Imperfect commonsense granular determinism

摘要

Occurrences in nature or social or psychological phenomena are mostly causally determined by preceding events or natural laws. Deterministic knowledge is often both imperfect and granular. Causal reasoning perceptions play an essential role in human decision-making. Relationships with a known cause/effect relationship have a high decision value. In many ways, the interest in data mining is the hope of discovering causal, or at least, predictive relationships. Causality is often imprecisely granular. Knowledge of at least some causal elements is inherently imperfect. Causal complexes are groupings of smaller causal relations that make up a large grained causal object. Explorations often use commonsense reasoning. Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many fine-grained events. However, the larger-grained causal objects are necessarily more imprecise. A satisfying approach might be to develop large-grained solutions and then only use the finer-grain when the impreciseness of the large-grain is unsatisfactory. Questions concerning imprecise causal complexes are: To what extent can the causal grain size be increased and still have useful causal information? Conversely, can a large-grained causal event be the starting point and then lead to the development of a finer-grained structure? Can the imprecision in changing grain size be measured and/or controlled? Can causal complexity burdens be reduced?.