Crisis Forge
Vancouver, BC
Feb 2024
CrisisForge is an initiative to improve simulations training, particularly for better inter-organizational collaboration during crises, and to create tools for use during crises.
This project started as a Hackathon hosted by UBC's Emerging Media Lab with mentorship provided by members of the Justice Institute of British Columbia and Conversa Corps. With a focus on JIBC's "Praxis" tabletop simulation tool, participants made a wide range of projects ranging from UI/UX designs to functional prototypes:
1. Nonlinear Crisis Timeline Interface with AI-Assisted Injects
- Problem: Traditional crisis management timelines are too linear and hard to modify dynamically.
- Solution: A redesigned user interface for the PRACTICE system that supports branching timelines and AI-generated injects.
- Features:
- Drag-and-drop inject reordering.
- AI-generated visuals for injects.
- Enhanced buttons, added “Cancel” and “Back to Dashboard.”
- Categorization and sub-inject numbering system (e.g., 2.1, 2.2).
- Goal: Improve usability and realism of crisis simulations, especially for administrators and controllers.
2. AI-Supported Debriefing Tool for Decision Traceability
- Problem: Experts only see the final decision and rationale, missing insights from the full conversation.
- Solution: Use of LangChain and AI prompts to analyze chat logs and generate:
- All possible decisions discussed.
- Contributions of individual participants.
- A percentage breakdown of how much each comment influenced each decision.
- Prototype Features:
- Review reports with full chat history.
- AI-generated potential decisions.
- Linked rationale and contributors per decision.
- Future Potential: Applying this to tools like Slack or Zoom for structured insight capture.
3. Dynamic Crisis Simulation Engine with Recursive AI Prompts
- Problem: Need for responsive, iterative crisis simulations that evolve with participant input.
- Solution: A modular system that separates simulation into:
- Baseline prompt (overall situation).
- Rolling situational updates.
- Ongoing user assessments and actions.
- Architecture:
- Containerized microservices (for scalability).
- Uses prompt chaining and AI evaluation to simulate crisis evolution.
- Efficient token usage and data storage.
- Outcome: A scalable, Zork/AI Dungeon-style real-time crisis interaction system.
4. Real-Time Slide Deck with Live Data and GPT Integration
- Problem: Static slides lack adaptability in crisis simulations.
- Solution: A prototype for slide decks that dynamically pull real-time data and GPT outputs per slide.
- Use Case: Command centers or practice simulations with evolving real-world info (e.g., wildfire updates).
- Features:
- Slides auto-refresh with live or pseudo-live data (e.g., from Wikipedia or news).
- Future vision includes live inputs from websites or APIs to drive simulation updates.
- Challenges: Debugging inconsistent live data rendering.
5. After Action Report (AAR) Generator + Decision Dynamics Evaluator
- Problem: Generating quality AARs is time-consuming and subject to human oversight.
- Solution:
- Use GPT to generate AARs based on FEMA templates.
- Evaluate team decision-making dynamics from transcripts.
- Experiments Included:
- Testing with fictional input (e.g., Star Trek, Council of Elrond) to simulate decisions.
- Added a fictional disruptive participant to evaluate how the system handled poor input.
- Result: Dynamic scoring, feedback, and insights into group performance and individual contributions.
6. Sphere Handbook RAG (Retrieval-Augmented Generation) Assistant
- Problem: Humanitarian guidelines are complex and hard to access in real-time.
- Solution: LangChain-based tool that uses a vector database of the Sphere Handbook (~450 pages).
- Features:
- AI answers questions like “How do we get water to isolated communities?”
- Cites specific handbook segments and page references (with some formatting issues to fix).
- Goal: Reliable, non-hallucinated AI access to expert humanitarian protocols.
Additional Contributions and Observations:
- Early GPT-based scenario generation tool: Prompt-based assistant trying to generate JSON scenarios (MVP level).
- A few teams collaborated closely and learned tools like LangChain, vector stores, prompt engineering, and GPT-based API integration on the fly.
- Suggestions for future work:
- Integration with RAG (Retrieval-Augmented Generation) pipelines for knowledge bases.
- Incorporating Reinforcement Learning (e.g., RH models) for more accurate simulations.
- Expanded containerization and microservice architecture for scale and deployment.
General Themes:
- Emphasis on nonlinear thinking, human-centered interface design, and modular AI pipelines.
- Strong orientation toward applicability in both simulations and real-world crisis scenarios.
- Tools developed were portable, flexible, and grounded in practical crisis management needs.
Presentations from the Hackathon may be seen here: https://www.youtube.com/watch?v=DTsG-YLgj1M
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