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.
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."
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.