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This paper proposes a complete set of systems thinking skills for use across many different disciplines. The paper places particular emphasis on the ability to assess each of the skills quantitatively, a comprehensible description of the skills, and the completeness of the set. The proposed skills were derived from a review of the literature, the application of systems thinking experience, and the application of systems thinking to itself. Several different sets of systems thinking skills can be found throughout the systems community, but common key concepts can be distilled from these sets. When combinations of these concepts are considered separately, holistically, and together as a system, a single, cohesive set of skills emerges. Systems thinking is widely believed to be of critical importance across many different fields; it has been said that skillful use of systems thinking skills could have prevented such disasters as World War II, the Great Depression, and the Challenger space shuttle disaster, as well as lessened or avoided the effects of many major environmental disasters. At the opposite send of the spectrum, systems thinking can be used to enhance health care, improve the economy, improve technology, laws, international and interpersonal relationships, schools, organizations, and so much more. However, this very useful skill set still lingers outside mainstream education. To address this problem, a set of assessable, comprehensible systems thinking skills is required. Such skills are defined, described, and detailed in this paper.

This paper proposes a complete set of systems thinking skills for use across many different disciplines. The paper places particular emphasis on the ability to assess each of the skills quantitatively, a comprehensible description of the skills, and the completeness of the set. The proposed skills derive from a review of the literature, the application of systems thinking experience, and the application of systems thinking to itself. Several different sets of systems thinking skills exist throughout the systems community, but common key concepts distill from these sets. When we consider combinations of these concepts separately, holistically, and together as a system, a single, cohesive set of skills emerges. Systems thinking is widely believed to be of critical importance across many different fields; some say that skillful use of systems thinking skills could have prevented such disasters as World War II, the Great Depression, and the Challenger space shuttle disaster, as well as lessened or avoided the effects of many major environmental disasters. At the opposite send of the spectrum, systems thinking can enhance health care, improve the economy, improve technology, laws, international and interpersonal relationships, schools, organizations, and so much more. However, this very useful skill set still lingers outside mainstream education. To address this problem requires a set of assessable, comprehensible systems thinking skills. This paper defines, describes, and details such skills.

A swarm of autonomous flying robots is implemented in simulation to cooperatively gather situational awareness data during the first few hours after a major natural disaster. In computer simulations, the swarm is successful in locating over 90% of survivors in less than an hour. The swarm is controlled by new sets of reactive behaviors which are presented and evaluated. The reactive behaviors integrate collision avoidance, battery recharge, formation control, altitude maintenance, and a variety of search methods to optimize the coverage area of camera and heartbeat locator sensors mounted on the robots. The behaviors are implemented in simulation on swarms of sizes from 1 to 20 robots. The simulation uses actual location data, including post-disaster satellite imagery, real locations of damaged and inundated buildings, and realistic victim locations based on personal interviews and accounts. The results demonstrate the value of using behavior-based swarming algorithms to control autonomous unmanned aerial vehicles for post-disaster search and assessment. Three examples of algorithms that have been effective in simulation are presented.