Computational modeling in psychiatry has generally followed from efforts to understand cognitive processes (McClelland and Rumelhart 1986) or the nervous system (Hodgkin and Huxley 1952). This stands to reason: psychiatric disorders are disorders of thought and central nervous system activity. Although there are few contributions to psychiatry from probability theorists and engineers (Shewhart 1938; Miner 1945; Lusser 1958), the tools developed for quality control of metal fatigue and failed rockets may point to a useful approach for thinking about mental illness. This chapter argues that the computational science of collapse, which describes the manner and likelihood of failures in complex systems, provides a framework in which to use computational modeling for relating mechanisms to behavioral outcomes. This science, known as reliability engineering, is a branch of applied probability theory that has now been used for almost a century to help understand and predict how inorganic, complex systems break down. The idea of a fault tree analysis is introduced, a tool developed in reliability engineering which may be able to incorporate and provide a broader structure for more traditional computational models. Finally, Some of the current challenges of psychiatric classification are unpacked, and discussion follows on how this framework might be adapted to provide a unifying framework for classification and etiology.