Causality and model abstraction Export

@article{citeulike:6075165, abstract = {Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation.}, author = {Iwasaki, Yumi and Simon, Herbert A.}, citeulike-article-id = {6075165}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/0004-3702(94)90014-0}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/0004370294900140}, comment = {The bathtub example is fascinating!}, doi = {10.1016/0004-3702(94)90014-0}, issn = {00043702}, journal = {Artificial Intelligence}, keywords = {ai, causality, computation, model, theory}, month = {May}, number = {1}, pages = {143--194}, posted-at = {2009-11-05 16:33:24}, priority = {2}, title = {Causality and model abstraction}, url = {http://dx.doi.org/10.1016/0004-3702(94)90014-0}, volume = {67}, year = {1994} }

TY - JOUR ID - citeulike:6075165 L3 - citeulike-article-id:6075165 N2 - Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation. IS - 1 JF - Artificial Intelligence SN - 00043702 EP - 194 TI - Causality and model abstraction VL - 67 SP - 143 KW - ai KW - causality KW - computation KW - model KW - theory N1 - The bathtub example is fascinating! AU - Iwasaki, Yumi AU - Simon, Herbert PY - 1994/05// UR - http://dx.doi.org/10.1016/0004-3702(94)90014-0 ER -