Personnel Flow Management Toolbox: A Complex Adaptive Systems Approach

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$70,000.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
N00014-04-M-0166
Agency Tracking Number:
N041-118-1070
Solicitation Year:
2004
Solicitation Topic Code:
N04-118
Solicitation Number:
2004.1
Small Business Information
ORBITAL RESEARCH, INC.
4415 Euclid Avenue, Suite 500, Cleveland, OH, 44103
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
557510336
Principal Investigator:
Mike Kovacina
Software Engineer
(216) 649-0399
kovacina@orbitalresearch.com
Business Contact:
Frederick Lisy
President
(216) 649-0399
lisy@orbitalresearch.com
Research Institution:
n/a
Abstract
Every year, billions of dollars are lost to bad decisions. Without the proper tools to analyze and evaluate decisions before implementation, even more money will be lost. Decision problems that must contend with autonomous entities are the areas most subject to erroneous decisions due to the complexity and cost-prohibitive nature of extended simulation and testing of hypothetical decisions. Advances in the area of Complex Adaptive Systems show that simple abstractions of complex phenomena can produce generalized results applicable to broad classes of problems, thus creating opportunities to analyze complex multi-agent problems without full-fidelity simulation. In order to address the need for a multi-agent discrete event simulation tool, with specific applications to the management of shipboard manpower and personnel, Orbital Research, Inc. proposes the development of a graph-theoretic multi-agent simulation tool that will incorporate analysis tools based on Complex Adaptive Systems theory. This Phase I work will: 1. Develop a generic multi-agent simulation tool based on graph-theoretic abstractions of real-world environments. 2. Investigate the applicability of Complex Adaptive Systems theory to the analysis of multi-agent systems. 3. Perform a proof-of-concept simulation of a real-world multi-agent scenario and correlate the data, showing that the abstracted simulation produces the same overall trends.

* information listed above is at the time of submission.

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