Michał Sikorski

The relevance principle plays a central role in the methodology of forensic science. Recently, it has been argued that it should also be applied in other scientific disciplines. The principle rules which information experts should use for evaluating evidence. A precise formulation has been given in terms of probabilistic relevance. In this paper, we focus on this probabilistic version and put it to the test by applying it to different causal scenarios and by discussing it to the background of two different assumptions about the trier of fact: the trier of fact as a causal reasoner vs. the trier of fact being agnostic about the causal structure. Finally, we point to problems with the probabilistic version of the relevance principle and propose improved versions that capture the principle’s underlying idea better than the purely probabilistic version does.

This encyclopedia entry examines the epistemology of replicability, arguing that replicability, the ability of scientific results to be obtained again under similar conditions, is a central marker of the reliability, objectivity, and trustworthiness of scientific results. The entry clarifies the concept by distinguishing different forms of replication (direct, partial, and conceptual) and situates the recent Replicability Crisis within a broader epistemological framework. It identifies key factors undermining replicability, including low statistical power, misuse of methodological flexibility, failures of representativeness, and weak or poorly specified theories. While acknowledging debates about whether replicability should function as a universal epistemic standard across disciplines, the entry defends it as an indispensable goal of empirical science. Finally, it surveys proposed proposals aimed at promoting replicability, including preregistration, use of well-powered designs and representative samples, and theory formalization, and outlines strategies for selecting and conducting replication studies, concluding that replicability remains essential for scientific self-correction and the production of reliable results.

In our paper, we reassess the role of non-epistemic values in scientific practice by drawing lessons from machine learning and the automation of science. Due to several influential arguments (e.g., Rudner 1953, or Longino 1990), traditional philosophy of science has largely converged on the view that non-epistemic values are necessary for the justification of scientific claims. Recently, renewed support of this view has been made by appealing to the No Free Lunch theorems in the context of machine learning—that there is no universally optimal machine learning algorithm (Dotan 2020). The argument claims that the No Free Lunch theorems entail that epistemic values are insufficient for discriminating hypotheses. In the negative part of our paper, we critique Dotan’s argument. We argue that NFL theorems do not entail that ‘all hypotheses have the same average expected error’. We also discuss what NFL theorems do in fact say about inductive inference and theory choice. In the positive part, we argue that the possibility of ‘value-free science’ (understood as a science that involves only epistemic values in the context of justification) seems to be supported by developments in the automation of science. For example, Robot Scientist presented in King et al. (2004) can automatically propose hypotheses, design and conduct experiments, and interpret the results. We argue that in these systems, only epistemic values are involved in hypothesis construction and testing.

Despite its crucial role, sample representativeness remains a controversial topic in medical science methodology. There is an ongoing debate not only about how best to define and ensure the representativeness of a sample (e.g., Rudolph et al., 2023; Porta, 2016), but also about whether representativeness is worth pursuing at all (e.g., Rothman et al., 2013). We present a new definition of representativeness in terms of causal models and argue that it is more precise and more useful than existing alternatives. We use examples to demonstrate the types of evidence that can support assumptions of representativeness.

In my article, I present a new version of a probabilistic truth prescribing semantics for natural language indicative conditionals. The proposed truth conditions can be paraphrased as follows: an indicative conditional is true if the corresponding conditional probability is high and the antecedent is positively probabilistically relevant for the consequent or the probability of the antecedent of the conditional equals 0. In the paper, the truth conditions are defended and some of the logical properties of the proposed semantics are described.

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.

The principle of total evidence says that all relevant information should be considered when making an inference about a hypothesis. In this article, we argue that the criminalist’s paradox from the literature on the methodology of forensic science constitutes a counterexample against the principle of total evidence. The paradox arises, for example, when a forensic scientist uses the results from other forensic procedures to inform their own analysis. In such cases, their results can become more reliable, but at the same time also dependent on those other forensic results and therefore less useful for decisions in court cases. Consequently, such influences are generally avoided in forensic science. We argue that structurally similar problems plague also other scientific disciplines, anticipate an objection to the counterexample, and propose two new versions of the principle of total evidence that do not fall prey to the criminalist’s paradox.

The Value-free ideal of science (VFI) is a view that claims that scientists should not use non-epistemic values when they are justifying their hypotheses, and is widely considered to be obsolete in the philosophy of science. I will defend the ideal by demonstrating that acceptance of non-epistemic values, prohibited by VFI, necessitates legitimizing certain problematic scientific practices. Such practices, including biased methodological decisions or Questionable Research Practices (QRP), significantly contribute to the Replication Crisis. I will argue that the realizability of VFI is not a necessary condition for its validity. Then, I will show how some of the prominent proposals of value-laden science legitimize problematic scientific practices, provide real-world examples, and generalize the argument. Finally, I will show how value-laden methodological decisions contribute to the Replicability Crisis and discuss two strategies for realizing VFI.

The overreliance on verbal models and theories in psychology has been criticized for hindering the development of reliable research programs (Harris, 1976; Yarkoni, 2020). We demonstrate how the conceptual space framework can be used to formalize verbal theories and improve their precision and testability. In the framework, scientific concepts are represented by means of geometric objects. As a case study, we present a formalization of an existing three-dimensional theory of emotion which was developed with a spatial metaphor in mind. Wundt posits that the range of human emotion can be represented along three axes of basic emotions (pleasure/displeasure, excitement/inhibition, tension/relaxation), just as color vision can be represented using three basic colors. We use dimensions to represent basic emotions, points to represent emotional states, and regions to represent broader emotional concepts. We then compare our formalization to an existing structuralist formalization of Wundt’s theory. Further, we discuss the empirical predictions that our formalization generates, such as comparisons of similarity and intensity. We conclude by demonstrating how the tools developed in the conceptual space framework can be used to formulate a theory of emotion based on empirical observation.

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.

Replicability is widely regarded as one of the defining features of science and its pursuit is one of the main postulates of meta-research, a discipline emerging in response to the replicability crisis. At the same time, replicability is typically treated with caution by philosophers of science. In this paper, we reassess the value of replicability from an epistemic perspective. We defend the orthodox view, according to which replications are always epistemically useful, against the more prudent view that claims that it is useful in very limited circumstances. Additionally, we argue that we can learn more about the original experiment and the limits of the discovered effect from replications at different levels. We hold that replicability is a crucial feature of experimental results and scientists should continue to strive to secure it.

In my article, I present a new version of a probabilistic truth prescribing semantics for natural language indicative conditionals. The proposed truth conditions can be paraphrased as follows: an indicative conditional is true if the corresponding conditional probability is high and the antecedent is positively probabilistically relevant for the consequent or the probability of the antecedent of the conditional equals 0. In the paper, the truth conditions are defended and some of the logical properties of the proposed semantics are described.

The chapter is devoted to the probability and acceptability of indicative conditionals. Focusing on three influential theses, the Equation, Adams’ thesis, and the qualitative version of Adams’ thesis, Sikorski argues that none of them is well supported by the available empirical evidence. In the most controversial case of the Equation, the results of many studies which support it are, at least to some degree, undermined by some recent experimental findings. Sikorski discusses the Ramsey Test, and Lewis’s triviality proof, with special attention dedicated to the popular ways of blocking it. Sikorski concludes that the role of the three theses in future studies of conditionals should be re-thought, and he presents alternative proposals.

The results of forensic science are believed to be reliable, and are widely used in support of verdicts around the world. However, due to the lack of suitable empirical studies, we actually know very little about the reliability of such results. In this paper, I argue that phenomena analogous to the main culprits for the replication crisis in psychology are also present in forensic science. Therefore forensic results are significantly less reliable than is commonly believed. I conclude that in order to obtain reliable estimates for the reliability of forensic results, we need to conduct studies analogous to the large-scale replication projects in psychology. Additionally, I point to some ways for improving the reliability of forensic science, inspired by the reforms proposed in response to the replicability crisis.

The Ramsey Test is considered to be the default test for the acceptability of indicative conditionals. I will argue that it is incompatible with some of the recent developments in conceptualizing conditionals, namely the growing empirical evidence for the _Relevance Hypothesis_. According to the hypothesis, one of the necessary conditions of acceptability for an indicative conditional is its antecedent being positively probabilistically relevant for the consequent. The source of the idea is _Evidential Support Theory_ presented in Douven (2008). I will defend the hypothesis against alleged counterexamples, and show that it is supported by growing empirical evidence. Finally, I will present a version of the Ramsey test which incorporates the relevance condition and therefore is consistent with growing empirical evidence for the relevance hypothesis.

In the last decade, many problematic cases of scientific conduct have been diagnosed; some of which involve outright fraud others are more subtle. These and similar problems can be interpreted as caused by lack of scientific objectivity. The current philosophical theories of objectivity do not provide scientists with conceptualizations that can be effectively put into practice in remedying these issues. We propose a novel way of thinking about objectivity for individual scientists; a negative and dynamic approach.We provide a philosophical conceptualization of objectivity that is informed by empirical research. In particular, it is our intention to take the first steps in providing an empirically and methodologically informed inventory of factors that impair the scientific practice. The inventory will be compiled into a negative conceptualization, which could in principle be used by individual scientists to assess objectivity of scientific practice. We propose a preliminary outline of a usable and testable instrument for indicating the objectivity of scientific practice.

Contextualist accounts of aesthetic predicates have difficulties explaining why we feel that speakers are disagreeing when they make true and compatible but superficially contradictory aesthetic judgments. One possible way to account for the disagreement is hybrid expressivism, which holds that the disagreement happens at the level of pragmatically conveyed, clashing contents about the speakers’ conative states. Marques defends such a strategy, combining dispositionalism about value, contextualism, and hybrid expressivism. This paper critically evaluates the plausibility of the suggested pragmatic mechanisms in conveying the kind of contents Marques takes to explain disagreements. The positive part suggests an alternative account of how aesthetic judgments are sources of information about speakers’ conative aesthetic states.