new light on the relations between simplicity and truth approximation, which are briefly discussed in the context of both machine learning and the philosophy of science.

Multiple discoveries, inevitability, and scientific realism

When two or more (groups of) researchers independently investigating the same domain arrive at the same result, a multiple discovery occurs. The pervasiveness of multiple discoveries in science suggests the intuition that they are in some sense inevitable-that one should view them as results that force themselves upon us, so to speak. We argue that, despite the intuitive force of such an "inevitabilist insight," one should reject it. More specifically, we distinguish two facets of the insight and argue that: (a) the profusion of multiple discoveries in scientific practice does not support the inevitabilist side of the inevitability/contingency of science controversy; and (b) the crucial role of background knowledge in scientific inquiry complicates the attempt to interpret the pervasiveness of multiple discoveries in realist terms.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.