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Public and private organizations are increasingly aware of the potential value of data and analytics to improving organizational performance and outcomes. The U.S. Department of Defense (DoD) is one of those organizations. Its size, complexity, security needs, and culture have created a challenging environment for successful use of data in decisionmaking. DoD's acquisition data lay an important part of the foundation for decisions about weapon systems. Because the private sector faces similar data challenges, the authors examined commercial data practices that might translate to the DoD acquisition community in the areas of data governance and analytics. Benchmarking select private-sector data governance and analytics practices helps establish a baseline against which DoD practices can be compared. That comparison can be used to identify areas in which DoD could improve and suggests actions or approaches to make those improvements. The authors determined that functions associated with the office of a chief data officer and associated data governance and data management are foundational requirements to pursue an analytics strategy in any organization.
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The U.S. Army faces two analytical and management challenges because its data are locked away in siloed and proprietary databases and it lacks access to modern, commonplace analytical tools. To solve these two problems, the authors developed a case study with Army Contracting Command (ACC) to determine if there is a simple and effective way to overcome these challenges and found an effective, efficient, and quick path forward. The authors conducted a proof of concept for data sharing and analytics with ACC, which has high volume and value of annually awarded contracts. They migrated large contracting data sets from ACC, built a robust querying and analytics platform for exploring that data, piloted a method for accessing heretofore inaccessible unstructured text data from contracts, and conducted a pilot machine-learning analysis highlighting how a cloud-based contract analysis system for ACC could lead to cost savings. The team found that the Army can achieve immediate cost savings and efficiencies through advanced data analytics and the use of currently available commercial off-the-shelf technology. The Army should immediately conduct multiple similar proofs of concept that take siloed and inaccessible data to the cloud to be analyzed using modern analytical tools to validate the methodology from this report across multiple commands.
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In the years following the 2008 financial crisis, significant attention was paid to systemic risk within heavily interconnected financial networks. The academic discussions on interbank network structure, market stability, and contagion gave rise to a policy debate about whether major banks had become both too big and too interconnected to fail. However, despite the focus on systemic risk—the risk of market collapse resulting from firm-level risks—within the financial sector, little attention was paid to systemic risks in the economy at large. The authors of this report address that gap in research. To begin to measure the potential magnitude of systemic risk in the broad economy, the authors estimated firm-to-firm connections across sectors of the U.S. economy. Using network analysis on observed firm-level networks to elucidate heavily interconnected firms and areas of centrality (i.e., firms of significant network importance), statistical inference, and network calibration, the authors provide a new approach to modeling the economy at the firm level that expands on the traditional sector-level input-output modeling by estimating firm-level input-output flows. The result allows one to use traditional input-output modeling to estimate the size of potential idiosyncratic shocks and to use economically weighted measures of centrality to reveal systemically important firms. The approach is a contribution to the growing literature on the microfoundations of economic risk, with the potential for use across a wide range of applications from financial stability to natural disasters.
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RAND researchers explored the capabilities and limitations of future weapon systems incorporating artificial intelligence and machine learning (AI/ML) through two wargame experiments. The researchers modified and augmented the rules and engagement statistics used in a commercial tabletop wargame to enable (1) remotely operated and fully autonomous combat vehicles and (2) vehicles with AI/ML–enabled situational awareness to show how the two types of vehicles would perform in company-level engagements between Blue (U.S.) and Red (Russian) forces. Those rules sought to realistically capture the capabilities and limitations of those systems, including their vulnerability to selected enemy countermeasures, such as jamming. Future work could improve the realism of both the gameplay and representation of AI/ML–enabled systems. In this experiment, participants played two games: a baseline game and an AI/ML game. Throughout play in the two game scenarios, players on both sides discussed the capabilities and limitations of the remotely operated and fully autonomous systems and their implications for engaging in combat using such systems. These discussions led to changes in how the systems were employed by the players and observations about which limitations should be mitigated before commanders were likely to accept a system and which capabilities needed to be fully understood by commanders so that systems could be employed appropriately. This research demonstrated how such games, by bringing together operators and engineers, could be used by the requirements and acquisition communities to develop realizable requirements and engineering specifications for AI/ML systems.
Command and control systems --- Games of strategy (Mathematics) --- Information warfare --- Forecasting.
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Social movement research is becoming increasingly important, as information and communications technologies (ICTs) have altered the ways movements form, organize, mobilize, and act, as well as the ways in which they are surveilled and disrupted. The authors of this report explore the use of agent-based modeling as a method for studying the effects of ICTs on the formation, maintenance, and dissolution of social movements over time. The authors first reviewed selected research on recent technologies and social movements and conducted case studies of the Arab Spring protests in Egypt in 2010, the civil uprising in Syria in 2010, and the Hong Kong protests in 2019. They then developed and tested an agent-based model (ABM) that simulates the role of technology on specific features of social movements. The authors present conclusions from this exploratory research and discuss how to better employ ABMs as a tool for understanding the dynamics of social movements.
Social movements --- Information technology --- Communication in social action. --- Internet and activism. --- Research. --- Political aspects.
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The 2019 National Defense Authorization Act mandated a study on artificial intelligence (AI) topics. In this report, RAND Corporation researchers assess the state of AI relevant to the U.S. Department of Defense (DoD), and address misconceptions about AI; they carry out an independent and introspective assessment of the Department of Defense's posture for AI; and they share a set of recommendations for internal actions, external engagements, and potential legislative or regulatory actions to enhance the Department of Defense's posture in AI.
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