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Title: Mechanistic Causality: Dispositions vs. Structures


1
Mechanistic Causality Dispositions vs. Structures
  • Lorenzo Casini
  • L.Casini_at_kent.ac.uk

2
Outline
  • Mechanisms complex systems
  • Glennans account latest views
  • Dispositionalist interpretation
  • The case of asset pricing
  • Between dispositions and structures

3
Complex systems
  • Systems whose behaviour result from
    (rule-based) interactions of many (different)
    components and exchange (of e.g. energy, mass,
    information) with environment
  • Can display one or more among
  • Nonlinearities
  • Sensitivity to initial conditions
  • Self-organisation
  • Adaptivity

4
Mechanistic causality
  • The given
  • Complex systems sciences study mechanisms (cf.
    Bechtel Richardson, BA, Kuhlmann, etc.)
  • In C S S, talk of causal relations and of
    mechanisms often go together
  • Working hypothesis
  • causal relations in complex systems have to do
    with mechanisms
  • Desiderata for account
  • informative about truth conditions
  • provide explanation of phenomena

5
Glennans account
  • (C) Event A causes event B iff there is a
    mechanism (M) which connects them (1996 49, 56,
    64)
  • All sorts of mechanisms between any two events.
    How to select the right one?
  • (M) A mechanism for a behavior is a complex
    system that produces that behavior by the
    interaction of a number of parts, where the
    interactions between parts can be characterized
    by direct, invariant, change-relating
    generalizations (2002 S344)
  • Interactions are only so characterised what are
    they?
  • Events are related by mechanism (complex
    system) complex system object whence, events
    mediated by an object not a process ?
    Relation between object and process?

6
Latest views
  • Causal claim relates events (property instances)
    and has the form
  • Event c causes event e (in background conditions
    B) in virtue of properties P (of c, e, or B)
    (...) For instance, Bob's coughing (c) caused
    Carol to wake up (e) in virtue of cough's
    loudness (P). (2010a 364)

production relevance
relates events relates properties
singular general
non-counterfactual counterfactual
truth conditions explanation
  • production provides truth conditions
  • to say that one event produced another is to say
    that in fact the causative event is connected to
    the effect via a continuous chain of causal
    processes (2010a365-6)

7
(i) relation processobject
  • Mechanism is both a system and a process which
    are so related
  • Mechanism" is used to describe two distinct but
    related sorts of structures. First, mechanisms
    are systems consisting of a collection of parts
    that interact with each other in order to produce
    some behavior. () Second, mechanisms are
    temporally extended processes in which sequences
    of activities produce some outcome of the
    mechanisms operation. () There is a natural
    relationships between processes and systems, for
    the operations of systems give rise to processes.
    (2008 376)
  • (Couldnt it be other way round ? )

8
(ii) nature of interactions
  • Interactions are only characterised as ..
    what are they?
  • an interaction is an occasion on which a change
    in a property of one part brings about a change
    in a property of another part. For instance, a
    change in the position of one gear within a clock
    mechanism may bring about the change in the
    position of an interlocking gear. Interaction
    is a causal notion that must be understood in
    terms of the truth of certain counterfactuals.
    (2002 S344)
  • What makes counterfactual assertion true?
  • singular determination, i.e. exercise of power
  • When a change in a produces a change in b, it
    follows (with the usual caveats about
    overdetermination, etc.) that if a had not
    changed, b would not have changed. But the
    counterfactual locution should be understood not
    as a claim about non-actual worlds, but a claim
    about the determining power of a in this world.
    (2010b, sec.5)

9
Ambiguity remains
  • What is the truth maker of a causes b ?
  • determining power of a
  • or
  • continuous chain of causal processes between a
    and b
  • Other sources of ambiguity
  • Glennan also talks of causal rel between events
    as if it is relevance rel
  • the set of events causally sufficient to bring
    about an effect are typically large, so that when
    we speak of the cause of an event, we are using
    pragmatic criteria to single out a certain event
    as especially salient. (2010a 364-5)
  • Powers are usually ascribed to objects not
    events can be OK but we need a
    (dispositionalist?) story here..

10
A dispositionalist interpretation
  • Chakravartty (2007)
  • A causal property is a property conferring to
    particulars that have it dispositions to behave
    in certain ways when in the presence or absence
    of other particulars with causal properties of
    their own (p.108)
  • causation is a relation of de re necessity
    between properties, or property instances
  • account of de re necessity follows from account
    of causal properties identity (pp. 113-114)
    (DIT)
  • what makes a causal property the property that
    it is are the dispositions it confers to the
    objects that have it (p 129)
  • causal phenomena are the result of continuous
    processes of interaction among particulars with
    causal properties

11
  • talk of events as relata is convenient but
    elliptical for description of aspects of such
    processes
  • identity of particulars (objects, events,
    processes) is derivative from identity of causal
    properties.
  • Position entails holism, or ontological
    circularity
  • All laws (general relations between properties)
    and all causal properties are fixed at once given
    a set of properties and their distribution
  • What is the truth maker of a causal claim?
  • (...) it is a consequence of DIT that networks
    of causal properties have a holistic nature. This
    furnishes a more radical solution to the problem
    of truthmaking than it is generally appreciated.
    The existence of any one causal property is a
    sufficient truthmaker for counterfactuals about
    all possible relations applicable to the world in
    which that property is found (p. 146)

12
  • All this seems in line with complex systems
    scientists views
  • Causal relations are not something extra added
    to predefined noncausal objects. They appear
    simultaneously with objects in a world that
    becomes, as a result of systematic individuation,
    a complex causal network of things and events.
    Causal relations obtain among states of things in
    static conditions and among events in dynamic
    conditions. An example of a static causal
    relation is the suspension of the Golden Gate
    Bridge by steel cables. Two states or events are
    causally relatable if they are connectible by a
    process, which can be stationary, related if they
    are so connected. If the connecting process is
    the change of a thing, then the thing is the
    agent of interaction. () (Auyang 1998, p. 260)
  • Weed (2005) Auyangs conception of a state
    space, prior to analysis is that of a reality
    composed of actually indefinite strings of
    activity.

13
  • For Chakravartty, if the account of de re
    necessity is viable, then
  • it gives criterion to distinguish causal /
    accidental regularities and
  • this criterion is explanatory (p. 130)
  • How does Glennans revised account fare wrt (1)
    and (2) in complex systems?
  • Are dispositions and de re necessity helpful
    tools?
  • First, whilst guaranteeing existence of
    sufficient truth maker, holism (obviously)
    doesnt help determine minimally sufficient
    (local) truth conditions. But these may
    nonetheless exist, whenever system is
    sufficiently isolated / carefully described.
  • Let us consider an example then..

14
(Apoptosis)
Weinberg (2007), p. 354
15
Asset pricing. Stylised facts
  • Prob that tomorrows price goes up equals goes
    down given available evidence (conditional
    distribution is approx Gaussian). Yet
  • big (/little) price changes follow big (/little)
    price changes changes not uniformly distributed
    (volatility clustering)
  • asset returns at different t show a dependency
    (volatility persistence) autocorrelation
    (correlation between values at different t, as
    function of t difference) of squared returns
    decays slowly
  • distributions of unconditional returns at
    frequencies of less than one month are
    fat-tailed too many observations near the mean,
    too few in mid range and too many in the tails to
    be normally distributed.
  • Mechanistic account needs to answer, e.g.
  • What causes crash/bubble?
  • What explains time series?

Gaussian and other distributions
16
Asset pricing. Time series
Lux Marchesi (1999), p. 397
17
Asset pricing. Model
  • Lux Marchesi (1999) analogy with phase
    transition phenomena in physics

Summary from Kuhlmann (2009)
18
Structural vs. mechanistic account
  • What are the truth conditions for, e.g., Switch
    of fundamentalists into chartists caused the
    bubble?
  • What explains, e.g., specific event (crash) or
    general pattern (fat tails, volatility
    clustering/persistence)?
  • Smith (1998) no ontological commitment is needed
  • fit between geometrical structure of model and
    model of data is sufficient for approximate
    truth
  • explanation of behaviour just is a geometrical
    feature of dynamical model property of
    representation of a concrete structure (cf.
    Goldstein, 1996 Huneman, forthcoming)
  • no appeal to causal notions

19
  • For Glennan, instead, more is needed
  • It is possible to formulate a mechanical model
    using a state space representation but not all
    state space models are mechanical models. The
    requirements for a model being a description of a
    mechanism place substantive constraints on the
    choice of state variables (such as the fact that
    state variables should refer to properties of
    parts), parameters, and laws of succession and
    coexistence. The satisfaction of these additional
    constraints is what accounts for the explanatory
    power of mechanical models. (2005 447-448)
  • The point is whether these additional constraints
    can be met in complex systems..

20
  • Kuhlmann (2011) is halfway between Smith and
    Glennan
  • although doesnt reify structure/geometry,
  • contrasts compositionally complex mechanisms
    (MDC, BA) and dynamically complex mechanisms
    (e.g. nonlinear systems, chaotic systems, CA)
  • Similarity essential to explanation of both
  • reference to interactions of systems parts (e.g.
    agents, comparison of profits)
  • behaviour of the whole system must show some
    degree of robustness (high volatility for wide
    rage of parameter values, thresholds for
    transitions)
  • Difference these features must be filled in
    differently
  • behaviour of c.c.m.
  • ontological details are important
  • parts maintain identity and function throughout
    process
  • behaviour of d.c.m. (e.g. apoptosis, asset
    pricing)
  • ontological details are less important
    /irrelevant (e.g. whether and what specific
    agent buys or sells)
  • parts can change identity and function
    (fundamentalists become chartists, optimistic
    become pessimistic)

21
  • Result entities dispositions in complex
    systems explain less than we hoped explanation
    depends largely on structural features of the
    arrangement. Depending on this
  • A has the capacity to produce B if not interfered
  • A has the capacity to prevent B if not interfered
  • And vice versa (B has the capacity to
    produce/prevent A if not interfered)
  • Analogously, for apoptosis
  • Depending on geometry, XIAP, by binding to Casp3,
    which would normally prevent apoptosis, can
    also promote it, due to XIAPs inability to
    inhibit Casp9, which is then left free to trigger
    Casp3
  • Caspases are synthetised as inactive and become
    active by proteolitic cleavage one can say they
    are disposed to become active, but mechanists
    (seem to) view procaspases and caspases as
    different things with different functions

22
Summary
  • Glennans account and his latest views have
    ambiguities as regards the nature of truth makers
    and explanation
  • His account can be made coherent by employing a
    dispositionalist metaphysics (Chakravartty)
  • In complex systems, talk of mechanisms and
    causality as involving properties and (dynamical)
    relations is legitimate
  • However, reference to specific parts with stable
    identities and functions to explain behaviour and
    provide truth makers is less appropriate
    vis-à-vis structural features of the arrangement

23
References
  • Auyang, S. (1998). Foundations of complex-system
    theories. Cambridge CUP
  • Bechtel, W., and Abrahamsen, A. (2005)
    Explanation A mechanist alternative. Studies in
    History and Philosophy of Biological and
    Biomedical Sciences, 36 421-441.
  • Bechtel, W., and Abrahamsen, A. (forthcoming)
    Complex biological mechanisms Cyclic,
    oscillatory, and autonomous. In Collier, J., and
    Hooker, C.A. (eds.) Handbook of the Philosophy
    of Science, Vol. 10 Philosophy of Complex
    Systems. New York Elsevier.
  • Glennan, S. (1996). Mechanisms and the nature of
    causation. Erkenntnis, 44, 4971.
  • Glennan, S. (2002). Rethinking mechanistic
    explanation. Philosophy of Science, 69,
    S342S353.
  • Glennan, S. (2005). Modeling mechanisms. Stud.
    Hist. Phil. Biol. Biomed. Sci., 36, 443464
  • Glennan, S. (2008). Mechanisms. In Psillos, S.
    and Curd, M. (eds) The Routledge Companion to
    the Philosophy of Science, 376384
  • Glennan, S. (2010). Mechanisms, causes, and the
    layered model of the world. Philosophy and
    Phenomenological Research, 81(2)362-381.
  • Glennan, S. (2011). Singular and general causal
    relations a mechanist perspective. In Illari,
    P., Russo, F. and Williamson, J. (eds.)
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  • Goldstein, J. (1996). Causality and Emergence in
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  • Huneman, P. (forthcoming). Topological
    explanations and robustness in biological
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  • Kuhlmann, M. (2011). Mechanisms in dynamically
    complex systems. In Illari, P., Russo, F. and
    Williamson, J. (eds.) Causality in the Sciences.
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  • Lux, T., and M. Marchesi (1999) Scaling and
    criticality in a stochastic multi-agent model of
    a financial market, Nature 397 498-
  • 500.
  • Machamer, P., Darden, L., and C. Craver (2000)
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  • Smith, P. (1998). Explaining chaos. Cambridge
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  • Weinberg, R. A. (2007). The biology of cancer.
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