Configurable Operator Decision Models for Constructive Test and Evaluation Simulations

SBIR · Phase: BOTH · Topic MDA27BZ02-DV009 · Solicitation 27.BZ

Government problem: Missile defense test and evaluation rely on constructive simulations to examine large numbers of scenarios, explore operational concepts, evaluate system-of-systems interactions, and reduce risk before live flight events. These simulations can represent physical platforms, sensors, weapons, communications, terrain, and threat laydowns, but operator decision effects are often represented by fixed scripts, simplified timing rules, or manually coded behaviors. This can cause analysis results to overstate or understate operational performance because human choices influence detection, classification, engagement, prioritization, communication, and response timing across the kill chain. Capability gap: The Government lacks a reusable and validated way to generate, configure, execute, and assess operator decision representations across many scenario variations and proficiency levels. Current approaches can require extensive custom programming for each model, scenario, or operator role; may not provide traceable rationale for actions taken during simulation; and may be difficult to compare across test events, analysis teams, or simulation environments. The gap affects the Government’s ability to understand sensitivity to operator behavior, evaluate alternate tactics, and support VV&A with documented evidence of intended use, assumptions, limitations, data pedigree, and risk. Why innovative solutions are likely available: The Government believes innovative small businesses can apply advances from artificial intelligence (AI)-enabled simulation, human behavior representation, synthetic data generation, reinforcement learning, design of experiments, digital engineering, development, security, and operations, and modular simulation integration to address this problem. Related industry capabilities already demonstrate AI-assisted constructive simulation, synthetic data generation, intelligent agents, and integration of commercial simulation toolchains with Government simulation environments. This topic seeks to mature those types of innovations into a Government-purpose capability suitable for missile defense test and evaluation workflows. Government interest: The Government is not seeking a vendor white paper, a visualization-only training game, or a single hard-coded operator model. The Government is seeking a Direct-to-Phase-II prototype capability that can be adapted to multiple missile defense analysis contexts; that can use Government-provided scenarios, models, data, and subject matter expert (SME) input; that can be evaluated against explicit measures of performance and effectiveness; and that can produce artifacts needed for future transition, reuse, and accreditation. Desired characteristics: •Open, modular interfaces for integration with constructive simulations and analysis workflows. •Configurable operator roles and behavior profiles that can be reviewed and adjusted by authorized Government analysts or SMEs. •Support for scenario ensembles, design of experiments, and repeatable Monte Carlo-style analysis. •Decision traceability, data provenance, uncertainty characterization, and explainable outputs suitable for analyst review. Responsible AI, cyber, data-management, and VV&A considerations incorporated from the beginning of Phase II.

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification