A graph model for probabilities of nested conditionals

Abstract We define a model for computing probabilities of right-nested conditionals in terms graphs representing Markov chains. This is an extension the simple from Wójtowicz and (Erkenntnis, 1–35. 10.1007/s10670-019-00144-z , 2019). The makes it possible to give formal yet description different interpretations compute their mathematically rigorous way. In this study we focus on problem conditionals; do not discuss questions concerning logical metalogical issues such as setting up axiomatic framework, inference rules, defining semantics, proving completeness, soundness etc. Our theory motivated by possible-worlds approach (the direct inspiration Stalnaker Bernoulli models); however, our generally more flexible. paper conditionals, discussing them detail. graph account unified way both shallow deep former being typical spaces, latter McGee’s Kaufmann’s causal models). particular, status Import-Export Principle PCCP. briefly some methodological constraints admissible models analyze with respect them. also illustrates general finding explications philosophically important notions applying mathematical methods analyzing philosophical issues.