Workshop 1C: Student Presentation: Decision-Making Swarms

While swarms that execute decisions are well known in the swarm community, swarms that exhibit this capability a priori have never before been achieved. We demonstrate a methodology, based on the Hamiltonian method of swarm design, that enables the design and implementation of swarms that exhibit decision-making capability. We develop the theoretical structure of the method and apply it to the development of an ant algorithm and a swarm capable of deciding whether its density exceeds a specific predetermined value. The swarm designs are validated in simulation

Report of the President / May 17, 2017

To provide Trustees with a report of activities and efforts of note, I organize the Report of the President, where appropriate, around the Priorities for 2016-2017 (a) Finalize and publish an “IMSA Operational Excellence Dashboard;” (b) Strengthen identity as a learning laboratory as expressed through grand challenges; (c) Develop and nurture my direct reports’ leadership orientation, equity commitment, and systems level management; and (d) Prepare original material suitable for publication as evidence of IMSA thought leadership and the Leadership Profile Components used by the Trustees to evaluate the President’s performance: (1) Institutional Planning (and Executing); (2) Financial/Business Model; (3) Innovations in Teaching and Learning and in STEM Talent Development; (4) Institutional Research and Scholarship on Program Effectiveness and RoI; (5) Thought Leadership in STEM Education Policy; and (6) Stakeholder’s Positive Action(s) on Behalf of IMSA. For additional information and updates on IMSA, please see my Personal Reflections that provide general observations shared with IMSA stakeholders throughout the year

Decision making swarms

While swarms that execute decisions are well known in the swarm community, swarms that exhibit this capability a pri- ori have never before been achieved. We demonstrate a methodology, based on the Hamiltonian method of swarm design, that enables the design and implementation of swarms that exhibit decision-making capability. We develop the the- oretical structure of the method and apply it to the develop- ment of an ant algorithm and a swarm capable of deciding whether its density exceeds a specific predetermined value. The swarm designs are validated in simulation