The Economic Approach To Human Behavior

Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable-though imperfect-clinical decisions or suggestions In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians' use and adoption of AI Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians However, assessing the magnitude and impact of human trust on AI technology demands substantial attention Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use

Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians.

Social media applications have proven to be a dependable communication channel even when traditional methods fail. Their application to emergency management offers new benefits to the domain. For instance, analysis of information as the event unfolds may increase situational awareness, news and alerts may reach larger audiences in less time and decision makers may monitor public activities as well as coordinate with stakeholders. With such benefits, it seems the adoption of social media applications to emergency management should be automatic. However, their implementation introduces risks as well. To better understand the benefits and challenges, a review and analysis of the literature regarding the application of social media to emergency management was conducted. Identified research gaps were mapped into social and technological challenges. These challenges were then analyzed to set research directions for practitioners and researchers.

Social media applications and emergency management: A literature review and research agenda

This paper presents results from a research project on the behavior of complex systems after they experience disruptive events that impact their performance. As systems become more complex, the probability increases that they will exhibit emergent behavior that could lead to system failures or widespread and prolonged service interruptions. A complex adaptive system CAS approach is used to conceptualize a complex network system that has been impacted by disruptions and perturbations. A combination of network analysis and agent-based modeling is used to measure the performance of the system as it responds to disruptive events and restoration efforts. This system-level behavior is an emergent property of the complex network and represents system resilience. Various resilience measures are used to quantify system resilience and assess the effectiveness of strategies system owners employ to restore the system. We illustrate our techniques by characterizing a critical infrastructure system network as a CAS, and applying an agent-based simulation with an adaptive algorithm.

Resilience in Complex Systems: An Agent-Based Approach

: The Future Combat Systems (FCS) program was the largest and most ambitious planned acquisition program in the Army s history. As a program it was intended to field not just a system, but an entire brigade: a system of systems developed from scratch and integrated by means of an advanced, wireless network. Moreover, the FCS-equipped brigade would operate with novel doctrine that was being developed and tested along with the materiel components of the unit. To paraphrase the Army at the time, FCS was Army modernization. In 2009 the FCS program was cancelled, although some of its efforts continued on as follow-on programs. The FCS program had garnered considerable attention throughout its existence, but few studies have been released documenting the lessons from the program to aid the Army in moving forward from such a large acquisition termination. In 2010, the Army s Acquisition Executive asked RAND Arroyo Center to conduct an after-action analysis of the FCS program in order to leverage its successes and learn from its problems.

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Lessons from the Army's Future Combat Systems Program

Systems Engineering (SE) is in the midst of a digital transformation driven by advanced modeling tools, data integration, and resulting “digital twins.” Like many other domains, the engineering disciplines will see transformational advances in the use of artificial intelligence (AI) and machine learning (ML) to automate many routine engineering tasks. At the same time, applying AI, ML, and autonomation to complex and critical systems needs holistic, system-oriented approaches. This will encourage new systems engineering methods, processes, and tools. It is imperative that the SE community deeply understand emerging AI and ML technologies and applications, incorporate them into methods and tools, and ensure that appropriate SE approaches are used to make AI systems ethical, reliable, safe, and secure. This chapter presents a road mapping activity undertaken by the Systems Engineering Research Center (SERC). The goal is to broadly identify opportunities and risks that might appear as this evolution proceeds as well as potentially provide information that guides further research in both SE and AI/ML.

Artificial Intelligence and Future of Systems Engineering

This paper discusses a multi-year program to introduce systems engineering students to social, economic, and policy aspects of complex engineered systems via systems research and master's degree level capstone programs. Within the programs, students are led to view engineered systems as a sociotechnical systems architecture. They use a tool framework that encourages the project participants to conceptualize their system as a response to a larger sociotechnical situation, and build a conceptual model that considers external social, economic, and policy constructs as part of the system design. They then conduct system trades that attempt to balance engineered system performance with external system benefits, particularly improvement of the human communities that use the systems. The paper describes a set of thinking tools used to help the systems engineers conceptualize their solutions within the broader sociotechnical context. We address a gap between systems engineering and systems thinking: tools that connect a system design to more holistic aspects of its value to stakeholders, and competencies that systems engineers can develop through more holistic analysis and design practices.

Training systems engineers to model sociotechnical aspects of complex engineered systems

A human‐focused, holistic model of community resilience

Model‐based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject‐matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model. The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.

Tom McDermott

Papers

Artificial Intelligence and Future of Systems Engineering

Training systems engineers to model sociotechnical aspects of complex engineered systems

A human‐focused, holistic model of community resilience

Towards Developing Metrics to Evaluate Digital Engineering

The Future Exchange of Digital Engineering Data and Models: an Enterprise Systems Analysis