Near-Decomposability and the Timescale Relativity of Causal Representations

Weinberger, Naftali

doi:10.1086/710760

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…These models take a framework that was designed for static sets of equations giving the causal relationships among simultaneous variables and generalize the framework to model systems in which some of the variables are away from equilibrium. Here we will not dwell too much on the causal interpretation of these models (Rescher and Simon 1966;Dash and Druzdzel 2001;Weinberger 2020), but will use them primarily as a way to keep track of the temporal relationships among the variables for the governor.…”

Section: Modeling the Watt Governormentioning

confidence: 99%

Static-Dynamic Hybridity in Dynamical Models of Cognition

Weinberger

Allen²

2022

Philos. sci.

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Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

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Section: Modeling the Watt Governormentioning

confidence: 99%

Static-Dynamic Hybridity in Dynamical Models of Cognition

Weinberger

Allen²

2022

Philos. sci.

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“…I introduce these models with a single example that can be modeled both statically and dynamically. Readers interested in further details may consult Simon and Rescher (1966), Iwasaki and Simon (1994), Dash (2003), Weinberger (2019Weinberger ( , 2020, and the "Appendix" below. Simon (1953) considered what makes causal relationships asymmetric, given that the equations stating scientific laws are typically symmetric.…”

Section: Statics Dynamics and Causal Modelsmentioning

confidence: 99%

“…Rather, such equations indicate that the variables adjust their values to one another so quickly that we can model the relationships as if they were instantaneous. See Anderson (2020) and Weinberger (2019Weinberger ( , 2020 for further discussion.…”

Section: Statics Dynamics and Causal Modelsmentioning

confidence: 99%

“…More generally, Y serves as a bottleneck (rate-limiting factor) delaying all downstream variables from adjusting to changes in X (thanks to Shannon Nolen for pointing this out). So dynamic causal models do not just distinguish between faster-and slower-occurring interactions, but also partition sets of variables such that variables within subsets influence one another locally and rapidly, while causal influences across subsets occur more slowly(Weinberger 2020).…”

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confidence: 99%

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Intervening and Letting Go: On the Adequacy of Equilibrium Causal Models

Weinberger

2021

Erkenn

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Causal representations are distinguished from non-causal ones by their ability to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain variable to fix its value, or ‘lets go’ of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument (Dash in Caveats for causal reasoning with equilibrium models. University of Pittsburgh. PhD thesis, 2003) against relying upon causal models inferred from equilibrium data.

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“…The notion of noise, a typical open systems phenomenon, also plays an important role in psychology and cognitive science (see, e.g., Kahneman, Sibony, & Sunstein, 2021). Other examples include network theory (see, e.g., Jackson, 2010), dynamical causal modeling (Weinberger, 2020), complex systems theory (see, e.g., Thurner et al, 2018), and the construal of agents as open systems in artificial intelligence (see, e.g., Briegel & De las Cuevas (2012), Dunjko & Briegel (2018) and Russell & Norvig (2021)).…”

Section: Introductionmentioning

confidence: 99%

The Open Systems View

Cuffaro¹,

Hartmann²

2021

Preprint

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There is a deeply entrenched view in philosophy and physics, the closed systems view, according to which isolated systems are conceived of as fundamental. On this view, when a system is under the influence of its environment this is described in terms of a coupling between it and a separate system which taken together are isolated. We argue against this view, and in favor of the alternative open systems view, for which systems interacting with their environment are conceived of as fundamental, and the environment's influence is represented via the dynamical equations that govern the system's evolution. Taking quantum theories of closed and open systems as our case study, and considering three alternative notions of fundamentality: (i) ontic fundamentality, (ii) epistemic fundamentality, and (iii) explanatory fundamentality, we argue that the open systems view is fundamental, and that this has important implications for the philosophy of physics, the philosophy of science, and for metaphysics.

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Near-Decomposability and the Timescale Relativity of Causal Representations

Cited by 5 publications

References 13 publications

Static-Dynamic Hybridity in Dynamical Models of Cognition

Static-Dynamic Hybridity in Dynamical Models of Cognition

Intervening and Letting Go: On the Adequacy of Equilibrium Causal Models

The Open Systems View

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