game-theoryAdvanced game theory analysis for crypto protocols, DeFi mechanisms, governance systems, and strategic decision-making. Use when analyzing tokenomics, evaluating protocol incentives, predicting adversarial behavior, designing mechanisms, or understanding strategic interactions in web3.
Install via ClawdBot CLI:
clawdbot install sp0oby/game-theoryStrategic analysis framework for understanding and designing incentive systems in web3.
"Every protocol is a game. Every token is an incentive. Every user is a player. Understand the rules, or become the played."
For any protocol or mechanism, ask:
## Protocol: [Name]
### Players
- Player A: [Role, objectives, constraints]
- Player B: [Role, objectives, constraints]
- ...
### Strategy Space
- Player A can: [List possible actions]
- Player B can: [List possible actions]
### Payoff Structure
- If (A does X, B does Y): A gets [payoff], B gets [payoff]
- ...
### Information Structure
- Public information: [What everyone knows]
- Private information: [What only some players know]
- Observable actions: [What can be seen on-chain]
### Equilibrium Analysis
- Nash equilibrium: [Stable outcome where no player wants to deviate]
- Dominant strategies: [Strategies that are always best regardless of others]
- Potential exploits: [Deviations that benefit attackers]
### Recommendations
- [Design changes to improve incentive alignment]| Document | Use Case |
|----------|----------|
| Nash Equilibrium | Finding stable outcomes in strategic interactions |
| Mechanism Design | Designing systems with desired equilibria |
| Auction Theory | Token sales, NFT drops, liquidations |
| MEV Game Theory | Adversarial transaction ordering |
| Tokenomics Analysis | Evaluating token incentive structures |
| Governance Attacks | Voting manipulation and capture |
| Liquidity Games | LP strategies and impermanent loss |
| Information Economics | Asymmetric information and signaling |
A state where no player can improve their payoff by unilaterally changing strategy. The "stable" outcome of a game.
Crypto application: In a staking system, Nash equilibrium determines the stake distribution across validators.
A strategy that's optimal regardless of what others do.
Crypto application: In a second-price auction, bidding your true value is dominant.
An outcome where no one can be made better off without making someone worse off.
Crypto application: AMM fee structures try to be Pareto efficient for traders and LPs.
"Reverse game theory" - designing rules to achieve desired outcomes.
Crypto application: Designing token vesting schedules to align long-term incentives.
A solution people converge on without communication.
Crypto application: Why certain price levels act as psychological support/resistance.
When truthful behavior is optimal for participants.
Crypto application: Oracle designs where honest reporting is the dominant strategy.
Everyone knows X, everyone knows everyone knows X, infinitely recursive.
Crypto application: Public blockchain state creates common knowledge of balances/positions.
Structure: Shared resource, individual incentive to overuse, collective harm.
Crypto examples:
Solution approaches:
Structure: Individual rationality leads to collective irrationality.
Crypto examples:
Solution approaches:
Structure: Multiple equilibria, players want to coordinate but may fail.
Crypto examples:
Solution approaches:
Structure: One party acts on behalf of another with misaligned incentives.
Crypto examples:
Solution approaches:
Structure: Information asymmetry leads to market breakdown.
Crypto examples:
Solution approaches:
Structure: Hidden action after agreement leads to risk-taking.
Crypto examples:
Solution approaches:
Players: Users, searchers, builders, validators
Key insight: Transaction ordering is a game; users are often the losers
See: MEV Strategies
Players: LPs, traders, arbitrageurs
Key insight: Impermanent loss is the cost of being adversely selected against
See: Liquidity Games
Players: Token holders, delegates, protocol team
Key insight: Rational apathy + concentrated interests = capture
See: Governance Attacks
Players: Stakers, validators, delegators
Key insight: Security budget must exceed attack profit
See: Tokenomics Analysis
Players: Data providers, consumers, attackers
Key insight: Profit from manipulation must be less than cost
See: Mechanism Design
Single-shot games often have bad equilibria. Repetition enables cooperation through:
Crypto application: Why anonymous actors behave worse than doxxed teams.
Strategies that survive competitive selection. Relevant for:
Games with incomplete information. Players have beliefs about others' types.
Crypto application: Trading with unknown counterparties, evaluating anonymous teams.
When players can form binding coalitions.
Crypto application: MEV extraction coalitions, validator cartels, governance blocs.
Computational aspects of game theory.
Crypto application: On-chain game computation limits, gas-efficient mechanism design.
Generated Mar 1, 2026
Analyze a new DeFi protocol's token distribution and incentive mechanisms to identify potential exploits or misaligned incentives that could lead to attacks like governance capture or liquidity mining mercenaries. Use the Five Questions framework to evaluate player strategies and equilibrium outcomes.
Assess a governance proposal for a DAO by modeling voting mechanisms and predicting how rational actors, such as large token holders or delegates, might behave. Identify potential manipulation risks like vote-buying or Sybil attacks to ensure incentive compatibility.
Model adversarial transaction ordering games for validators in a proof-of-stake network to understand MEV extraction strategies and their impact on network stability. Analyze equilibrium outcomes to recommend slashing mechanisms or fee structures that mitigate negative externalities.
Design an auction mechanism for an NFT drop by applying auction theory to ensure fairness and efficiency. Evaluate strategies like bid shading or sniping to create rules that encourage truthful bidding and maximize revenue for creators.
Analyze the strategic interactions between validators or nodes in a cross-chain bridge system to identify attack vectors like collusion or free-riding. Use game theory to model the tragedy of the commons and propose incentive structures like staking rewards or penalties to enhance security.
Offer game theory analysis services to blockchain projects for auditing tokenomics, designing governance systems, or evaluating mechanisms. Provide detailed reports with equilibrium analysis and recommendations to improve incentive alignment and security.
Develop an online platform with interactive tutorials, templates, and simulation tools for game theory in web3. Target developers and analysts seeking to apply strategic frameworks to protocol design or risk assessment.
Create a software-as-a-service product that automates game theory analysis for DeFi protocols, identifying vulnerabilities like MEV exploits or governance attacks. Integrate with on-chain data to provide real-time alerts and mitigation strategies.
💬 Integration Tip
Integrate this skill by applying the Five Questions framework to any crypto protocol analysis, starting with identifying players and their strategies to predict equilibrium outcomes and recommend improvements.
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