RIPPLES: Mapping Cause and Effect

Understanding RIPPLES: Mapping Cause and Effect

Every decision creates waves. A policy change, a budget cut, a new regulation—each sends consequences rippling outward into areas the original decision-makers may never have considered. RIPPLES is our attempt to make these hidden connections visible.

What Is a RIPPLE?

A RIPPLE is a documented cause-and-effect relationship that connects one domain of civic life to another. It captures the unintended consequences, the indirect impacts, the externalities that traditional policy analysis often misses.

When a government announces "operational efficiencies" in healthcare, the immediate framing is fiscal: dollars saved. But the actual impact cascades through employment, social services, housing markets, local businesses, mental health, and community stability. These cascading effects are RIPPLES.

Why RIPPLES Matter

Artificial intelligence, for all its capabilities, thinks linearly. When processing "healthcare budget reduction," it sees a financial adjustment. It doesn't automatically trace the path from budget line → layoff notice → EI application → mortgage stress → family strain → children's anxiety → school performance decline.

Humans make these leaps intuitively because you've lived in systems. You've witnessed the coworker who got laid off and struggled. You know the restaurant near the hospital that closed months after staffing cuts. That's embodied knowledge that no training data captures systematically.

RIPPLES creates a human-curated causal graph—something that doesn't exist elsewhere. Not in academic literature (too siloed by discipline), not in government policy analysis (too focused on intended outcomes), not in AI training data (too implicit). RIPPLES makes hidden connections explicit and machine-readable.

How RIPPLES Work in the Ecosystem

Each forum topic on CanuckDUCK can have a special thread titled "RIPPLE." This thread is reserved for documenting cause-and-effect relationships. Users contribute individual RIPPLE comments, each describing a single connection between the topic and another domain.

These contributions are processed by AI to extract structured data:

• Source Domain: Where does the effect originate? (e.g., Healthcare Workforce)
• Target Domain: What area is impacted? (e.g., Local Economy)
• Direction: Does it increase or decrease something?
• Mechanism: How does the cause create the effect?
• Strength: How much of the original impact carries through?

The extracted relationships feed into Ducklings, our civic simulation environment. When students or citizens model budget decisions, the simulation can show not just the direct fiscal impact, but the cascading consequences documented by the community.

How to Write a Good RIPPLE

A well-written RIPPLE follows this pattern:

Example RIPPLE

Ripple Attenuation

RIPPLES don't propagate at full force forever. Each hop through the causal chain carries a diminishing impact:

• An initial event might create a strong first-order effect (80% impact)
• That effect creates second-order effects at reduced strength (perhaps 50% of the first-order impact)
• Third-order effects are weaker still

This natural attenuation prevents infinite loops while still capturing meaningful cascades. The simulation limits propagation to 2-3 degrees of separation, focusing on the most significant downstream effects.

Bidirectional Effects

The same trigger can create effects in opposite directions:

Healthcare budget cut →

• Staff count decreases (primary effect)
• Remaining staff workload increases
• Remaining staff overtime pay increases (short term)
• Remaining staff burnout increases (medium term)
• Wait times increase
• Administrative efficiency might increase (if well-managed)

This is where community input becomes essential. AI cannot predict which direction is more likely in a given context. But humans who've lived through restructuring can share: "in my experience, what actually happens is..."

The Uncomfortable Connections

Some RIPPLES trace dark paths that policy-makers would rather not acknowledge. Budget cuts don't just affect balance sheets—they affect human beings. And when people lose employment, they don't just lose income:

• Financial stress increases
• Mental health often declines
• Sense of self-worth (which society ties to employment) erodes
• In extreme cases, suicide risk increases

These connections are uncomfortable but real. RIPPLES doesn't shy away from documenting them. The goal isn't to be pessimistic—it's to ensure decision-makers understand the full weight of their choices.

What RIPPLES Is Not

RIPPLES is not prediction. We're not claiming to forecast exactly what will happen. We're surfacing the questions that should be asked before a decision is made.

RIPPLES is not partisan. Effects flow regardless of which party makes the decision. A tax cut and a spending increase both have ripples.

RIPPLES is not complete. This is an experimental, evolving system. We're open-sourcing cause and effect, throwing ideas at the wall to see what sticks. As more humans contribute their lived experience, the causal graph becomes richer and more useful.

Contributing to RIPPLES

Anyone can contribute a RIPPLE. Your lived experience—watching what happened when the local plant closed, when the school budget was cut, when the new regulation took effect—is valuable data that no academic study captures.

To contribute:

1. Navigate to a forum topic on Pond
2. Find the RIPPLE thread (or request one be created)
3. Add a comment describing a single cause-effect relationship
4. Follow the WHEN/THEN/BECAUSE/STRENGTH/EVIDENCE pattern

Your contribution will be processed by AI to extract structured data, validated for consistency, and added to the causal graph that powers civic simulation.

The Vision

Imagine a future where, before any major policy decision, citizens can see:

"Based on documented ripple effects from N community contributors, this decision may impact:

• Employment: ↓ moderate to strong
• Social services load: ↑ moderate
• Local economy (affected communities): ↓ moderate
• Healthcare access: ↓ moderate to strong
• Mental health outcomes: ↓ weak to moderate

Average confidence: X based on Y supporting observations."

That's what we're building. Not certainty—visibility. The hidden costs made explicit. The unintended consequences anticipated. The full picture, contributed by the people who actually live with the results.

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RIPPLES is part of the CanuckDUCK civic technology ecosystem. For questions or feedback, visit the forums or contact us at [email protected].