game-theoretic-analysis

Name: game-theoretic-analysis
Availability: InStock
Author: Anduril

Model players, strategies, payoffs, and timing to diagnose Nash versus Pareto equilibria.

7|2|Updated Mar 5, 2026

Search Tags:#multi-agent systems #game theory #negotiation #incentive design #mechanism design #equilibrium analysis

Ready to run

npx skills add https://github.com/AndurilCode/craftwork --skill game-theoretic-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: game-theoretic-analysis
Source: https://github.com/AndurilCode/craftwork/tree/main/skills/game-theoretic-analysis
Command: npx skills add https://github.com/AndurilCode/craftwork --skill game-theoretic-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you diagnose why multiple actors behave strategically and why “rational” local incentives create bad system-level outcomes.

Core Features & Use Cases

Game definition: Identify players, strategies, payoffs, information, and timing to make the strategic interaction explicit.
Equilibrium & diagnosis: Classify the game and compare Nash equilibrium vs Pareto optimal outcomes to spot structural misdesign (social dilemmas).
Mechanism design: Recommend incentive-compatible rule changes (payoffs, information visibility, timing, enforcement, or removing the game).

Quick Start

Use this skill to analyze a failing multi-agent negotiation by asking it to map the players, incentives, and likely equilibria and then propose mechanism-design changes that align self-interest with the system goal.

Frequently Asked Questions about game-theoretic-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze multi-agent incentives to fix bad system-level outcomes?▼

To analyze multi-agent incentives, model the players, strategies, payoffs, and timing to classify the game and compare Nash equilibrium versus Pareto optimal outcomes, identifying structural misdesign causing bad system-level results.

What is the best way to design mechanism changes for incentive misalignment?▼

The best way to design mechanism changes for incentive misalignment is to model the strategic interaction, diagnose the current equilibrium, and recommend incentive-compatible rule changes like modifying payoffs, information visibility, or enforcement.

How does equilibrium analysis predict stable behavior in multi-agent systems?▼

Equilibrium analysis predicts stable behavior in multi-agent systems by modeling self-interested players and their payoffs to identify the Nash equilibrium, revealing the likely outcome when no player benefits from changing their strategy unilaterally.

Why does rational local decision-making cause cooperation failures in multi-agent systems?▼

Rational local decision-making causes cooperation failures when individual payoffs incentivize self-interested play that diverges from the Pareto optimal outcome, creating a social dilemma where the system-level outcome is structurally misdesigned.

Can I use game theory to realign self-interest with system goals in negotiation protocols?▼

Yes, you can apply game theory to realign self-interest with system goals in negotiation protocols by diagnosing the current game dynamics and implementing mechanism design recommendations to adjust payoffs, timing, or information visibility.

When should I not use game-theoretic analysis for multi-agent coordination?▼

You should not use game-theoretic analysis for multi-agent coordination when interactions lack clear strategic payoffs or timing dynamics, as the equilibrium diagnostics require explicit player strategies to model self-interested play accurately.