Tom McDermott

Bio: Tom McDermott is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Systems thinking & Sociotechnical system. The author has an hindex of 5, co-authored 48 publications receiving 135 citations. Previous affiliations of Tom McDermott include Georgia Institute of Technology & Georgia Tech Research Institute.

Use of systemigrams to identify emergence in complex adaptive systems

Abstract: This paper presents the application of a sociotechnical modeling framework to capture the wider global system impacts of the U.S. shale gas boom in a context of the larger global energy marketplace. The global energy markets represent a complex adaptive system. Existing models are not sufficient to reflect the complexity of changes presently occurring in the system, and fail to trace interactions across a wide enough system of interest. This project introduces conceptual models of flow and interaction in the global energy marketplace in order to inform the design of analytical models that identify more complex behaviors of the system. This is an example of a set of emerging methods and tools for the design of complex systems and represents an important first stage in a research program to integrate complexity analysis tools with traditional system design tools.

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

Abstract: 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

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

Abstract: 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.

Resilience in Complex Systems: An Agent-Based Approach

Abstract: 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.

Lessons from the Army's Future Combat Systems Program

Abstract: : 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.