What makes Repeated Games & Reputation notable?

1K downloads. Trust evidence available

How should Repeated Games & Reputation be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Repeated Games & Reputation

Activate when: user asks how to build trust with a repeat counterparty, whether to retaliate after a partner defected, how to design a reputation system, whe... Skill: Repeated Games & Reputation Owner: deciqai Summary: Activate when: user asks how to build trust with a repeat counterparty, whether to retaliate after a partner defected, how to design a reputation system, whe... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:13:41.749Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/repeated-games-reputation.json) v1.0.4 | 2026-

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:repeated-games-reputation

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-repeated-games-reputation/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

143,360 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: repeated-games-reputation
description: "Activate when: 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.' Do NOT activate when: interaction is genuinely one-shot with no third-party observers (use prisoners-dilemma), or situation is zero-sum competition where repetition entrenches rivalry. More: deciqai.com/c/repeated-games-reputation"
---

# Repeated Games & Reputation

## Overview

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 where parties don't repeat directly. Composes with `prisoners-dilemma` · `second-order-thinking` · `signaling-games`.

## When to Use

Apply when:
- A relationship is **expected to continue** between the same parties (supplier-buyer, employer-employee, GP-LP, founder-investor, customer-platform)
- Even in a one-shot direct interaction, **third parties observe** the move and adjust their willingness to play with you
- You're designing **a platform or marketplace** that needs strangers to cooperate — reputation infrastructure is the architectural question
- You're trying to **escape a defection trap** and the candidate escape is "repetition" or "reputation"
- A partnership keeps fragmenting — diagnose whether δ is too low or observation is broken
- Trust/safety reputation is shaping who wins **AI adoption and AI-native competition** — where capability converges, a bad launch or safety incident reprices every future round of enterprise adoption (and the AI capex supercycle only lengthens the shadow of the future)

**When NOT to use:**
- Genuinely one-shot with no third-party observability → use `prisoners-dilemma`
- Parties are **about to exit** (last round of finite game) — backward induction risk; standard repeated-game logic can fail
- Zero-sum situation — repetition can entrench rivalry rather than dissolve it
- "Repetition" is only nominal — rotating counterparties who don't talk = effectively one-shot

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete repeated/reputational situation → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line what-it-is: one-shot rationality says "defect"; repeated rationality says "cooperate if the future matters enough an

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "repeated-games-reputation",
  "version": "1.0.5",
  "publishedAt": 1784225621749
}

references/sources.md

# Sources — repeated-games-reputation

> *Primary sources for the [repeated-games-reputation](../SKILL.md) skill.*

- Axelrod, R. (1984). *The Evolution of Cooperation*. Basic Books. The primary text reporting both computer tournaments (1979–80 and 1980–81), Rapoport's TFT victories, and Axelrod's analysis extracting the four properties (nice, retaliatory, forgiving, clear). ISBN 978-0465021215. Updated edition with additional chapters: Axelrod, R. (2006). *The Evolution of Cooperation*, Revised Edition. Basic Books.
- Axelrod, R., & Hamilton, W. D. (1981). "The Evolution of Cooperation." *Science*, 211(4489), pp. 1390–1396. The original peer-reviewed publication of the tournament findings, co-authored with evolutionary biologist W. D. Hamilton. https://doi.org/10.1126/science.7466396
- Friedman, J. W. (1971). "A Non-cooperative Equilibrium for Supergames." *Review of Economic Studies*, 38(1), pp. 1–12. The early formulation of the folk theorem — cooperation is sustainable in infinitely repeated games when the discount factor is high enough. https://doi.org/10.2307/2296617
- Fudenberg, D., & Maskin, E. (1986). "The Folk Theorem in Repeated Games with Discounting or with Incomplete Information." *Econometrica*, 54(3), pp. 533–554. The full statement of the folk theorem with rigorous discount-factor conditions. https://doi.org/10.2307/1911307
- Nowak, M. A., & Sigmund, K. (1992). "Tit for Tat in Heterogeneous Populations." *Nature*, 355, pp. 250–253. The discovery that Generous TFT outperforms TFT under observation noise. https://doi.org/10.1038/355250a0
- Nowak, M. A., & Sigmund, K. (1993). "A Strategy of Win-Stay, Lose-Shift That Outperforms Tit-for-Tat in the Prisoner's Dilemma Game." *Nature*, 364, pp. 56–58. Pavlov strategy formal introduction. https://doi.org/10.1038/364056a0
- Kreps, D. M., Milgrom, P., Roberts, J., & Wilson, R. (1982). "Rational Cooperation in the Finitely Repeated Prisoners' Dilemma." *Journal of Economic Theory*, 27(2), pp. 245–252. The "gang of four" paper showing that cooperation can be sustained in finitely-repeated games when there's incomplete information about opponent types. https://doi.org/10.1016/0022-0531(82)90029-1

### Contemporary context (2024–2026 AI example)

- European Union (2024). Regulation (EU) 2024/1689 (the "AI Act") — the EU's risk-based AI regulation, which entered into force in 2024 with obligations phasing in through 2025–2026. Establishes a durable, publicly documented disclosure/reporting regime that functions as part of the *observation* and *persistence* layers of AI reputation infrastructure. Official text: https://eur-lex.europa.eu/eli/reg/2024/1689/oj
- Public AI-lab safety documentation (2023–2025). Model cards, system cards, usage policies, and published safety / responsible-scaling frameworks from major AI labs (e.g. OpenAI, Anthropic, Google DeepMind) — the widely adopted industry practice of disclosing model capabilities and safety evaluations. Cited here as the real-world *legibilit

examples/ai-trust-reputation-enterprise-adoption-2024-2026.md

# Method in Action: Trust Reputation as Strategy in the 2024–2026 AI Race

> *Example for the [repeated-games-reputation](../SKILL.md) skill.*

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 running a one-shot transaction. They are opening an indefinitely repeated relationship, and they know it. In that structure, a single bad launch — a safety incident, a data-handling failure, a reckless capability release — is not a one-time cost. It reprices every future round of the game.

This example runs the anchor case through the skill's own **Repeated-Game Analysis**. The point is not to rank the labs; it is to show *why* reputation is the strategic asset it became, and where the analysis says a defection actually bites.

### 1. Establish true repetition

The relationship between an AI vendor and its enterprise customers, its regulators, and the broader developer public is **indefinitely repeated**, and it is repeated on two layers at once:

- **Bilateral repetition:** a specific enterprise customer renews, expands seats, adds workloads, and re-buys as new model versions ship. Each release is a fresh round with the same counterparty.
- **Reputational / third-party layer:** thousands of *other* buyers, regulators, and journalists observe how the vendor handled the last incident and adjust their willingness to play. This is the more powerful layer, because the audience is huge and the moves are public.

There is no known, fixed endpoint — new model generations keep arriving, so folk-theorem logic applies rather than backward-induction unraveling. **Horizon: indefinite. Repetition: both bilateral and reputational.**

### 2. Estimate δ (the shadow of the future)

The discount factor here is high for the labs, and that is the whole game. AI is a capital-intensive, subscription-and-usage-revenue business: the value of a customer is overwhelmingly in the *stream* of future renewals and expansion, not the first contract. When most of a customer's lifetime value sits in future rounds, δ is high, and the folk theorem says cooperation (ship responsibly, honor commitments, don't cut safety corners for a launch) is sustainable — *because* the discounted future cooperative payoff swamps the one-time gain from a reckless "win this quarter" defection.

Using the cooperation threshold δ ≥ (T − R) / (T − P): the temptation T (rush a flashy but unsafe release, harvest short-term headlines and signups) is real but bounded; the reward R (a durable, renewi

examples/robert-axelrod-computer-tournament-1979-1981.md

# Method in Action: Robert Axelrod's Computer Tournament, 1979–1981

> *Example for the [repeated-games-reputation](../SKILL.md) skill.*

The empirical foundation of modern repeated-game theory was not derived. It was **observed** — under conditions of unprecedented procedural rigor for social science — in two computer tournaments run by political scientist Robert Axelrod at the University of Michigan from 1979 to 1981.

The setup was direct. Axelrod sent invitations to game theorists, economists, mathematicians, sociologists, computer scientists, and psychologists who had published on the Prisoner's Dilemma. Each submitter wrote a computer program implementing a strategy for the iterated Prisoner's Dilemma. The programs were entered into a round-robin tournament: each strategy played each other strategy (and a clone of itself, and one random-defector control) over a long series of rounds, with payoffs accumulated. The payoff matrix was the canonical PD (T=5, R=3, P=1, S=0). The rule was: highest cumulative score wins.

The **first tournament (1979–1980)** received 14 entries. The strategies ranged from extraordinarily complex (multi-state automata that attempted to model the opponent's strategy and respond optimally) to extraordinarily simple. The shortest entry was submitted by **Anatol Rapoport**, a mathematical psychologist at the University of Toronto best known for his work on conflict resolution and for an earlier book co-authored with Albert Chammah on Prisoner's Dilemma experiments. Rapoport's program was four lines of FORTRAN. Its strategy:

> "Tit for Tat starts with a cooperative choice, and thereafter does what the other player did on the previous move."

— Axelrod, R., *The Evolution of Cooperation* (Basic Books, 1984), p. 31.

Tit-for-Tat won. The complex strategies — including several that attempted to detect cooperators and exploit them — finished lower. The result was striking enough that Axelrod organized a second tournament with full disclosure: he published the strategies of all 14 first-round entries along with their performance data, gave participants months to study the results, and invited fresh submissions. **The second tournament (1980–1981) received 62 entries from six countries** — from professional game theorists, including several Nobel-prize-track economists, and from amateurs who had read the first-tournament writeup and wanted to test their own ideas.

Many of the second-round entries were explicitly designed to beat TFT. Some attempted to identify when they were playing TFT and exploit it; others tried elaborate detection-and-punishment schemes; one submitted strategy waited 30 moves to defect, hoping to extract a one-time gain before TFT's retaliation kicked in. Tit-for-Tat — the same four-line FORTRAN program, resubmitted by Rapoport without modification — won again.

Axelrod's analysis of what made TFT win is the part of the literature most worth reading carefully. He extracted four properties:

> "What accou

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