Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out— they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all the merits of a CBN representation of mechanisms.
Combining causal Bayes nets and cellular automata: A hybrid modelling approach to mechanisms / Gebharter, Alexander; Koch, Daniel. - In: BRITISH JOURNAL FOR THE PHILOSOPHY OF SCIENCE. - ISSN 0007-0882. - 72:3(2021), pp. 839-864. [10.1093/bjps/axy049]
Combining causal Bayes nets and cellular automata: A hybrid modelling approach to mechanisms
Gebharter Alexander
Co-primo
Writing – Original Draft Preparation
;
2021-01-01
Abstract
Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out— they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all the merits of a CBN representation of mechanisms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
