── ── Strategy
Repeated Games & Reputation
When parties repeat — or third parties observe — defection costs tomorrow's cooperation, flipping the Prisoner's Dilemma. Axelrod's 1979–1981 tournaments proved cooperation wins empirically; the Folk Theorem (Fudenberg & Maskin 1986) proved it mathematically. This skill diagnoses when cooperation is sustainable (discount factor check), selects the right strategy (TFT vs Generous TFT vs Pavlov), and engineers reputation infrastructure for markets…
Run Repeated Games & Reputation on a real problem
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How it works
Run the Repeated-Game Analysis across these steps:
1. Establish true repetition. Indefinite/infinite → folk-theorem logic applies. Finite with known endpoint → backward induction risk; add uncertainty or commitment devices. 2. Estimate δ. Cooperation threshold: δ ≥ (T − R) / (T − P). Below threshold → change the structure first; no strategy design saves it. 3. Confirm observability. Perfect → TFT variants work. Noisy → Generous TFT (cooperate ~1/3 of the time after apparent defection) or Contrite TFT. Pure TFT under noise → recrimination spirals. 4. Select strategy. TFT (clean bilateral) · Generous TFT (noisy) · Pavlov (mixed populations) · Grim Trigger (high-stakes, credible threat only) · benchmarks: Always Defect / Always Cooperate. 5. For reputation infrastructure: design Observation · Aggregation · Persistence · Manipulation resistance — all four required; missing one breaks the system. 6. Stress-test endgame. Mitigations: endpoint uncertainty; legacy concerns; successor obligations; overlapping generations. 7. Stop-rule: lifetime cooperative payoff must beat one-shot defection by margin sufficient to absorb noise.
When to use it
- user asks how to build trust with a repeat counterparty, whether to retaliate after a partner defected, how to design a reputation system, whether a long-term relationship can survive betrayal, or says 'shadow of the future / tit-for-tat / burn this bridge / they'll remember this / build credibility.'
When not to use it
interaction is genuinely one-shot with no third-party observers (use prisoners-dilemma), or situation is zero-sum competition where repetition entrenches rivalry.
Worked example
Trust Reputation as Strategy in the 2024–2026 AI Race
Between 2024 and 2026, the competition among frontier AI labs and platforms (OpenAI, Anthropic, Google DeepMind, Meta, and others) became one of the clearest live demonstrations of repeated-game logic in a market visible to everyone. Model capability converged fast: for many enterprise tasks, the leading models were close substitutes. That convergence pushed a second variable to the front of the buying decision — trust and safety reputation. Enterprises signing multi-year contracts, embedding a model in regulated workflows, and exposing customer data to it are not…
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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