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Optimized Robust Combinatorial Human Incentive Design Structures (ORCHID)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: N6523622P0023
Agency Tracking Number: D21I-30-0445
Amount: $224,974.31
Phase: Phase I
Program: SBIR
Solicitation Topic Code: HR001121S0007-30
Solicitation Number: HR001121S0007.I
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-31
Award End Date (Contract End Date): 2023-02-08
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138-4555
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Austin Crumpton
 (617) 491-3474
Business Contact
 Mark Felix
Phone: (617) 491-3474
Research Institution

While the DoD has tried to take advantage of collaborative APIs across many programs, parties often make a series of seemingly unimportant decisions that unintentionally lead to the development of customized systems and APIs. As a result, there has been a stream of stove-piped system development that puts the DoD at a disadvantage in rapid evolution, restricting innovation, and making the integration of new capabilities more complex. Many of these current processes result from policies that cannot be easily changed; instead, the DoD needs a system of incentive mechanisms that drives stakeholders away from stove-piping and encourages the use of collaborative APIs. To address these needs, Charles River Analytics proposes to design and demonstrate the feasibility of a platform to explore Optimized Robust Combinatorial Human Incentive Design Structures (ORCHID). ORCHID will draw from the empirical foundations of behavioral economics, mechanism design, and judgment and decision-making science to provide decision-shaping nudges through probabilistic, incentive-structure choice architecture models. To test and optimize these incentive structures to contexts and decision-making bottlenecks, ORCHID will combine a first order simulated model of DoD ecosystems and actors with discrete combinatorial optimization via stochastic search, assessing the most effective incentive configurations through Monte Carlo simulations.

* Information listed above is at the time of submission. *

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