Mechanistic Causality: Dispositions vs. Structures

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Mechanistic Causality Dispositions vs. Structures

• Lorenzo Casini
• L.Casini_at_kent.ac.uk

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Outline

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

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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

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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

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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?

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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)

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(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 ? )

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(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)

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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..

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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

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• 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)

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• 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.

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• 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..

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(Apoptosis)
Weinberg (2007), p. 354
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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
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Asset pricing. Time series
Lux Marchesi (1999), p. 397
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Asset pricing. Model

• Lux Marchesi (1999) analogy with phase
  transition phenomena in physics

Summary from Kuhlmann (2009)
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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

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• 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..

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• 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)

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• 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

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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

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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.)
  Causality in the Sciences. Oxford OUP.
• Goldstein, J. (1996). Causality and Emergence in
  Chaos and Complexity Theories. In Sulis, W. H.
  and Combs, A. (eds.). Nonlinear Dynamics in Human
  Behavior, pp. 161-190. World Scientific
  Publishing.
• Huneman, P. (forthcoming). Topological
  explanations and robustness in biological
  sciences. Synthese
• Kuhlmann, M. (2011). Mechanisms in dynamically
  complex systems. In Illari, P., Russo, F. and
  Williamson, J. (eds.) Causality in the Sciences.
  Oxford OUP.
• 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)
  Thinking about mechanisms, Philosophy of Science,
  67 1-25.
• Smith, P. (1998). Explaining chaos. Cambridge
  CUP
• Weed, L. E. (2005). Sunny Auyang on complex
  systems theory a field being perspective in
  philosophy of science.
• Weinberg, R. A. (2007). The biology of cancer.
  Garland Science, Taylor Francis Group, New York.