Interest Analysis: Score the "Why" Behind Beliefs

Topic: ISE Framework > Conflict Resolution > Interest Analysis

The Proposal: Stop debating only positions ("what do you want?") and start scoring motivations ("why do you want it?"). For every belief, we generate two scores that make conflict measurable instead of vibes-based:

1. Linkage Accuracy (0-100): Are we right about which interests motivate which groups?
2. Interest Validity (0-100): Even if it's real, how legitimate is that interest compared to others?

What We Build: The Interest Profile

Every belief gets an Interest Profile that's editable, evidence-driven, and auditable:

• Supporter interests: Why people agree
• Opponent interests: Why people disagree
• % breakdown: Estimated share of each group driven by each interest
• Two scores: Linkage Accuracy + Interest Validity

The point isn't to "purify" debate. It's to make motives testable and make compromises buildable.

See: Stakeholder Interest Analysis Framework

Score 1: Linkage Accuracy (Stop Strawman Motives)

People constantly argue by guessing the other side's motives ("they just hate X"). Linkage Accuracy puts that guess on a leash: if you claim a motive, you need evidence.

What drives Linkage Accuracy:

• Evidence strength: Surveys, interviews, public statements (Evidence tiers)
• Behavioral match: Do actions align with the stated motive?
• Revealed preferences: Votes, donations, spending, time allocation
• Expert validation: Do credible analysts confirm it?
• Historical consistency: Does this pattern repeat in similar groups/context?

Example: Tax Policy

Scores update as users add arguments and evidence: better data raises confidence; speculation drops it.

See: Evidence-to-Conclusion Linkage Methodology

Score 2: Interest Validity (Not All Motives Are Equal)

Some interests are about safety or survival. Some are about status. Some are about domination. Validity ranks interests so debates stop treating everything as equally sacred.

Maslow-Informed Validity Framework:

Within-level ranking criteria:

1. Impact scope: How many people, how severely affected
2. Reversibility: Irreversible harms rank higher
3. Alternative satisfaction: Fewer alternatives means higher priority
4. Universal test: What if everyone pursued it?
5. Reciprocity: Would you accept it applied to you?

See: Insisting on Objective Criteria

"Ugly" Interests Don't Get Ignored—They Get Bad Scores

We don't pretend bad motivations don't exist. We just stop letting them run the show. Interests that fail ethical tests get de-weighted (typically 0-20):

• Domination interests: Universal application creates perpetual conflict
• Zero-sum tribal winning: "I want them to lose" isn't a public goal
• Pretextual motives: Stated reason contradicts behavior
• Manufactured interests: Based on misinformation, no evidence base

See: Truth Scoring System, Cognitive Bias Detection

Debating the Scores

Arguments supporting linkage/validity:

• Survey data, behavioral evidence, expert analysis
• Historical patterns, revealed preferences
• Universal application produces beneficial results
• Satisfying interest improves wellbeing

Arguments challenging linkage/validity:

• Stated interest contradicts actual behavior
• Confounding factors mask real motivation
• Universal application creates problems
• Pursuing interest harms others, violates reciprocity

Scored using ReasonRank, updating as new evidence emerges.

See: Cost-Benefit Analysis Framework

Finding Shared Interests

Interest profiling reveals where opposing groups share motivations—foundation for compromise.

Example: Healthcare Reform

Build solutions on high-validity shared interests first.

See: Inventing Options for Mutual Gain

Automation: Faster Mapping, Less Duplication

Once interests are "data," we automate the boring work and save humans for the hard parts:

• NLP extraction: Pulls interests from submissions
• Semantic clustering: Merges duplicates/synonyms
• Compatibility analysis: Maps overlaps and trade-offs
• Visualizations: Generates matrices, heatmaps, trade-off curves
• Dynamic updates: Recalculates scores when new evidence/arguments appear

Output: System surfaces high-accuracy + high-validity shared interests as foundation for compromise and actionable options.

See: Interest Analysis Implementation Code

Multi-Dimensional Interest Profiles

Each interest gets four complementary scores:

• Need Intensity (N): How critical? (Maslow level sets baseline)
• Motivational Depth (D): Connection to core values
• Relational Complexity (R): Network effects with other interests
• Contextual Relevance (C): Linkage strength to specific belief

How to Contribute

1. Suggest interests for a belief (supporter + opponent)
2. Estimate percentages (dominant vs. fringe motivations)
3. Add evidence supporting/challenging the linkage
4. Argue validity using ethical + practical criteria
5. Flag shared interests to build compromise options

See: Brainstorming Interests for Policy Debates

Why This Matters

Scoring the why behind beliefs turns tribal argument into structured problem-solving: less mind-reading, more evidence, clearer priorities, and compromise built on real shared interests.

See: Separating People from the Problem, Automated Conflict Resolution

Related Resources

• Stakeholder Interest Analysis Framework
• Applying "Getting to Yes" Principles
• Values Framework
• Insisting on Objective Criteria
• Linkage Scoring Methodology

Contribute

Contact me to help develop interest analysis tools or map interests for specific beliefs.

GitHub for technical implementation.