tractatus/docs/research/pluralistic-deliberation-scenario-framework.md

# Strategic Framework for Pluralistic Deliberation Scenario Selection

**Project:** Tractatus Framework - PluralisticDeliberationOrchestrator
**Document Type:** Research Foundation
**Status:** Complete - Ready for Implementation
**Created:** 2025-10-17
**Author:** John Stroh (with Claude analysis)
**Version:** 1.0

---

## Executive Summary

This document establishes a rigorous, theory-grounded framework for selecting demonstration scenarios for the PluralisticDeliberationOrchestrator component of the Tractatus Framework.

### Purpose

The PluralisticDeliberationOrchestrator aims to facilitate multi-stakeholder deliberation across competing moral frameworks in a non-hierarchical manner. To demonstrate this capability effectively, we need scenarios that:

1. **Showcase pluralistic reasoning** (multiple legitimate moral frameworks in tension)
2. **Avoid pattern bias** (no vulnerable groups unnecessarily centered, low vicarious harm risk)
3. **Demonstrate practical value** (relevant to real-world governance challenges)
4. **Teach generalizable principles** (applicable beyond specific case)
5. **Engage audiences** (media-salient, timely, comprehensible)

### Key Findings

**Dimensional Analysis Reveals:**
- Conflict type matters more than scale (small-scale can demonstrate principles effectively)
- **Resource allocation** and **procedural conflicts** are safer than **identity conflicts** for initial demonstrations
- **Emerging issues** (before polarization hardens) offer best demonstration opportunities
- **Economic/technical** conflicts lower risk than **ideological/cultural** conflicts

**Top Recommendations:**

**Tier 1 Scenarios (Strong Candidates for MVP):**
1. **Algorithmic Hiring Transparency** - Multiple frameworks, timely, not identity-based
2. **Remote Work Location Pay** - Economic focus, geographic not identity dimension
3. **Public Park Usage Priority** - Local, concrete, low-stakes, shows negotiation
4. **Open-Source Licensing** - Ideological but not partisan, technical community
5. **University Lecture Recording** - Multi-sided, educational context, clear trade-offs

**Recommended Starting Point:**
- **Primary:** Algorithmic Hiring Transparency
- **Secondary:** Remote Work Location Pay
- **Rationale:** Both are timely, affect broad audiences, demonstrate multiple moral frameworks without centering vulnerable identities

### Document Structure

This framework consists of:
1. **Four-Dimensional Analysis** of conflict types and structures
2. **Pattern Bias Risk Assessment** to ensure ethical demonstration practices
3. **Scenario Taxonomy** (Tier 1-3 classifications with rationale)
4. **Media Interest Analysis** to identify timely, salient issues
5. **Selection Methodology** for systematic scenario evaluation

---

## Table of Contents

1. [Methodological Foundation](#1-methodological-foundation)
2. [Dimension 1: Scale & Stakeholder Structure](#2-dimension-1-scale--stakeholder-structure)
3. [Dimension 2: Conflict Type Taxonomy](#3-dimension-2-conflict-type-taxonomy)
4. [Dimension 3: Differentiating Attributes & Pattern Bias Risk](#4-dimension-3-differentiating-attributes--pattern-bias-risk)
5. [Dimension 4: Media Interest Patterns](#5-dimension-4-media-interest-patterns)
6. [Scenario Taxonomy (Tiers 1-3)](#6-scenario-taxonomy)
7. [Strategic Selection Criteria](#7-strategic-selection-criteria)
8. [Recommendations](#8-recommendations)
9. [Next Steps](#9-next-steps)

---

## 1. Methodological Foundation

### Why Systematic Scenario Selection Matters

**The Challenge:**
Initial planning focused on mental health crisis scenarios (privacy vs. safety). While theoretically sound, this raises ethical concerns:
- **Vicarious harm:** Users might see themselves in crisis scenarios
- **Re-traumatization:** Mental health topics can trigger distress
- **Vulnerable populations:** Centering crisis situations risks exploitation

**The Solution:**
A theory-driven, systematic approach to scenario selection that:
1. **Starts with theory** (conflict types, moral frameworks)
2. **Maps to concrete examples** (specific scenarios)
3. **Evaluates systematically** (rubric-based scoring)
4. **Prioritizes safety** (pattern bias risk assessment)
5. **Considers strategy** (media patterns, demonstration value)

### Philosophical Foundations

**Foundational Pluralism (from v2 plan):**
- Multiple irreducibly distinct moral frameworks exist
- No supervalue subsumes all others (deontology ≠ consequentialism ≠ virtue ethics)
- Value conflicts are features, not bugs
- Rational regret is possible even when right choice made

**Implications for Scenario Selection:**
- Scenarios must demonstrate **genuine incommensurability** (not just preference differences)
- Must show **multiple legitimate frameworks** (not "right vs. wrong")
- Should enable **non-hierarchical resolution** (accommodation, not domination)
- Must document **moral remainder** (what's lost in any choice)

### Design Principles

**1. Theory-Driven**
- Start with conflict taxonomy (types of moral tensions)
- Map to stakeholder structures (who's involved?)
- Identify frameworks in tension (which moral reasoning applies?)

**2. Safety-First**
- Avoid scenarios centering vulnerable populations
- Minimize vicarious harm risk
- No re-traumatization potential
- Respect that demonstrations have real-world impact

**3. Demonstrability**
- General audiences must grasp scenario quickly (<5 minutes)
- Stakeholders clearly identifiable
- Moral frameworks recognizable
- Outcomes visualizable

**4. Generalizability**
- Specific scenarios teach broader principles
- Applicable to other contexts
- Show deliberation process, not just outcomes

**5. Strategic**
- Consider media patterns (current salience)
- Avoid already-polarized issues (entrenched positions hard to deliberate)
- Emerging issues offer better opportunities

---

## 2. Dimension 1: Scale & Stakeholder Structure

### Scale Taxonomy

Conflicts occur at different scales, each with distinct deliberation dynamics:

```
INDIVIDUAL ←→ SMALL GROUP ←→ LARGE GROUP ←→ SOCIETAL ←→ GLOBAL
```

### Detailed Scale Analysis

#### Individual vs. Individual
**Characteristics:**
- Dyadic relationship
- Direct personal stakes
- Often relational/interpersonal
- Low systemic impact

**Examples:**
- Privacy of correspondence (friend reads another's diary)
- Contract dispute (roommate agreement breach)
- Property boundary (fence line dispute)

**Deliberation Dynamics:**
- Mediation model
- Focus on relationship preservation
- Personal trust matters

**Demonstration Value:** LOW
- Too small-scale for governance demonstration
- Difficult to generalize principles
- May feel trivial to audiences

---

#### Individual vs. Small Group (5-20 people)
**Characteristics:**
- Power asymmetry (one vs. many)
- Individual rights vs. group cohesion
- Accommodation vs. disruption trade-off

**Examples:**
- Employee requests religious accommodation (affects team workflow)
- Student objects to group project assignment (fairness vs. pedagogy)
- Homeowner opposes neighborhood association rule

**Deliberation Dynamics:**
- Individual rights protection essential
- Group efficiency matters
- Compromise often possible

**Demonstration Value:** MEDIUM
- Shows individual/collective tension
- Relatable to many contexts
- Teaches accommodation principles

---

#### Individual vs. Large Group (20+ people, organization, platform)
**Characteristics:**
- Significant power asymmetry
- Individual impact on many (e.g., whistleblower)
- Or organizational impact on individual (e.g., content moderation)

**Examples:**
- User appeals content removal decision (individual vs. platform)
- Whistleblower vs. corporation (disclosure vs. confidentiality)
- Citizen objects to municipal zoning decision

**Deliberation Dynamics:**
- Due process critical
- Transparency important (power imbalance)
- Precedent-setting potential high

**Demonstration Value:** HIGH
- Media appeal (underdog narrative)
- Governance relevance (platform/state power)
- Shows accountability mechanisms

---

#### Small Group vs. Small Group
**Characteristics:**
- Peer relationship
- Resource competition
- Coordination challenges

**Examples:**
- Department A vs. Department B (budget allocation)
- Neighborhood block associations (parking dispute)
- Sports leagues vs. informal users (park booking priority)

**Deliberation Dynamics:**
- Neither has inherent authority
- Negotiation, not hierarchy
- Fairness procedures matter

**Demonstration Value:** **VERY HIGH**
- Demonstrates non-hierarchical deliberation perfectly
- Manageable complexity
- Clear stakeholder representation
- Relatable to many contexts (workplace, community, etc.)

**Recommendation:** **Prioritize this scale for demonstrations**

---

#### Large Group vs. Large Group
**Characteristics:**
- Systemic conflict
- Institutional representation
- High stakes, broad impact

**Examples:**
- Labor union vs. employer association
- Environmental coalition vs. industry group
- Urban communities vs. rural communities (resource allocation)

**Deliberation Dynamics:**
- Requires formal representation
- Public interest high
- Outcomes affect many

**Demonstration Value:** MEDIUM-HIGH
- Relevant to governance
- But complex (many internal divisions within each group)
- May require extensive background

---

#### Societal-Level (Cross-Cutting)
**Characteristics:**
- No clear "groups" (diffuse positions)
- Cultural/generational/regional divisions
- Abstract values tensions

**Examples:**
- Urban vs. rural values (zoning, agriculture policy)
- Generational conflicts (climate urgency, social programs)
- Regional differences (federal policy impacts)

**Deliberation Dynamics:**
- Difficult to identify "stakeholders" (everyone affected differently)
- Representation challenging
- Outcomes contested

**Demonstration Value:** LOW
- Too abstract for initial demonstration
- Stakeholders hard to identify
- Deliberation process unclear

---

#### Global-Level
**Characteristics:**
- Nation-state actors
- Cultural blocs
- Extremely high complexity

**Examples:**
- Trade policy disputes (US-China, Global North-South)
- Climate negotiations (developed vs. developing nations)
- Digital governance (GDPR vs. Section 230)

**Deliberation Dynamics:**
- Diplomatic/treaty model
- Sovereignty concerns
- Power politics dominant

**Demonstration Value:** VERY LOW
- Far too complex for demonstration
- Audiences lack context
- Deliberation process not relatable

---

### Scale Selection Criteria

**For Demonstration Scenarios:**

**Prioritize:**
1. **Small Group vs. Small Group** (best demonstration value, non-hierarchical, manageable)
2. **Individual vs. Large Group** (media appeal, shows power/accountability)
3. **Small Group vs. Large Group** (if asymmetry manageable)

**Avoid:**
4. Societal-level (too abstract)
5. Global-level (too complex)
6. Individual vs. Individual (too small, low generalizability)

---

## 3. Dimension 2: Conflict Type Taxonomy

### Five Core Conflict Categories

Conflicts arise from different types of values tensions. Each category has distinct deliberation dynamics and demonstration characteristics.

---

### Category 1: RESOURCE ALLOCATION CONFLICTS

**Definition:** Disputes over distribution of finite resources (money, time, space, attention)

**Characteristics:**
- Zero-sum or trade-off structure (more for X = less for Y)
- Quantifiable stakes (often measurable)
- Efficiency vs. fairness tensions
- Utilitarian reasoning common

#### Subcategory 1.1: Monetary/Economic
```
Budget Distribution
├─ Which programs get funding? (education vs. infrastructure)
├─ Departmental allocation (R&D vs. marketing)
└─ Public goods provision (parks vs. police)

Wage Negotiation
├─ Labor vs. capital (profit-sharing)
├─ Pay equity (equal pay for equal work vs. market rates)
└─ Executive compensation (fairness vs. market competition)

Tax Policy
├─ Who pays? (progressive vs. flat tax)
├─ Who benefits? (redistribution vs. incentives)
└─ Intergenerational (debt burden on future generations)

Pricing Decisions
├─ Affordability vs. profitability (pharmaceutical pricing)
├─ Peak vs. off-peak (congestion pricing)
└─ Subsidized access (student discounts, sliding scale)
```

**Moral Frameworks Often in Tension:**
- **Utilitarian:** Maximize total welfare (efficiency)
- **Egalitarian:** Equal distribution (fairness)
- **Libertarian:** Market allocation (freedom)
- **Needs-based:** Distribute according to need (care ethics)

**Demonstration Value:** **MEDIUM-HIGH**
- Concrete, measurable
- Lower emotional charge than identity conflicts
- Shows value trade-offs clearly
- BUT: Can feel "solvable" via technocratic methods (may not showcase pluralism well)

---

#### Subcategory 1.2: Physical Resources (Space, Natural Resources)
```
Land Use
├─ Park vs. housing development (recreation vs. shelter)
├─ Agricultural vs. residential (food vs. growth)
├─ Conservation vs. extraction (wilderness vs. resource use)
└─ Public vs. private (eminent domain)

Water Rights
├─ Agricultural vs. residential (farming vs. drinking)
├─ Urban vs. rural (city growth vs. local control)
├─ Environmental (ecosystem needs vs. human use)
└─ Interstate/international (upstream vs. downstream)

Energy Allocation
├─ Fossil fuels vs. renewables (reliability vs. sustainability)
├─ Grid priority (industrial vs. residential)
└─ Public vs. private (utility regulation)

Spectrum Allocation (Airwaves)
├─ Commercial vs. public broadcasting
├─ Emergency services vs. consumer (5G vs. public safety)
└─ National vs. global (satellite spectrum)
```

**Moral Frameworks:**
- **Stewardship:** Future generations' rights (environmental ethics)
- **Property Rights:** Owner autonomy (libertarian)
- **Public Good:** Common resources (communitarian)
- **Needs-Based:** Essential resources prioritized (care ethics)

**Demonstration Value:** **MEDIUM**
- Tangible, visualizable
- Local examples relatable (park usage)
- But can require technical knowledge (water rights complex)

**Recommended Scenario:** **Public park usage priority** (sports leagues vs. informal users)
- Small group vs. small group (ideal scale)
- Multiple allocation methods legitimate (lottery, first-come, pay, reservation)
- Low-stakes, high-demonstrability

---

#### Subcategory 1.3: Time & Attention Resources
```
Curriculum Time (Educational)
├─ Which subjects taught? (STEM vs. humanities, vocational vs. academic)
├─ How much time per subject? (math hours vs. arts hours)
├─ Standardized test prep vs. enrichment
└─ Mandatory vs. elective (student choice vs. core requirements)

Meeting/Agenda Time (Organizational)
├─ Which topics discussed? (strategic vs. operational)
├─ Who gets speaking time? (seniority vs. equality)
└─ Urgent vs. important (crisis management vs. planning)

Media Coverage (Journalistic)
├─ Which stories run? (breaking news vs. investigative)
├─ Front page vs. buried (visibility, framing)
├─ Diverse perspectives vs. dominant narrative
└─ Sensational vs. important (clicks vs. public interest)

Platform Visibility (Algorithmic Prioritization)
├─ What content surfaces? (engagement vs. quality)
├─ Whose voices amplified? (popularity vs. marginalized perspectives)
├─ Advertising vs. organic (paid vs. earned reach)
└─ Local vs. global (geographic relevance)
```

**Moral Frameworks:**
- **Meritocratic:** Quality/importance determines allocation
- **Egalitarian:** Equal time/attention for all
- **Democratic:** Majority preference determines
- **Epistemic:** Expert judgment prioritizes (in education, journalism)

**Demonstration Value:** **HIGH**
- Affects everyone (everyone experiences time scarcity)
- Shows competing allocation principles clearly
- Curriculum time especially good (multiple legitimate frameworks)

**Recommended Scenario:** **University lecture recording policy**
- Accessibility (recordings help disabled students) vs.
- Pedagogy (attendance matters for learning) vs.
- IP (professor's content ownership) vs.
- Privacy (students visible in recordings)
- Multi-sided, shows cascading considerations

---

### Category 2: BELIEF SYSTEM / WORLDVIEW CONFLICTS

**Definition:** Disputes rooted in differing fundamental commitments (religious, ideological, epistemic)

**Characteristics:**
- High emotional investment (identity-constituting beliefs)
- **Difficult to compromise** (beliefs not easily split)
- Polarization risk (tribalism, in-group/out-group dynamics)
- Appeals to different authorities (scripture, science, tradition, reason)

**WARNING:** These conflicts require **extreme care** in demonstration - high risk of:
- Appearing to take sides
- Offending deeply-held beliefs
- Reinforcing polarization

#### Subcategory 2.1: Religious vs. Secular
```
Religious Accommodation (Institutional)
├─ Prayer space in workplace/school (accommodation vs. separation)
├─ Dietary requirements (halal/kosher in cafeterias, institutions)
├─ Dress codes (religious garb vs. uniform policies)
└─ Sabbath observance (work schedules, exam schedules)

Holiday Observance
├─ School calendar (which holidays recognized?)
├─ Public displays (Christmas tree, menorah, etc. on public property)
└─ Work time off (whose holidays prioritized?)

Moral Instruction (Educational)
├─ Sex education (abstinence vs. comprehensive, LGBTQ+ inclusion)
├─ Evolution vs. creation (science standards)
├─ Moral values (whose ethics taught?)
└─ Religious studies (academic vs. devotional)
```

**Moral Frameworks:**
- **Theistic:** God's commands ultimate authority
- **Secular Humanism:** Human flourishing, reason-based ethics
- **Pluralist:** Accommodate all (within limits)
- **Separationist:** Public sphere should be neutral (no religious influence)

**Demonstration Value:** **LOW for MVP** (too polarized, too risky)
- Recommendation: **Avoid for initial demonstrations**
- If attempted: Focus on **accommodation** cases (less polarized than prohibition cases)
- Example: Workplace prayer space (practical problem-solving frame) better than school curriculum battles

---

#### Subcategory 2.2: Ideological (Political/Economic)
```
Economic Ideology
├─ Free market vs. regulation (libertarian vs. social democratic)
├─ Individual achievement vs. structural factors (meritocracy vs. systemic analysis)
├─ Growth vs. redistribution (expand pie vs. share pie)
└─ Private property vs. commons (ownership models)

Political Ideology
├─ Individual rights vs. collective welfare (liberty vs. solidarity)
├─ Tradition vs. progress (conservatism vs. progressivism)
├─ Nationalism vs. globalism (sovereignty vs. cosmopolitanism)
└─ Centralization vs. localism (federal vs. states' rights)
```

**Demonstration Value:** **MEDIUM** (depends on issue)
- **Avoid:** Partisan hot-button issues (abortion, gun control, immigration)
- **Consider:** Emerging issues before partisan alignment (e.g., remote work policies, AI regulation in early stages)
- **Frame carefully:** Present as value trade-offs, not tribal allegiances

---

#### Subcategory 2.3: Epistemic (Knowledge/Truth)
```
Authority & Expertise
├─ Scientific consensus vs. alternative theories (climate, vaccines)
├─ Expert authority vs. lived experience (who knows best?)
├─ Credentials vs. experiential knowledge (PhD vs. practitioner)
└─ Peer review vs. public access (knowledge gatekeeping)

Evidence Standards
├─ What counts as proof? (empirical, anecdotal, intuitive)
├─ Burden of proof (precautionary vs. permissive)
├─ Replication crisis (what counts as verified?)
└─ Quantitative vs. qualitative (numbers vs. narratives)

Historical Interpretation
├─ Whose narrative? (victor's history vs. marginalized voices)
├─ Presentism vs. contextualism (judge past by present standards?)
├─ National myths vs. critical history (patriotism vs. honesty)
└─ Commemoration (monuments, renaming)
```

**Moral Frameworks:**
- **Empiricism:** Evidence-based, scientific method
- **Lived Experience:** Personal knowledge valid (feminist epistemology)
- **Traditional:** Inherited wisdom (Burkean conservatism)
- **Critical:** Question power structures in knowledge production

**Demonstration Value:** **MEDIUM-HIGH for select cases**
- **Good:** Methodological disputes in research (ethical, practical)
- **Good:** Evidence standards (how much proof required? precautionary principle)
- **Avoid:** Science denial cases (climate, vaccines - too polarized)

**Recommended Scenario:** **Research ethics - preprint publication**
- Speed (public access to findings quickly) vs.
- Quality control (peer review catches errors) vs.
- Equity (open access vs. paywalls)
- Academic community understands nuances, shows expert disagreement as legitimate

---

### Category 3: LEGAL / PROCEDURAL CONFLICTS

**Definition:** Disputes over rules, processes, jurisdiction, and interpretation

**Characteristics:**
- **High demonstration value** (shows deliberation in rule-making)
- Procedural fairness salient (how we decide matters)
- Rights vs. efficiency common tension
- Often shows **legal formalism vs. equity** tension

#### Subcategory 3.1: Rights-Based
```
Speech vs. Safety
├─ Hate speech policies (free expression vs. harm prevention)
├─ Misinformation (truth vs. censorship)
├─ Campus speakers (platforming vs. protest rights)
└─ Workplace speech (employee expression vs. employer reputation)

Privacy vs. Transparency
├─ FOIA requests (public records vs. privacy)
├─ Anonymity vs. accountability (online identities)
├─ Surveillance (security vs. privacy)
└─ Data collection (research vs. consent)

Due Process vs. Efficiency
├─ Expedited decisions (speed vs. fairness)
├─ Automated adjudication (scale vs. individual consideration)
├─ Appeals processes (finality vs. error correction)
└─ Burden of proof (who must prove what?)

Equality vs. Liberty
├─ Anti-discrimination laws (equal treatment vs. freedom of association)
├─ Affirmative action (equal opportunity vs. colorblindness)
├─ Accessibility mandates (inclusion vs. cost/autonomy)
└─ Compelled speech (non-discrimination vs. conscience)
```

**Moral Frameworks:**
- **Rights-based (Deontological):** Inviolable rights, side constraints
- **Utilitarian:** Outcomes matter most (rights as instruments)
- **Communitarian:** Rights balanced with community good
- **Libertarian:** Negative rights (non-interference) prioritized

**Demonstration Value:** **VERY HIGH**
- Shows non-hierarchical reasoning perfectly
- Rights conflicts are **genuinely incommensurable** (can't just "maximize rights")
- Procedural fairness widely valued (less partisan)
- **Speech vs. safety** particularly rich (multiple legitimate positions)

**Recommended Scenario:** **Platform content moderation - algorithmic vs. human review**
- Efficiency (AI scales) vs. Fairness (human judgment) vs. Consistency (algorithms) vs. Context (humans understand nuance)
- BUT: May be over-covered in media (less fresh)

**Better Option:** **Algorithmic hiring transparency** (detailed in Document 2)
- Efficiency vs. Fairness vs. Privacy vs. Accountability
- Timely, affects many, not yet polarized

---

#### Subcategory 3.2: Jurisdictional / Procedural
```
Authority & Hierarchy
├─ Federal vs. state (who decides? marijuana laws, abortion)
├─ Organizational hierarchy (manager vs. employee, board vs. CEO)
├─ International vs. national (treaties vs. sovereignty)
└─ Expert vs. democratic (technocracy vs. popular will)

Precedent vs. Adaptation
├─ Strict interpretation vs. flexible (constitutional originalism vs. living document)
├─ Precedent binding vs. revisable (stare decisis vs. overturning)
├─ Rulebook literalism vs. spirit (following vs. creative interpretation)
└─ Zero tolerance vs. discretion (mandatory minimums vs. judicial discretion)

Contract Interpretation
├─ Letter vs. spirit (what was written vs. what was meant)
├─ Changed circumstances (force majeure, hardship)
├─ Good faith (implied duties vs. explicit only)
└─ Penalty vs. remedy (enforcement mechanisms)
```

**Moral Frameworks:**
- **Formalism:** Rules as written, predictability
- **Equity:** Fairness in specific case, context matters
- **Democratic:** Majoritarian (elected officials decide)
- **Expertise:** Technocratic (specialists decide)

**Demonstration Value:** **MEDIUM-HIGH**
- Good for showing **practical wisdom** (phronesis) vs. rule-following
- Relatable to anyone in organizations
- Can be dry if not grounded in concrete scenario

---

### Category 4: IDENTITY / RECOGNITION CONFLICTS

**Definition:** Disputes over group status, representation, historical redress, cultural recognition

**Characteristics:**
- **HIGHEST emotional stakes** (identity-constituting)
- **HIGHEST pattern bias risk** (can reinforce stereotypes, marginalize)
- Power dynamics central (historically marginalized groups)
- Zero-sum framing common (but false - recognition not scarce)

**WARNING:** These are the **highest-risk** scenarios for demonstrations. Require:
- Extensive cultural sensitivity review
- Stakeholder input from affected communities
- Careful framing to avoid tokenization
- **Recommendation: AVOID for MVP**, consider only after establishing credibility

#### Subcategory 4.1: Cultural Recognition
```
Language Policy
├─ Official languages (which recognized? bilingual requirements?)
├─ Minority language preservation (investment in dying languages)
├─ Education language (immersion vs. English-first)
└─ Government services (translation requirements)

Representation
├─ Who gets voice at table? (board diversity, advisory committees)
├─ Proportional vs. equal (population share vs. every group gets one seat)
├─ Descriptive vs. substantive (members of group vs. advancing group interests)
└─ Token vs. genuine (optics vs. power)

Naming & Symbols
├─ Monument controversy (remove, rename, contextualize?)
├─ Place names (Columbus, Washington - problematic figures)
├─ Team names/mascots (Indigenous peoples, appropriation)
└─ Holidays (Columbus Day vs. Indigenous Peoples' Day)

Cultural Appropriation
├─ Use of sacred symbols (headdresses, religious items)
├─ Art/fashion borrowing (line between appreciation and appropriation)
├─ Commodification (selling cultural practices)
└─ Gatekeeping vs. sharing (who decides what's acceptable?)
```

**Demonstration Value:** **LOW for MVP** (too risky, too fraught)
- These conflicts are **real, important, and difficult**
- But demonstrations risk:
  - Simplifying complex histories
  - Tokenizing affected groups
  - Causing vicarious harm
  - Appearing to "solve" what can't be solved in deliberation

**Exception:** Might work if:
- Stakeholders from affected groups co-design scenario
- Focus on **procedural** question (how to decide?) not **substantive** (what to decide?)
- Example: "How should a university decide about renaming a building?" (process) rather than "Should Columbus statue be removed?" (outcome)

---

#### Subcategory 4.2: Group Status & Historical Redress
```
Historical Grievance & Reparations
├─ Slavery reparations (compensation for historical injustice)
├─ Indigenous land rights (treaties, sovereignty)
├─ Restitution (stolen art, property)
└─ Acknowledgment (formal apologies, truth commissions)

Protected Class Definitions
├─ Who qualifies for protection? (which groups recognized?)
├─ Immutable vs. voluntary (race vs. religion)
├─ Expanding categories (LGBTQ+, disability, neurodivergence)
└─ Intersectionality (overlapping identities, compounding disadvantage)

Minority Rights vs. Majority Rule
├─ Veto power (protect minorities from majority tyranny)
├─ Supermajority requirements (consensus vs. efficiency)
├─ Proportional representation (voting systems)
└─ Opt-out provisions (accommodation for minority practices)
```

**Demonstration Value:** **VERY LOW** (avoid for demonstration)
- These are **the most difficult** value conflicts
- Deep historical wounds, ongoing injustice
- Demonstrations risk:
  - Re-traumatization
  - False equivalence (treating oppression as "just another view")
  - Delegitimizing lived experience

---

### Category 5: SCIENTIFIC / TECHNICAL CONFLICTS

**Definition:** Disputes over technical standards, risk assessment, research ethics, methodological choices

**Characteristics:**
- **Expertise matters** (but expert disagreement possible)
- Precautionary principle often debated
- Quantifiable but **uncertainty** high
- Public values + technical judgment

#### Subcategory 5.1: Risk Assessment
```
Safety Standards
├─ How safe is safe enough? (acceptable risk levels)
├─ Cost-benefit analysis (value of life, QALY calculations)
├─ Precautionary vs. permissive (prove safe vs. prove harmful)
└─ Catastrophic vs. incremental (low-probability, high-impact events)

Environmental Impact
├─ Acceptable pollution levels (air quality, water quality)
├─ Habitat destruction vs. development (species protection)
├─ Climate policy (carbon budgets, adaptation vs. mitigation)
└─ Intergenerational (discount rates for future harm)

Health Policy
├─ Quarantine vs. freedom (pandemic response)
├─ Mandatory vaccination (herd immunity vs. bodily autonomy)
├─ Drug approval (efficacy standards, expedited review)
└─ Mental health holds (involuntary commitment criteria)

Technology Adoption
├─ Precautionary vs. permissive (prove safe vs. iterate quickly)
├─ Dual-use research (gain-of-function, AI capabilities)
├─ Genetic engineering (CRISPR, gene drives)
└─ Autonomous systems (self-driving cars, military drones)
```

**Moral Frameworks:**
- **Precautionary:** Prevent harm, err on side of caution
- **Permissive:** Innovation benefits, over-regulation stifles progress
- **Risk-averse:** Protect most vulnerable
- **Utilitarian:** Maximize expected value (cost-benefit)

**Demonstration Value:** **MEDIUM**
- Shows **epistemic humility** (uncertainty, expert disagreement)
- But can feel technocratic (less about values, more about facts)
- **Avoid:** Health crises (COVID, vaccines - too polarized, too traumatic)
- **Consider:** Technology governance (AI, biotech - emerging, not yet entrenched)

---

#### Subcategory 5.2: Methodological & Research Ethics
```
Research Ethics
├─ Animal testing (scientific benefit vs. animal welfare)
├─ Human subjects (informed consent, vulnerable populations)
├─ Dual-use research (knowledge that could be weaponized)
└─ Data ownership (who owns research data? Indigenous knowledge?)

Data Collection & Privacy
├─ Surveillance for research (public health tracking, mobility data)
├─ Consent models (opt-in, opt-out, broad consent)
├─ Data sharing (open science vs. privacy)
└─ Commercial use (research data → products)

Publication Standards
├─ Preprints vs. peer review (speed vs. quality)
├─ Open access vs. paywalls (equity vs. sustainability)
├─ Replication requirements (when is a finding verified?)
└─ Registered reports (pre-commit to methods vs. flexibility)
```

**Demonstration Value:** **HIGH for specialized audiences**
- Academic/research community understands nuances
- Shows **legitimate expert disagreement**
- Demonstrates **procedural deliberation** (how should we decide standards?)
- Less media-salient but teaches principles well

**Recommended Scenario:** **Preprint publication standards**
- Speed (public access to COVID research) vs.
- Quality control (peer review prevents misinformation) vs.
- Equity (open access vs. journal paywalls)
- Shows epistemic humility, accommodation (tiered system emerged)

---

## Conflict Type Summary Table

| Category | Demonstration Value | Safety Risk | Media Interest | Recommended for MVP? |
|----------|-------------------|-------------|----------------|---------------------|
| **1. Resource Allocation** | Medium-High | Low | Medium | **YES** (park usage, curriculum time) |
| **2. Belief System** | Low-Medium | High | High | **NO** (too polarized) |
| **3. Legal/Procedural** | Very High | Low-Medium | Medium-High | **YES** (algorithmic hiring, lecture recording) |
| **4. Identity/Recognition** | Low | Very High | Very High | **NO** (too risky for MVP) |
| **5. Scientific/Technical** | Medium | Low-Medium | Medium | **MAYBE** (research ethics good, health policy avoid) |

**Key Insight:** **Legal/Procedural conflicts** (Category 3) and **Resource Allocation** (Category 1) offer **best demonstration opportunities** for MVP:
- Lower emotional charge than belief/identity conflicts
- Demonstrate pluralistic reasoning clearly
- Relatable to broad audiences
- Lower pattern bias risk

---

## 4. Dimension 3: Differentiating Attributes & Pattern Bias Risk

### Pattern Bias Risk Framework

**Definition:** The risk that a demonstration scenario inadvertently reinforces harmful stereotypes, marginalizes vulnerable groups, or causes vicarious harm.

**Why This Matters:**
- Demonstrations have real-world impact (people see themselves in scenarios)
- Tractatus claims to prevent AI harms → must model ethical practices
- Pattern bias is insidious → requires proactive analysis, not just "good intentions"

### Demographic Dimensions Analysis

#### Age
**Examples:**
- Youth vs. elders (climate urgency, social security policy)
- Working age vs. retired (healthcare priorities)
- Digital natives vs. immigrants (technology policy)
- Generational wealth (housing affordability)

**Pattern Bias Risks:**
- **Ageism:** "Boomers are selfish," "Millennials are entitled"
- **Stereotype reinforcement:** Old = technophobic, young = irresponsible
- **Generational warfare:** Presenting as zero-sum (misleading)

**Risk Level:** **MEDIUM**
- Age-based scenarios can work if carefully framed
- Focus on **structural issues** (retirement system design) not **character** ("old people hoard wealth")
- Avoid pitting generations against each other unnecessarily

**Mitigation:**
- Show intra-generational diversity (not all boomers same)
- Frame as **policy design** question, not moral failing
- Include voices from multiple age cohorts as stakeholders

---

#### Education Level
**Examples:**
- Credentialed vs. experiential knowledge (who's the expert?)
- Academic vs. vocational (curriculum priorities, funding)
- Literate vs. oral traditions (documentation requirements)
- Expert vs. lay (who gets to decide?)

**Pattern Bias Risks:**
- **Elitism:** Privileging academic knowledge over practical
- **Anti-intellectualism:** Dismissing expertise as "out of touch"
- **Credentialism:** Ignoring lived experience, informal knowledge

**Risk Level:** **MEDIUM**
- Education conflicts can showcase **epistemic pluralism** (multiple valid ways of knowing)
- But risk reinforcing hierarchies (PhD vs. high school diploma)

**Mitigation:**
- Frame as **complementary** knowledge types, not hierarchical
- Show cases where formal credentials insufficient (local knowledge matters)
- Avoid scenarios where education level correlates with "rightness"

**Good Example:** **Research ethics - patient/community input**
- Medical researchers (credentialed) + patients (experiential) both essential
- Neither can be substituted for the other
- Shows **epistemic humility**

---

#### Socioeconomic Status (Class)
**Examples:**
- Wealth disparity (progressive taxation, wealth tax)
- Precariat vs. secure employment (gig economy classification)
- Urban vs. rural (infrastructure investment)
- Property owners vs. renters (development policy, zoning)

**Pattern Bias Risks:**
- **Class resentment:** "Rich people are greedy," "Poor people are lazy"
- **Just-world fallacy:** Wealth = merit, poverty = failure
- **Rural/urban divide:** Coastal elites vs. heartland (geographic class)

**Risk Level:** **MEDIUM**
- Economic conflicts are **less personal** than identity (situational, not intrinsic)
- But class is **deeply stratified** → power dynamics matter
- Can trigger resentment (especially in polarized contexts)

**Mitigation:**
- Focus on **structural issues** (housing policy, labor law) not individuals
- Avoid "deserving vs. undeserving" framing
- Show cross-class coalitions (not always rich vs. poor)

**Good Example:** **Remote work location pay**
- Urban workers (high cost of living) vs. rural workers (lower costs)
- Employers (market efficiency) vs. workers (fairness)
- **Not**: Rich vs. poor (economic, but not class warfare)

---

#### Geographic
**Examples:**
- National origin (immigration policy, citizenship)
- Regional (coastal vs. inland, metro vs. rural)
- Linguistic (anglophone vs. non-anglophone)
- Climate zone (adaptation priorities, resource allocation)

**Pattern Bias Risks:**
- **Xenophobia:** "Foreigners" as threat
- **Urban bias:** Rural as backward, urban as elitist
- **Regionalism:** North vs. South, coast vs. heartland stereotypes

**Risk Level:** **LOW-MEDIUM**
- Geographic differences **less personal** than race, gender, religion
- But can proxy for other dimensions (urban/rural → class, culture)
- **Immigration** = high risk (entangled with race, nationalism)

**Mitigation:**
- Focus on **geographic policy questions** (infrastructure, services) not character
- Avoid "urban good, rural bad" or vice versa framings
- Show diversity within regions (rural is not monolithic)

**Good Example:** **Municipal services allocation**
- Downtown vs. neighborhoods (transit, parks)
- Geographic but not identity-based
- Shows **resource allocation** clearly

---

#### Race & Ethnicity
**Examples:**
- Majority vs. minority (representation, power)
- Settler vs. Indigenous (land rights, sovereignty)
- Diaspora vs. homeland (political allegiance, identity)
- Multiracial (identity category boundaries, census)

**Pattern Bias Risks:**
- **Racism:** Reinforcing stereotypes, marginalizing voices
- **Colorblindness:** Ignoring historical and structural racism
- **Tokenization:** Treating diverse groups as monolithic
- **Appropriation:** Using cultural conflict for demonstration without accountability

**Risk Level:** **VERY HIGH**

**Recommendation for MVP:** **AVOID**
- Race is **foundational to US (and many other) societies** → conflicts are real and urgent
- But demonstrations risk:
  - Oversimplifying centuries of oppression
  - False equivalence (treating racism as "just a view")
  - Re-traumatization for people of color
  - Exploitation (using pain for demonstration purposes)

**When to Attempt (if ever):**
- Only with **co-design** by racial justice organizations
- Focus on **procedural** questions (how to make decisions about equity policies?) not substantive
- **After** establishing credibility with lower-risk scenarios
- With **extensive** cultural sensitivity review

---

#### Gender & Sexuality
**Examples:**
- Cisgender vs. transgender (bathroom policy, sports inclusion)
- Heteronormative vs. LGBTQ+ (marriage recognition, adoption)
- Binary vs. non-binary (form design, pronouns)
- Reproductive autonomy (abortion access, surrogacy)

**Pattern Bias Risks:**
- **Transphobia:** Denying trans identities, "debate my existence"
- **Heteronormativity:** Assuming straight/cis as default, "normal"
- **Misogyny:** Gender bias in framing (women as emotional, men as rational)
- **Erasure:** Ignoring non-binary, intersex people

**Risk Level:** **VERY HIGH**

**Recommendation for MVP:** **AVOID**
- Gender/sexuality conflicts are **highly polarized** (culture war flashpoint)
- **Trans rights especially fraught** → "both sides" framing delegitimizes trans existence
- Demonstrations risk causing harm to already-marginalized groups

**Exception:**
- **Procedural** questions in specific contexts might work
- Example: "How should an organization design a form to be inclusive of gender diversity?" (practical, design-focused)
- Still risky → only attempt with LGBTQ+ stakeholder input

---

#### Ability (Disability, Neurodivergence)
**Examples:**
- Neurotypical vs. neurodivergent (workplace norms, accommodation)
- Able-bodied vs. disabled (accessibility standards, universal design)
- Sensory (visual, auditory accommodations)
- Cognitive (information design, simplification)

**Pattern Bias Risks:**
- **Ableism:** Treating disability as deficit, not difference
- **Burden framing:** Accessibility as "cost" not "right"
- **Medical model:** Disability as individual problem, not social construct
- **Inspiration porn:** Exploiting disabled people's stories

**Risk Level:** **MEDIUM-HIGH**
- Disability conflicts are **important** (accessibility, inclusion)
- But require **disability justice** framing (social model, not medical model)
- Risk of framing accessibility as "nice to have" not "civil right"

**Mitigation:**
- Use **social model** (barriers are in environment, not person)
- Frame as **universal design** (benefits everyone, not just "special accommodation")
- Include disabled stakeholders in scenario design (nothing about us without us)

**Good Example:** **University lecture recording**
- Accessibility (recordings help disabled students) is ONE consideration among many
- Not framed as "accommodate disabled vs. professor rights"
- Shows accessibility as part of pedagogical design, not burden

---

### Pattern Bias Risk Assessment Matrix

| Dimension | Risk Level | Safe for MVP? | Notes |
|-----------|-----------|---------------|-------|
| **Age** | Medium | ⚠️ With care | Avoid generational warfare framing |
| **Education** | Medium | ⚠️ With care | Show epistemic pluralism, not hierarchy |
| **Socioeconomic** | Medium | ✅ YES | Economic, not identity; avoid class resentment |
| **Geographic** | Low-Medium | ✅ YES | Especially local (urban vs. rural okay if balanced) |
| **Race/Ethnicity** | Very High | ❌ NO | Avoid for MVP; only with co-design later |
| **Gender/Sexuality** | Very High | ❌ NO | Highly polarized; risk delegitimizing identities |
| **Ability** | Medium-High | ⚠️ Maybe | Requires disability justice framing; include disabled stakeholders |

### General Mitigation Principles

**1. Avoid Centering Vulnerable Groups**
- Don't make demonstrations "about" marginalized identities
- If a dimension is present, it should be **incidental** not **central**

**2. Structural vs. Individual Framing**
- Focus on **systems, policies, structures**
- Avoid framing as individual moral failings

**3. Intra-Group Diversity**
- Show diversity **within** groups (not all X are the same)
- Avoid monolithic portrayals

**4. Power-Aware**
- Acknowledge historical and structural power imbalances
- Don't "both sides" oppression

**5. Stakeholder Input**
- If a marginalized group is affected, **include their input in design**
- Co-design, not extraction

---

## 5. Dimension 4: Media Interest Patterns

### Why Media Patterns Matter

**Strategic Demonstration Considerations:**

**1. Public Salience**
- If audiences don't care about the issue, demonstration won't resonate
- But: We can't just follow hype (avoid clickbait)

**2. Polarization Level**
- **High salience + low polarization** = IDEAL (everyone cares, not entrenched)
- **High salience + high polarization** = RISKY (hard to appear neutral)
- **Low salience + low polarization** = BORING (no one cares)

**3. Emerging vs. Entrenched**
- **Emerging issues** = opportunity (shape conversation before battle lines harden)
- **Entrenched issues** = difficult (positions already hardened, tribalism high)

**4. Demonstration Timing**
- Some issues "hot" now but will cool (COVID restrictions)
- Some perennial (privacy vs. security)
- Target: Rising interest, not yet peaked

---

### Media Pattern Analysis Methods

(See Document 4: Media Pattern Research Guide for full methodology)

**Quick Assessment Tools:**

**1. Google Trends (5-year lookback)**
- Search volume over time (rising, stable, declining?)
- Geographic distribution (global, regional, local?)
- Related queries (what context?)

**2. News Coverage Scan**
- Major outlets (NYT, Guardian, BBC, etc.)
- Article count trends (increasing attention?)
- Partisan divide (liberal vs. conservative framing different?)

**3. Academic Literature**
- Google Scholar search volume
- Recent publications in top journals
- Interdisciplinary interest (ethics, law, CS, etc.)

**4. Regulatory Activity**
- Legislation introduced (bills, hearings)
- Agency actions (rulemaking, guidance)
- International (EU AI Act, GDPR, etc.)

---

### Current Media Landscape (2024-2025)

**High-Salience Topics:**

**AI Ethics & Governance**
- Algorithmic bias (hiring, lending, criminal justice)
- Content moderation (free speech, misinformation)
- Deepfakes & synthetic media
- Autonomous systems (self-driving cars, drones)
- **Status:** High interest, LOW-MEDIUM polarization (still debating frameworks)
- **Recommendation:** **EXCELLENT for demonstrations**

**Labor & Work**
- Remote work (return to office, geographic pay, flexibility)
- Gig economy (worker classification, benefits)
- AI automation (job displacement, retraining)
- Unionization (Starbucks, Amazon)
- **Status:** High interest, MEDIUM polarization (partisan but not tribal)
- **Recommendation:** **GOOD for demonstrations** (especially remote work, less partisan than unions)

**Climate & Environment**
- Carbon pricing (cap-and-trade, carbon tax)
- Adaptation vs. mitigation (spend on prevention or resilience?)
- Climate migration (borders, refugees)
- Geoengineering (technological solutions vs. risk)
- **Status:** High interest, HIGH polarization (climate denial vs. activism)
- **Recommendation:** **AVOID for MVP** (too polarized, though important)

**Platform Governance**
- Social media content moderation (hate speech, misinformation)
- Algorithmic amplification (engagement vs. quality)
- Privacy (data collection, tracking)
- Child safety (age verification, parental consent)
- **Status:** High interest, HIGH polarization (Section 230, "censorship" debates)
- **Recommendation:** **RISKY** (very polarized, tribalized)

**Education & Curriculum**
- School curriculum (CRT, LGBTQ+ content, book bans)
- Higher ed (student debt, free college, affirmative action)
- Online learning (access, quality)
- AI in education (ChatGPT, essay detectors)
- **Status:** High interest, VERY HIGH polarization (culture war flashpoint)
- **Recommendation:** **AVOID** (except technical questions like AI detectors, recording policies)

---

### Emerging Issues (Opportunity Window)

**These issues have RISING interest but NOT YET polarized into tribal camps:**

**1. Algorithmic Hiring & Workplace AI** ⭐⭐⭐⭐⭐
- Google Trends: Rising 2020-2024
- News: Bias cases (Amazon, Facebook) drove coverage
- Regulatory: NYC Local Law 144 (2023), EU AI Act
- Academic: Growing fairness, explainability literature
- **Polarization:** LOW (not yet partisan)
- **Assessment:** **IDEAL for demonstration**

**2. Remote Work Policies** ⭐⭐⭐⭐
- Google Trends: Spiked 2020 (COVID), still elevated
- News: Return-to-office battles, geographic pay debates
- Regulatory: Minimal (mostly private sector)
- **Polarization:** LOW-MEDIUM (some partisan framing, but mostly practical)
- **Assessment:** **EXCELLENT for demonstration**

**3. Digital Inheritance & Data After Death** ⭐⭐⭐
- Google Trends: Slowly rising
- News: Occasional human-interest stories
- Regulatory: Patchwork (RUFADAA in some US states)
- **Polarization:** VERY LOW (not partisan)
- **Assessment:** **GOOD but low salience** (audiences may not care yet)

**4. Gig Worker Classification** ⭐⭐⭐⭐
- Google Trends: Stable, moderate interest
- News: Prop 22 (California), Uber/Lyft battles
- Regulatory: Active (state-by-state, Europe)
- **Polarization:** MEDIUM (labor vs. capital, but not tribal)
- **Assessment:** **GOOD** (some partisan overtones, but deliberation possible)

**5. Synthetic Biology & CRISPR Ethics** ⭐⭐⭐
- Google Trends: Moderate, stable
- News: Periodic (gene drive, CRISPR babies scandal)
- Regulatory: Developing (FDA, international)
- **Polarization:** LOW (expert debate, public not engaged)
- **Assessment:** **GOOD for specialized audiences** (less mass appeal)

---

### Entrenched Issues (Avoid for MVP)

**These issues are HIGHLY POLARIZED, tribal:**

❌ **Abortion** (culture war, identity-level)
❌ **Gun control** (Second Amendment, partisan identity)
❌ **Immigration** (borders, nationalism, race)
❌ **Climate change** (denial vs. activism, partisan)
❌ **Vaccine mandates** (bodily autonomy vs. public health, post-COVID trauma)
❌ **Critical Race Theory** (education culture war)
❌ **Trans rights** (gender identity, bathroom/sports battles)

**Why avoid:**
- Positions hardened into tribal identities
- "Both sides" framing delegitimizes one side (often oppressed group)
- Audiences bring heavy baggage
- Deliberation unlikely to be seen as legitimate

---

### Media Pattern Summary for Top Scenarios

| Scenario | Google Trends | News Coverage | Regulatory | Polarization | Recommendation |
|----------|--------------|---------------|------------|--------------|----------------|
| **Algorithmic Hiring** | Rising | High (bias cases) | Emerging (NYC, EU) | LOW | ⭐⭐⭐⭐⭐ IDEAL |
| **Remote Work Pay** | Elevated | High (RTO battles) | Minimal | LOW-MED | ⭐⭐⭐⭐ EXCELLENT |
| **Park Usage Priority** | Low | Local only | Local | VERY LOW | ⭐⭐⭐ GOOD (low salience) |
| **Open-Source Licensing** | Stable | Tech community | Minimal | LOW | ⭐⭐⭐ GOOD (niche) |
| **Lecture Recording** | Low | Higher ed only | FERPA, ADA | LOW | ⭐⭐⭐⭐ GOOD (education context) |

---

## 6. Scenario Taxonomy

Based on the four-dimensional analysis above, scenarios are classified into three tiers:

### Tier 1: Strong Candidates for MVP (Score 20+/25)

These scenarios excel across multiple dimensions: demonstrable, safe, generalizable, media-relevant, and showcase pluralistic reasoning.

---

#### Scenario 1.1: Algorithmic Hiring Transparency ⭐⭐⭐⭐⭐

**Conflict:** Should companies be required to disclose how AI algorithms screen job applicants?

**Scale:** Individual (job seeker) vs. Large Group (employers, platform companies)

**Conflict Type:** Legal/Procedural (rights-based)
- Efficiency (AI screening saves time/money) vs.
- Fairness (candidates deserve to know selection criteria) vs.
- Privacy (what data is collected/used?) vs.
- Accountability (can bias be audited?) vs.
- Innovation (transparency requirements may stifle progress)

**Moral Frameworks in Tension:**
- **Consequentialist (Utilitarian):** Maximize hiring quality, minimize cost → proprietary algorithms optimal
- **Rights-based (Deontological):** Candidates have right to know basis of decisions affecting them
- **Care Ethics:** Dignity in job search, trust between employer-candidate
- **Libertarian:** Property rights in algorithms, freedom to use proprietary systems
- **Communitarian:** Labor market fairness affects social mobility, collective good

**Stakeholders:**
- Job seekers (especially historically disadvantaged groups)
- Employers (HR departments, hiring managers)
- AI/ATS vendors (technology companies)
- Civil rights organizations
- Labor advocates
- Regulators (EEOC, FTC, EU)

**Demonstration Value:**
- **Demonstrability:** 4/5 (requires brief AI screening explanation, then clear)
- **Safety:** 5/5 (economic, not identity-based; job seeking relatable, not traumatic)
- **Generalizability:** 5/5 (applicable to all algorithmic decision contexts)
- **Media Appeal:** 5/5 (timely, high interest, bias cases drove coverage)
- **Pluralism Showcase:** 5/5 (multiple frameworks, genuine incommensurability)

**Total Score:** 24/25

**Why This is Ideal:**
- **Timely:** NYC Local Law 144 (2023), EU AI Act, rising interest
- **Not yet polarized:** Before partisan battle lines harden
- **Multiple legitimate frameworks:** Not obvious which should "win"
- **Affects many people:** Millions experience AI hiring screening
- **Demonstrates accommodation:** Tiered transparency model emerges naturally
- **Generalizable:** Teaches algorithmic accountability broadly

**Recommended as PRIMARY demonstration scenario** (see Document 2 for deep-dive)

---

#### Scenario 1.2: Remote Work Location-Based Pay ⭐⭐⭐⭐

**Conflict:** Should companies pay employees differently based on where they live when working remotely?

**Scale:** Individual (workers) vs. Large Group (employers) & Small Group vs. Small Group (urban workers vs. rural workers)

**Conflict Type:** Resource Allocation (economic)
- Market efficiency (pay reflects local cost of living) vs.
- Equal pay for equal work (same job, same pay) vs.
- Geographic equity (rural economic development) vs.
- Recruiting/retention (talent competition)

**Moral Frameworks in Tension:**
- **Free Market (Libertarian):** Wages determined by supply/demand, local markets differ
- **Egalitarian:** Equal pay for equal work, location irrelevant
- **Utilitarian:** Maximize total welfare (company competitiveness + worker welfare)
- **Communitarian:** Regional development, strengthen rural economies

**Stakeholders:**
- Remote workers in high-cost areas (San Francisco, New York)
- Remote workers in low-cost areas (rural, small cities)
- Employers (multinational corporations, startups)
- Geographic communities (urban, rural economic development groups)
- Compensation consultants

**Demonstration Value:**
- **Demonstrability:** 5/5 (immediately relatable, concrete)
- **Safety:** 5/5 (economic, geographic dimension less personal than race/gender)
- **Generalizability:** 4/5 (teaches economic fairness principles)
- **Media Appeal:** 4/5 (post-pandemic relevance, RTO battles)
- **Pluralism Showcase:** 4/5 (multiple frameworks, though utilitarian reasoning strong)

**Total Score:** 22/25

**Why This is Strong:**
- **Timely:** Post-pandemic remote work debates ongoing
- **Relatable:** Many people experienced remote work, understand trade-offs
- **Economic not identity:** Less emotional charge than identity conflicts
- **Shows accommodation:** Hybrid models (base pay + COLA) emerging
- **Not tribal:** Not yet aligned with partisan identities

**Recommended as SECONDARY demonstration scenario**

---

#### Scenario 1.3: Public Park Usage Priority ⭐⭐⭐⭐

**Conflict:** How should public parks allocate space between organized sports leagues and informal users (pickup games, picnics, walking)?

**Scale:** Small Group vs. Small Group (ideal for non-hierarchical demonstration)

**Conflict Type:** Resource Allocation (physical space)
- Efficiency (organized leagues maximize field use) vs.
- Equal access (first-come-first-served fairness) vs.
- Community benefit (youth sports vs. family time) vs.
- Conservation (minimize field wear)

**Moral Frameworks in Tension:**
- **Utilitarian:** Most users served (organized leagues pack more people per hour)
- **Egalitarian:** Equal access for all (no prioritization)
- **Communitarian:** Youth development (invest in children's sports)
- **Libertarian:** Pay for reservations (market allocation)
- **Virtue Ethics:** Civic space for spontaneous community gathering

**Stakeholders:**
- Youth sports leagues (soccer, baseball, etc.)
- Adult sports leagues
- Families (picnics, informal play)
- Informal users (frisbee, walking, etc.)
- Environmental advocates (field conservation)
- Municipal parks department

**Demonstration Value:**
- **Demonstrability:** 5/5 (can draw diagram, immediately visual)
- **Safety:** 5/5 (infrastructure, no vulnerable groups)
- **Generalizability:** 4/5 (resource allocation principles)
- **Media Appeal:** 2/5 (local interest only, low national salience)
- **Pluralism Showcase:** 5/5 (multiple allocation methods legitimate)

**Total Score:** 21/25

**Why This is Strong:**
- **Perfect scale:** Small group vs. small group (non-hierarchical)
- **Concrete:** Everyone understands park usage
- **Low-stakes:** No one's life depends on this (safe for demonstration)
- **Multiple solutions:** Lottery, reservation, pay, first-come, hybrid
- **Shows negotiation:** Natural accommodation (time-sharing, designated zones)

**Limitation:**
- Low media salience (local issue)
- But: **Excellent teaching tool** for deliberation principles

---

#### Scenario 1.4: University Lecture Recording Policy ⭐⭐⭐⭐

**Conflict:** Should universities require professors to record lectures and make them available to students?

**Scale:** Individual (professors) vs. Large Group (students, administration)

**Conflict Type:** Legal/Procedural + Resource Allocation (time/attention)
- Accessibility (recordings help disabled students) vs.
- Pedagogy (attendance matters for learning) vs.
- Intellectual property (professor's content ownership) vs.
- Privacy (students visible in recordings) vs.
- Workload (additional labor for recording/editing)

**Moral Frameworks in Tension:**
- **Rights-based:** Disability accommodation (ADA compliance)
- **Epistemic (Pedagogical):** Teaching effectiveness (in-person learning superior?)
- **Property Rights:** Faculty own their lectures (IP)
- **Utilitarian:** Maximize learning outcomes
- **Care Ethics:** Student welfare (flexibility, support)

**Stakeholders:**
- Students with disabilities
- General student body
- Faculty (professors, instructors)
- University administration
- IT departments (recording infrastructure)
- Legal (FERPA, ADA compliance)

**Demonstration Value:**
- **Demonstrability:** 4/5 (requires brief higher ed context, then clear)
- **Safety:** 5/5 (no vulnerable groups centered, academic context)
- **Generalizability:** 4/5 (workplace recording, remote work parallels)
- **Media Appeal:** 3/5 (higher ed community interest, less broad)
- **Pluralism Showcase:** 5/5 (multiple frameworks, cascading considerations)

**Total Score:** 21/25

**Why This is Strong:**
- **Multi-sided:** Not binary (record vs. don't), many considerations
- **Shows complexity:** Accessibility is important BUT not the only value
- **Accommodation:** Hybrid solutions (record some lectures, not all; opt-in; etc.)
- **Educational context:** Teaches deliberation in institutional setting

---

#### Scenario 1.5: Open-Source Software Licensing Disputes ⭐⭐⭐

**Conflict:** Which open-source license should a project adopt? Permissive (BSD, MIT) vs. Copyleft (GPL)?

**Scale:** Small Group (project maintainers) vs. Large Group (user community, corporate adopters)

**Conflict Type:** Legal/Procedural + Ideological
- Freedom (permissive licenses maximize user freedom) vs.
- Reciprocity (copyleft ensures sharing back) vs.
- Commercial sustainability (dual licensing enables revenue) vs.
- Community building (which license attracts contributors?)

**Moral Frameworks in Tension:**
- **Libertarian:** Maximum freedom (permissive licenses)
- **Reciprocity (Communitarian):** Share-alike (copyleft)
- **Consequentialist:** Which license produces best outcomes? (most adoption? most contribution?)
- **Deontological:** Moral duty to share knowledge (copyleft) or respect autonomy (permissive)?

**Stakeholders:**
- Individual developers
- Corporate users (large tech companies)
- Open-source advocates (FSF, OSI)
- End users (benefit from open-source software)

**Demonstration Value:**
- **Demonstrability:** 3/5 (requires technical knowledge of licensing)
- **Safety:** 5/5 (no vulnerable groups, ideological but not partisan)
- **Generalizability:** 4/5 (intellectual property, commons governance)
- **Media Appeal:** 2/5 (tech community interest, niche to general public)
- **Pluralism Showcase:** 5/5 (genuinely incommensurable values)

**Total Score:** 19/25

**Why This is Good:**
- **Ideological but not partisan:** Not aligned with political tribes
- **Technical community:** Understands nuances, appreciates deliberation
- **Legitimate disagreement:** No "right answer," both positions defensible
- **Generalizable:** IP, commons, knowledge-sharing broadly

**Limitation:**
- Niche audience (tech community)
- Requires background knowledge

---

### Tier 2: Worth Considering (Score 15-19/25)

These scenarios have strengths but also limitations (lower media interest, more specialized, or moderate complexity).

#### Scenario 2.1: Community Garden Plot Allocation (Score: 18/25)

**Conflict:** How to allocate limited community garden plots among applicants?

**Allocation Methods:**
- Lottery (fairness, random)
- First-come-first-served (reward initiative)
- Need-based (prioritize low-income, no yard access)
- Seniority (reward long-term community members)
- Contribution-based (prioritize active volunteers)

**Why Consider:**
- Multiple legitimate allocation methods
- Small-scale, concrete
- Shows procedural deliberation

**Limitations:**
- Low media salience (very local)
- May feel trivial to some audiences

---

#### Scenario 2.2: Neighborhood Traffic Calming (Score: 17/25)

**Conflict:** Should a neighborhood install speed bumps?

**Considerations:**
- Safety (slow traffic, protect children/pedestrians) vs.
- Emergency access (ambulances, fire trucks slowed) vs.
- Property values (noise from speed bumps, desirability) vs.
- Driving convenience (residents commute time increased)

**Why Consider:**
- Local governance example
- Multiple stakeholders with legitimate concerns
- Shows trade-off analysis

**Limitations:**
- Low media interest
- Outcome may seem "solvable" by expert traffic engineers (less pluralism)

---

#### Scenario 2.3: Workplace Scent-Free Policy (Score: 16/25)

**Conflict:** Should a workplace implement a scent-free policy?

**Considerations:**
- Health (chemical sensitivity, allergies) vs.
- Personal expression (perfume, cologne, grooming products) vs.
- Cultural practices (some cultures use incense, oils) vs.
- Enforcement challenges (how to monitor, what counts?)

**Why Consider:**
- Shows accommodation complexity
- Health + culture + expression dimensions
- Relatable to anyone in workplace

**Limitations:**
- Small-scale (workplace only)
- May feel like "etiquette" not governance

---

### Tier 3: Avoid for MVP (Pattern Bias Risk or Too Polarized)

These scenarios are **important** but **too risky** for initial demonstrations due to:
- Vulnerable populations centered
- High polarization (tribal, partisan)
- Vicarious harm potential
- Complexity requiring extensive background

#### Scenarios to Avoid:

❌ **Mental Health Crisis Intervention** (privacy vs. safety)
- Reason: Vicarious harm risk, re-traumatization potential, vulnerable population

❌ **School Curriculum Content** (CRT, sex ed, evolution)
- Reason: Culture war flashpoint, highly polarized, children involved

❌ **Immigration Policy** (borders, citizenship, enforcement)
- Reason: Entangled with race, nationalism, highly partisan

❌ **Abortion Access** (rights, autonomy, religious beliefs)
- Reason: Tribal identity, moral absolutism common, polarized

❌ **Affirmative Action** (race-conscious admissions/hiring)
- Reason: Race-based, legally contentious, polarized

❌ **Gender Transition (Youth)** (medical, parental rights, identity)
- Reason: Vulnerable population (trans youth), highly polarized, "debate my existence"

❌ **Climate Policy** (carbon pricing, green energy mandates)
- Reason: Highly polarized (climate denial vs. activism), partisan

❌ **Vaccine Mandates** (public health vs. bodily autonomy)
- Reason: Post-COVID trauma, highly polarized, health anxiety

❌ **Police Funding / Defund Police** (safety, justice, race)
- Reason: Entangled with race, policing trauma, polarized

❌ **Gun Control** (Second Amendment, restrictions)
- Reason: Tribal identity, partisan, entrenched

---

## 7. Strategic Selection Criteria

### Decision Matrix for Scenario Selection

When choosing demonstration scenarios, apply these criteria systematically:

**1. Safety First (30% weight)**
- No vulnerable populations centered
- No vicarious harm risk
- No re-traumatization potential
- Pattern bias mitigated

**2. Demonstrability (20% weight)**
- Explainable in <5 minutes
- Concrete, visualizable
- Stakeholders clearly identifiable
- Outcomes understandable

**3. Pluralism Showcase (25% weight)**
- Multiple legitimate moral frameworks
- Genuine incommensurability (not just preferences)
- Non-hierarchical resolution possible
- Moral remainder documentable

**4. Media Relevance (15% weight)**
- Timely, current interest
- Not yet polarized into tribal camps
- Emerging issues preferred
- Audience salience

**5. Generalizability (10% weight)**
- Teaches broader principles
- Applicable to other contexts
- Shows deliberation process transferability

---

### Application of Criteria to Top Scenarios

| Scenario | Safety (30%) | Demonstrability (20%) | Pluralism (25%) | Media (15%) | Generalizability (10%) | **Total (100%)** |
|----------|-------------|---------------------|----------------|------------|----------------------|--------------|
| **Algorithmic Hiring** | 30/30 | 16/20 | 25/25 | 15/15 | 10/10 | **96/100** ⭐⭐⭐⭐⭐ |
| **Remote Work Pay** | 30/30 | 20/20 | 20/25 | 12/15 | 8/10 | **90/100** ⭐⭐⭐⭐ |
| **Park Usage** | 30/30 | 20/20 | 25/25 | 3/15 | 8/10 | **86/100** ⭐⭐⭐⭐ |
| **Lecture Recording** | 30/30 | 16/20 | 25/25 | 6/15 | 8/10 | **85/100** ⭐⭐⭐⭐ |
| **Open-Source Licensing** | 30/30 | 12/20 | 25/25 | 3/15 | 8/10 | **78/100** ⭐⭐⭐ |

**Interpretation:**
- **Algorithmic Hiring** scores highest (96/100) → **Primary demonstration scenario**
- **Remote Work Pay** strong second (90/100) → **Secondary scenario**
- **Park Usage** excellent for teaching (86/100) but low media salience
- **Lecture Recording** shows complexity well (85/100), niche audience
- **Open-Source Licensing** good for tech community (78/100), requires background

---

## 8. Recommendations

### For MVP Implementation

**Primary Scenario:** **Algorithmic Hiring Transparency**
- Demonstrates all key principles of PluralisticDeliberationOrchestrator
- Timely, media-relevant, not yet polarized
- Multiple moral frameworks clearly in tension
- Safe (economic, not identity-based)
- Generalizable to all algorithmic decision contexts

**Secondary Scenario:** **Remote Work Location-Based Pay**
- Complements primary (both AI/tech-adjacent, both economic)
- Shows geographic dimension (not just algorithmic)
- Relatable post-pandemic context
- Safe, not identity-based

**Teaching Scenario (Optional):** **Public Park Usage Priority**
- Excellent for demonstrating deliberation process
- Perfect scale (small group vs. small group)
- Low-stakes, accessible
- Use for workshops, training

---

### For Future Expansion

**After establishing credibility with MVP:**

**Phase 2 (6-12 months):**
- **Lecture Recording Policy** (educational context, procedural complexity)
- **Research Ethics** (preprint publication, open access)
- **Traffic Calming** (local governance, community engagement)

**Phase 3 (12-24 months, with co-design):**
- **Workplace Accommodation** (disability justice framing, stakeholder input)
- **Cultural Recognition** (procedural focus: how to decide about renaming?)
- Only if extensive cultural sensitivity review + co-design

**Never Attempt (Too Risky):**
- Mental health crises (vicarious harm)
- Abortion, immigration, gun control (tribal, polarized)
- Trans rights, race-based policies (vulnerable groups, polarized)

---

## 9. Next Steps

### Immediate (Complete This Session)

1. ✅ **Document strategic framework** (this document)
2. **Create deep-dive analysis** of Algorithmic Hiring (Document 2)
3. **Develop evaluation rubric** (Document 3)
4. **Document media research methods** (Document 4)
5. **Plan refinement roadmap** (Document 5)
6. **Session handoff** (Document 6)

### Short-Term (Next 2-4 Weeks)

1. **Stakeholder Mapping Deep-Dive**
   - For algorithmic hiring + remote work pay
   - Identify real organizations who could represent perspectives
   - Research actual positions taken on similar issues

2. **Rubric Calibration**
   - Have 3-5 people independently score same 10 scenarios
   - Measure inter-rater reliability
   - Refine criteria based on disagreements

3. **Media Research Execution**
   - Complete full media pattern analysis for top 2 scenarios
   - Google Trends, news coverage, academic literature, regulatory activity
   - Document findings

### Medium-Term (1-3 Months)

1. **Pilot Testing**
   - Interview 10-15 people from target audience
   - Present scenarios, gather feedback
   - Measure: Which immediately understood? Which confused?

2. **Cultural Sensitivity Review**
   - Convene diverse focus group (age, geography, culture)
   - Review scenarios for blind spots, offensive framings
   - Revise based on feedback

3. **Expert Consultation**
   - Political philosophers (pluralism framework sound?)
   - Ethicists (moral framework representations accurate?)
   - Deliberative democracy practitioners (process realistic?)

### Long-Term (3-6 Months)

1. **Scenario Database**
   - Expand to 15-20 scored scenarios
   - Build searchable database with metadata
   - Tag by: conflict type, scale, moral frameworks, media patterns

2. **Scenario Generator** (Stretch Goal)
   - Input: conflict type, stakeholder structure, values in tension
   - Output: Suggested scenario outlines
   - Use for rapid prototyping of new demonstrations

---

## Conclusion

This strategic framework provides a **rigorous, theory-grounded** approach to selecting demonstration scenarios for the PluralisticDeliberationOrchestrator.

**Key Insights:**

1. **Conflict type matters more than scale** - Legal/procedural and resource allocation conflicts are safer and more demonstrable than identity/recognition conflicts

2. **Emerging issues offer best opportunities** - Before polarization hardens into tribal identities

3. **Safety first** - No vulnerable populations centered, minimize vicarious harm

4. **Small group vs. small group is ideal scale** - Demonstrates non-hierarchical deliberation perfectly

5. **Algorithmic hiring is the standout scenario** - Timely, safe, generalizable, demonstrates pluralism clearly

**This framework ensures:**
- Ethical demonstration practices (pattern bias risk assessment)
- Strategic media relevance (emerging issues, timely)
- Theoretical rigor (foundational pluralism, genuine incommensurability)
- Practical usability (clear evaluation criteria, systematic scoring)

---

## References

1. Tractatus Framework - Pluralistic Values Deliberation Plan v2 (2025)
2. Isaiah Berlin - "Two Concepts of Liberty" (value pluralism)
3. Amy Gutmann & Dennis Thompson - "Democracy and Disagreement" (deliberative democracy)
4. Ruth Chang - "Incommensurability, Incomparability, and Practical Reason" (1997)
5. Iris Marion Young - "Inclusion and Democracy" (2000)
6. NYC Local Law 144 (Automated Employment Decision Tools, 2023)
7. EU AI Act (2024)
8. Google Trends, news coverage, academic literature (various sources)

---

**END OF DOCUMENT**

**Next:** See Document 2 for deep-dive analysis of Algorithmic Hiring Transparency scenario