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To assist the U.S. Department of Defense and other stakeholders addressing the subsiding interest in and knowledge of a continued nuclear threat, RAND Corporation game designers and subject-area experts developed A Strange Game, named in reference to the 1983 techno-thriller movie, Wargames, which presents thermonuclear war as a conflict that no one can win. A Strange Game, however, very much concerns the present day and the future in that it gives players an opportunity to explore the problems of near- or longer-term future intra-war deterrence where the taboo against the use of nuclear weapons has broken down. This report provides the scientific and theoretical background that informed the creation of the A Strange Game prototype. This report also offers relevant context that went into game design. Therefore, it should be useful to those considering adopting the game, game masters who will want to conduct fully informed gaming sessions, and players wanting a first-hand account of the diplomatic and military concepts used in the game. The information presented in this report can also assist other game designers as they work toward the development of further games that immerse participants in environments designed to improve decisionmaking before, during, and after conflict.

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Every few years, the Air Force develops the Strategic Environment Assessment (AFSEA). The 2016 AFSEA is a 30-year look into the future for Air Force planning. As part of this process, Air Force asked RAND researchers to identify plausible futures based on nine trends in the categories of Geopolitical, Military & Warfare, and Human & Workforce to assist Air Force strategic planning in developing the AFSEA. The RAND team generated a range of future projections based on each of these trends and then convened a collaborative structured workshop to identify important interactions between these trends and to develop a set of future worlds during a 30-year time frame to assist the Air Force during the AFSEA. The workshop was a two-day event during which the RAND trend experts (1) presented their trend assessments and plausible futures; (2) conducted a cross-consistency analysis to look for any combinations of futures that they felt would be inconsistent with each other; (3) identified "interesting pairings" of futures; and (4) developed future worlds using different combinations of trend futures. The purpose of the workshop was to create a set of future worlds that represented diverse contexts and potential challenges for the Air Force to consider. This report presents the results of that workshop.

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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.