Collaborative Systems Thinking Research: Exploring Systems Thinking within Teams

This paper describes ongoing research that seeks to develop an empirical basis for collaborative systems thinking, defined as “an emergent behavior of teams resulting from the interactions of team members and utilizing a variety of thinking styles, design processes, tools, and communication media to consider system attributes, interrelationships, context and dynamics towards executing systems design”. This type of thinking is critically important to addressing engineering systems challenges, and the research seeks to inform and enable effective systems engineering practice in contemporary engineering enterprises. Focusing on the aerospace domain, collaborative systems thinking is examined through the alignment of enterprise culture and standard technical processes. This paper draws on a variety of literature to compose a definition of collaborative systems thinking and propose a research agenda going forward

Investigating the readability of formal specification languages

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (leaves 90-92).by Marc Kenton Zimmerman.S.M

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments

Exploration of the mechanisms enabling team systems thinking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 197-214).Aerospace systems are among the most complex anthropogenic systems and require large quantities of systems knowledge to design successfully. Within the aerospace industry, an aging workforce places those with the most systems experience near retirement at a time when fewer new programs exist to provide systems experience to the incoming generation of aerospace engineers and leaders. The resulting population will be a set of individuals who by themselves may lack sufficient systems knowledge. It is therefore important to look at teams of aerospace engineers as a new unit of systems knowledge and thinking. By understanding more about how teams engage in collaborative systems thinking (CST), organizations can better determine which types of training and intervention will lead to greater exchanges of systems-level knowledge within teams. Following a broad literature search, the constructs of team traits, technical process, and culture were identified as important for exploring CST. Using the literature and a set of 8 pilot interviews as guidance, 26 case studies (10 full and 16 abbreviated) were conducted to gather empirical data on CST enablers and barriers. These case studies incorporated data from 94 surveys and 65 interviews. From these data, a regression model was developed to identify the five strongest predictors of CST and facilitate validation. Eight additional abbreviated case studies were used to test the model and demonstrate the results are generalizable beyond the initial sample set. To summarize the results, CST teams are differentiable from non-CST teams.(cont.) Among the most prevalent differentiators is a team's self-reported balance between individual and consensus decision making. Teams that engage in consensus decision making reported stronger engagement in collaborative systems thinking. Another differentiator is the median number of past program experiences on a team. Teams whose members reported more past similar program experiences also reported more engagement in collaborative systems thinking. Data show the number of past similar programs worked is a better predictor than years of industry experience. The apparent enabling effects of qualitative team traits are also discussed. The conclusions of this document propose ways in which these findings may be used to improve training and team intervention within industry, academia, and government.by Caroline Marie Twomey Lamb.Ph.D