| name | game-theory |
| description | Advanced 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. |
| metadata | {"clawdbot":{"emoji":"","homepage":"https://github.com/tedkaczynski-the-bot/game-theory"}} |
Game Theory for Crypto
Strategic 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."
When to Use This Skill
- Analyzing tokenomics for exploits or misaligned incentives
- Evaluating governance proposals and voting mechanisms
- Understanding MEV and adversarial transaction ordering
- Designing auction mechanisms (NFT drops, token sales, liquidations)
- Predicting how rational actors will behave in a system
- Identifying attack vectors in DeFi protocols
- Modeling liquidity provision strategies
- Assessing protocol sustainability
Core Framework
The Five Questions
For any protocol or mechanism, ask:
- Who are the players? (Users, LPs, validators, searchers, governance token holders)
- What are their strategies? (Actions available to each player)
- What are the payoffs? (How does each outcome affect each player?)
- What information do they have? (Complete, incomplete, asymmetric?)
- What's the equilibrium? (Where do rational actors end up?)
Analysis Template
## 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]
Reference Documents
Quick Concepts
Nash Equilibrium
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.
Dominant Strategy
A strategy that's optimal regardless of what others do.
Crypto application: In a second-price auction, bidding your true value is dominant.
Pareto Efficiency
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.
Mechanism Design
"Reverse game theory" - designing rules to achieve desired outcomes.
Crypto application: Designing token vesting schedules to align long-term incentives.
Schelling Point
A solution people converge on without communication.
Crypto application: Why certain price levels act as psychological support/resistance.
Incentive Compatibility
When truthful behavior is optimal for participants.
Crypto application: Oracle designs where honest reporting is the dominant strategy.
Common Knowledge
Everyone knows X, everyone knows everyone knows X, infinitely recursive.
Crypto application: Public blockchain state creates common knowledge of balances/positions.
Analysis Patterns
Pattern 1: The Tragedy of the Commons
Structure: Shared resource, individual incentive to overuse, collective harm.
Crypto examples:
- Gas price bidding during congestion
- Governance token voting apathy
- MEV extraction degrading UX
Solution approaches:
- Harberger taxes
- Quadratic mechanisms
- Commitment schemes
Pattern 2: The Prisoner's Dilemma
Structure: Individual rationality leads to collective irrationality.
Crypto examples:
- Liquidity mining mercenaries (farm and dump)
- Race-to-bottom validator fees
- Bridge security (each chain wants others to secure)
Solution approaches:
- Repeated games (reputation)
- Commitment mechanisms (staking/slashing)
- Mechanism redesign
Pattern 3: The Coordination Game
Structure: Multiple equilibria, players want to coordinate but may fail.
Crypto examples:
- Which L2 to use?
- Token standard adoption
- Hard fork coordination
Solution approaches:
- Focal points (Schelling points)
- Sequential moves (first mover advantage)
- Communication mechanisms
Pattern 4: The Principal-Agent Problem
Structure: One party acts on behalf of another with misaligned incentives.
Crypto examples:
- Protocol team vs token holders
- Delegates in governance
- Fund managers
Solution approaches:
- Incentive alignment (token vesting)
- Monitoring (transparency)
- Bonding (skin in game)
Pattern 5: Adverse Selection
Structure: Information asymmetry leads to market breakdown.
Crypto examples:
- Token launches (team knows more than buyers)
- Insurance protocols (risky users more likely to buy)
- Lending (borrowers know their risk better)
Solution approaches:
- Signaling (lock-ups, audits)
- Screening (credit scores, history)
- Pooling equilibria
Pattern 6: Moral Hazard
Structure: Hidden action after agreement leads to risk-taking.
Crypto examples:
- Protocols with insurance may take more risk
- Bailout expectations encourage leverage
- Anonymous teams may rug
Solution approaches:
- Monitoring and transparency
- Incentive alignment
- Reputation systems
Common Crypto Games
The MEV Game
Players: Users, searchers, builders, validators
Key insight: Transaction ordering is a game; users are often the losers
See: MEV Strategies
The Liquidity Game
Players: LPs, traders, arbitrageurs
Key insight: Impermanent loss is the cost of being adversely selected against
See: Liquidity Games
The Governance Game
Players: Token holders, delegates, protocol team
Key insight: Rational apathy + concentrated interests = capture
See: Governance Attacks
The Staking Game
Players: Stakers, validators, delegators
Key insight: Security budget must exceed attack profit
See: Tokenomics Analysis
The Oracle Game
Players: Data providers, consumers, attackers
Key insight: Profit from manipulation must be less than cost
See: Mechanism Design
Red Flags in Protocol Design
Tokenomics Red Flags
- Insiders can sell before others (vesting asymmetry)
- Inflation benefits few, dilutes many
- No sink mechanisms (perpetual selling pressure)
- Rewards without risk (free money = someone else paying)
Governance Red Flags
- Low quorum thresholds (minority capture)
- No time delay (flash loan attacks)
- Token voting only (plutocracy)
- Delegates with no skin in game
Mechanism Red Flags
- First-come-first-served (bot advantage)
- Sealed bids without commitment (frontrunning)
- Rebates/refunds (MEV extraction)
- Complex formulas (hidden exploits)
Advanced Topics
Repeated Games and Reputation
Single-shot games often have bad equilibria. Repetition enables cooperation through:
- Trigger strategies (cooperate until defection)
- Reputation building (costly to destroy)
- Future value (patient players cooperate more)
Crypto application: Why anonymous actors behave worse than doxxed teams.
Evolutionary Game Theory
Strategies that survive competitive selection. Relevant for:
- Which protocols survive long-term
- Memetic competition between narratives
- Bot strategy evolution
Bayesian Games
Games with incomplete information. Players have beliefs about others' types.
Crypto application: Trading with unknown counterparties, evaluating anonymous teams.
Cooperative Game Theory
When players can form binding coalitions.
Crypto application: MEV extraction coalitions, validator cartels, governance blocs.
Algorithmic Game Theory
Computational aspects of game theory.
Crypto application: On-chain game computation limits, gas-efficient mechanism design.
Methodology
Step 1: Model the Game
- Identify all players (including those not obvious)
- Map complete strategy spaces
- Define payoff functions precisely
- Specify information structure
Step 2: Find Equilibria
- Check for dominant strategies
- Compute Nash equilibria
- Identify Pareto improvements
- Consider trembling-hand perfection
Step 3: Stress Test
- What if players collude?
- What if new players enter?
- What if information leaks?
- What if parameters change?
Step 4: Recommend
- Mechanism changes to improve equilibrium
- Monitoring to detect deviations
- Parameter bounds to maintain stability
Resources
Foundational Texts
- "Theory of Games and Economic Behavior" - von Neumann & Morgenstern
- "A Beautiful Mind" (Nash's life, accessible intro)
- "The Strategy of Conflict" - Schelling
- "Mechanism Design Theory" - Myerson (Nobel lecture)
Crypto-Specific
- "Flash Boys 2.0" - MEV paper
- "SoK: DeFi Attacks" - Systemization of DeFi exploits
- "Clockwork Finance" - MEV and mechanism design
- Paradigm research blog
Tools
- Nashpy (Python game theory library)
- Gambit (game theory software)
- Agent-based modeling frameworks
