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    "sections": [
      {
        "title": "Game Theory for Crypto",
        "body": "Strategic analysis framework for understanding and designing incentive systems in web3.\n\n\"Every protocol is a game. Every token is an incentive. Every user is a player. Understand the rules, or become the played.\""
      },
      {
        "title": "When to Use This Skill",
        "body": "Analyzing tokenomics for exploits or misaligned incentives\nEvaluating governance proposals and voting mechanisms\nUnderstanding MEV and adversarial transaction ordering\nDesigning auction mechanisms (NFT drops, token sales, liquidations)\nPredicting how rational actors will behave in a system\nIdentifying attack vectors in DeFi protocols\nModeling liquidity provision strategies\nAssessing protocol sustainability"
      },
      {
        "title": "The Five Questions",
        "body": "For any protocol or mechanism, ask:\n\nWho are the players? (Users, LPs, validators, searchers, governance token holders)\nWhat are their strategies? (Actions available to each player)\nWhat are the payoffs? (How does each outcome affect each player?)\nWhat information do they have? (Complete, incomplete, asymmetric?)\nWhat's the equilibrium? (Where do rational actors end up?)"
      },
      {
        "title": "Analysis Template",
        "body": "## Protocol: [Name]\n\n### Players\n- Player A: [Role, objectives, constraints]\n- Player B: [Role, objectives, constraints]\n- ...\n\n### Strategy Space\n- Player A can: [List possible actions]\n- Player B can: [List possible actions]\n\n### Payoff Structure\n- If (A does X, B does Y): A gets [payoff], B gets [payoff]\n- ...\n\n### Information Structure\n- Public information: [What everyone knows]\n- Private information: [What only some players know]\n- Observable actions: [What can be seen on-chain]\n\n### Equilibrium Analysis\n- Nash equilibrium: [Stable outcome where no player wants to deviate]\n- Dominant strategies: [Strategies that are always best regardless of others]\n- Potential exploits: [Deviations that benefit attackers]\n\n### Recommendations\n- [Design changes to improve incentive alignment]"
      },
      {
        "title": "Reference Documents",
        "body": "DocumentUse CaseNash EquilibriumFinding stable outcomes in strategic interactionsMechanism DesignDesigning systems with desired equilibriaAuction TheoryToken sales, NFT drops, liquidationsMEV Game TheoryAdversarial transaction orderingTokenomics AnalysisEvaluating token incentive structuresGovernance AttacksVoting manipulation and captureLiquidity GamesLP strategies and impermanent lossInformation EconomicsAsymmetric information and signaling"
      },
      {
        "title": "Nash Equilibrium",
        "body": "A state where no player can improve their payoff by unilaterally changing strategy. The \"stable\" outcome of a game.\n\nCrypto application: In a staking system, Nash equilibrium determines the stake distribution across validators."
      },
      {
        "title": "Dominant Strategy",
        "body": "A strategy that's optimal regardless of what others do.\n\nCrypto application: In a second-price auction, bidding your true value is dominant."
      },
      {
        "title": "Pareto Efficiency",
        "body": "An outcome where no one can be made better off without making someone worse off.\n\nCrypto application: AMM fee structures try to be Pareto efficient for traders and LPs."
      },
      {
        "title": "Mechanism Design",
        "body": "\"Reverse game theory\" - designing rules to achieve desired outcomes.\n\nCrypto application: Designing token vesting schedules to align long-term incentives."
      },
      {
        "title": "Schelling Point",
        "body": "A solution people converge on without communication.\n\nCrypto application: Why certain price levels act as psychological support/resistance."
      },
      {
        "title": "Incentive Compatibility",
        "body": "When truthful behavior is optimal for participants.\n\nCrypto application: Oracle designs where honest reporting is the dominant strategy."
      },
      {
        "title": "Common Knowledge",
        "body": "Everyone knows X, everyone knows everyone knows X, infinitely recursive.\n\nCrypto application: Public blockchain state creates common knowledge of balances/positions."
      },
      {
        "title": "Pattern 1: The Tragedy of the Commons",
        "body": "Structure: Shared resource, individual incentive to overuse, collective harm.\n\nCrypto examples:\n\nGas price bidding during congestion\nGovernance token voting apathy\nMEV extraction degrading UX\n\nSolution approaches:\n\nHarberger taxes\nQuadratic mechanisms\nCommitment schemes"
      },
      {
        "title": "Pattern 2: The Prisoner's Dilemma",
        "body": "Structure: Individual rationality leads to collective irrationality.\n\nCrypto examples:\n\nLiquidity mining mercenaries (farm and dump)\nRace-to-bottom validator fees\nBridge security (each chain wants others to secure)\n\nSolution approaches:\n\nRepeated games (reputation)\nCommitment mechanisms (staking/slashing)\nMechanism redesign"
      },
      {
        "title": "Pattern 3: The Coordination Game",
        "body": "Structure: Multiple equilibria, players want to coordinate but may fail.\n\nCrypto examples:\n\nWhich L2 to use?\nToken standard adoption\nHard fork coordination\n\nSolution approaches:\n\nFocal points (Schelling points)\nSequential moves (first mover advantage)\nCommunication mechanisms"
      },
      {
        "title": "Pattern 4: The Principal-Agent Problem",
        "body": "Structure: One party acts on behalf of another with misaligned incentives.\n\nCrypto examples:\n\nProtocol team vs token holders\nDelegates in governance\nFund managers\n\nSolution approaches:\n\nIncentive alignment (token vesting)\nMonitoring (transparency)\nBonding (skin in game)"
      },
      {
        "title": "Pattern 5: Adverse Selection",
        "body": "Structure: Information asymmetry leads to market breakdown.\n\nCrypto examples:\n\nToken launches (team knows more than buyers)\nInsurance protocols (risky users more likely to buy)\nLending (borrowers know their risk better)\n\nSolution approaches:\n\nSignaling (lock-ups, audits)\nScreening (credit scores, history)\nPooling equilibria"
      },
      {
        "title": "Pattern 6: Moral Hazard",
        "body": "Structure: Hidden action after agreement leads to risk-taking.\n\nCrypto examples:\n\nProtocols with insurance may take more risk\nBailout expectations encourage leverage\nAnonymous teams may rug\n\nSolution approaches:\n\nMonitoring and transparency\nIncentive alignment\nReputation systems"
      },
      {
        "title": "The MEV Game",
        "body": "Players: Users, searchers, builders, validators\nKey insight: Transaction ordering is a game; users are often the losers\n\nSee: MEV Strategies"
      },
      {
        "title": "The Liquidity Game",
        "body": "Players: LPs, traders, arbitrageurs\nKey insight: Impermanent loss is the cost of being adversely selected against\n\nSee: Liquidity Games"
      },
      {
        "title": "The Governance Game",
        "body": "Players: Token holders, delegates, protocol team\nKey insight: Rational apathy + concentrated interests = capture\n\nSee: Governance Attacks"
      },
      {
        "title": "The Staking Game",
        "body": "Players: Stakers, validators, delegators\nKey insight: Security budget must exceed attack profit\n\nSee: Tokenomics Analysis"
      },
      {
        "title": "The Oracle Game",
        "body": "Players: Data providers, consumers, attackers\nKey insight: Profit from manipulation must be less than cost\n\nSee: Mechanism Design"
      },
      {
        "title": "Tokenomics Red Flags",
        "body": "Insiders can sell before others (vesting asymmetry)\nInflation benefits few, dilutes many\nNo sink mechanisms (perpetual selling pressure)\nRewards without risk (free money = someone else paying)"
      },
      {
        "title": "Governance Red Flags",
        "body": "Low quorum thresholds (minority capture)\nNo time delay (flash loan attacks)\nToken voting only (plutocracy)\nDelegates with no skin in game"
      },
      {
        "title": "Mechanism Red Flags",
        "body": "First-come-first-served (bot advantage)\nSealed bids without commitment (frontrunning)\nRebates/refunds (MEV extraction)\nComplex formulas (hidden exploits)"
      },
      {
        "title": "Repeated Games and Reputation",
        "body": "Single-shot games often have bad equilibria. Repetition enables cooperation through:\n\nTrigger strategies (cooperate until defection)\nReputation building (costly to destroy)\nFuture value (patient players cooperate more)\n\nCrypto application: Why anonymous actors behave worse than doxxed teams."
      },
      {
        "title": "Evolutionary Game Theory",
        "body": "Strategies that survive competitive selection. Relevant for:\n\nWhich protocols survive long-term\nMemetic competition between narratives\nBot strategy evolution"
      },
      {
        "title": "Bayesian Games",
        "body": "Games with incomplete information. Players have beliefs about others' types.\n\nCrypto application: Trading with unknown counterparties, evaluating anonymous teams."
      },
      {
        "title": "Cooperative Game Theory",
        "body": "When players can form binding coalitions.\n\nCrypto application: MEV extraction coalitions, validator cartels, governance blocs."
      },
      {
        "title": "Algorithmic Game Theory",
        "body": "Computational aspects of game theory.\n\nCrypto application: On-chain game computation limits, gas-efficient mechanism design."
      },
      {
        "title": "Step 1: Model the Game",
        "body": "Identify all players (including those not obvious)\nMap complete strategy spaces\nDefine payoff functions precisely\nSpecify information structure"
      },
      {
        "title": "Step 2: Find Equilibria",
        "body": "Check for dominant strategies\nCompute Nash equilibria\nIdentify Pareto improvements\nConsider trembling-hand perfection"
      },
      {
        "title": "Step 3: Stress Test",
        "body": "What if players collude?\nWhat if new players enter?\nWhat if information leaks?\nWhat if parameters change?"
      },
      {
        "title": "Step 4: Recommend",
        "body": "Mechanism changes to improve equilibrium\nMonitoring to detect deviations\nParameter bounds to maintain stability"
      },
      {
        "title": "Foundational Texts",
        "body": "\"Theory of Games and Economic Behavior\" - von Neumann & Morgenstern\n\"A Beautiful Mind\" (Nash's life, accessible intro)\n\"The Strategy of Conflict\" - Schelling\n\"Mechanism Design Theory\" - Myerson (Nobel lecture)"
      },
      {
        "title": "Crypto-Specific",
        "body": "\"Flash Boys 2.0\" - MEV paper\n\"SoK: DeFi Attacks\" - Systemization of DeFi exploits\n\"Clockwork Finance\" - MEV and mechanism design\nParadigm research blog"
      },
      {
        "title": "Tools",
        "body": "Nashpy (Python game theory library)\nGambit (game theory software)\nAgent-based modeling frameworks"
      }
    ],
    "body": "Game Theory for Crypto\n\nStrategic analysis framework for understanding and designing incentive systems in web3.\n\n\"Every protocol is a game. Every token is an incentive. Every user is a player. Understand the rules, or become the played.\"\n\nWhen to Use This Skill\nAnalyzing tokenomics for exploits or misaligned incentives\nEvaluating governance proposals and voting mechanisms\nUnderstanding MEV and adversarial transaction ordering\nDesigning auction mechanisms (NFT drops, token sales, liquidations)\nPredicting how rational actors will behave in a system\nIdentifying attack vectors in DeFi protocols\nModeling liquidity provision strategies\nAssessing protocol sustainability\nCore Framework\nThe Five Questions\n\nFor any protocol or mechanism, ask:\n\nWho are the players? (Users, LPs, validators, searchers, governance token holders)\nWhat are their strategies? (Actions available to each player)\nWhat are the payoffs? (How does each outcome affect each player?)\nWhat information do they have? (Complete, incomplete, asymmetric?)\nWhat's the equilibrium? (Where do rational actors end up?)\nAnalysis Template\n## Protocol: [Name]\n\n### Players\n- Player A: [Role, objectives, constraints]\n- Player B: [Role, objectives, constraints]\n- ...\n\n### Strategy Space\n- Player A can: [List possible actions]\n- Player B can: [List possible actions]\n\n### Payoff Structure\n- If (A does X, B does Y): A gets [payoff], B gets [payoff]\n- ...\n\n### Information Structure\n- Public information: [What everyone knows]\n- Private information: [What only some players know]\n- Observable actions: [What can be seen on-chain]\n\n### Equilibrium Analysis\n- Nash equilibrium: [Stable outcome where no player wants to deviate]\n- Dominant strategies: [Strategies that are always best regardless of others]\n- Potential exploits: [Deviations that benefit attackers]\n\n### Recommendations\n- [Design changes to improve incentive alignment]\n\nReference Documents\nDocument\tUse Case\nNash Equilibrium\tFinding stable outcomes in strategic interactions\nMechanism Design\tDesigning systems with desired equilibria\nAuction Theory\tToken sales, NFT drops, liquidations\nMEV Game Theory\tAdversarial transaction ordering\nTokenomics Analysis\tEvaluating token incentive structures\nGovernance Attacks\tVoting manipulation and capture\nLiquidity Games\tLP strategies and impermanent loss\nInformation Economics\tAsymmetric information and signaling\nQuick Concepts\nNash Equilibrium\n\nA state where no player can improve their payoff by unilaterally changing strategy. The \"stable\" outcome of a game.\n\nCrypto application: In a staking system, Nash equilibrium determines the stake distribution across validators.\n\nDominant Strategy\n\nA strategy that's optimal regardless of what others do.\n\nCrypto application: In a second-price auction, bidding your true value is dominant.\n\nPareto Efficiency\n\nAn outcome where no one can be made better off without making someone worse off.\n\nCrypto application: AMM fee structures try to be Pareto efficient for traders and LPs.\n\nMechanism Design\n\n\"Reverse game theory\" - designing rules to achieve desired outcomes.\n\nCrypto application: Designing token vesting schedules to align long-term incentives.\n\nSchelling Point\n\nA solution people converge on without communication.\n\nCrypto application: Why certain price levels act as psychological support/resistance.\n\nIncentive Compatibility\n\nWhen truthful behavior is optimal for participants.\n\nCrypto application: Oracle designs where honest reporting is the dominant strategy.\n\nCommon Knowledge\n\nEveryone knows X, everyone knows everyone knows X, infinitely recursive.\n\nCrypto application: Public blockchain state creates common knowledge of balances/positions.\n\nAnalysis Patterns\nPattern 1: The Tragedy of the Commons\n\nStructure: Shared resource, individual incentive to overuse, collective harm.\n\nCrypto examples:\n\nGas price bidding during congestion\nGovernance token voting apathy\nMEV extraction degrading UX\n\nSolution approaches:\n\nHarberger taxes\nQuadratic mechanisms\nCommitment schemes\nPattern 2: The Prisoner's Dilemma\n\nStructure: Individual rationality leads to collective irrationality.\n\nCrypto examples:\n\nLiquidity mining mercenaries (farm and dump)\nRace-to-bottom validator fees\nBridge security (each chain wants others to secure)\n\nSolution approaches:\n\nRepeated games (reputation)\nCommitment mechanisms (staking/slashing)\nMechanism redesign\nPattern 3: The Coordination Game\n\nStructure: Multiple equilibria, players want to coordinate but may fail.\n\nCrypto examples:\n\nWhich L2 to use?\nToken standard adoption\nHard fork coordination\n\nSolution approaches:\n\nFocal points (Schelling points)\nSequential moves (first mover advantage)\nCommunication mechanisms\nPattern 4: The Principal-Agent Problem\n\nStructure: One party acts on behalf of another with misaligned incentives.\n\nCrypto examples:\n\nProtocol team vs token holders\nDelegates in governance\nFund managers\n\nSolution approaches:\n\nIncentive alignment (token vesting)\nMonitoring (transparency)\nBonding (skin in game)\nPattern 5: Adverse Selection\n\nStructure: Information asymmetry leads to market breakdown.\n\nCrypto examples:\n\nToken launches (team knows more than buyers)\nInsurance protocols (risky users more likely to buy)\nLending (borrowers know their risk better)\n\nSolution approaches:\n\nSignaling (lock-ups, audits)\nScreening (credit scores, history)\nPooling equilibria\nPattern 6: Moral Hazard\n\nStructure: Hidden action after agreement leads to risk-taking.\n\nCrypto examples:\n\nProtocols with insurance may take more risk\nBailout expectations encourage leverage\nAnonymous teams may rug\n\nSolution approaches:\n\nMonitoring and transparency\nIncentive alignment\nReputation systems\nCommon Crypto Games\nThe MEV Game\n\nPlayers: Users, searchers, builders, validators Key insight: Transaction ordering is a game; users are often the losers\n\nSee: MEV Strategies\n\nThe Liquidity Game\n\nPlayers: LPs, traders, arbitrageurs Key insight: Impermanent loss is the cost of being adversely selected against\n\nSee: Liquidity Games\n\nThe Governance Game\n\nPlayers: Token holders, delegates, protocol team Key insight: Rational apathy + concentrated interests = capture\n\nSee: Governance Attacks\n\nThe Staking Game\n\nPlayers: Stakers, validators, delegators Key insight: Security budget must exceed attack profit\n\nSee: Tokenomics Analysis\n\nThe Oracle Game\n\nPlayers: Data providers, consumers, attackers Key insight: Profit from manipulation must be less than cost\n\nSee: Mechanism Design\n\nRed Flags in Protocol Design\nTokenomics Red Flags\nInsiders can sell before others (vesting asymmetry)\nInflation benefits few, dilutes many\nNo sink mechanisms (perpetual selling pressure)\nRewards without risk (free money = someone else paying)\nGovernance Red Flags\nLow quorum thresholds (minority capture)\nNo time delay (flash loan attacks)\nToken voting only (plutocracy)\nDelegates with no skin in game\nMechanism Red Flags\nFirst-come-first-served (bot advantage)\nSealed bids without commitment (frontrunning)\nRebates/refunds (MEV extraction)\nComplex formulas (hidden exploits)\nAdvanced Topics\nRepeated Games and Reputation\n\nSingle-shot games often have bad equilibria. Repetition enables cooperation through:\n\nTrigger strategies (cooperate until defection)\nReputation building (costly to destroy)\nFuture value (patient players cooperate more)\n\nCrypto application: Why anonymous actors behave worse than doxxed teams.\n\nEvolutionary Game Theory\n\nStrategies that survive competitive selection. Relevant for:\n\nWhich protocols survive long-term\nMemetic competition between narratives\nBot strategy evolution\nBayesian Games\n\nGames with incomplete information. Players have beliefs about others' types.\n\nCrypto application: Trading with unknown counterparties, evaluating anonymous teams.\n\nCooperative Game Theory\n\nWhen players can form binding coalitions.\n\nCrypto application: MEV extraction coalitions, validator cartels, governance blocs.\n\nAlgorithmic Game Theory\n\nComputational aspects of game theory.\n\nCrypto application: On-chain game computation limits, gas-efficient mechanism design.\n\nMethodology\nStep 1: Model the Game\nIdentify all players (including those not obvious)\nMap complete strategy spaces\nDefine payoff functions precisely\nSpecify information structure\nStep 2: Find Equilibria\nCheck for dominant strategies\nCompute Nash equilibria\nIdentify Pareto improvements\nConsider trembling-hand perfection\nStep 3: Stress Test\nWhat if players collude?\nWhat if new players enter?\nWhat if information leaks?\nWhat if parameters change?\nStep 4: Recommend\nMechanism changes to improve equilibrium\nMonitoring to detect deviations\nParameter bounds to maintain stability\nResources\nFoundational Texts\n\"Theory of Games and Economic Behavior\" - von Neumann & Morgenstern\n\"A Beautiful Mind\" (Nash's life, accessible intro)\n\"The Strategy of Conflict\" - Schelling\n\"Mechanism Design Theory\" - Myerson (Nobel lecture)\nCrypto-Specific\n\"Flash Boys 2.0\" - MEV paper\n\"SoK: DeFi Attacks\" - Systemization of DeFi exploits\n\"Clockwork Finance\" - MEV and mechanism design\nParadigm research blog\nTools\nNashpy (Python game theory library)\nGambit (game theory software)\nAgent-based modeling frameworks"
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