On the Assessed Strength of Agents' Bias

Recent work in social epistemology has shown that, in certain situations, less communication leads to better outcomes for epistemic groups. In this paper, we show that, ceteris paribus, a Bayesian agent may believe less strongly that a single agent is biased than that an entire group of independent agents is biased. We explain this initially surprising result and show that it is in fact a consequence one may conceive on the basis of commonsense reasoning

What's Hot in Mathematical Philosophy. Formal Epistemology of Medicine

“Formal Epistemology of Medicine” is a new strand of research analysing issues arising in medical epistemology by examining the interaction of methodological, social and regulatory dimensions in medicine. The motivation for adopting a formal approach stems from its higher capability to describe the “rules of the game” and to provide an analytic explanatory account of the investigated phenomena. Formalisation of scientific inference within the Bayesian epistemology tradition has generally aimed at providing mathematical explanations of various inferential phenomena in the sciences: confirmatory support of coherent evidence, confirmatory role of the explanatory power, the role of replication in assessing the reliability of evidence, the no-alternatives and no-miracles arguments. Drawing on this tradition we exploit the confirmatory support of heterogeneous sources of evidence, and expand the justificatory toolset in such domains as drug risk management and policy-making (Landes J. Osimani B. Poellinger R. (2017) Epistemology of causal inference in pharmacology. Towards a framework for the assessment of harms. European Journal for Philosophy of Science). This also goes in the direction advocated by Gelman (Gelman A. Working through some issues. Significance 12.3 (2015): 33-35.) and Marsman et al. (A Bayesian bird's eye view of ‘Replications of important results in social psychology’. R Soc Open Sci. 2017, 4(1): 160426), both invoking a more comprehensive approach to evidence, in the aftermath of the “reproducibility crisis”. We also investigate the joint interaction of diverse dimensions of evidence (reliability, consistency, strength, variety) by developing a Bayesian model of hypothesis confirmation which takes into account both random and systematic error (Osimani B., Landes J. (2021). Consistently with previous attemps to formalize the Variety of Evidence, our model also shows violation of the VET; however, the area of failure is considerably smaller and depends on the ratio of false to true positives of the biased vs. reliable instrument affected by random error. The take-home message is that replication with the same instrument is favoured when the noise of the reliable instrument exceeds the systematic error of the biased one

Reliability: an introduction

none3openBonzio S.; Landes J.; Osimani B.Bonzio, S.; Landes, J.; Osimani, B

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study. Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework. Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses

Osimani B., Poellinger R. (2020) A Protocol for Model Validation and Causal Inference from Computer Simulation. In: Bertolaso M., Sterpetti F. (eds) A Critical Reflection on Automated Science. Human Perspectives in Health Sciences and Technology, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-25001-0_9

The philosophical literature on modelling is increasingly vast, however clear formal analyses of computational modelling in systems biology are still lacking. We present a general, theoretical scheme which (i) visualizes the development and repeated refinement of a computer simulation, (ii) explicates the relation between different key concepts in modelling and simulation, and (iii) facilitates tracing the epistemological dynamics of model validation. To illustrate and motivate our conceptual scheme, we analyse a case study, the discovery of the functional properties of a specific protein, E-cadherin, which seems to have a key role in metastatic processes by way of influencing cell growth and proliferation signalling. To this end we distinguish two types of causal claims inferred from a computer simulation: (i) causal claims as plain combinations of basic rules (capturing the causal interplay of atomic behaviour) and (ii) causal claims on the level of emergent phenomena (tracing population dynamics). In formulating a protocol for model validation and causal inference, we show how, although such macro-level phenomena cannot be subjected to direct causal tests qua intervention (as, e.g., formulated in interventionist causal theories), they possibly suggest further manipulation tests at the basic micro-level. We thereby elucidate the micro-macro-level interaction in systems biology

Varieties of Error and Varieties of Evidence in Scientific Inference, Forthcoming in The British Journal for Philosophy of Science

According to the Variety of Evidence Thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than e.g. replications of analogous studies. This thesis is known to fail Bovens and Hartmann (2003), Claveau (2013). How- ever, the results obtained by the former only concern instruments whose evidence is either fully random or perfectly reliable; instead in Claveau (2013), unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model which formalises both reliability, and unreliability, differently. Our instruments are either reliable, but affected by random error, or they are biased but not deterministically so. Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of “too-good-to- be-true” evidence. This happens precisely because they neither contemplate the role of systematic bias, nor unavoidable random error of reliable instruments. In our model the Variety of Evidence Thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann (2003), Claveau (2013) and holds for (the majority of) realistic cases (that is, where biased instruments are very biased). The essential mechanism which triggers VET failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency

Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review

Contemporary debates about scientific institutions and practice feature many proposed reforms. Most of these require increased efforts from scientists. But how do scientists' incentives for effort interact? How can scientific institutions encourage scientists to invest effort in research? We explore these questions using a game-theoretic model of publication markets. We employ a base game between authors and reviewers, before assessing some of its tendencies by means of analysis and simulations. We compare how the effort expenditures of these groups interact in our model under a variety of settings, such as double-blind and open review systems. We make a number of findings, including that open review can increase the effort of authors in a range of circumstances and that these effects can manifest in a policy-relevant period of time. However, we find that open review's impact on authors' efforts is sensitive to the strength of several other influences

Science as a Weapon of Mass Distraction.

With the COVID-19 pandemic the relationship between science and warfare seems to have scaled up to a new level. in the current information war, science seems to be used as the weapon itself, instrumentalized by different parties featuring diverse vested interests with the aim to advance their agendas. In such circumstances information may be manipulated in several ways. The paper ranks different forms of “persuasion” in ascending order, from paternalism to full-blown authoritarianism, as exemplified by various episodes during the COVID-19 emergency. Finally, it advances some proposals regarding science policy approaches, in particular the development of virtuous mechanisms that reward overall public and individual health, instead of just reimbursing interventions (with the consequent spiral of increasing insurance costs). As Tallacchini (2019) underlines, authoritarianism and nudging are not the only possible routes to be explored. A third way is a new confidence pact between institutions, private sector and citizens, and a new Hippocratic oath between patients and doctors, fostered by the right mechanisms, both for the social planner and for the entrepreneur, in view of the long term wellbeing and welfare of the population

Real and Virtual Clinical Trials: a Formal Analysis

If well-designed, the results of a Randomised Clinical Trial (RCT) can justify a causal claim between treatment and effect in the study population; however, additional information might be needed to carry over this result to another population. RCTs have been criticized exactly on grounds of failing to provide this sort of information (Cartwright & Stegenga 2011), as well as to black-box important details regarding the mechanisms underpinning the causal law instantiated by the RCT result. On the other side, so-called In-Silico Clinical Trials (ISCTs) face the same criticisms addressed against standard modelling and simulation techniques, and cannot be equated to experiments (see, e.g., Boem & Ratti, 2017, Parker, 2009, Parke, 2014, Diez Roux, 2015 and related discussions in Frigg & Reiss, 2009, Winsberg, 2009, and Beisbart & Norton, 2012). We undertake a formal analysis of both methods in order to identify their distinct contribution to causal inference in the clinical setting. Britton et al.'s study (Britton et al., 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes. We deduce that, by predicting variability through interpolation, ISCTs aid with problems regarding extrapolation of RCTs results, and therefore in assessing their external validity. Furthermore, ISCTs can be said to encode “thick” causal knowledge (knowledge about the biological mechanisms underpinning the causal effects at the clinical level) – as opposed to “thin” difference-making information inferred from RCTs. Hence, ISCTs and RCTs cannot replace one another but rather, they are complementary in that the former provide information about the determinants of variability of causal effects, while the latter can, under certain conditions, establish causality in the first place

Pharmacovigilance as Personalized Medicine. In: Chiara Beneduce and Marta Bertolaso (eds.) Personalized Medicine: A Multidisciplinary Approach to Complexity, Springer Nature.

Personalized medicine relies on two points: 1) causal knowledge about the possible effects of X in a given statistical population; 2) assignment of the given individual to a suitable reference class. Regarding point 1, standard approaches to causal inference are generally considered to be characterized by a trade-off between how confidently one can establish causality in any given study (internal validity) and extrapolating such knowledge to specific target groups (external validity). Regarding point 2, it is uncertain which reference class leads to the most reliable inferences. Instead, pharmacovigilance focuses on both elements of the individual prediction at the same time, that is, the establishment of the possible causal link between a given drug and an observed adverse event, and the identification of possible subgroups, where such links may arise. We develop an epistemic framework that exploits the joint contribution of different dimensions of evidence and allows one to deal with the reference class problem not only by relying on statistical data about covariances, but also by drawing on causal knowledge. That is, the probability that a given individual will face a given side effect, will probabilistically depend on his characteristics and the plausible causal models in which such features become relevant. The evaluation of the causal models is grounded on the available evidence and theory