Alexander Gebharter

Compatibilist libertarianism proposes a new solution to the problem of an apparent incompatibility of free will and determinism. It drives a wedge between ontological levels and claims that free will is possible as a higher-level phenomenon even if the fundamental physical level is governed by determinism. After highlighting an inconsistency in the current version of compatibilist libertarianism, we discuss how one of its essential metaphysical assumptions (in particular: supervenience) can be modified in order to avoid this problem. Finally, we discuss the pros and cons of pushing the position to the limits in this way.

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.

Goldman proposed that a subject s knows p if and only if p is appropriately causally connected to s's believing p. He later on abandoned this theory. The main objection to the theory is that the causal connection required by Goldman is compatible with certain problematic forms of luck. In this paper we argue that Goldman's causal theory of knowledge can overcome the luck problem if causation is understood along interventionist lines. We also show that the modified theory leads to the correct results in contexts involving other prominent forms of epistemic luck and compare it with other accounts on the market.

Schurz (Synthese 164:201–234, 2008) proposed a justification of creative abduction on the basis of the Reichenbachian principle of the common cause. In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework. We identify necessary conditions for such inferences and investigate their unificatory power. We also sketch several interesting applications of modeling creative abduction Bayesian style. In particular, we discuss use-novel predictions, confirmation, and the problem of underdetermination in the context of abductive inferences.

In this chapter I develop a novel reconstruction of Woodward’s interventionist theory of causation within the theory of causal nets. This endeavor allows one to see in which respects the two theories agree and in which respects they diverge from each other. It also allows for uncovering several weak points of Woodward’s theory which may have been overlooked otherwise. I highlight some of these weak points of Woodward’s interventionist theory of causation and suggest several modifications of the theory to avoid them. This results in two alternative versions of an interventionist theory. The basic causal notions of these alternative versions turn out to coincide with the corresponding causal notions within the theory of causal nets under suitable conditions.

This chapter provides a brief introduction to the book as well as an overview of the subsequent chapters. The book consists of two main parts: A more theoretical part on the philosophical foundations of the theory of causal Bayes nets and a more application oriented part. The former part (Chap. 4 ) supports the theory of causal Bayes nets by an inference to the best explanation. It can also be seen as a response to David Hume’s skeptical challenge about whether causation is something ontologically real. The latter part is more application oriented. In particular, I try to shed some new light on Woodward’s (2003) interventionist theory of causation (Chap. 5 ) and the new mechanist debate (Chap. 6 ). Chapters 2 and 3 are of introductory nature. Chapter 2 presents the formal preliminaries needed for subsequent chapters; Chap. 3 provides a self-contained introduction to the causal Bayes net framework.

Compatibilist libertarianism proposes a new solution to the problem of an apparent incompatibility of free will and determinism. It drives a wedge between ontological levels and claims that free will is possible as a higher-level phenomenon even if the fundamental physical level is governed by determinism. After highlighting an inconsistency in the current version of compatibilist libertarianism, we discuss how one of its essential metaphysical assumptions (in particular: supervenience) can be modified in order to avoid this problem. Finally, we discuss the pros and cons of pushing the position to the limits in this way.

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.

Many causal systems studied by sciences such as biology, pharmacology, and economics feature causal cycles. Most accounts of causal modelling currently on the market are, however, explicitly designed to study acyclic structures. This chapter focuses on causal cycles and the challenges such cycles pose for causal modelling. First, we distinguish between different types of causal cycles. Then we introduce causal models and discuss a selection of general challenges for cyclic models when it comes to representation, prediction, and causal discovery. Finally, we zoom in on a concrete case from biochemistry: the PI3K/mTOR signalling network that plays a crucial role in regulating the cell cycle. Using this case we discuss some possible pitfalls for the application of causal modelling tools to complex biological cases.

Recently, Dardashti, Hartmann, Thébault, and Winsberg (2019) proposed a Bayesian model for establishing Hawking radiation by analogical inference. In this paper we investigate whether their model would work as a general model for analogical inference. We study how it performs when varying the believed degree of similarity between the source and the target system. We show that there are circumstances in which the degree of confirmation for the hypothesis about the target system obtained by collecting evidence from the source system goes down when increasing the believed degree of similarity between the two systems. We then develop an alternative model in which the direction of the variation of the degree of confirmation always coincides with the direction of the believed degree of similarity. Finally, we argue that the two models capture different types of analogical inference.

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.

In his Critique of Pure Reason Immanuel Kant presents four anti- nomies. In his attempt to solve the first of these antinomies he examines and analyzes"thesis" and "antithesis" more thoroughly and employs the terms `part', `whole' and `boundary' in his argumentation for their validity. According to Kant, the whole problem surrounding the antinomy was caused by applying the concept of the world to nature and then using both terms interchangeably. While interesting, this solution is still not that much more than a well thought out idea if it does not also include an adequate formal explication. Since the aforementioned terms all have counterparts in modern mereotopology, a discipline that has seen significant progress in recent times, we will apply these concepts to Kant's analysis in an attempt to evaluate Kant's solution in light of modern analytic philosophy.

One of the central problems within the free will debate lies in the apparent incompatibility of an agent's ability to do otherwise and determinism. Recently, compatibilist libertarianism was proposed as an actualist position intended to finally reconcile both. In this article, we argue that in order to maintain consistency, this position must be understood as a variant of classical compatibilism rather than a version of libertarianism. Though this seems to be an undesired consequence for proponents of compatibilist libertarianism, we think that it is not that bad. We show that recent objections to this position can be avoided by embracing its compatibilist nature and argue that a modified version of compatibilist libertarianism might very well be as close to an actualist account of free will in a deterministic world as one can hope for.

Mental causation is notoriously threatened by the causal exclusion argument. A prominent strategy to save mental causation from causal exclusion consists in subscribing to an interventionist account of causation. This move has, however, recently been challenged by several authors. In this paper, we do two things: We (i) develop what we consider to be the strongest version of the interventionist causal exclusion argument currently on the market and (ii) propose a new way how it can in principle be overcome. In particular, we propose to replace strict supervenience in the assumption that the mental supervenes on the physical by probabilistic supervenience and show how this move has the potential to license the inference to mental causation. Finally, we argue that probabilistic supervenience captures some of the most important intuitions that strict supervenience captures and discuss possible objections to weakening strict supervenience in the way we suggest.

Strong notions of free will are closely connected to the possibility to do otherwise as well as to an agent’s ability to causally influence her environment via her decisions controlling her actions. In this paper we employ techniques from the causal modeling literature to investigate whether a notion of free will subscribing to one or both of these requirements is compatible with naturalistic views of the world such as non-reductive physicalism to the background of determinism and indeterminism. We argue that from a causal modeler’s perspective the only possibility to get both requirements consists in subscribing to reductive physicalism and indeterminism.

Causal Bayes nets (CBNs) provide one of the most powerful tools for modelling coarse-grained type-level causal structure. As in other fields (e.g., thermodynamics) the question arises how such coarse-grained characterisations are related to the characterisation of their underlying structure (in this case: token-level causal relations). Answering this question meets what is called a “coherence-requirement” in the reduction debate: How are different accounts of one and the same system (or kind of system) related to each other. We argue that CBNs as tools for type-level causal inference are abstract enough to roughly fit any current token-level theory of causation as long as certain modelling assumptions are satisfied, but accounts of actual causation, i.e. accounts that attempt to infer token-causation based on CBNs, for the very same reason, face certain limitations.

We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly increasing the complexity of the underlying causal structure shows that both measures can easily disagree with explanatory power. The upshot of this is that even sophisticated causally constrained measures for unification ultimately fail to track explanatory relevance. This shows that unification and explanation are not as closely related as many philosophers thought.

The SOPhiA conferences are intended to give young predoctoral philosophers the possibility to actively attend a professional conference, to tackle current, as well as classical, philosophical problems, and to discuss their own approaches with promising students from many dierent countries as well as with wellestablished experts. We are firmly convinced that this is a natural and necessary step for promoting the next generation of analytic philosophers and thus, strengthening analytic philosophy in general. Because we believe that the methods of analytic philosophy can be fruitfully applied to all areas of philosophy, the SOPhiA conferences do not have a thematic focus; this is also reflected by the fact that the talks given at SOPhiA 2011 covered all areas of philosophy.

Psychological studies show that the beliefs of two agents in a hypothesis can diverge even if both agents receive the same evidence. This phenomenon of belief polarisation is often explained by invoking biased assimilation of evidence, where the agents’ prior views about the hypothesis affect the way they process the evidence. We suggest, using a Bayesian model, that even if such influence is excluded, belief polarisation can still arise by another mechanism. This alternative mechanism involves differential weighting of the evidence arising when agents have different initial views about the reliability of their sources of evidence. We provide a systematic exploration of the conditions for belief polarisation in Bayesian models which incorporate opinions about source reliability, and we discuss some implications of our findings for the psychological literature.

Goldman (1967) proposed that a subject s knows p if and only if p is appropriately causally connected to s’s believing p. He later on abandoned this theory (Goldman, 1976). The main objection to the theory is that the causal connection required by Goldman is compatible with certain problematic forms of luck. In this paper we argue that Goldman’s causal theory of knowledge can overcome the luck problem if causation is understood along interventionist lines. We also show that the modified theory leads to the correct results in contexts involving other prominent forms of epistemic luck and compare it with other accounts on the market.

Supporters of the autonomy of higher-level causation (or explanation) often appeal to proportionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modeling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this paper we apply ideas from this literature to the issue of higher vs. lower-level causation (and explanation). Surprisingly, proportionality turns out to be irrelevant for the question of whether higher-level causes (or explanations) can be autonomous; specificity is a much more informative notion for this purpose.

List (2014, 2019) has recently argued for a particular view of free will as a higher-level phenomenon compatible with determinism. According to List, one could refute his account by showing that determinism at the physical level implies the impossibility of doing otherwise at the agential level. This paper takes up that challenge. Based on assumptions to which List’s approach is committed, I provide a simple probabilistic model that establishes the connection between physical determinism and the impossibility of doing otherwise at the agential level that is needed to refute free will as a higher-level phenomenon.

Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that kicks in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore.

Schurz (Synthese 164:201–234, 2008) proposed a justification of creative abduction on the basis of the Reichenbachian principle of the common cause. In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework. We identify necessary conditions for such inferences and investigate their unificatory power. We also sketch several interesting applications of modeling creative abduction Bayesian style. In particular, we discuss use-novel predictions, confirmation, and the problem of underdetermination in the context of abductive inferences.