Systems Statistical Engineering – Systems Hierarchical Constraint Propagation

Cotter (ASEM-IAC 2012, 2015, 2016, 2017): (1) identified the gaps in knowledge that statistical engineering needed to address and set forth a working definition of and body of knowledge for statistical engineering; (2) proposed a systemic causal Bayesian hierarchical model that addressed the knowledge gap needed to integrate deterministic mathematical engineering causal models within a stochastic framework; (3) specified the modeling methodology through which statistical engineering models could be developed, diagnosed, and applied to predict systemic mission performance; and (4) proposed revisions to and integration of IDEF0 as the framework for developing hierarchical qualitative systems models. In the last work, Cotter (2017) noted that a necessary dimension of the systems statistical engineering body of knowledge is hierarchical constraint propagation to assure that imposed environmental economic, legal, political, social, and technical constraints are consistently decomposed to subsystems , modules, and components and that modules, and subsystems socio-technical constraints are mapped to systemic mission performance. This paper presents systems theory, constraint propagation theory, and Bayesian constrained regression theory relevant to the problem of systemic hierarchical constraint propagation and sets forth the theoretical basis for their integration into the systems statistical engineering body of knowledge