Adjudication Rules#

Individual Participant Decision Model (V7.4)#

Critical Change: Each round, each participant makes individual decisions for their assigned industries (1 or more industries per participant; recommend 2). Unlike V6 (team deliberation), V7.4 has individual participants deciding for their industries directly.

Scoring Logic:

• Explicit Industry Decisions (If Submitted): Scored normally using banded framework ({-2, 0, +2} per dimension, +/-3 if red-flag)
• Industries Without Explicit Actions: Receive pre-defined base case fallback scores from fallback bank (small fixed deltas: +/-1 per dimension, deterministic, plausible). Fallbacks are automatic and deterministic.

Each industry decision is scored independently on the same 3 dimensions. There is no "sector-level" decision; all scoring happens at the industry level. The 11 industries are:

1. Retail — scored using retail-specific context (omnichannel, demand forecasting, inventory)
2. CPG — scored using CPG-specific context (brand management, R&D cycles, DTC)
3. Healthcare Provider — scored using provider-specific context (clinical AI, FDA, patient safety)
4. Healthcare Payer — scored using payer-specific context (claims automation, fraud detection, MLR)
5. Finance — scored using finance-specific context (underwriting, trading, fair lending, fraud)
6. Consulting — scored using consulting-specific context (copilot adoption, vertical AI expertise, pricing models, junior talent pipeline)
7. Law — scored using law-specific context (billable hour model, bar rule compliance, malpractice liability, associate leverage)
8. Manufacturing — scored using manufacturing-specific context (predictive maintenance, OT/IT integration, union relations)
9. Logistics — scored using logistics-specific context (route optimization, autonomous vehicles, driver adoption)
10. Big Tech — scored using Big Tech-specific context (cloud, ads, devices, enterprise software; excludes AI lab/model development)
11. B2B/B2C SaaS — scored using SaaS-specific context (AI feature integration, pricing pressure, startup disruption)

Participants decide individually; facilitators score each industry decision on its own merits, in its own context. Fallback industries do not require participant justification; facilitator applies the pre-defined score from the fallback bank.

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Scoring Framework#

Each industry decision is scored on three dimensions:

Total per decision: Sum of three dimensions (range: -6 to +6).

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Banded Scoring During Play#

Default scoring bands during live rounds:

Why banded? Prevents false precision. Participants understand three clear buckets per dimension, not nine.

Exception: If a red-flag/plausibility trigger fires (see Section 2), unlock +/-3 scoring.

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Scoring Guidance by Dimension#

Strategic Fit (-3 to +3)#

Execution Risk (-3 to +3)#

Execution Risk maps from banded inputs: Spend/Commitment, Time-to-Impact, Execution Complexity, Dependency, and Scale.

Examples:

• Deploying proven AI copilots (Absorbable, 0-3mo, Low, Internal, Pilot) = +2
• Enterprise-wide AI deployment with vendor integration (Material, 3-12mo, Medium, Vendor, National) = 0
• Building proprietary AI from scratch (Transformational, 2+ years, Very high, Ecosystem, Global) = -2 to -3

Tail Risk (-3 to +3)#

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Band-to-Score Translation Reference#

Use this table to quickly map banded inputs to expected Execution Risk scores:

Quick rule: Absorbable + 0-3mo + Low = +2. Each band downgrade (Material, Transformational, Existential) or timeline extension (3-12mo, 1-2yr, 2+yr) or complexity increase (Medium, High, Very High) or dependency widening (Vendor, Regulator, Ecosystem) shifts score down by roughly 0.5-1.0 per dimension.

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Red-Flag Triggers (Band Combinations)#

Red-flag triggers now reference band combinations instead of granular details:

When a red-flag band combination appears: Challenge the participant. Offer a narrower scope, longer timeline, lower complexity, or lower spend. Unlock +/-3 exception scoring only if participant acknowledges and addresses the constraint.

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Decision Specificity Checklist#

Before scoring ANY decision, verify participant specified:

Scoring rule: If >2 items are missing -> ask for specificity before scoring.

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When to Challenge vs. When to Accept#

Challenge (Ask for Clarification)#

Ask for specificity if proposal is vague on WHO/WHAT/WHERE/WHEN/HOW/HOW MUCH/RISK.

Template: "I appreciate the direction. I need clarity on [specific issue]. Can you walk me through [detail]? That changes the execution risk profile significantly."

Accept (If Specificity Met)#

Once all 7 items are clear, score the decision per framework above.

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Red-Flag Triggers (When to Unlock +/-3)#

Red-flag triggers are plausibility gates based on band combinations and strategic archetypes. If a red-flag fires, unlock +/-3 exception scoring (see Plausibility Decision Trees).

Quantitative Requirements (Only Required When...)#

Only quantify when the move is M&A, major capex build, or regulatory/legal commitment. Otherwise, bands + justification is sufficient.

• M&A: Deal size, close timeline, integration plan required
• Major Capex: Capex amount, ROI, payback period required
• Regulatory/Legal Commitment: Regulatory timeline, legal exposure, severance obligations required
• Otherwise: Band classification + concise justification suffices

Red-Flag Categories (Band-Based)#

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Facilitation Language for Industry-Level Decisions#

When challenging a vague decision, reference the industry explicitly to keep context clear:

Challenging a Vague Decision (Industry-Specific)#

"I want to score this Retail decision, but I need specificity. Right now you've said 'deploy AI across stores and online.' But I need to know:

• Where are you starting? Which 500 stores? All stores? Which e-commerce functions?
• How much are you spending on the Retail initiative? $5M or $50M?
• How will you staff it? Do you have retail tech talent, or do you need to hire?
• What's the rollback plan if customer trust erodes?

Once you answer those, I can score your Retail decision. Note: If you also submit a Law decision, it will be scored separately on Law-specific metrics (billable hour impact, bar compliance, malpractice exposure, etc.)."

Challenging a Red-Flag#

"I appreciate the ambition here. But this proposal triggers a plausibility concern for me. Let me walk you through:

• You're proposing FDA approval in 12 months for a novel diagnostic system.
• FDA approval for novel diagnostics typically takes 18-24 months, even with a complete application.
• In 12 months, you could submit an application or run a pilot with investigational status, but not full deployment.

Here's what I can score: Proposing a pilot with human oversight and FDA submission (not approval). That's Strategic Fit +1 (good direction), Execution Risk -1 (regulatory timeline is long), Tail Risk 0 (human oversight limits downside). Total: 0/6.

Or, if you want higher execution feasibility, propose a different approach. What would you prefer?"

Accepting a Strong Industry Decision#

"Okay, I have all the specificity I need for your Retail decision. Let me score:

• Strategic Fit: +2 -- Direct response to inventory margin loss; aligns with omnichannel strategy.
• Execution Risk: +1 -- Demand forecasting AI is proven; you have experience; 500-store pilot is feasible.
• Tail Risk: +1 -- Phased pilot allows learning; if brand backlash emerges, you can narrow scope.
• Total: +4/6 -- This is a strong Retail decision. Expected probability of success: 70%+. If you also submit a Consulting decision, it will be scored separately."

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Scoring Reference Table#

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Base Case Fallback Scoring#

After scoring all explicit industry decisions, apply base case fallbacks to any industry that did not receive an explicit action from any participant.

Fallback Scoring Process:

1. Identify industries without explicit actions (variable count based on how many industries each participant covers)
2. Reference the fallback bank (see Base Case Fallback Bank)
3. Apply the pre-defined fallback score for that industry (deterministic, small fixed deltas: +/-1 per dimension, plausible)
4. Fallback industries are applied automatically (no participant input required)
5. Post fallback score alongside explicit decisions for transparency

Example: Participant covering Retail and CPG submits explicit Retail decision (+2/6). CPG receives no explicit action. Reference fallback bank -> CPG falls back to {0, +1, 0} = +1/6 (defensive but plausible cost-reduction move). Post both scores.

Fallback scoring is deterministic, not participant-controlled. Facilitator applies it directly from the fallback bank.

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Industry Health Signals (End of Round)#

• During play: Score explicit decisions only (3 dimensions, sum per decision).
• Apply base case fallbacks after explicit scoring (reference fallback bank).
• End of round: Update cumulative aggregate score for each industry (running total across all rounds). Look up Industry Health condition (Surge/Tailwind/Steady/Headwind/Crisis) from Industry Health Signal Tables.
• Announce conditions at start of next round (~2 min). Apply Headwind/Crisis constraints as applicable.

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Summary Checklist (Per Explicit Decision)#

• Industry identified (Retail? CPG? Finance? Healthcare Provider? Consulting? Law? etc.)
• Participant specified all 7 items (WHO/WHAT/WHERE/WHEN/HOW/HOW MUCH/RISK)
• Band mapping clear (Spend/Commitment, Time-to-Impact, Complexity, Dependency, Scale)
• No red-flag band combinations (if yes, unlock +/-3 scoring and reference decision trees)
• Only quantify if M&A, major capex, or regulatory/legal commitment; otherwise bands + justification suffices
• Score Strategic Fit: {-2, 0, +2} (or +/-3 if red-flag)
• Score Execution Risk: {-2, 0, +2} (or +/-3 if red-flag) -- reference band translation table
• Score Tail Risk: {-2, 0, +1} (or +/-3 if red-flag)
• Sum total (range: -6 to +6)
• Post score and brief rationale to participant
• After round: Apply base case fallbacks to industries without explicit actions (reference Base Case Fallback Bank)
• End of round: Update cumulative scores and look up Industry Health conditions (reference Industry Health Signal Tables)