Approximate Causal Abstraction

Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on work of Rubenstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model offers only an approximation of the underlying system. We show how the resulting account handles the discrepancy that can arise between low- and high-level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to probabilistic causal models, indicating how and where uncertainty can enter into an approximate abstraction

Approximate Causal Abstraction

Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on work of Rubenstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model offers only an approximation of the underlying system. We show how the resulting account handles the discrepancy that can arise between low- and high-level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to probabilistic causal models, indicating how and where uncertainty can enter into an approximate abstraction

Unfolding Shape Graphs

Shape graphs have been introduced in [Ren04a, Ren04b] as an abstraction to be used in model checking object oriented software, where states of the system are represented as graphs. Intuitively, the graphs modeling the states represent the structure of objects dynamically allocated in the heap. State transitions are then generated by applying graph transformation rules corresponding to the statements of the program. Since the state space of such systems is potentially unbounded, the graphs representing the states are abstracted by shape graphs. Graph transformation systems may be analyzed [BCK01, BK02] by constructing finite structures that approximate their behaviour with arbitrary accuracy, by using techniques developed in the context of Petri nets. The approach of [BK02] is to construct a chain of finite under-approximations of the Winskel’s style unfolding of a graph grammar, as well as a chain of finite over-approximations of the unfolding, where both chains converge to the full unfolding. The approximations may then be used to check properties of the underlying graph transformation system. We apply this technique to approximate the behaviour of systems represented by shape graphs and graph tranformation rules

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

CONE

Abstraction and its Limits: Finding Space for Novel Explanation.

Several modern accounts of explanation acknowledge the importance of abstraction and idealization for our explanatory practice. However, once we allow a role for abstraction, questions remain. I ask whether the relation between explanations at different theoretical levels should be thought of wholly in terms of abstraction, and argue that changes of variable between theories can lead to novel explanations that are not merely abstractions of some more detailed picture. I use the example of phase transitions as described by statistical mechanics and thermodynamics to illustrate this, and to demonstrate some details of the relationship between abstraction, idealization, and novel explanation

Kinds of Tropes without Kinds

In this article, we propose a new trope nominalist conception of determinate and determinable kinds of quantitative tropes. The conception is developed as follows. First, we formulate a new account of tropes falling under the same determinates and determinables in terms of internal relations of proportion and order. Our account is a considerable improvement on the current standard account (Campbell 1990; Maurin 2002; Simons 2003) because it does not rely on primitive internal relations of exact similarity or quantitative distance. The internal relations of proportion and order hold because the related tropes exist; no kinds of tropes need be assumed here. Second, we argue that there are only pluralities of tropes in relations of proportion and order. The tropes mutually connected by the relations of proportion and order form a special type of plurality, tropes belonging to the same kind. Unlike the recent nominalist accounts, we do not identify kinds of tropes with any further entities (e.g. sets) or abstractions from entities (e.g. pluralities of similar tropes)