Alexander Gebharter
Biographic Data
| ID | 976576 |
|---|---|
| NAME | Alexander Gebharter |
| GIVEN NAMES | Alexander |
| FAMILY NAME | Gebharter |
| SIGNATURE | GEBHARTER A |
| AFFILIATIONS | Marche Polytechnic University |
| ORCID | 0000-0003-1708-2949 |
| VERIFIED | Yes |
| TOTAL WORKS | 23 |
| TOTAL CITATIONS | 75 |
| AUTHOR COUNT | 23 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 4 |
The Formal Structure(s) of Analogical Inference
Recently, Dardashti et al. (Stud Hist Philos Sci Part B Stud Hist Philos Mod Phys 67:1–11, 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 th…
A causal theory of suppositional reasoning
Suppositions can be classified as indicative versus subjunctive and full versus partial. We propose a causal account of suppositional reasoning that naturally unifies all four types of reasoning based on this classification, provides a justification of the rather heterogenous canonical update rules, and gives rise to a new update rule for the partial subjunctive case in terms of generalized imaging
Probabilistic supervenience and agential possibilities
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 a…
Causal Bayes Nets and Token-Causation: Closing the Gap between Token-Level and Type-Level
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 characterizations are related to the characterization 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. It provides deta…
The Criminalist's Paradox as a Counterexample to the Principle of Total Evidence
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 anal…
Supervenient fixity and agential possibilities
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 thi…
Mental causation, interventionism, and probabilistic supervenience
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 an…
Unification and explanation from a causal perspective
Quantifying Proportionality and the Limits of Higher-Level Causation and Explanation
Supporters of the autonomy of higher-level causation (or explanation) often appeal to pro- portionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modelling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this article we apply ideas from this literature to the issue of higher ver…
Free Will, Control, and the Possibility to do Otherwise from a Causal Modeler’s Perspective
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 t…
A Causal Bayes Net Analysis of Glennan’s Mechanistic Account of Higher-Level Causation (and Some Consequences)
One of Glennan’s ([1996]) most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms. In this article I employ causal Bayes nets (CBNs) to reconstruct his analysis. This allows for identifying general assumptions that have to be satisfied to get the analysis working. I show that once these assumptions are in place, they imply (against the background of the CBN machi…
The causal theory of knowledge revisited: An interventionist approach
Goldman proposed that a subject knows if and only if is appropriately causally connected to 's believing . 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 th…
A causal Bayes net analysis of dispositions
In this paper we develop an analysis of dispositions in terms of causal Bayes nets. In particular, we analyze dispositions as generic cause–effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and fin…
The role of source reliability in belief polarisation
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 anoth…
Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms
Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out—they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automa…
Introduction to the special issue “Logical perspectives on science and cognition”
A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: Fixing the chemical factory
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 si…
Introduction to the special issue “Causation, probability, and truth—the philosophy of Clark Glymour”
Causality as a theoretical concept: Explanatory warrant and empirical content of the theory of causal nets
A Modeling Approach for Mechanisms Featuring Causal Cycles
Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also…
Constitutive Relevance, Mutual Manipulability, and Fat-Handedness
The first part of this article argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation—for which it is explicitly designed—it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance, or it gives rise to the unwanted consequence that constitutio…
Causal Exclusion and Causal Bayes Nets
In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion…
A Formal Framework for Representing Mechanisms
In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) interven…
Constitutive Relevance, Mutual Manipulability, and Fat-Handedness
The first part of this article argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation—for which it is explicitly designed—it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance, or it gives rise to the unwanted consequence that constitutio…
Causality as a theoretical concept: Explanatory warrant and empirical content of the theory of causal nets
Causal Exclusion and Causal Bayes Nets
In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion…
A Formal Framework for Representing Mechanisms
In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) interven…
A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: Fixing the chemical factory
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 si…
A Modeling Approach for Mechanisms Featuring Causal Cycles
Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also…
A Causal Bayes Net Analysis of Glennan’s Mechanistic Account of Higher-Level Causation (and Some Consequences)
One of Glennan’s ([1996]) most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms. In this article I employ causal Bayes nets (CBNs) to reconstruct his analysis. This allows for identifying general assumptions that have to be satisfied to get the analysis working. I show that once these assumptions are in place, they imply (against the background of the CBN machi…
Quantifying Proportionality and the Limits of Higher-Level Causation and Explanation
Supporters of the autonomy of higher-level causation (or explanation) often appeal to pro- portionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modelling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this article we apply ideas from this literature to the issue of higher ver…
A causal Bayes net analysis of dispositions
In this paper we develop an analysis of dispositions in terms of causal Bayes nets. In particular, we analyze dispositions as generic cause–effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and fin…
The role of source reliability in belief polarisation
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 anoth…
Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms
Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out—they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automa…
A Formal Framework for Representing Mechanisms
In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) interven…
Causal Exclusion and Causal Bayes Nets
In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion…
Introduction to the special issue “Causation, probability, and truth—the philosophy of Clark Glymour”
Causality as a theoretical concept: Explanatory warrant and empirical content of the theory of causal nets
A Modeling Approach for Mechanisms Featuring Causal Cycles
Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also…
Constitutive Relevance, Mutual Manipulability, and Fat-Handedness
The first part of this article argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation—for which it is explicitly designed—it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance, or it gives rise to the unwanted consequence that constitutio…
Introduction to the special issue “Logical perspectives on science and cognition”
A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: Fixing the chemical factory
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 si…
The causal theory of knowledge revisited: An interventionist approach
Goldman proposed that a subject knows if and only if is appropriately causally connected to 's believing . 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 th…
A causal Bayes net analysis of dispositions
In this paper we develop an analysis of dispositions in terms of causal Bayes nets. In particular, we analyze dispositions as generic cause–effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and fin…
The role of source reliability in belief polarisation
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 anoth…
Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms
Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out—they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automa…
Free Will, Control, and the Possibility to do Otherwise from a Causal Modeler’s Perspective
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 t…
A Causal Bayes Net Analysis of Glennan’s Mechanistic Account of Higher-Level Causation (and Some Consequences)
One of Glennan’s ([1996]) most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms. In this article I employ causal Bayes nets (CBNs) to reconstruct his analysis. This allows for identifying general assumptions that have to be satisfied to get the analysis working. I show that once these assumptions are in place, they imply (against the background of the CBN machi…
Unification and explanation from a causal perspective
Quantifying Proportionality and the Limits of Higher-Level Causation and Explanation
Supporters of the autonomy of higher-level causation (or explanation) often appeal to pro- portionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modelling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this article we apply ideas from this literature to the issue of higher ver…
Supervenient fixity and agential possibilities
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 thi…
Mental causation, interventionism, and probabilistic supervenience
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 an…
Probabilistic supervenience and agential possibilities
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 a…
Causal Bayes Nets and Token-Causation: Closing the Gap between Token-Level and Type-Level
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 characterizations are related to the characterization 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. It provides deta…
The Criminalist's Paradox as a Counterexample to the Principle of Total Evidence
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 anal…
The Formal Structure(s) of Analogical Inference
Recently, Dardashti et al. (Stud Hist Philos Sci Part B Stud Hist Philos Mod Phys 67:1–11, 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 th…
A causal theory of suppositional reasoning
Suppositions can be classified as indicative versus subjunctive and full versus partial. We propose a causal account of suppositional reasoning that naturally unifies all four types of reasoning based on this classification, provides a justification of the rather heterogenous canonical update rules, and gives rise to a new update rule for the partial subjunctive case in terms of generalized imaging
Epistemology (21 works) · Philosophy (20 works) · Philosophy and History of Science (18 works) · Computer Science (13 works) · Mathematics (12 works) · Metaphysics (11 works) · Causation (9 works) · Psychology (9 works) · Artificial Intelligence (8 works) · Causal model (8 works)