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Project Summary
Decision-making in complex systems is no longer limited by information scarcity, but by the inability to interpret interdependencies, feedback loops, and second-order effects. Across AI governance, climate systems, public health, economic policy, and institutional design, researchers and policymakers drown in data while starving for clarity on how systems actually behave.
This translates complexity into structured, navigable representations of system behaviour for public good ethically. The output makes leverage points, constraints, and intervention pathways legible and actionable for operators in high-complexity environments. The long-term goal is strengthening collective sensemaking and improving decision quality in critical global systems.
I am Megan, Principal Investigator. I hold a PhD in Systems Engineering from George Washington University and conducted research at MIT focused on systems behaviour, decision-making under uncertainty, and nonlinear dynamics in complex adaptive systems. My professional track record spans twenty years across governments, multilaterals, NGOs, and private sector organisations in international development, sustainability, and organisational transformation. I have provided advisory and applied systems engineering work across multiple sectors. In each role, I translated systems thinking into operational tools for decision-making, strategy development, and institutional analysis. The Systems Intelligence Lab extends this twenty-year track record into a general-purpose infrastructure for systems intelligence designed to operate across domains rather than within a single institutional context.
What are this project's goals? How will you achieve them?
Goals:
1. Build and validate a minimum viable prototype for the systems intelligence infrastructure
2. Develop a formal systems representation framework defining actors, incentives, dependencies, and feedback loops
3. Convert real-world domains into structured system maps showing leverage points and intervention pathways
4. Test outputs with early users in policy, research, and systems domains
5. Iterate based on structured feedback and real-world use
How I Will Achieve Them (3–6 Month MVP Phase):
Phase 1: Systems Framework Development (Months 1–2)
- Develop formal systems representation framework: actors, incentives, dependencies, feedback loops
- Map interdependencies across AI governance, climate, health, economic systems, institutional design
- Create computational models for nonlinear dynamics in complex adaptive systems
Phase 2: Prototype Build (Months 2–4)
- Build working prototype converting real-world domains into structured system maps
- Implement systems modelling framework with visualisation of leverage points
- Develop intervention pathway identification algorithms
- Create lightweight infrastructure for deployment
Phase 3: User Validation (Months 4–6)
- Test outputs with early users in policy, research, systems domains
- Conduct structured feedback sessions with 10–15 early adopters
- Iterate based on real-world use and feedback
- Design and document outputs for broader adoption
The approach combines applied systems engineering, computational modelling, and iterative user validation. My twenty years across governments, multilaterals, NGOs, and private sector provide access to 10–15 early adopters for validation. PhD-level systems engineering expertise and MIT research on nonlinear dynamics enable efficient frameworks.
How will this funding be used?
Total: $50,000 USD for 3–6 Month MVP and Validation Phase
| Category | Amount |
| Principal Investigator (3–6 months, full-time) | $30,000 |
| Systems modelling and framework development | $5,000 |
| Software development, infrastructure, and tools | $8,000 |
| User testing and validation (10–15 early adopters) | $4,000 |
| Documentation, design, and outreach materials | $2,000 |
| Operations, hosting, and iteration costs | $1,000 |
What This Enables:
- Full-time focus on prototype development
- Functional MVP capable of generating structured representations
- 10–15 early user validations
- Complete systems representation framework
- Deployable prototype with documentation
Who is on your team? What's your track record on similar projects?
Team: Founder-led and lean. Additional collaborators will be brought in as the prototype and validation process evolves.
My Track Record (PhD and Twenty Years Across Academia, Governments, Multilaterals, NGOs, and Private Sector):
- PhD Systems Engineering (George Washington University): Complex adaptive systems, institutional design, applied systems thinking
- Research: Systems behaviour, decision-making under uncertainty, nonlinear dynamics in complex adaptive systems
- Twenty Years Across Governments, Multilaterals, NGOs, and Private Sector: Translating systems thinking into operational tools for decision-making, strategy development, institutional analysis
- International Development and Sustainability: Organisational transformation in complex environments
- Applied Systems Thinking: Strategy development, institutional analysis, decision-making tools
Why My Track Record Makes This Realistic:
1. Twenty years across academia, governments, multilaterals, NGOs, and private sector provides access to 10–15 early adopters for validation
2. PhD in complex adaptive systems plus MIT research enables efficient framework development
3. Applied systems thinking tools experience demonstrates ability to translate abstract concepts into usable outputs
4. International development work demonstrates understanding of high-complexity decision environments
What are the most likely causes and outcomes if this project fails?
Key Risks:
1. Insufficient funding to complete prototype phase — Probability: 20%. $50K may not cover all costs if project scales.
2. Difficulty translating systems concepts into usable outputs — Probability: 30%. Systems thinking is abstract; making it actionable is challenging.
3. Challenges achieving strong validation with early users — Probability: 25%. Users may not see value in system maps.
4. Limited adoption despite functional performance — Probability: 15%. Market may not need additional systems intelligence tools.
Most Likely Outcome if Project Fails:
Incomplete or under-adopted prototype that still contributes to understanding how my expertise is operationalised.
Fallback Options:
- Insufficient funding: Apply to additional funders for expansion phase
- Unusable outputs: Simplify framework, focus on 1–2 domains first
- Poor user validation: Iterate based on feedback, target different user segments
- Limited adoption: Publish open-source framework, seek institutional partnerships
Even If Partially Failing, I Deliver:
- Framework
- Prototype
- User feedback
Bottom line: Even with failure, tangible outputs worth $20K–$40K are delivered.
How much money have you raised in the last 12 months, and from where?
No external funding has been received in the past 12 months. Work to date has been supported by me. This grant ($50K) would be dedicated funding for the systems intelligence project. It would enable me to:
- Dedicate full-time to prototype development
- Build functional MVP with systems modelling framework
- Validate with 10–15 early users
- Deploy prototype with documentation
Funding Request
Requested funding: $50,000 USD
Phase 1 — MVP and Validation ($50,000):
- Build and validate prototype
- Develop core systems representation framework
- Test with early users in relevant domains
Closing
The Systems Intelligence Lab builds foundational infrastructure for improving how complex systems are understood and navigated. The immediate goal is a validated prototype. The long-term goal is scalable systems intelligence for high-stakes decision environments.
Megan L. Peters, PhD, Principal Investigator, brings twenty years of track record combining PhD-level systems engineering, MIT research on nonlinear dynamics, and applied advisory work across governments, multilaterals, NGOs, and private sector organisations. This positions the lab to deliver systems intelligence infrastructure that operates across domains.
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