Opportunity Information: Apply for BRO 20 SOMAII
The Department of Defense grant opportunity titled "Award for Fundamental Research in Socio-Mathematics of Information and Influence" (Funding Opportunity Number BRO 20 SOMAII) supports basic, exploratory research aimed at understanding and countering modern disinformation and influence operations. The program frames information as a current and growing domain of warfare, where adversaries do not just attack systems or platforms, but instead manipulate content and narratives to trigger specific psychological and social reactions. These campaigns often leverage social media at scale, using increasingly sophisticated automated bots along with coordinated human operators who can steer bot networks, adapt tactics quickly, and refine messaging strategies. The central concern is that in high-uncertainty information environments, open societies can lose trust and cohesion, creating instability that adversaries may seek to exploit. While existing forensic and network-science methods can often reveal manipulation after the fact, the solicitation argues that this is not enough, because delayed detection limits the ability to respond effectively.
The core objective is to build a deeper theoretical foundation for future information warfare so that manipulation attempts can be detected, tracked, and predicted quickly, including in real time and under adaptive, adversarial conditions. The DoD is explicitly looking for work that goes beyond applying current analytics or machine learning tools to static datasets. Instead, it calls for the creation of new mathematical foundations that can represent and predict human social behavior across multiple scales (from individuals to groups to nations) in complex, dynamic, multilayer environments. A major theme is integration: the program wants mathematics and the social sciences to be merged more tightly than is typical today, so that models of networks and agents reflect realistic cognitive, emotional, and social processes rather than assuming fully rational behavior. This matters because influence operations often succeed by exploiting non-rational human responses, which increases uncertainty and makes prediction harder if models ignore psychology and sociology.
On the technical side, the opportunity highlights shortcomings of existing methods such as standard machine learning and other data-analytic approaches (with examples like topological data analysis and manifold reconstruction). These methods can be powerful for pattern-finding, but the program emphasizes that they are not designed for opponents who actively adapt, change strategies, and effectively "move the goalposts" during detection. It also stresses that the underlying networks themselves are complicated and evolving: influence spreads across intertwined cyber, media, and social layers, and the scaling properties of real-world networks may be poorly understood or changing over time as technology and social conditions shift. The solicitation positions these as fundamental open problems where incremental improvements are not enough.
On the social-science side, the program is concerned with how increasingly fast, personalized, and high-volume information exposure (including the emergence of 5G and beyond) could change social trust, learning, communication, and group dynamics. It raises questions about whether faster and more customizable manipulation creates new, subtler pathways to shape opinions, whether those pathways can be detected, and who becomes an agent of manipulation (intentionally or unintentionally) in such systems. The program expects proposals to treat these as first-class modeling concerns, not as afterthoughts, and to ultimately unify social and mathematical perspectives into coherent, multi-scale frameworks.
The solicitation lists several example research directions, while making clear that the list is not exhaustive and that novelty and rigorous underpinnings are heavily weighted. One area is developing careful mathematical abstractions grounded in behavioral science to represent psychological and social variables such as emotion, cognition, intent, belief, and group dynamics, including multi-scale clustering methods that connect individual behavior to larger aggregates. Another is applying or extending game theory and machine learning, such as multi-agent reinforcement learning or distributional reinforcement learning, with an emphasis on modeling mixed populations that include both rational and irrational human agents as well as artificial agents like bots. A third area is designing efficient algorithms to infer malicious intent, learn objectives, and characterize behavior from limited, noisy observations, reflecting the reality that defenders rarely have complete information. A fourth area is strategy optimization or "inverse design" for defenses, which can include counter-messaging or network interventions, with the broader idea of actively shaping conditions to reduce influence effectiveness rather than only labeling content after it spreads.
A particularly encouraged theme is a generative, co-evolutionary approach that models threats and defenses as adapting against each other over time. The program is looking for conceptual frameworks that can help anticipate future threat scenarios and evaluate defensive postures before harm occurs, rather than only explaining what happened afterward. Proposals are expected to be exploratory and can use publicly available data, synthetic datasets, or real data that performers can obtain on their own. Importantly, applicants are told not to depend on the government providing non-public data that does not already exist in public form.
Administratively, the opportunity is a mandatory DoD grant in the science and technology research and development category (CFDA 12.630). Eligible applicants include public and state-controlled institutions of higher education and private institutions of higher education. The award ceiling is listed as $3,000,000, with an expectation of making 2 awards. The original posting date is July 16, 2020, with an original closing date of August 28, 2020.Apply for BRO 20 SOMAII
- The Department of Defense in the science and technology and other research and development sector is offering a public funding opportunity titled "Award for Fundamental Research in Socio-Mathematics of Information and Influence" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.630.
- This funding opportunity was created on Jul 16, 2020.
- Applicants must submit their applications by Aug 28, 2020. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $3,000,000.00 in funding.
- The number of recipients for this funding is limited to 2 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education, Private institutions of higher education.
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Frequently Asked Questions (FAQs)
What is the title of this Department of Defense grant opportunity?
The opportunity is titled "Award for Fundamental Research in Socio-Mathematics of Information and Influence."
What is the Funding Opportunity Number (FON) for this program?
The Funding Opportunity Number is BRO 20 SOMAII.
What is the program trying to accomplish overall?
The program supports basic, exploratory research aimed at building a deeper theoretical foundation to understand and counter modern disinformation and influence operations. The goal is to enable faster detection, tracking, and prediction of manipulation attempts, including in real time and under adaptive, adversarial conditions.
How does the solicitation frame the problem of disinformation and influence operations?
It frames information as a current and growing domain of warfare where adversaries manipulate content and narratives to trigger psychological and social reactions. These campaigns can involve social media at scale, automated bot networks, and coordinated human operators who steer and adapt tactics quickly.
Why does the program say after-the-fact detection is not enough?
The solicitation argues that while forensic and network-science methods can often reveal manipulation after it happens, delayed detection limits the ability to respond effectively. The program emphasizes the need for approaches that can operate quickly, potentially in real time, even as adversaries adapt.
What kind of research does the DoD want to fund under this program?
The DoD is looking for fundamental research that goes beyond applying existing analytics or machine learning tools to static datasets. It calls for new mathematical foundations that can represent and predict human social behavior across multiple scales in complex, dynamic, multilayer environments.
What does "socio-mathematics" mean in the context of this opportunity?
In this program, socio-mathematics refers to tightly integrating mathematics with the social sciences to create models that reflect realistic cognitive, emotional, and social processes, rather than relying on purely rational-agent assumptions.
Why is integration of mathematics and social science a major theme?
The solicitation emphasizes that influence operations often succeed by exploiting non-rational human responses. If models ignore psychology and sociology, uncertainty grows and prediction becomes harder, so proposals are expected to unify social and mathematical perspectives into coherent multi-scale frameworks.
What limitations of existing technical approaches does the solicitation highlight?
It highlights that standard machine learning and other data-analytic approaches (including examples like topological data analysis and manifold reconstruction) may be powerful for pattern-finding but are not designed for opponents who actively adapt and change strategies during detection.
What makes the underlying networks challenging in this problem space?
The program notes that influence spreads across intertwined cyber, media, and social layers, and that the networks themselves can be complicated and evolving. It also suggests that scaling properties of real-world networks may be poorly understood or may change over time as technology and social conditions shift.
What social-science concerns are explicitly called out?
The solicitation points to increasingly fast, personalized, and high-volume information exposure (including the emergence of 5G and beyond) and asks how this could change social trust, learning, communication, and group dynamics, as well as whether faster customization creates new and subtler pathways for manipulation.
Does the program care about who becomes an agent of manipulation?
Yes. It raises questions about who becomes an agent of manipulation in these systems, intentionally or unintentionally, and expects proposals to treat such dynamics as first-class modeling concerns rather than afterthoughts.
What are some example research directions mentioned in the solicitation?
The solicitation provides examples (not an exhaustive list), including: (1) developing mathematical abstractions grounded in behavioral science for variables like emotion, cognition, intent, belief, and group dynamics; (2) multi-scale clustering methods connecting individuals to larger aggregates; (3) extending game theory and machine learning approaches such as multi-agent reinforcement learning or distributional reinforcement learning to mixed populations of rational and irrational humans plus artificial agents (bots); (4) efficient algorithms to infer malicious intent and objectives from limited, noisy observations; and (5) strategy optimization or inverse design for defenses, including counter-messaging or network interventions.
Is the list of research topics exhaustive?
No. The solicitation explicitly states the list is not exhaustive and signals that novelty and rigorous underpinnings are heavily weighted.
What does the program mean by modeling across multiple scales?
It refers to modeling human social behavior from individuals to groups to nations, and connecting micro-level psychological and behavioral variables to higher-level aggregate outcomes in complex environments.
What does the solicitation mean by "mixed populations" of agents?
It refers to environments where both rational and irrational human agents interact alongside artificial agents such as bots, with influence operations emerging from the combined behavior of these different agent types.
Why does the program emphasize limited and noisy observations?
It reflects the practical reality that defenders rarely have complete information, so the solicitation encourages algorithms and theories that can infer intent, objectives, and behavioral patterns from partial, uncertain, and noisy data.
What is meant by "inverse design" or strategy optimization for defenses?
It refers to actively designing defensive strategies (such as counter-messaging or network interventions) that shape conditions to reduce the effectiveness of influence operations, rather than only labeling content after it has already spread.
What is the "generative, co-evolutionary approach" the program encourages?
It is an approach that models threats and defenses as adapting against each other over time, with the aim of anticipating future threat scenarios and evaluating defensive postures before harm occurs.
Does the program prefer predictive and real-time capabilities?
Yes. A central objective is to enable manipulation attempts to be detected, tracked, and predicted quickly, including in real time and in the presence of adaptive adversaries.
What types of data can proposals use?
The solicitation indicates proposals can use publicly available data, synthetic datasets, or real data that performers can obtain on their own.
Can applicants rely on the government to provide non-public datasets?
No. Applicants are told not to depend on the government providing non-public data that does not already exist in public form.
What is the administrative category and CFDA listing for this opportunity?
It is a mandatory DoD grant in the science and technology research and development category, listed under CFDA 12.630.
Who is eligible to apply?
Eligible applicants include public and state-controlled institutions of higher education and private institutions of higher education.
What is the maximum award amount (award ceiling)?
The award ceiling is listed as $3,000,000.
How many awards does the DoD expect to make?
The opportunity states an expectation of making 2 awards.
When was the opportunity originally posted?
The original posting date is July 16, 2020.
What was the original closing date?
The original closing date is August 28, 2020.
What is the key differentiator the DoD is looking for compared to typical analytics projects?
The solicitation emphasizes new, rigorous mathematical foundations integrated with social-science realism, designed for dynamic, multilayer systems and adaptive adversaries, rather than static-dataset analysis or incremental improvements to existing methods.
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