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Team Wargames - 2022 Technology, Innovation & Great Power Competition

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Team Wargames - 2022 Technology, Innovation & Great Power Competition

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Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Wargames

Technology Innovation and Great Power Competition,TIGPC, Gordian knot Center, DIME-FIL, department of defense, dod, intlpol 340, joe felter, ms&e296, raj shah, stanford, Steve blank, AI, ML, AI/ML, china, Wargames

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Team Wargames - 2022 Technology, Innovation & Great Power Competition

  1. Lessons Learned Original Statement The U.S. needs a way, given a representative simulation, to rapidly explore a strategy for possible novel uses of existing platforms and weapons. David Song B.S. CS Yujing Zhang M.A. East Asian Studies Shashank Rammoorthy M.S. CS Jonathan Sepulveda M.S. Materials Science Project Wargames 30+ interviews Current Statement Strategic wargames stand to benefit from a stronger integration of AI+ML but are struggling to find adoption and usage. How can this be addressed? 1
  2. Lessons Learned Defining a Problem: Research Weeks 2 3 4 5 6 7 8 9 Scavenging the Solution Space: Testing Future Exploring the Problem 2
  3. Lessons Learned 3
  4. Lessons Learned But what is a wargame? 4 1. Physics-based simulators 2. TTXs for campaigns 3. High-level diplomacy wargames
  5. Lessons Learned Then we played with wargames and talked to experts... 5
  6. Lessons Learned 6 So we went ‘back to the drawing board’... Picking one case study of interest, we focused on the Cross-Straits scenario… ● We visited talks on Taiwan’s Economic Security to understand variables often overlooked ● The sad reality is, wargame players often have very little time to anticipate or prepare for wargames, and are not concerned with non- combatant variables ● The most important element of wargames ought to be to teach people to think creatively
  7. Lessons Learned We realized wargaming was not easy Then we started learning and asking… ● What are CoAs? ● Differences between wargaming vs. M&S? ● Why does the DoD want innovation within such traditional processes? ● What hasn’t been attempted already? 7 Excerpt from Battle! Practical Wargaming by Charles Grant
  8. Lessons Learned Problems we found with wargaming 8 We immediately found out that there are… ● Many moving parts & elements: researchers → designers → wargamers → Red and Blue teams ● Little data is shared amongst designers and team members ● There are multiple types of wargames ● Most games remain TTXs ● Too difficult to build de novo a robust, AI-capable Red adversary That left us thinking: ● Virtual wargames are often used for training, but seldom as formal proceedings; why? ● Should we seek to replace wargamers altogether with AI+ML ‘magic’?
  9. Lessons Learned What some of our interviewees told us… 9 Sebastian Bae CNA Corp. | Wargamer Yuna Wong IDA | Defense Analyst Jacquelyn Schneider Hoover, CISAC | Researcher Pete Pellegrino U.S. NWC | Game Design Lead "There are a lot of reasons for why AI/ML hasn't been used in wargaming in the DoD…technical illiteracy within the DoD…policies are inflexible, and systems are difficult to change"
  10. Lessons Learned What some of our interviewees told us… 10 Sebastian Bae CNA Corp. | Wargamer Yuna Wong IDA | Defense Analyst Jacquelyn Schneider Hoover, CISAC | Researcher Pete Pellegrino U.S. NWC | Game Design Lead "We should never use new wargames, but rather well-studied wargames and already-studied ones in predicting our outcomes & enhancing our adjudications”
  11. Lessons Learned What some of our interviewees told us… 11 Sebastian Bae CNA Corp. | Wargamer Yuna Wong IDA | Defense Analyst Jacquelyn Schneider Hoover, CISAC | Researcher Pete Pellegrino U.S. NWC | Game Design Lead "Wargames need data to eat from. You need a volume of data that AI+ML can learn from and be helpful; where is that data coming from a wargame? Wargames don’t generate simulation data in copious amounts”
  12. Lessons Learned What some of our interviewees told us… 12 Sebastian Bae CNA Corp. | Wargamer Yuna Wong IDA | Defense Analyst Jacquelyn Schneider Hoover, CISAC | Researcher Pete Pellegrino U.S. NWC | Game Design Lead "We put too much emphasis on the Red...players are generally not from Russia or China, and so you struggle to extrapolate specific country foreign policy decisions versus general human reactions to international crises.” Many different objections to AI+ML!
  13. Lessons Learned We got out of the building, went to an NPS Conference… 13 ● Existing AI+ML capabilities have to be better leveraged ● AI+ML does not need to make drastic technical advances to be feasible for wargaming uses ● There’s no readily accessible central repository of past wargame data outcomes to help future teams ● Ideally, forecast decision spaces as opposed to having AI+ML play moves
  14. Lessons Learned It’s really hard to build Alphastar with the simulations so far 14
  15. Lessons Learned Defining a Problem: Research Weeks 2 3 4 5 6 7 8 9 Scavenging the Solution Space: Testing Future 15 Pivot: Let’s help humans play wargames better with AI tools instead of replacing humans completely with AI
  16. Lessons Learned Defining a Problem: Research Weeks 2 3 4 5 6 7 8 9 Scavenging the Solution Space: Testing Future 16 Identifying Recommendations
  17. Lessons Learned 17 1. Digitize wargames to increase iterations "Accelerating and making wargames more efficient will come down to a series of technologies that could very well be unclassified in development” Major defense startup company
  18. Lessons Learned 18 2. Create AI tools for digitized wargames “Not many people in the U.S. have access to the decision- making teams you find in Red teams [the PLA and the CCP].” Elizabeth Bartels | RAND
  19. Lessons Learned 19 3. Implement organizational changes We read-up on traditional approaches to building wargames, noting its strengths.. ● We found more support that cross-talk between wargamers & designers and players is important ● Academics do not differ that sharply from the assessment of wargame players insofar as AI+ML integration is concerned ● Information asymmetry predominates within wargames–not an attribute of UCC commanders ● Need more collaborations among wargamers, designers, combat & command
  20. Lessons Learned Defining a Problem: Research Weeks 2 3 4 5 6 7 8 9 Scavenging the Solution Space: Testing Future 20 Next steps 1. Validate our recommendations 2. Shadow a wargame 3. Continue working with our sponsor

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