AFOSR - Information and Networks

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The Information and Networks Team within the Engineering and Information Science Branch is organized to support many U.S. Air Force priority areas including autonomy, space situational awareness, and cyber security. The research programs within this team lead the discovery and development of foundational issues in mathematical, information and network oriented sciences. They are organized along three themes: Information, Decision Making, and Networks.

The information theme addresses the critical challenges faced by the U.S. Air Force which lie at the intersection of the ability to collect, mathematically analyze, and disseminate large quantities of information in a time critical fashion with assurances of operation and security.

Closely aligned with the mathematical analysis of information is the need for autonomous decision making. Research in this theme focuses on the discovery of mathematical laws, foundational scientific principles, and new, reliable and robust algorithms, which underlie intelligent, mixed human-machine decision-making to achieve accurate real- time projection of expertise and knowledge into and out of the battle space.

Information analysis and decision making rarely occur in the context of a single source. The networks theme addresses critical issues involving how the organization and interaction among large collections of information providers and consumers contributes to an understanding of the dynamics of complex information systems.

The Information and Networks (AFOSR/RTA2) Program Officers and topics are: 

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Computational Cognition and Machine Intelligence

Program Description: This program supports innovative basic research on the fundamental principles and methodologies needed to enable intelligent machine behavior, particularly in support of mixed-initiative (i.e., human-machine teaming) systems. The overall vision of this program is that future computational systems will achieve high levels of performance, adaptation, flexibility, self-repair, and other forms of intelligent behavior in the complex, uncertain, adversarial, and highly dynamic environments faced by the U.S. Air Force. This program covers the full spectrum of computational and machine intelligence, from cognitively plausible reasoning processes that are responsible for human performance in complex problem-solving and decision-making tasks, to non-cognitive computational models of intelligence necessary to create robust intelligent systems. Robustness in this context is the ability to achieve high performance given at least some or all of the following factors: uncertainty, incompleteness or errors in knowledge; limitations on sensing; real-world complexity and dynamic change; adversarial factors; unexpected events including system faults; and out-of-scope requirements on system behavior. In the midst of this spectrum are the technologies explicitly needed to seamlessly incorporate intelligent computational systems into mixed human-machine teams. The program is divided into three sub-areas that span the full spectrum of computational and machine intelligence. They are: Computational Cognition, Human-Machine Teaming and Machine Intelligence.

The program encourages cross-disciplinary teams with collaboration including computer scientists, neuroscientists, cognitive scientists, mathematicians, statisticians, operation and management science researchers, information scientists, econometricians and game theoreticians, etc., especially when the research pertains to common issues and when collaboration is likely to generate bidirectional benefits. This program is aggressive, accepts risk, and seeks to be a pathfinder for U.S. Air Force research in this area. Proposals that may lead to breakthroughs or highly disruptive results are especially encouraged.

Basic Research Objectives: The Computational Cognition sub-area supports innovative basic research on high-order cognitive processes that are responsible for good human performance in complex problem solving and decision-making tasks – we only want to model the things people excel at. The sub-area also seeks to support research on building computational systems that derive from and/or integrate cognitive and biological models of human and animal intelligence. The overall objective is to understand and exploit these processes to create computational models that perform as well as or better than the reasoning systems they emulate. This sub- area seeks basic research that pertains to exploiting the capabilities of the mind and brain (human or animal) for creating more intelligent machines, as well as cognitively plausible mechanisms inspired by human (or animal) reasoning. This includes computational models based on human and animal performance in perception, attention, memory, learning, reasoning, and decision making in order to improve machine performance.

This sub-area does NOT, however, support statistical approaches to machine learning (e.g., “Deep Learning”), or related variants, as fundamental science in that area is addressed by the Science of Information, Computation, Fusion and Learning program described elsewhere in this BAA.

The Machine Intelligence sub-area supports innovative basic research on fundamental principles and methodologies of computational intelligence necessary to create robust intelligent systems. These methodologies may be cognitively inspired, or non- cognitive in nature, taking full advantage of the strengths embodied in mathematical and computational systems, such as the ability to reason with complex formal logic.

This sub-area encourages research enabling the creation of computational systems that embody intelligent behavior based on cognitively inspired or purely mathematical approaches. Proposals that lead to advances in the basic principles of machine intelligence for memory, reasoning, planning, scheduling, and cognitively-inspired learning (i.e., NOT “Deep Learning” or other statistical means), action, and communication are desired insofar as these contribute directly towards robustness as defined above.

The Human-Machine Teaming sub-area is primarily concerned with the machine-side of mixed human-machine decision-making, which appears at all levels of U.S. Air Force operations and pervades every stage of U.S. Air Force missions. To that end, new theoretical and empirical guidance is needed to prescribe maximally effective mixtures of human and machine decision making in environments that are becoming increasingly complex and demanding as a result of the high uncertainty, complexity, time urgency, and rapidly changing nature of military missions. This sub-area seeks new empirical and theoretical basic research that enables intelligent machines to perform as true “teammates,” adapting their behavior to accommodate changes in the environment, as well as augmenting the performance of human teammates when needed. This includes basic science in collaborative human-machine teams to aid the machine-side of inference, analysis, prediction, planning, scheduling, and decision making.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.



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Computational Mathematics

Program Description: This program seeks to develop innovative mathematical methods and fast, reliable and scalable algorithms aimed at making radical advances in computational science and large-scale engineering and design. Research in computational mathematics underpins the fundamental understanding of complex physical phenomena and leads to predictive simulation capabilities that are crucial to the design and control of future U.S. Air Force systems, and their lifetime expectancy. Proposals to this program should focus on fundamental scientific and mathematical innovations, and should have the potential to address some of the most important computational challenges in science and engineering. Additionally, it is desirable to frame the basic research ideas in the context of applications relevant to the U.S. Air Force, which can serve simultaneously to focus the research and to provide avenues for transition of basic research outcomes into practice. Applications of current Air Force interest include, but are not limited to, quantum physics and quantum information systems, plasma dynamics, turbulence (e.g., in fluids, combustion, plasma), lasers and directed energy, aero-thermo-dynamics, information science, data analysis (including machine learning), biophysics, and material and structural sciences.

Basic Research Objectives: Research under this program has traditionally emphasized schemes that address the discretization and numerical solution of complex systems of equations, generally partial differential equations derived from physical models. However, alternative computational approaches are of keen interest, particularly in connection with emerging and multidisciplinary applications.

Increased emphasis in this portfolio is placed on approaches that can handle a very high number of dimensions, uncertainty and stochasticity for non-Markovian processes, far from equilibrium conditions, and/or a wide range of scales (space, time, physical parameters, or complexity). Research areas of particular interest currently include:

  • Innovative methods for quantum many-body physics, especially strongly correlated systems and environmental interactions; of special interest are approaches based on concepts derived from high-energy physics, and the exploration of relationships with information processing by neural networks.
  • Mathematical methods for complexity reduction of high-dimensional, non- linear and multiscale problems, e.g., via projection-based methods and/or new machine-learning concepts. Such systems may have continuous, discrete or mixed representations, and may reside on graphs with evolving topology.
  • Mathematical approaches to the modeling of non-equilibrium statistical processes and turbulent dynamics with multiple physical interactions and large parameter spaces; of special interest are methods which effectively allow bi-directional transfer of information across scales, and can simultaneously reduce the computational burden while preserving the correct physics of interaction, including conservation laws and instability regimes.
  • Highly efficient and accurate methods for high-dimensional, nonlinear and stochastic dynamics with constraints. In particular, we are seeking revolutionary approaches to solving Hamilton-Jacobi-Bellman equations, optimal transport problems, and inverse problems for highly complex conditions. Of particular interest are applications in large-scale game theory, self-organized criticality and cascades, and the prediction of rare and extreme events.
  • Traditional computational methods involving high-order spatial and temporal algorithms remain of interest, if they have the potential for significant breakthrough and are able to meet the formidable computational challenges associated with current and future engineering problems of interest to the U.S. Air Force.

The list above is not exhaustive and other approaches can be suggested to the Program Officer, who can then determine if a proposal is warranted and of potential interest. All proposed methods must be innovative, have quantifiable measures of fidelity, efficiency and adaptively, must be based on rigorous analysis and preferably demonstrated on canonical challenge and grand challenge problems.

You are encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Dynamics and Control

Program Description: This program emphasizes the interplay of dynamical systems and control theories with the future aim of developing innovative synergistic strategies for the design and analysis of controlled systems that enable radically enhanced capabilities, including performance and operational efficiency for future U.S. Air Force systems. Proposals should focus on the fundamental science and mathematics first, but should also include possible connectivity to appropriate Air Force applications of the future. These applications currently include information systems, as well as autonomous/semi-autonomous aerial vehicles, munitions, and space vehicles.

The dramatic increase in complexity of Air Force systems provides unique challenges for the Dynamics and Control Program. Meeting these challenges may require interdisciplinary approaches as well as deeper studies within single disciplines.

Lastly, note that the Dynamics and Control Program places special emphasis on mathematically rigorous techniques addressing realistic treatment of applications, complexity management, semi-autonomous systems, and real-time operation in stochastic and adversarial environments.

Basic Research Objectives: Current research interests include: methods of dynamical analysis of complex systems for the purpose of real-time control, control of ensemble and infinite dimensional systems, deterministic time and/or real-time reachability set calculation and verification and validation of hybrid systems, distributed and decentralized decision making and control for coordinated autonomous/semi- autonomous aerospace vehicles considering constraints, uncertain, information rich, dynamically changing, networked environments with time-varying topologies; understanding how to optimally account for humans in the design space; novel schemes that enable challenging multi-agent aerospace tracking in complex, cluttered scenarios; robust and adaptive non-equilibrium (e.g., set-based) control of nonlinear processes where the primary objective is enhanced operability rather than just local stability; new methods for understanding and mitigating the effects of uncertainties in dynamical processes where uncertainty distribution is non-Gaussian; novel theory for control of hybrid systems that can intelligently manage actuator, sensor, and processor communications in a complex, spatially distributed and evolving system of systems; sensor rich, data driven adaptive control; and applying control concepts motivated by studies of biological systems. In general, interest in the control of large complex, multi- scale, hybrid, highly uncertain nonlinear systems is increasing. Further, new mathematics in clear support of dynamics and control is of fundamental importance.

In this regard, some areas of interest include, but are not limited to, hybrid dynamical systems theory, geometric and algebraic methods of dynamics and control, stochastic and adversarial systems, control of cyber physical systems, emerging areas of control theory, graph theoretic control theory over nonlinear dynamics, partial and corrupted information, max-plus and idempotent methods, nonlinear control and estimation, and novel computational techniques specifically aimed at control of systems with large data.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Dynamic Data Driven Applications Systems (DDDAS)

Program Description: The DDDAS concept entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability for an application to dynamically steer the measurement components. Key developments are sought to improve the modeling of systems under dynamic conditions, achieve effective instrumentation management, and architect control of dynamic and heterogeneous resources, including networks of models, sensors, and embedded resources. DDDAS encourages multidisciplinary research, especially synergistic and systematic collaborations between domain researchers in mathematics and statistics, computer sciences, and the design and implementation of measurement and control systems for modeling, diagnostics, and analytics.

Basic Research Objectives: Foster individual and multidisciplinary research, technology development, and system analysis over emerging science and technology frontiers.

Domain modeling: Methods are sought to leverage large-scale simulations for real- time control, in concert with heterogeneous data collection, model updates, and system processing. Research advances should describe different levels of detail and modalities, invoke appropriate models, and include interfaces of applications to measurements and other data systems. Solutions will, for example, engender an integration of large scale simulations, models, and data to advance traditional controls paradigms.

Mathematical and Statistical Algorithms: Design methods for stable and robust convergence properties under perturbations induced by time-dependent (periodic and non- periodic, scheduled and event-driven) data inputs, multiple scales and model variations.

Address enhanced asynchronous algorithms with stable communication between networked resources, multimodal modeling, and uncertainty quantification. Solutions will, for example, dynamically invoke models requiring elegant methods of uncertainty quantification, management, and propagation.

Measurement Systems and Methods: Innovate instrumentation platforms for collecting data, registering measurements, controlling sampling rates, and multiplexing multisource information. Solutions will, for example, determine heterogeneous and embedded distributed sensor networks architectures, information fusion paradigms, and operationally robust performance.

Computational Hardware/Software Methods: Design platforms that provide runtime support coupling data workflows, architecture processing, and systems-oriented execution. Solutions will, for example, address computational capabilities within envisioned hardware/software advances to direct realizable and tractable systems for real-world performance.

Areas of interest to the AF and which can benefit from DDDAS advances, include areas such as: (a) autonomy (e.g., leveraging large-scale modeling of mission planning, collaborative/cooperative control, and data learning for data analytics); (b) agility (e.g., designing computational methods of sensor-based processing, ad-hoc network configurations, and multi-scale multi-physics simulations for decision support); (c) authority (e.g., coupling high-performance aircraft health monitoring, space situational awareness, and ground operations for command and control); and (d) robustness (e.g., understanding materials stresses and degradation; embedded diagnostics, complex adaptive systems verification and validation, and cognitive performance augmentation for situational understanding).

DDDAS seeks new approaches for combining computational, theoretical, and analytical methods for interactive testing of multiple scientific and engineering hypotheses. Programmatic activities that will be launched under this initiative will support research in individual areas, but mostly in the context of multidisciplinary research across the Basic Area Objectives mentioned above.

You are highly encouraged to contact the Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Information Assurance and Cybersecurity

Program Description: Securing cyberspace, i.e. defending against and preventing cyber-attacks are not new challenges but these have become increasingly pressing in the light of technological advancements. Software and protocols are continuously becoming more complex to meet application demands. More flexible computing environments, such as distributed systems, demand new ways of thinking how to ensure secure end-to-end functionalities, even though components are only known to be individually secure. The emergence of nanoscale devices and quantum information processing and communication also portends new technological challenges for cybersecurity. By the same token, these new technologies potentially offer unparalleled security solutions to the existing or future problems.

Although engineering practices continue to provide short-term and temporary relieves to these pressing needs, new scientific ideas are required to address the lack of security and the explosive growth of hostile actions in cyberspace, especially taking into account of emerging technologies. Many fundamental concepts are still eluding precise formulation and awaiting rigorous responses. The goal of this Basic Research program is to explore novel, promising concepts and methodologies that can establish a firm scientific foundation for cybersecurity and potentially tackle the difficult technical hurdles described above.

Basic Research Objectives: Recent developments and advances in the following research areas of computer science and mathematics are expected to provide valuable insights into various cybersecurity problems: dependent type theory, cryptographic protocols for interactive computation and communication, interactive and automated theorem proving, language-based techniques in software and hardware for formal specification and verification, secure protocols, game theory with strong security content, obfuscation and fully homomorphic encryption, model categories, formalized mathematics. Broadly speaking, cross-fertilization of mathematical formalisms and logical constructs will likely continue to play a central role in the construction and verification of security invariants, and in the study of security models or security principles.

These scientific advances are expected to contribute fresh ideas to a number of fundamental cybersecurity topics: composition of security properties and protocols in distributed interactive systems without the need of trusted third parties; rigorous techniques to enable persistent and secure operations on unsecure or untrusted systems; information flow security and non-interference in dynamic and distributed settings; new security invariants that can readily be computed and interpreted, especially for systems endowed with rich geometric dynamics; rigorous proofs and construction of obfuscation techniques for programs and circuits to enhance security; formal verification and certification of the correctness of complex large-scale mathematical proofs and critical computer systems.

Aside from software and secure protocols, nanoscale material properties and quantum effects should offer added security capabilities for future computing devices that cannot be realized by today’s technologies. They potentially enable physical construction of cryptographic primitives that are traditionally described by algorithms and typically implemented by software. Random Number Generators and Physical Unclonable Functions are simplest examples of such construction. At the same time, securing future unconventional technologies will require the introduction of new security principles and security models that may substantially deviate from the traditional approaches. In fact, various concepts in quantum information science and quantum computation such as quantum resources (entanglement, non-locality, contextuality, etc.) and quantum computational/communication complexity are highly relevant to the security of future communication and computing systems in which classical and quantum devices interact.

Research areas of interest to this program include, but are not limited to, the methodologies and topics described above. Highest priority will be given to projects with novel scientific ideas that potentially deliver new DoD/Air Force capabilities.

You are highly encouraged to contact the Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Optimization and Discrete Mathematics

Program Description: The program goal is the development of mathematical methods for the optimization of large and complex models that will address future decision problems of interest to the U.S. Air Force. Areas of fundamental interest include resource allocation, planning, logistics, engineering design, and scheduling. Increasingly, the decision models will address problems that arise in the design, management and defense of complex networks, in robust decision making, in performance, operational efficiency, and optimal control of dynamical systems, and in artificial intelligence and information technology applications.

Basic Research Objectives: There will be a focus on the development of new nonlinear, integer, and combinatorial optimization algorithms, including those with stochastic components. Techniques designed to handle data that are uncertain, evolving, incomplete, conflicting, or overlapping are particularly important.

As basic research aimed at having the broadest possible impact, the development of new computational methods will include an emphasis on theoretical underpinnings, on rigorous convergence analysis, and on establishing provable bounds for (meta-) heuristics and other approximation methods.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Science of Information, Computation, Learning, and Fusion

Program Description: The U.S. Air Force collects vast amounts of data through various modes at various times in order to extract and derive needed “information” from these large and heterogeneous (mixed types) data sets. Some data, such as those collected from magnetometers, register limited information content which is more identifiable at the sensor level but beyond human’s sensory reception. Other types of data, such as video cameras or text reports, possess more semantic information that is closer to human cognition and understanding. Nevertheless, these are instances of disparate data which encapsulate different types of “information” pertained to, perhaps, the same event(s) captured by different modalities through sensing and collection.

In order to understand and interpret information contained in various data sources, it is necessary to extract relevant pieces of information from these datasets and to make inferences based on prior knowledge and probabilities. This bottom-up processing direction needs conceptually driven reasoning to integrate or fuse the previously extracted snippets of information by leveraging domain knowledge. Furthermore, the top-down processes can offer causal explanation or causal inference, generate new hypotheses, verify or test hypotheses in light of observed datasets. Between the data- driven and conceptually-driven ends, there may reside different levels of abstraction in which information is partially extracted and aggregated based on the nature of applications.

Basic Research Objectives: With the rationale and guiding principles outlined in the above paragraph, this program seeks fundamental research that potentially leads to scientific advancements in informatics, computation, and learning that can support processing and making sense of complex disparate information sources. After all, information processing can formally and fundamentally be described as computing and reasoning on various knowledge representations. Successes in addressing the research sub-areas stated below would give the U.S. Air Force new capabilities to: (1) shift emphasis from sensing to information awareness; (2) understand the underpinning of autonomy; (3) relieve human’s cognitive overload in dealing with the data deluge problem; (4) enhance human-machine interface in information processing.

To accomplish the research objectives, this program focuses on, but is not limited to, new techniques in mathematics, computing science, statistics and logic which have potentials to: (1) cope with various complex disparate data/information types; (2) integrate a diversity of unique reasoning and learning components collaborating simultaneously (e.g., multi-strategy reasoning and learning); (3) bridge correlational with causal discovery; (4) determine solutions or obstructions to local-to-global data- fusion problems; (5) mechanize reasoning/learning and computing in the same computational environment; (6) yield provably efficient procedures to enable or facilitate data analytics; (7) deal with high-dimensional and massive datasets with provably guaranteed performance.

You are highly encouraged to contact our Program Officer prior to developing a full proposal to briefly discuss the current state-of-the-art, how your research would advance it, and the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.


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Trust and Influence

Program Description: Trust and Influence is an interdisciplinary basic research portfolio with two overarching goals. The first, is to advance our basic understanding of human reliance and teaming, to elucidate how people establish, maintain, and repair trust in agents, both human and machine. In particular, it supports research to build the scientific foundations for designing high-performing, mixed humans-machine teams, through properly calibrated trust. Research on the human social and cognitive process that inform the design of systems composed of human and machine agents and the development of novel man-machine interfaces and interaction techniques is of particular interest. The second goal is to advance the science of social influence within the context of national security. For instance, researchers in the program strive to understand the variables that influence human behavior, attitudes and beliefs, sufficient for measurement and forecasting of social phenomena. There is particular interest in developing computational approaches and using large-scale data sets to understand social and cultural behavior. Trust and Influence invests in the discovery of foundational concepts of effective influence, deterrence, trust-building, trust calibration, and counter-terrorism operations. Multidisciplinary and transdisciplinary approaches are encouraged, to include contributions from cognitive science, neuroscience, anthropology, sociology, linguistics, economics, computer science and mathematics. Research designs that incorporate laboratory studies, modeling or field research leading to transformative novel theories are encouraged.

Basic Research Objectives: The research interests under this program can be defined broadly by three areas: trust in autonomous systems, socio-digital influence, and computational methods in social science. In the area of trust in autonomous systems there is particular interest in (1) empirical studies to examine drivers of trust between humans and intelligent, autonomous or robotic agents, (2) laboratory and field studies to examine the impact of socially-designed cues or physical features such as appearance, voice, personality, and other social elements on human trust and system performance, (3) development of trust metrics and other relevant constructs in human- machine teaming with a particular focus on real-time and dynamic assessment, and (4) modeling of human-machine teaming that supports adaptive and continuous improvement of joint performance in complex environments. In the area of socio- digital influence, research is needed towards understanding how social and digital media are used to influence populations, spread ideas and change beliefs. The portfolio is concerned with behavioral effects, but also the cognitive processes that give rise to behavior and the neural underpinnings of those cognitive processes. There is a need for (1) laboratory and field studies to reveal sources of influence and persuasion in social media and across different cultural groups, (2) social, cognitive, and neural mechanisms of influence and persuasion (3) modeling and measuring the relationship between online and real-world behaviors, and (4) empirical studies to discover new theories of influence as it pertains to the cyber domain. In the area of computational social science, there is interest in (1) developing methods employing computational and data sciences to further understanding of human behavioral, social and cultural processes, 2) computational analysis of social networks and social media content, leading to new theories and metrics of behavior and intent, (3) understanding how online or virtual communities affect politics and violence and what role social media plays in popular movements (4) research to understand the psychological and behavioral effects of new weapons systems such as armed drones, directed energy weapons, and cyber-based operations.

You are encouraged to contact our Program Officer prior to developing a full proposal to discuss alignment of your ideas with our program goals, your proposed methods, the scope of your proposed effort, and if there are any specific submission target dates.



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Complex Networks

Program Description: Networks are pervasive to the U.S. Air Force and DOD operations. They occur at multiple hierarchies of scale (networks-of-networks), and involve multiple types of structure, data, and functionality. For example, communication and computer networks are intimately coupled to logistical and resource networks; depending on the topology, failure of critical nodes in one can trigger failure in the other, and a cascade event with potentially catastrophic consequence. Networks describe the fundamentally structural aspect of interactions between individual agents, and network science possesses universal qualities that allows it to analyze dynamics, stability, and design optimization for a wide variety of problems, e.g.: cyber-networks, logistics, complex engineered systems, socio-economic behavior, epidemiology, ecology, etc. Even individual platforms such as modern aircrafts are very complex assemblies of a large number of components, interacting with each other via physical coupling or information exchange, in a network. The software system in these platforms is, on its own, a complex network. Multiple aircrafts operating in synchrony with each other and coupled to global communication and ISR platforms form a higher-level network. As operational strategies increasingly shift towards un-manned platforms with increasing levels of autonomy, the network complexity increases and its scalability, optimization, stability and robustness become even more critically important considerations for the Air Force. This portfolio is aimed at fundamental, mathematical approaches to study, understand, analyze and design complex networks at multiple scales. Only innovative approaches with far-reaching potential, agnostic to the information content or specifics of the information processing, will be considered of interest. The networks of interest will have arbitrary topologies and heterogeneous nodes and data types, will have dynamical properties on multiple time scales, and will be subject to uncertain conditions, ranging from a stochastic environment to deliberate adversarial actions affecting both nodes and links. Applications range from any type of complex, engineering network to natural, physical, chemical, socio-economical, biological and neurological networks

Basic Research Objectives: The mathematical methods of interest should aim at solving one or more of the following problems of interest: rapid design and reconfiguration of optimal networks, rigorous analysis of stability, robustness and resilience, and optimal information recovery from multi-scale variability of topology, coupling strength/bandwidth and node functionality (continuous and discontinuous changes). Furthermore, we are also interested in network inference, e.g. topology, functionality, and stability, from limited observations. Also of particular importance are predictive capabilities for rare events, i.e. triggered cascades of failures across multiple network layers, and better understanding of emergent, global behaviors from local interactions and measurements, either qualitatively or quantitatively. Finally, we are interested in methods leading to complexity reduction of the network dynamics and functionality, for tractable and/or very rapid simulations of very large-scale network behavior, with complex, non-linear node functions. Approaches may include, but are not limited to: algebraic topology, differential geometry, information theory, control theory, graph theory, Bayesian methods, random Markov fields, optimal transport, game theory, differential equations and differential inclusions, reduced-order models and Galerkin projections, Koopman modes, Tensor Network States, machine-learning and neural-networks.

You are highly encouraged to contact the Program Officer, preferably by email, prior to developing a full proposal, to briefly discuss the current state-of-the-art, how your research would advance it, the approximate cost for a three (3) to five (5) year effort, and if there are any specific submission target dates.

We are currently searching/hiring a new Program Officer, but there is a temporary custodian until a new one is selected. Emails sent to the email address below will go to the temporary custodian indicated below.


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Computational Social Sciences

Program Description: There is an increased interest in the ability to better understand and predict social behavior, driven mostly by the availability of new mathematical methods and computer capabilities, combined with the explosive growth of data on social networks. Commercial applications, for consumer marketing and trend analysis for example, have been a major factor behind both the interest in the field, and the progress made in developing analysis tools. The computational modeling of social behavior also finds its use in economics and urban planning, but also in areas that are directly relevant to issues of interest to national security. In epidemiology, for example, the prediction of crowd behavior is directly linked to the ability to control the spread of disease amongst the population at large. Similarly, crowd response to catastrophic events, natural or due to human intervention (e.g. attacks), or even the mere threat of the event, is an important factor in determining the effectiveness of defensive postures and/or military missions. Social behavior, in a broad sense of the term, is therefore an important and pervasive subject that needs to be better understood, characterized and predicted. The social dynamics do not occur in vacuo, and are strongly coupled to the physical environment (e.g. transportation network, weather, communication), which itself may respond non-linearly to the social behavior (e.g. congestion, breakdown). Furthermore, the individual agents of a human population can have complex and variable objectives driving their decisions, often leading to irrational actions and high susceptibility to spontaneous organization. While there has been some spectacular success in reproducing many features of social behavior using very simple rules, it is also quite clear that deviations from average dynamics, rational objectives and simple strategies must be considered in order to greatly improve our predictive capabilities of social behavior.

Basic Research Objectives: This portfolio aims at developing new approaches, methods and tools to model socio-cultural dynamics at unprecedented levels of detail in agent strategies, interacting within complex and dynamic networks, physical and socio- economic. The research should leverage or advance the state of the art in one or more fields, such as mathematics, computer science, psychology, sociology, political science, economics, and anthropology, towards the construction of accurate numerical models of behavior. Topics of interest include, but are not limited to: advanced mathematical models of cognitive functions, rational and irrational thinking, information processing, cognitive bias, influence, social norms. Advances in agent modeling should be combined with counter-parts in the network modeling at various scales and with dynamic features, e.g. mobility, clique formation and destruction, communication uncertainty and breakdown, and multi-type agents (including adversarial). Finally, the inverse problem is also of high interest, i.e. inferring agent strategies from partial observations or finding optimal network characteristics or forcing functions to achieve specific equilibria.

You are encouraged to contact our Program Officer prior to developing a full proposal to discuss alignment of your ideas with our program goals, your proposed methods, the scope of your proposed effort, and if there are any specific submission target dates.


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