Information transmission and the mosaic of causal theory
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Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution.
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Information transmission and the mosaic of causal theory
- 1. Information Transmission and the Mosaic of Causal Theory Federica Russo Philosophy & ILLC | University of Amsterdam russofederica.wordpress.com |@federicarusso A longstanding collaboration with Phyllis Illari
- 2. Overview Causality, between ‘the epistemic’ and ‘the ontic’ Central in several epistemic practices Arguably, the ‘cement of the universe’ Causal pluralism and the causal mosaic How to keep the epistemic and the ontic together into the same account Information transmission A thin metaphysics of causation that works across domains and epistemic practices 2
- 3. Causality and epistemic practices 3
- 4. The centrality of causality in epistemic practices Explanation Inference and Prediction Control Reasoning Arguably, in all these practices causes and causal factors figure prominently They are not all there is about these practices, but they are certainly central and prominent, and across different scientific domains 4
- 5. The ‘cement of the universe’ 5
- 6. Metaphysical questions What is ‘causality’? What are ‘causes’? How do causes ‘produce’ their effects? We are interested in making metaphysical claims about causation, Cashing out the idea that causation is what ‘cement things together’, to borrow old good Mackie 6
- 7. What makes causal metaphysics so hard? Most of our causal theories are discipline-oriented: Physics stuff causes physics stuff Social stuff causes social stuff Biological stuff causes biological stuff And yet, there are plenty of cases in which we cross disciplinary boundaries, and try to cement ‘things’ of different nature E.g.: social factors causing biological outcomes in health and disease (and vice-versa) What we try to ‘cement’ is often technologically constructed Exposure research, high energy physics, agent-based models in social science, … 7
- 8. Metaphysics and epistemology We can easily make sense of the plurality of epistemic practices in which causality is central: Go for pluralism! But how is this compatible with causal metaphysics? 8
- 9. The mosaic of causal theory 9
- 10. 10
- 11. 11 Causal pluralism: Causality cannot be reduced to one single concept but has to be analysed using several concepts
- 13. Types of causing Anscombian pluralism: pulling, pushing, binding, … Aristotelian causes: material, formal, efficient, final Concepts of causation Dependence vs production Types of inferences Inferential bases, inferential targets Sources of evidence Correlations and mechanisms Different concepts for different scientific fields Physics, social science, medicine, biology … Methods for causal inference Quantitative, qualitative, observational, experimental, … 13
- 15. 5 philosophical questions Metaphysics What is causality? What kind of things are causes and effects? Semantics What does it mean that C causes E? Epistemology What notions guide causal reasoning? How can we use C to explain E? Methodology How to establish whether C causes E? Or how much of C causes E? Use What to do once we know that C causes E? 5 scientific problems Inference Does C cause E? To what extent? Prediction What to expect if C does (not) cause E? Explanation How does C cause or prevent E? Control What factors to hold fixed to study the relation between C and E? Reasoning What considerations enter in establishing whether / how / to what extent C causes E? 15
- 18. Allocating the fragments into the ‘causal mosaic’ • A (causal) mosaic is picture made of tiles • Each fragment has a role that • Is determined by the scientific challenge / philosophical question it addresses • Stands in a relation with neighboring concepts • The causal mosaic is dynamic, and also depends on scientists’ / philosophers’ perspectives 18
- 19. Tiles for the Causal Mosaic … necessary and sufficient; levels; evidence; probabilistic causality; counterfactuals; manipulation and invariance; processes; mechanisms; information exogeneity; Simpson’s paradox; dispositions; regularity; variation; action and agency; inference; validity; truth; … Philosophical Questions Metaphysics, Semantics, Epistemology, Methodology, Use Scientific Problems Inference, Prediction, Explanation, Control, Reasoning 19
- 21. The ‘cement’ revisited: information transmission 21
- 22. Use Epistemology Methodology Metaphysics Semantics 22 Establish: how C produces E how C and E are linked
- 23. Accounts of causal productions Physical processes Tied to description of processes in physics Only one type of linking Mechanisms Give organization of parts and their interactions Course-grained linking Capacities, Powers, Dispositions Give modal properties of some parts of mechanisms Not about linking 23 Illari, P., Russo, F. Information Channels and Biomarkers of Disease. Topoi (2016)
- 24. Use Epistemology Methodology Metaphysics Semantics Information transmission: the most general notion of causal production ➢ Helps address: ✓ Problem of omissions ✓ Heterogeneity of causal factors ➢ Compatible with mechanisms, processes, dispositions Establish: how C produces E how C and E are linked 24
- 25. The prospects of an informational approach to causal production 25
- 26. What is information? An information-theoretic approach See Philosophy of Information Semantic information Agreed, the most difficult to quantify But: the most versatile, as it allows us to express informationally virtually anything It is related to an epistemic agent expressing informationally something It is related to language, but not reduced to it 26 General Definition of Information: p is an instantiation of information, understood as semantic content, if, and only if: (GDI1) p consists of data; (GDI2) data in p are well-formed; (GDI3) well-formed data are meaningful. (GDI4) meaningful well-formed data are truthful.
- 27. The transmission of information A development of process theory, or rather going back to the very origins: A process is causal if it is capable of transmitting marks Problem: counterfactual formulation Reformulate question in (onto)epistemological terms: We check whether a process is causal by looking at which marks are (not) transmitted It is not formulated in terms of counterfactuals Marks are already there Techno-scientific practices are exactly about finding and tracing these marks What is most important is the tracking of the transmission 27
- 28. Why information Gives a way of describing reality, of any kind The transmission of information can be tracked, measured, described in quantitative or qualitative way See e.g.: biological information A light metaphysics, a versatile cement In line with Anscombian pluralism Activities and interactions is where we look for salient marks of information transmission Helps with: conceptualisation of link, picking up signal / signature, tracking marks It is a form of knowledge construction There is an ineliminable role of the epistemic agents in the process of tracking information 28
- 29. An episode of information transmission 29
- 30. The ATLAS experiment at the LHC at CERN A huge technological infrastructure for particle acceleration Testing the Standard Model: A bundle of theories about how matter interacts, and according to which forces The ATLAS experiment: Test the prediction of the Higgs boson Test predictions beyond the Standard Model Search for unforeseen physics processes Analysis of data from collision events in the LHC 30 • https://home.cern/science/physics/standar d-model • Karaca (2020), Two Senses of Experimental Robustness: Result Robustness and Procedure Robustness , BJP • Karaca (2017), A case study in experimental exploration: exploratory data selection at the Large Hadron Collider , Synthese
- 31. A techno-scientific experiment Technologies involved Experimental apparatus of the LHC Mathematical technology of Quantum Field Theory Digital computer technology What these technology allow us to do: Design the experimental set up Record all collision events Select ‘interesting events’ 31 • Karaca (2020), Two Senses of Experimental Robustness: Result Robustness and Procedure Robustness , BJP • Plotnitsky A. 2016 The future (and past) of quantum theory after the Higgs boson: a quantum- informational viewpoint. Phil. Trans. R. Soc. A • Stahlberg (2015),The Higgs Boson, The God Particle, and the Correlation Between Scientific and Religious Narratives, Open theology
- 32. What are ‘interesting events’? In the processes where particle collides, we look for signature or traces of these collisions, as predicted by SM E.g. particle decay ‘signatures’ We never observe collisions as such, or entities colliding as such! The causal interactions leave traces Between quantum objects Between quantum objects and measuring instruments 32
- 33. How to make sense of all this? In the midst of processes occurring, happening, intersecting we try to track, with sophisticated instrumentation bits of information that is transferred along these processes What we can actually detect are interactions that leave traces and that, according to our theory, are the right signs to look for (e.g. specific levels of decay of particles) [Processes, relations, interactions are ontologically prior to entities, but this is another episode!] 33
- 34. To sum up and conclude 34
- 35. Attempts to find The-One-Theory of causality notoriously failed One reason is that to account for the centrality of causality in epistemic practices and in ontological questions may require very distinct approaches Finding the ‘cement’ of the universe is all the more challenging because We often cross ‘domain’ boundaries and try to establish causal relations between relata of different nature We often construct relata and relations with sophisticated instruments and technologies In the causal mosaic we can distinguish phil questions and sci problems, and then reconnect them In the causal mosaic, information transmission is a thin metaphysics for causal production We can explain what links together causal factors, even if of different nature or technologically constructed This cement is not simply ‘out there’, but is the result of the epistemic practices performed by epistemic agents 35
- 36. Information Transmission and the Mosaic of Causal Theory Federica Russo Philosophy & ILLC | University of Amsterdam russofederica.wordpress.com |@federicarusso
Editor's Notes
- ABSTRACT Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution. But beyond having epistemic significance, causality is arguably the ‘cement of the universe’, to borrow the expression from the seminal work of John Mackie. What this means exactly is however made difficult in the light of the overspecialization of the sciences: is this cement the same in high energy physics and in molecular biology? Is it the same cement at the basis of social bonds and of disease onset? Two issues further complicate the picture. First, contemporary science, more often than not, crosses disciplinary boundaries, trying to establish causal relations across relata of different nature; for instance, we attempt to explain mental health conditions, such as depression, invoking biological and environmental factors, and how the two interact. Second, more often than not, contemporary science is techno-science, where instruments arguably allow for deeper and greater epistemic access to the (portion of the) world under investigation, but they do so by (partly) ‘constructing’ the object of study. For instance, the process of detection and measurement of biomarkers is not a simple and direct process giving access to a clearly identified entity, but is instead a complex process in which the ‘thing’ biomarker is much constructed via the technologies and theories employed. How can we make sense of this complicated metaphysical picture? How can we make a metaphysics of causality compatible with an epistemology? In this talk, I explore the prospects of an informational approach to causality, as one that can offer a thin metaphysics, derived from an epistemology of techno-scientific practices. Specifically, I shall try to support the view that the mosaic view of causality, as an epistemology of causality, needs to be accompanied by a (thin) metaphysics in which causality is cashed out as information transmission. This combination, I shall argue, helps make sense of causality across different techno-scientific contexts and domains.
- Floridi: constructionist, not representationalist. Knowledge is not a matter of hitting or finding, but of designing and constructing. reality is not a source but a resource for knowledge.
- ABSTRACT Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution. But beyond having epistemic significance, causality is arguably the ‘cement of the universe’, to borrow the expression from the seminal work of John Mackie. What this means exactly is however made difficult in the light of the overspecialization of the sciences: is this cement the same in high energy physics and in molecular biology? Is it the same cement at the basis of social bonds and of disease onset? Two issues further complicate the picture. First, contemporary science, more often than not, crosses disciplinary boundaries, trying to establish causal relations across relata of different nature; for instance, we attempt to explain mental health conditions, such as depression, invoking biological and environmental factors, and how the two interact. Second, more often than not, contemporary science is techno-science, where instruments arguably allow for deeper and greater epistemic access to the (portion of the) world under investigation, but they do so by (partly) ‘constructing’ the object of study. For instance, the process of detection and measurement of biomarkers is not a simple and direct process giving access to a clearly identified entity, but is instead a complex process in which the ‘thing’ biomarker is much constructed via the technologies and theories employed. How can we make sense of this complicated metaphysical picture? How can we make a metaphysics of causality compatible with an epistemology? In this talk, I explore the prospects of an informational approach to causality, as one that can offer a thin metaphysics, derived from an epistemology of techno-scientific practices. Specifically, I shall try to support the view that the mosaic view of causality, as an epistemology of causality, needs to be accompanied by a (thin) metaphysics in which causality is cashed out as information transmission. This combination, I shall argue, helps make sense of causality across different techno-scientific contexts and domains.