2013/03/20 by John Mark Agosta, Agosta, John Mark
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Cognitive Science and Mapping #FOS: Computer and information sciences #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.1303.5704
openalex publication_date 2013/03/20 · openalex created_date 2022/10/07 · openalex updated_date 2026/07/28
This paper examines the interdependence generated between two parent nodes\nwith a common instantiated child node, such as two hypotheses sharing common\nevidence. The relation so generated has been termed "intercausal." It is shown\nby construction that inter-causal independence is possible for binary\ndistributions at one state of evidence. For such "CICI" distributions, the two\nmeasures of inter-causal effect, "multiplicative synergy" and "additive\nsynergy" are equal. The well known "noisy-or" model is an example of such a\ndistribution. This introduces novel semantics for the noisy-or, as a model of\nthe degree of conflict among competing hypotheses of a common observation.\n