Imprecise nested granular complexes

Causal reasoning occupies a central position in human reasoning. Causality is granular in many ways. Knowledge of some causal effects is imprecise. Perhaps, complete knowledge of all possible factors might lead to crisp causal descriptions. However, it is unlikely that all possible factors can be known. Even if the precise elements are unknown, people recognize that a complex of elements can cause an effect. They may not know what events are in the complex; or, what constraints and laws impact the complex. Common sense understanding accepts imprecision, uncertainty and imperfect knowledge and is more successful reasoning with a few large-grain sized events than many fine-grained events. Perhaps, a satisfying solution would be to develop large-grained solutions and only go to an implicitly nested finer-grain when the impreciseness of the large-grain is unsatisfactory. Fuzzy Markov models might be used. It may be more computationally feasible to work on larger-grained representations.