The Computational Methods for Decision Making applied research program is composed of three thrusts — Large Scale Distributed Decision-making, Automated Image Understanding, and Resource Optimization.
Large Scale Distributed Decision-making: The amount of data that decision makers are facing today is much larger than any time before in human history. In addition, the data is much more complex, heterogeneous and fast changing. Analysis of such large and complex datasets is beyond the cognitive abilities of any single decision maker. The aim of this thrust is to develop new methods for extraction and analysis of relevant information from large-scale datasets, and to develop new tools for distributed information sharing and decision-making. To achieve this aim, it is required to advance fundamental understanding of networks (such as social and organizational networks), and to integrate rigorous methods from mathematical and computational sciences with methods from social sciences.
Research Concentration Areas
- Coordination and integration:
- Methods for aggregation of individual preferences, optimal allocation and matching mechanisms
- Methods for collaborative problem solving
- Methods for formalizing information content and communication rules
- Knowledge acquisition, assessment and dissemination:
- Methods for adaptive learning on individual and group levels
- Methods for large-scale assessment and evaluation of distributed information (such as peer reviewing and deliberation)
- Techniques for information representation and dissemination
- Network structure:
- Design of the optimal network structure for a given task complexity
- Analysis of network dynamics
- Structure formation, evolution, and adaptation over time
Research Challenges and Opportunities
- Artificial intelligence (AI) enhanced decision making: Design of new rules for communication and decision–making among large numbers of people using novel AI and machine learning algorithms
- Collective problem solving and decision-making: Exploration of the solution space, and trade-offs between exploitation of known solutions and exploration of new possibilities
- Expertise identification and development:
- Methods for identifying individuals with relevant expertise
- Methods for continuous learning and expertise assessment
- Approaches to automatically match individuals and their expertise, and coordinate their activities
How to Submit
For detailed application and submission information for this research topic, please refer to our broad agency announcement (BAA) No. N00014-22-S-B001.
Contracts: All white papers and full proposals for contracts must be submitted through FedConnect; instructions are included in the BAA.