Assessing, Monitoring, and Evaluating Army Security Cooperation: A Framework for Implementation

O’Mahony, Angela; Blum, Ilana; Armenta, Gabriela; Burger, Nicholas; Mendelsohn, Joshua; McNerney, Michael J.; Popper, Steven W.; Marquis, Jefferson P.; Szayna, Thomas S.

doi:10.7249/rr2165

Cited by 4 publications

(8 citation statements)

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“…Table 2 shows the units in which effort was denominated. The resourcing for each activity type is notional, but reflects values observed in an analysis of recent Army SC activities (O'Mahony et al, 2018). The resourcing for each activity type is notional, but reflects values observed in an analysis of recent Army SC activities (O'Mahony et al, 2018).…”

Section: Inputsmentioning

confidence: 97%

Robust decision making and scenario discovery in the absence of formal models

Popper

2019

Futures & Foresight Science

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Robust decision making (RDM) is a method for aiding decision making under deep uncertainty that uses models not as predictors but as generators of cases exploring assumptions and outcomes. RDM was intended for use with formal models. However, we show a model‐less RDM application to a portfolio planning problem (selecting U.S. Army security cooperation activities with a partner country) seeking to achieve several objectives. In the absence of formal models, the analysis tests candidate actions against different explicit statements of causal relationships to allow more systematic reasoning over choices and outcomes. Doing so renders assumptions about complex systems explicit, characterizes uncertainties in terms of effect on weighting policy choices rather than as presently unknowable probabilities, provides a venue for planners and evaluators to share findings and insights, and yields explicit expressions of theories of causation (TOC) that themselves act as formal models where none had previously existed. RDM allows important scenarios for decision planning to be generated analytically. This may address for several scenario applications the question of determining important futures and also how scenario results may be used directly to inform policy and operational decisions.

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Section: Inputsmentioning

confidence: 97%

Robust decision making and scenario discovery in the absence of formal models

Popper

2019

Futures & Foresight Science

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“…4) humanitarian assistance and disaster response (HA/DR) operations. 5 Table 2.1 summarizes the specific measures we use for these posture elements and the sources of data for each. As is clear in the table, there are several forms of U.S. forward posture for which we do not have good data-in particular, the elements of footprint (where we lack good historical data on facilities and prepositioned materiel independent of the military personnel stationed there) and activities (where we lack adequate data on nonmateriel forms of security cooperation; ISR activities; and routine shows of force, such as freedom of naviga- 4 This definition is consistent with broad DoD usage of the term.…”

Section: Us Campaigning Instrumentsmentioning

confidence: 99%

“…In this report, we break out activities as a separate element. 5 For data reasons, we focus on relatively sizeable and overt activities. Broadly, activities such as senior leader engagements between a handful of officers would not be included, while a military field exercise involving thousands of personnel would be.…”

Section: Us Campaigning Instrumentsmentioning

confidence: 99%

“…4 The Logic of Escalation Any U.S. activity or investment with the potential to deter also has the potential to provoke unintended escalation. 5 If deterrence requires that a potential aggressor be convinced that the costs of aggression outweigh the benefits, the converse is also true: A potential aggressor must believe that the benefits of peace outweigh the costs. 6 If a country's leaders feel that strategic competition under peacetime is slowly eroding their country's security to the point where the country will become intensely vulnerable, they might choose even a high risk of defeat in war over what they perceive to be certain defeat in peace.…”

Section: The Logic Of Deterrencementioning

confidence: 99%

“…When a competitor can spend equivalent or greater resources on strategic competition in relation to the United States' expenditure, the efficiency of converting resources into desired strategic effects becomes essential. 5 In this report, we explore-in very broad terms-the rate of return on U.S. military overseas competition activities. More specifically, we focus on five questions:…”

Section: Introductionmentioning

confidence: 99%

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This report documents research and analysis conducted as part of a project entitled Evaluating the Return on Army Activities in Competition, sponsored by Futures and Concepts Center, Army Futures Command. The purpose of the project was to develop a framework for assessing the cost-benefit trade-offs of different types of competitive activities and apply the framework to select activities to help the U.S. Army better calibrate and prioritize its responses to Chinese and Russian hostile measures.This research was conducted within RAND Arroyo Center's Strategy, Doctrine, and Resources Program. RAND Arroyo Center, part of the RAND Corporation, is a federally funded research and development center (FFRDC) sponsored by the United States Army.RAND operates under a "Federal-Wide Assurance" (FWA00003425) and complies with the Code of Federal Regulations for the Protection of Human Subjects Under United States Law (45 CFR 46), also known as "the Common Rule," as well as with the implementation guidance set forth in DoD Instruction 3216.02. As applicable, this compliance includes reviews and approvals by RAND's Institutional Review Board (the Human Subjects Protection Committee) and by the U.S. Army. The views of sources utilized in this study are solely their own and do not represent the official policy or position of DoD or the U.S. Government.

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Robust Decision Making (RDM)

Lempert

2019

Decision Making Under Deep Uncertainty

138

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The quest for predictions-and a reliance on the analytical methods that require them-can prove counter-productive and sometimes dangerous in a fast-changing world. • Robust Decision Making (RDM) is a set of concepts, processes, and enabling tools that use computation, not to make better predictions, but to yield better decisions under conditions of deep uncertainty. • RDM combines Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future, and then to identify policy-relevant scenarios and robust adaptive strategies. • RDM embeds analytic tools in a decision support process called "deliberation with analysis" that promotes learning and consensus-building among stakeholders. • The chapter demonstrates an RDM approach to identifying a robust mix of policy instruments-carbon taxes and technology subsidies-for reducing greenhouse gas emissions. The example also highlights RDM's approach to adaptive strategies, agent-based modeling, and complex systems. • Frontiers for RDM development include expanding the capabilities of multiobjective RDM (MORDM), more extensive evaluation of the impact and effectiveness of RDM-based decision support systems, and using RDM's ability to reflect multiple world views and ethical frameworks to help improve the way organizations use and communicate analytics for wicked problems.

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Assessing, Monitoring, and Evaluating Army Security Cooperation: A Framework for Implementation

Cited by 4 publications

References 0 publications

Robust decision making and scenario discovery in the absence of formal models

Robust decision making and scenario discovery in the absence of formal models

Assessing the Value of Overseas Military Campaigning in Strategic Competition

Robust Decision Making (RDM)

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