But we also apply this Goldilocks heuristic in questionable ways. This presentation focuses on the idea that, in making choices when there are conflicting objectives – which are very common – choosing a middle path often fails to give you the best decision. In fact, sometimes it leads to the worst. We’ll examine an analytic framework that allows us to quantify what “often” and “sometimes” really mean, and we’ll also examine the psychological reasons that drive us to make these poor Goldilocks decisions.
a. 11:15am-12:00pm, Johnson 247 Title: Building a Successful Company – With Mathematicians???
In this talk, Dr. Butler will give an overview of his company,
Daniel H. Wagner,
its historic beginnings in 1963 through its current role today. In 1963, Dr. Dan Wagner
founded his eponymous company with two guiding principles in mind. First, Dan believed in
hiring young mathematicians and training them to solve real-world problems. Second, Dan felt the quality of the writing in the technical reports and briefings provided to the clients was nearly as important as the technical content itself. To this end, and while the company size was small, Dan personally reviewed every scrap of paper that went out under the company name. Through the years, the company developed an impressive reputation for mathematical analysis applied to the budding field of Search Theory (find the lost H-bomb, find the sunken treasure, find the enemy submarine, etc.), and this continues to be an area of expertise today. At the same time, the company demonstrated the breadth of its capabilities by working in areas as diverse as DNA sequencing, retirement planning, crane anti-sway, speech recognition, speaker verification, and random number generation on GPUs.
b. 2:20pm-3:10pm Weyandt Hall Room 32
Title: Decentralized and Autonomous Data Fusion Service (DADFS) for Heterogeneous Unmanned Vehicles (UVs)
In a joint project with Johns Hopkins University Applied Physics Laboratory (JHU/APL), Daniel H. Wagner Associates developed a full-scale prototype Decentralized and Autonomous Data Fusion Service (DADFS) for heterogeneous Unmanned Vehicles (UVs) that obtains contact/track data from real-world UVs and (1) creates a Common Operational Picture (COP) on each UV node using sensor data from all communicating UV nodes and any other available relevant additional data; (2) synchronizes this COP across all UV nodes within the constraints of the available limited and intermittent communications links; and (3) (when human operators are available) provides alerts, requests for assistance, and the relevant COP information to UV operators in an intuitive, easily comprehended manner.
In this talk, we will discuss data fusion and a few of the various mathematical algorithms
required to successfully implement a Data Fusion Service. As a concrete example of the use of DADFS, the Office of Naval Research (ONR) conducted a demonstration of this first-of-its-kind technology, which will allow any unmanned surface vehicle (USV) to not only protect Navy ships, but also, for the first time, “swarm” offensively on hostile vessels.