Download AIPS Conference October 28-30, 2016 Mechanistic Explanations

AIPS Conference October 28-30, 2016
International Academy for the Philosophy of the Sciences
Mechanistic Explanations,
Computability and Complex Systems
Brigitte Falkenburg (Dortmund) & Gregor Schiemann (Wuppertal)
Booklet of Abstracts (Preliminary)
Mechanistic Explanations, Computability and Complex Systems
When it turned out that the "classical" DN model of explanation cannot cope with many fields of scientific
research (starting with cases from physics), several other models of scientific explanation have been
developed. In a recent philosophical turn to scientific practice, mechanistic explanations have drawn much
attention. They explain how certain properties of a whole stem from the causal activities of its parts. This
kind of explanation is in particular employed in explanatory models of the behaviour of complex systems. It
is widely spread in biology and neuroscience, and hence was taken up mainly in the philosophy of biology
and neurobiology. In our 2016 conference, we want to broaden the scope of discussion. In recent philosophy
of physics, mechanistic explanations have not yet drawn the attention they deserve. This is a surprising
neglect, given that mechanistic explanations in other fields of research are modelled after the mechanical
models of physics. Well-known examples of mechanical explanations are the kinetic theory of heat, and the
constituent models of matter of atomic, nuclear, and particle physics. In the latter, however, the mechanisms
on which the explanations rely are no longer "mechanical" in a classical sense. Other kinds of mechanical
explanations are typical of computer science. In all these cases, questions of computability and its limitations
arise. We hope that a broader philosophical discussion of mechanical explanations, ranging from physics and
computer science to biology and cognitive neuroscience, will shed new light on the topics of emergence and
reduction in complex systems.
Friday October 28, 2016
I Mechanistic Explanation: Conceptions, Objections, and Defence
09:30 Stathis Psillos: Mechanisms, Then and Now
In this talk I will compare the current views of mechanisms with the mechanisms (and mechanical philosophy)
of the seventeenth century. I will argue that the current non-mechanical conception of mechanisms is
motivated by non-reductionist accounts of explanation in the sciences and though it has little in common
with the seventeenth century conception, it faces analogous problems, viz., it needs to rely on some robust
conception of laws (or other principles of connection) to account for the action of the mechanism.
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
10:15 Brigitte Falkenburg: Mechanistic Explanations: History, Scope, and Limitations
Here, he relations between mechanistic explanation and the top-down and bottom-up approaches of physics
and the follower sciences will be explained. A precursor of these approaches was Newton’s analytic-synthetic
method, which aimed at analysing the phenomena in terms of their components and causes. In order to shed
some light on the scope and limitations of mechanistic explanations today, it will be discussed to what extent
the current mechanistic explanations of physics or neuroscience still fit in Newton’s methodological tradition.
11:30 Dennis Dieks: Explanation and Mechanisms in Physics
Paradoxically, in spite of the fact that the nature of explanation is one of the oldest and most studied subjects
in the philosophy of science, philosophical consensus seems further away than ever. Indeed, many proposals
compete for the title of the correct analysis: DN-explanation, explanation by unification, constitutive
explanation, explanation by principle, interventionist explanation, causal explanation, mechanistic
explanation, to name only a few prominent attempts to “explain explanation”. All of these proposals possess
their paradigm cases that make them seem eminently reasonable; but all of them also face objections and
counterexamples.
In our contribution we will try to diagnose and remedy this, by arguing that the project to come up with one
unique and general definition of what explanation really is, is misguided. Instead, we will defend a pragmatic
and contextual analysis of explanation. Accordingly, what is the appropriate way of explaining a phenomenon
depends on the exact form of the demand for explanation and on the conceptual tools that are available.
The different analyses that are on offer will thus turn out to be alternatives that are complementary rather
than in conflict with each other. In particular, we will argue that mechanistic explanation is a worth-while
addition to the explanatory arsenal, but cannot claim to be the only true explanatory tool. We will illustrate
this situation with examples from physics.
Ref.: H.W. de Regt and D. Dieks. “A Contextual Approach to Scientific Understanding”, Synthese 144 (2005), 137-170.
12:15 Marco Buzzoni: Multilevel Reality, and Mechanistic Explanations
Levels of organization and multilevel research strategies have significant implications for philosophical
analyses of explanation, modelling, and representation (Malley et al. 2014). However, the most state-of-theart and scientifically plausible theory of levels of organization, that of Machamer, Darden, Bechtel, and Craver
(for example Craver 2002), has proved to be fundamentally problematic (cf. Strand and Oftedal 2009;
Potochnik and McGill 2011; Pâslaru 2009; Eronen 2013 and 2015).
The talk aims to show that a perspectival version of the agency account of causation makes it possible to
have a consistent notion of level (or scale) and to make room for causal interactions between the different
levels of a mechanism. More precisely, moving from a perspectival version of the agency theory of causality,
it aims to clarify a) how different explanatory levels correspond to different pragmatic interests and practical
possibilities; b) how this does not exclude the objective independence of the relation of cause and effect in
a specific explanatory context, and c) how interlevel explanations are to be understood.
14:30 Itala M. Loffredo D'Ottaviano: Autonomous Action in Complex Mechanical Systems: A Real Dilemma?
After proposing a general definition for self-organizing systems, we will analyse relationships between
autonomous action and self-organizing actions in mechanical systems (such as self-organizing robots) from
the perspective of the complex systems paradigm.
The following problem will guide our presentation: is there (in) compatibility between the concepts of
autonomous action and self-organizing mechanical action?
15:15 Joëlle Proust: Metacognition and the Role of Mechanistic Explanations in the Philosophy of Mind
Ref.: Joëlle Proust, The Philosophy of Metacognition: Mental Agency and Self-Awareness. Oxford University Press 2013.
2
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
16:30 Jan Faye: Causal Mechanism, Complexity, and the Environment
Apparently, scientific practice includes assuming a wide range of different ontologies. This is due to the fact
that scientists may use various conceptual descriptions in order to explain a single research system, and none
of these descriptions have ontological priority. What scientists take to be an explanation of their research
objects is often determined by dissimilar research interests. From a philosophical point of view this practice
can be justified if one acknowledges the naturalistic and pragmatic turn in philosophy of science. Because of
the existence of such a plurality of conceptual schemes there is no common way to determine a natural kind;
each natural kind to which any explanation refers is always defined with respect to a conceptual schema of
investigation and the explanatory interests of the scientists. In addition, conceptual pluralism implies that it
does not make sense to look for a basic ontological foundation to which all other conceptual descriptions can
be reduced. So what some have called horizontal pluralism entails vertical pluralism as well.
Thus there are cases where a system seems to possess properties which cannot be explained in terms of the
properties of its subsystems. In such cases many scientists, even physicists, believe that we are facing
properties that either supervene on the properties of the subsystems or allow so-called emergent behavior.
This can be emergent behavior of, for example, the superfluidity of 3He, isomers in chemistry, flocks of birds,
or mental states in neuroscience. Indeed, the appearances of emergent behavior at numerous levels seem
to give rise to scientific practices covering a large range of different ontological levels.
People often may have different stances in mind when discussing “pluralism” in science. We may distinguish
between epistemological, conceptual, and metaphysical pluralism. Conceptual pluralism is stronger than
epistemological pluralism, but weaker than metaphysical pluralism. Of course, one is immediately led to ask:
what does accepting conceptual pluralism imply with respect to the nature of emergent behavior? Should
we be antirealist or realist? Is the emergency found at many levels of scientific practice only a result of the
fact that we experience the behavior of complex systems and their subsystems differently reflecting that we
use different conceptual descriptions when we explain their behavior; or is emergency a result of the fact
that the world really consists of different ontological levels at which new properties and non-reductive
behavior emerges. If we take emergent behavior to be ontologically genuine, we may ask what its causal
status is. How do the various levels interact with each other?
It is standard to say that properties of higher-level systems arise out of the properties and relations that
characterize their constitutive elements. Most of these properties of higher-level systems are “resultant” but
some are “emergent”. Some philosophers have even suggested that the whole, which exhibits the emergent
behavior, in fact gives rise to this behavior because there is efficient downward causation from the higherlevel to the lower-level. Personally, I find the concept of efficient downward causation quite dubious, partly
because it is difficult to see how downward causation can meet some reasonable criteria of causation.
Instead of taking a strong ontological stance in favor of real emergent levels and the possibility of downward
causation in a couple of recent publications I have suggested that emergency and plurality have a much more
cognitive and pragmatic origin. In every domain of nature we make a division between relatively selfcontained or self-sustained systems and their environment. I believe this holds for small systems as well as
big systems. Indeed, one can call these domains of systems “complexity levels” as long as one does not hold
strong commitments to an ontological hierarchy: I suggest that a complex system is a self-contained system
encapsulated by another self-contained system where the encapsulating system functions as the
environment of the encapsulated system. Thus it is in the interaction between a complex system and its
environment that some properties of the system may be understood as features of supervenience or
emergency.
Thus a lesser complex system is encapsulated by an environment of greater complexity. In general, one may
say that a system is self-contained or self-sustained if causal mechanisms internal to the system determine
its surface properties, its limited rage of activity, and its organization. However, no system exists in isolation
from its environment, and I think that it is its causal interaction with the environment that is seen to give rise
to the emergent behavior. My assumption is that there is no hocus pocus in explaining so-called emergent
behavior, and the epistemic and ontological requirements of conceptual pluralism. The explanation of a
system’s causal behavior in relation to its environment is not reducible to an explanation in terms of the
system’s own internal causal mechanisms. I therefore distinguish between causal mechanism and causal
3
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
interaction. The internal causal mechanisms give only the system a disposition to interact in certain ways,
whereas the manifestation of this disposition requires the system’s interaction with the environment and its
disposition to interact with the system. The aim of my talk is to establish whether this rough model of
complexity has any explanatory virtues, and if so whether or not it can be used to understand emergent
behavior from atoms to galaxies and from cells to brains.
Ref.: Agazzi, E. & Montecucco (2002). Complexity and Emergence. World Scientific Publishing.
Faye, Jan (2014). “What Counts as Causation in Physics and Biology.” In: Maria Carla Gavalotti et al (eds.),
New Directions in the Philosophy of Science. Heidelberg: Springer, 173-189.
Kim, J. (1999). “Making sense of emergence”. In Philosophical Studies, 95: 3-36.
Ladyman, J. & Ross D., (2007) Every Thing Must Go, Oxford University Press.
17:15 Michael Ghins: Mechanistic Explanation: An Extension and a Defence
The mechanistic conception of explanation has been recently revived and revamped within the New
Mechanistic Philosophy advocated in Glennan (2000), Machamer et al. (2000) and Kuhlmann & Glennan
(2014) among others. To explain a process or behaviour is to describe the mechanism that produces it. As
Glennan puts it: « A mechanism for a behaviour is a complex system that produces that behaviour by the
interaction of a number of parts, where the interactions between parts can be characterized by direct,
invariant, change-relating generalizations. » (Glennan 2000, p. S344)
After a brief presentation of the new mechanistic explanation (NME), I will propose to enlarge the notion of
mechanism and take it as an organization of properties, which are considered to be the parts of the
mechanism. Such an organization or structure is not necessarily spatial unlike the organization of the parts
of classical mechanisms. These properties interact in accordance with “change-relating” generalizations,
which are causal laws. A causal law is a universal mathematical statement which contains a time derivative,
which refers to the effect, whereas the other terms in the law refer to the cause(s) (Blondeau & Ghins 2012).
The merits of this mechanistic causal view of explanation will be defended against some objections.
Ref.: Blondeau, J. & Ghins (2012), “Is There an Intrinsic Criterion for Causal Lawlike Statements?” International Studies
in the Philosophy of Science 26, 381-401.
Glennan, S. (2000). Rethinking mechanistic explanation. Philosophy of Science, 69(S3), S342–S353.
Kuhlmann, M., & Glennan, S. (2014). On the relation between quantum mechanical and neo-mechanistic
ontologies and explanatory strategies. European Journal for Philosophy of Science, 4(3), 337–359.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.
Saturday October 29, 2016
II Explaining Complex Systems: Case Studies from Physics and Biology
09:30 Mauro Dorato & Laura Felline: On Explaining Non-dynamically
the Quantum Correlations via Quantum Information Theory: What it Takes
In this paper we argue that quantum information theory can provide a kind of non-causal/non-dynamical
explanation of the quantum correlations. However, such an explanation per se does not rule out the
possibility of a dynamical/causal explanation of the quantum correlations, given in terms of some
interpretations (or alternative formulations) of quantum theory. In order to strengthen the claim that its
explanations of the quantum correlations are explanatory sufficient, the quantum information theory
approach should inquire into the possibility that the quantum correlations could be treated as “natural”, that
is, as phenomena that are physically fundamental. As such, they would admit only a structural explanation,
similarly to what happened in crucial revolutionary episodes in the history of physics.
4
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
10:15 Paul Busch: The Quantum-Classical Relationship:
An Instance of Theory Hierarchy or Coexistent Levels of Explanation?
In this talk I will present case studies highlighting conceptual issues in the relationship between quantum
mechanics and classical mechanics that have remained unresolved hitherto: I will show that (a) quantisation
methods are still reliant on the presence of classically described reference systems and (b) classical limit
operations are only capable of accounting for some but never all aspects of a physical system’s quasi-classical
behaviour. Hence the statements that quantum mechanics supersedes classical mechanics and that classical
mechanics derives as a limiting description of quantum mechanics cannot be maintained without
qualification. I will explore the question whether quantum mechanics can be consistently cast as arising from
a revision and generalisation of classical mechanics (as seems to be implied by Peter Mittelstaedt’s
programme of Rational Reconstructions of Modern Physics), or whether both theories are required for a
consistent explanation of the full range of microscopic and macroscopic physical phenomena (thus calling for
a pluralism of levels of description, as suggested by Günther Ludwig’s conception of a hierarchy of physical
theories).
11:30 Margaret Morrison: Turbulence and the Problems of Explanation in Multi-Scale Modelling
12:15 Michela Massimi: Model-Independent Searches.
Or, how Scientific Practice in Physics is Re-thinking the Metaphysics of Science
One of the central metaphysical questions in philosophy of science concerns the truth of our scientific
theories. The metaphysical stance associated with it goes under the broad family of realism. But realism—as
a metaphysical stance —is an ongoing concern for anyone with an interest in scientific practice. For a closer
look at scientific practice soon reveals the perspectival nature of our scientific knowledge:
(1) all our scientific knowledge is ‘from a particular vantage point’ (e.g. be it the measurement technique
we may employ, or the theoretical model we use to interpret the data); and
(2) there cannot be a perspective-independent approach to nature (see Giere 2006).
Let us call (1) and (2) ‘the perspectival argument’. Questions about the perspectival nature of models often
translate into questions about the perspectival nature of scientific representation (van Fraassen 2008, ch. 3).
Be it fMRI brain scan, galaxy clustering, or computer simulations in high energy physics, scientists have only
access to what the target system looks like within the chosen measurement set-up and theoretical model
endorsed, as opposed to what the target system really is (or so the ‘perspectival argument’ goes).
The destabilizing implications of the perspectival argument become evident if one considers whether it can
in fact be reconciled with realism (and what kind of realism is indeed compatible with it). The existing
literature has concentrated on theoretical inconsistencies that perspectivism brings along with it (Morrison
2011) with the consequent risk that perspectivism becomes what Morrison calls a “view from Everywhere”,
i.e. a sophisticated form of instrumentalism. Is the perspectival argument correct? And can it be reconciled
with realism? To answer these questions, one must take a second look at scientific practice, and whether or
not scientists do indeed develop methodologies designed to overcome the perspectival argument. Second,
even granted that the perspectival argument might be correct, one must investigate how scientists go about
in their daily practice handling perspectival considerations; and whether perspectival considerations may in
fact be heuristically advantageous (rather than a hindrance), by making possible novel testable predictions,
for example.
Model-independent searches in Beyond Standard Model (BSM) studies provide an example of a nonperspectival approach by trading the sensitivity of the data to the chosen experimental set-up for the breadth
of data, typical of Big Data. What are the implications of this new trend in high energy physics for the
‘perspectival argument’? Can perspectival considerations be, after all, a heuristically productive feature of
scientific inquiry? There are indeed situations in science where integration and cross-check of perspectival
techniques at work in multi-probe methods are routinely used to calibrate key parameters, for example.
5
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
What can we learn from this scientific practice, when it comes to issues of unification, integration, and novel
predictions? Can we uphold a metaphysical stance such as realism, despite the situated nature of our
knowledge? This paper addresses this metaphysical question by focusing on cutting-edge methodology in
contemporary high-energy physics.
14:30 Ion-Olimpiu Stamatescu: Explanation, the Progress of Physical Theories, and Computer Simulations
The three items in the title are in many ways related. In Duhem's terms, a physical theory should and could
never “explain” the world in the sense of leading to a metaphysical reality, but the better the theory becomes
in describing and predicting phenomena, “the more we intuit that [its] logical order is the reflex of an
ontological order”. This understanding of explanation, however, is strongly dependent on the ability of
exploring the theory “from first principles” - which in contemporary physics is a key word for computer
simulations. Another connection appears in the evolution of theories itself: in the physics of fundamental
phenomena, e.g., present day theories are understood as “effective theories” related to a hierarchy of scales,
and computer simulations provide the possibility to test this relation and the hypotheses for higher theories.
And computer simulations also allow a grasp on the internal connections in the theory and so help intuition
(in the sense of Heisenberg, of being able to overview the most immediate predictions of the theory). These
aspects will here be discussed.
15:15 Jesús Mosterín: Explanations in Cosmology
Who needs to explain? And what is in need of explanation? We require explanations for anomalies,
unexpected results and surprises. It is the sudden collapse of the bridge that has to be explained, not its usual
permanence. Let’s consider cases in cosmology.
In classical Greek astronomy, the circular unisonous movement of the stars in the sky was accepted as a
matter of course. The problem arose with the apparent anomalies, specifically the retrograde trajectories of
the planets, which seemed to go the wrong way for a while. The Ptolemaic system was the main effort to
provide an explanation. (Was it a “mechanistic” explanation?)
We will analyze several more recent cases: Work on redshifts. Hubble and the expansion of the universe. The
cosmic microwave background radiation. The anomalous movements of galaxies and dark matter. The arrival
of unexpectedly too few solar neutrinos to Earth’s surface. The acceleration of the expansion of the universe.
Finally, we will consider whether certain fashionable theories (like inflation, the multiverse or the anthropic
principle) really do explain anything that is in need of explanation. What is inflation supposed to explain?
Things like the absence of magnetic monopoles, the flatness of space or the relative homogeneity of the
CMBR. In how far are they in need of explanation, or in how far can they be taken as just descriptive features
of the universe, as brute facts?
16:30 Bernard Feltz: Philosophical Issues of Mathematical Modelling in Life sciences.
From Self-Organization to Autonomy.
Two parts in this presentation. In a first part, I will analyse two conceptions of explanation in biology –
mechanistic (A. Rosenberg), selectionist (M. Farlene-Burnet, E. Sober) – and show how mathematical
modelling modifies these two logics of explanation. In a second part, I would like to describe two concepts
of self-organization. Boolean automata networks can conduct to a spinozist conception of human behavior
(H. Atlan) while Neuronal Group Selection Theory can conduct to a conception of human behavior including
intentional stance and free will (G. Edelman and G. Tononi).
17:15 Jean Petitot: Emergent Properties in Complex Neural Systems. The Case of the Primary Visual Cortex
18:00 Jean Gayon: Repetitiveness, Invariance, and Reversibility in Evolution
It is a commun conviction among evolutionary biologists that evolution is a 'unique and irreversible' process.
Formulated in such a simple way, this assumption is questionable: contemporary evolutionary biology offers
significant examples of repetition, invariance, and reversibility, both at the theoretical and at the
6
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
experimental level. Most often, the claim of the uniqueness and irreversibility of evolution is commented on
with reference to macroevolution. My communication will concentrate mainly on microevolution,that is
evolution at the level of populations within species. At that level, the repetitiveness/uniqueness and
reversibility/irreversibility alternatives not an all-or-nothing issue.
The first part of the paper is about repetitiveness and reversibility in population genetics. The classical models
of population genetics involve significant aspects of both. However, one should distinguish the intuitive
invariant properties taken as a starting point for the construction of models (e.g. reproduction and life cycles),
and the properties that are discovered through the development of theoretical models. Repetitiveness
should also be distinguished from invariance through time. Similarly, the issue of reversibility requires to
specify what concept of reversibility is used. I will carefully distinguish several senses of reversibility: return
to a former point in a dynamical trajectory, return along the same trajectory, and mathematical reversibility
with respect to time, that is insensitivity of equations to substituting “-t” for “t”. Non trivial notions of
invariance and reversibility can be recognized in current models of theoretical population genetics. I will give
some examples, and examine some objections.
The second part of the paper examines the question of repetitiveness and reversibility in experimental
population genetics, esp. the traditional approach of population cages (Drosophila) used for approximately
41 years from the 1930sto the 1970s, and the more recent experiments by Lenski and Travisano on cultures
of bacteria. In both cases, population geneticists evolve towards an increasing skepticism towards the issue
of reversibility. Dobzhansky's comment in 1953 seems to be still relevant: 'The elementary components of
the evolutionary, the mutational and selectional steps, are both repeatable and reversible; evolution is
however unrepeatable and irreversible... the microevoluationary changes [are] fully reproductible and
repeatable, while the mesoevolutionary ones were repatalbe only to a limited extent.' I will show that the
most spectacular attempts to obtain repeatable results fail, an observation which conflates with the
important aspects of invariance and reversibility postulated in the models of theoretical population genetics.
But I will also report on more recent experiments that refute the claim of the total inadequacy of the notion
of reversibility in experimental evolution. Here again, as in the theoretical part of the paper, several senses
of the term “reversibility” will have to be cautiously analysed.
To conclude, I will evocate Dollo's 'law of irreversibility' in evolution, proposed .the late 19th century, with
reference to palaeontological materials, and therefore to macroevolution. In spite of Dollo's use of the term
'principle', this paleontologist was highly aware that irreversibility was more a matter of fact than an a priori
theoretical principle, a statement that remains as much plausible today at all levels of evolution as it was a
century ago.
Sunday October 30, 2016
III Explanation, Causation, Representation
09:30 Jure Zovko: Is Inference to the Best Explanation a Myth?
In connection with the traditional Hempel-Oppenheim model (deductive-nomological model), I analyse in my
paper what criteria permit one to infer that an explanation is the best explanation. A good explanation is
usually a causal explanation, but that in itself does not establish whether it is a well-founded explanation
(provide the basis for the inference to the best explanation). A well-founded explanation has among other
characteristics a greater extent and coverage than other explanations with regard to the explanandum. It
offers a higher degree of reliability and precision regarding description of causal mechanisms governing
phenomena and ability to predict their behaviour. Greater unification of data to be explained is a further
characteristic which enables the IBE.
7
AIPS Conference 2014: Booklet of Abstracts (Preliminary)
In my presentation, I challenge the applicability of universal criteria for the inference to the best explanation
in philosophy and the humanities. I show that the idea of there being strictly determinable and fixed universal
criteria for the IBE which are applicable in every context is a myth whose purpose is to promote scientific
realism, but which unfortunately disregards the complexity of the epistemic process and qualitative
differences among types of phenomena to be explained (for example, fundamental differences between the
types of ‘explanation’ required by the humanities, social sciences, even religion and the arts, and those types
of explanation indigenous to the natural sciences). A universally determinable IBE does not exist, and no such
supposedly universal IBE can answer the question of how one can decide for the best among competing
explanations for every area and type of human inquiry. The fact that throughout history scientific theories in
one way or another eventually prove to be inadequate to explain the phenomena as, leaves us little room
for optimism on IBE.
There are many cases of data interpretation where we assume that we can apply the Inference to the best
explanation (for example conclusion based on the basis of existing fossil and their morphological similarity
for the evolutionary selection of species). But there are also many cases in science where we can apply
specific general criteria in order to obtain the IBE. Nevertheless, in practice, application of IBE in specific
cases involves a complex process of cultivated analysis and competent judgment – which rely on a form of
IBE which cannot be determined on the basis of general criteria alone.
10:15 Vinzenco Fano: Computation and Reality
The aim of this paper is to address the question: when is a computation realized by a physical system? Our
strategy is to propose a new definition of realization that makes the above question more tractable and easier
to scrutinize. We then show that our definition has some advantages when dealing with classical arguments
moved against definitions of this type.
The paper will be structured in four parts: in part one, we will set some desiderata our definition ought to
fulfil; in part two, we will introduce some prerequisite notions that are necessary for a full comprehension of
our definition; in part three, we will introduce our definition of realization – it will turn out that our definition
can be identified by a specific kind of strategy that Piccinini (2015) dubs nomological mapping account; in
part four, we will assess our definition.
11:30 Gino Tarozzi: The Lack of Causal Explanation in Quantum Mechanics
Despite its extraordinary predictive power and the wideness of its range of description, quantum mechanics
in its standard, or orthodox, interpretation presents an essential lack of those explanatory requests
characterizing previous scientific theories, as has been very well condenses in Feynman’s famous statement
"it is all quite mysterious and we more we look at it the more mysterious it seems."
We propose to show how this character emerges with particular emphasis in the case of causal explanation,
whose possibility, in all its most well-known formulations - like Laplace’s deterministic mechanism, Kant’s
conformity to law, Mill’s principle of the uniformity of nature and even Hume’s spatial contiguity and time
ordering -, seems to be contradicted by the basic principles of quantum mechanics,
We conclude with the discussion of a further quantum causal anomaly violating the metaphysical tenet ex
nihilo nihil.
12:15 Hans Lenk: Scheme -Interpreting Causation, Determinism and/or Mental Causation
I shall apply my scheme-interpretational approach to the problems of describing and explaining the processes
of causing, whether deterministic or not, as well possibly also to the so-called mental causation. References
are to be discussed by referring to and criticising Hacking and Giere (modelism and scientific perspectivism),
Spohn (deterministic causation), Woodward (making happen), Meixner and the critique of the “myth” of
thorough-going determinism (Falkenburg) will be analyzed under the epistemological stance and
methodological perspectivism developed by the author in the decades since the early eighties (cf. Grasping
Reality 2003). The result will be an “indirectistic” realism compatible with methodological conceptions even
of statistical causation (Suppes) and phenomena as well as “processes” of mental causation.
8