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Naturalistic Decision Making (NDM) explains how decisions are made in dynamic, precise and sensitive settings such as Unmanned Aerial Vehicles (UAV) operations, fire events, nuclear power plant processes and put forward that people decide on a diagram arose from past experience before carrying the necessary action. The use of UAVs in the military is mounting due to a diminishing tolerance of societies to losses in human life, advances in technology, augmentation of the effective impact power of defense systems, and advances in information systems, robot, and artificial intelligence technologies. Conversely, the current state of UAV technology places additional demands on the human control pilot’s situational awareness and ability to adapt to dynamic combat environments, because intelligent autonomous systems capable of decreasing human piloting errors are still far away from the status quo. Understanding how decision errors in UAV operations arise in accident situations are vital for realignment of the organizational context to mitigate or eliminate decision errors. This paper reports an analysis of UAV operations within the context of Klein’s (1993) recognition-primed decision-making (RPD) model, proposes a modification to the RPD model to incorporate Crew Resource Management (UAV CRM-RPD), and sets forth a research agenda for future testing and validation of the UAV CRM-RPD model. [ABSTRACT FROM AUTHOR]

All organizations, including government, commercial and others, face numerous challenges in maintaining and upgrading long life-cycle, complex, mission critical systems. Maintaining and upgrading these systems requires the insertion and integration of new technology to avoid obsolescence (both hardware and software), improve performance, maintain and improve security, and extend useful life. Current research and experience show that the process of inserting and integrating new technology into these systems is difficult, expensive, slow, and ineffective. One the issues contributing to the difficulty of technology insertion is the lack of a design body of knowledge. An ontology is the foundation of any body of knowledge. To provide the necessary foundation, research was initiated to develop a core reference systems technology design ontology with technology insertion design as a quantum primitive taxonomic category. The technology insertion design category failed to materialize. To address this failure, the technology insertion design quantum primitive ontology was developed as a hierarchical quantum primitive core reference ontology subsumed with respect to the more general systems technology design ontology. The taxonomic and axiomatic structure of the hierarchical quantum primitive ontology revealed the deficiency in existing systems technology design methodology with respect to technology insertion design. This paper summarizes the development of the hierarchical quantum primitive ontology (taxonomic and axiomatic proofs are omitted), discusses the functional difference in systems technology design with respect to technology insertion design, and recommends improvements in systems technology design to integrate prediction of future technology insertion requirements into initial systems design. [ABSTRACT FROM AUTHOR]

In the early 1990s, most medical records were a hybrid of paper and electronic data deployed on combinations of mainframe and personal computers (Evans, 2016). The complexity and inadequacies of the mixed paper-electronic medical records was the driver behind the Institute of Medicine’s call to shift to a complete electronic medical record (EMR) system (Institute of Medicine, 1997). Advances in computing technology and the Internet during the last two decades has made access to health information online possible. However, as noted by Adler-Milstein (2017), the major technical issue still to be overcome is lack of semantics interoperability. This paper summarizes current approaches to resolving medical terminology differences, and identifies the interoperability errors driving the interoperability problem. The paper then reviews the theory behind quantum ontology primitives, proposes a new method for developing core reference hierarchical quantum ontology primitive trees integrated with ontology learning to mitigate interoperability errors among the primary medical terminologies, and discusses the potential for this method to increase semantics coverages among the medical terminologies. [ABSTRACT FROM AUTHOR]

Increasing levels of automated tasking in networked human-automation systems also produces increasing levels of complex decision-task interactions that are not fully understood and difficult to predict. The inability to fully predict complex human-automation task interactions in the design and subsequent management of these systems have resulted in mission failure and, worse, loss of life in safety-critical systems. This paper presents initial research results into the development of a human-automation decision-task workflow risk analysis method that integrates the identification and modeling of potential failure modes, occurrence distributions, and impacts on the performance and mission accomplishment of networked human-automation systems. [ABSTRACT FROM AUTHOR]

Beginning around 1990, efforts were initiated in the medical profession by the U.S. government to transition from paper based medical records to electronic medical records (EMR). By the late 1990s, EMR implementation had already encountered multiple barriers and failures. Then President Bush set forth the goal of implementing electronic health records (EHRs), nationwide within ten years. Again, progress toward EMR implementation was not realized. President Obama put new emphasis on promoting EMR and health care technology. The renewed emphasis did not overcome many of the original problems and induced new failures. Retrospective analyses suggest that failures were induced because programmers did not consider the medical socio-technical communications structures that had evolved around paper records. Transition to electronic records caused breakdowns in the medical socio-technical communications systems; induced inconsistencies in information exchanges among clinics, physicians, hospitals, laboratories, pharmacies, and health insurance providers; and resulted in the incorrect administration of prescriptions, errors in patient care, and unnecessary treatments and surgeries. With the rapid integration of machine intelligence (MI) in medical socio-technical systems, there is a potential to repeat the failures of EMR implementation. To address the MI integration issue, this paper reports research design into the development of a human-intelligent/machineintelligent (HI-MI) EMR core reference ontology around which EMR-MI knowledge can be encoded to form the basis for informed transition to artificially intelligent electronic medical records. [ABSTRACT FROM AUTHOR]

Inter-rater reliability can be regarded as the degree of agreement among raters on a given item or a circumstance. Multiple approaches have been taken to estimate and improve inter-rater reliability of the United States Department of Defense Human Factors Analysis and Classification System used by trained accident investigators. In this study, three trained instructor pilots used the DoD-HFACS to classify 347 U.S. Air Force Accident Investigation Board (AIB) Class-A reports between the years of 2000 and 2013. The overall method consisted of four steps: (1) train on HFACS definitions, (2) verify rating reliability, (3) rate HFACS reports, and (4) random sample to validate ratings reliability. Attribute agreement analysis was used as the method to assess inter-rater reliability. In the final training verification round, within appraiser agreement ranged 85.28% to 93.25%, each appraiser versus the standard ranged 77.91% to 82.82%, between appraisers 72.39%, and all appraisers versus the standard was 67.48%. Corresponding agreement for the random sample of HFACS rated summaries were within appraiser 78.89% to 92.78% and between appraisers 53.33%, which is consistent with prior studies. This pilot study indicates that the train-verify-rate-validate attribute agreement analysis approach has the potential to aid in improving HFACS ratings reliability and contributing to accurately capturing human factors contributions to aircraft mishaps. Additional full-scale studies will be required to verify and fully develop the proposed methodology. [ABSTRACT FROM AUTHOR]

In the ASEM-IAC 2012, Cotter (2012) identified the gaps in knowledge that statistical engineering needs to address, explored additional gaps in knowledge not addressed in the prior works, and set forth a working definition of and body of knowledge for statistical engineering. In the ASEM-IAC 2015, Cotter (2015) proposed a systemic causal Bayesian hierarchical model that addressed the knowledge gap needed to integrate deterministic mathematical engineering causal models within a stochastic framework. Missing, however, is the framework for specifying the hierarchical qualitative systems structures necessary and sufficient for specifying systemic causal Bayesian hierarchical models. In the ASEM-IAC 2016, Cotter (2016) specified the modeling methodology through which statistical engineering models could be developed, diagnosed, and applied to predict systemic mission performance. In the last research update, Cotter (2017) proposed revisions to and integration of IDEF0 as the framework for developing hierarchical qualitative systems models. In that work, Cotter noted that a hierarchical causal Bayesian socio-technical modeling body of knowledge was yet to be developed, validated, and peer reviewed. This paper reports research into development of a core taxonomy for the systems statistical engineering causal Bayesian socio-technical modeling body of knowledge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community's unstated assumption that "...every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas ..., sixty years later the AI community is not close to coding global human intelligence into AI." (Cotter, 2015). Thus, in complex mission-environment situations it is therefore still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence/machine-intelligence (HI-MI) decision capacity is required. Most important, there has been no research into the governance and management of human-intelligent/machine-intelligent decision processes. To address this gap, research has been initiated into an HIMI decision governance body of knowledge and discipline. This paper updates progress in one track of that research, specifically into establishing the ontological basis of HI-MI decision governance, which will form the theoretical foundation of a systemic HI-MI decision governance body of knowledge. [ABSTRACT FROM AUTHOR]

Driven by a growing requirement during the 21st century for the integration of rigorous statistical analyses in engineering research, there has been a movement within the statistics and quality communities to evolve a unified statistical engineering body of knowledge (Horel and Snee, 2010; Anderson-Cook, 2012). Outside of the 2014 Statistical Engineering Agreement among the ASQ Statistics Division, DOT&E, NASA, and IDA, there has been little formal progress toward this goal since the May 2011 NASA Symposium on Statistical Engineering in Williamsburg Virginia. In the ASEM-IAC 2012, Cotter (2012) identified the gaps in knowledge that statistical engineering needs to address, explored additional gaps in knowledge not addressed in the prior works, and set forth a working definition of and body of knowledge for statistical engineering. Again in the ASEM-IAC 2015, Cotter (2015) proposed a systemic causal Bayesian hierarchical model that addressed the knowledge gap needed to integrate deterministic mathematical engineering causal models within a stochastic framework. Missing, however, is the framework for specifying the hierarchical qualitative systems structures necessary and sufficient for specifying systemic causal Bayesian hierarchical models. This paper proposes revisions to and integration of IDEF0 as the framework for developing hierarchical qualitative systems models. [ABSTRACT FROM AUTHOR]

Flight deck automation has improved the safety and efficiency of commercial aviation, but a broad consensus has developed over the last 30 years that this technology is deficient in some areas. Pioneering research by Edwards, Billings, Wiener, Sarter, Woods, and Degani indicated flight deck automation development has focused on the technology rather than the human element, resulting in multiple cases of pilot-automation interactions adversely impacting decision-making and contributing to loss of life and aircraft. (Air France Flight 447, 2009; Turkish Airlines Flight 1951, 2009; Asiana Flight 214, 2013, and others). More research is needed to address this gap and improve pilot-automation decision governance. Our research seeks to address such questions as: (1) Do pilots trust and rely on current automated cockpit systems? (2) Does current cockpit design provide pilots the sufficient amount of correctly timed feedback to minimize decision risk under time pressure? (3) Do pilots think that automated cockpit systems should conform to Crew Resource Management (CRM) principles? This paper reports survey results of 77 pilots from different carriers and geographical regions and holding various type ratings. The survey was designed to measure pilot perception of cockpit automation across five dimensions: trust in cockpit automation, cockpit automation monitoring practices, effectiveness of cockpit automation interface design, flight automation/company policies, and sufficiency of training in cockpit automation. Line pilot surveys can guide the empirical research for development of improved cockpit automation which better supports decision making in complex and dynamic environments, under time pressure. [ABSTRACT FROM AUTHOR]