Predicting Decision-Maker Behavior
Funded by a subcontract from Human Resources Research Organization (HumRRO), the problem was to create and implement a modeling framework for predictions regarding what actions would be taken by well known leaders 6 weeks to 12 months in the future.
Dynamic Decision Networks
Funded by FCS Research through United States Military Academy, our effort developed over 20 DDN models for a variety of Army unmanned vehicles, unattended sensors, and manned activities (e.g., non‐line of sight fire control, dismounting from an armored vehicle) associated with the Army’s Future Combat System.
Funded by the U.S. Navy N81, IDI was tasked with developing a model to assess the deterrent effect of a specific U.S. Military Force on the decision making of a specific adversary in a conventional scenario.
IDI's Research Focus
IDI leads and supports a variety of research studies aimed at understanding how decision-makers think and decide—in groups or as individuals. For example, IDI has assisted research efforts in the field of critical thinking—a new approach to training decision-makers based on dialogue theory. The research focused on how dialogue and critical thinking might be related -- people first learn to think critically by stating views to others, getting response, defending or rethinking. This is internalized as an inner dialogue. Group decision making makes the process external again. So, training in critical thinking dialogue procedures might improve both individual and collaborative process.
Decision Aids and Expert Systems
IDI researches and builds prototype decision aids and models. As an example, IDI staff researched requirements for, and the feasibility of, developing a simulation tool that can be used to improve strategic decision analysis. The result of the project was a concept definition of an improved simulation tool for use by strategic decision-makers that included the use of a multi-criteria decision aid (MCDA), decision trees, and dynamic Bayesian probability updating.
IDI staff also investigated a means to specify sets of inter-related, cross-functional information needs and their satisfaction criteria, and to automatically task and schedule cross-functional operations to best satisfy a specified set of user information needs using Bayesian inferential algorithms.
Access to Additional Publications
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