opencompletion.com/research/activity45-game-theory-501.yaml
Claude b8bf7261cb
Add complete statistics and game theory curriculum (6 activities)
NEW ACTIVITIES:

activity40-statistics-101.yaml - Foundational statistics
- Central tendency, spread, probability, distributions
- Real-world applications and critical thinking

activity41-game-theory-101.yaml - Strategic fundamentals
- Prisoner's Dilemma, Nash Equilibrium, dominant strategies

activity42-game-theory-201.yaml - Advanced concepts
- Mixed strategies, repeated games, Tit-for-Tat

activity43-game-theory-301.yaml - Cooperative games
- Coalition formation, Shapley value, fair division

activity44-game-theory-401.yaml - Information asymmetry
- Signaling, screening, adverse selection

activity45-game-theory-501.yaml - Mechanism design
- Auction theory, Vickrey auctions, incentive compatibility

All activities:
- Validate with zero errors/warnings
- Follow expert guide requirements
- Include engaging examples
- Terminate properly
- Support language switching
2025-11-09 15:27:38 +00:00

73 lines
3.6 KiB
YAML

default_max_attempts_per_step: 3
sections:
- section_id: introduction
title: Game Theory 501
steps:
- step_id: welcome
title: Design the Game
content_blocks:
- "# Game Theory 501: Mechanism Design 🏗️"
- "**Reverse game theory: Design the game itself!**"
- "✓ Mechanism design (reverse game theory)"
- "✓ Auction theory"
- "✓ Voting theory"
- "✓ Incentive compatibility"
question: Ready to learn how to design strategic systems?
tokens_for_ai: Accept positive as 'ready', else 'off_topic'
buckets: [ready, set_language, off_topic]
transitions:
ready: {next_section_and_step: "mechanism_design:step_1"}
set_language: {metadata_add: {language: "the-users-response"}, counts_as_attempt: false, next_section_and_step: "introduction:welcome"}
off_topic: {counts_as_attempt: false, next_section_and_step: "introduction:welcome"}
- section_id: mechanism_design
title: Designing Strategic Systems
steps:
- step_id: step_1
title: Incentive Engineering
content_blocks:
- "## Mechanism Design: Engineering Incentives 🎯"
- ""
- "**The challenge:**"
- "Design rules so self-interested players produce desired outcomes"
- ""
- "**Revelation Principle:**"
- "Focus on mechanisms where truth-telling is optimal"
- "'Incentive compatible' mechanisms"
- ""
- "**Vickrey Auction (2nd-price sealed-bid):**"
- "- Everyone submits sealed bid"
- "- Highest bidder wins"
- "- Pays 2nd-highest bid"
- ""
- "**Why brilliant:**"
- "- Dominant strategy: Bid your true value!"
- "- Overbidding risks paying too much"
- "- Underbidding risks losing when you'd profit"
- "- Truthful bidding is optimal"
- ""
- "**Applications:**"
- "- eBay (proxy bidding)"
- "- Google AdWords"
- "- Organ donation matching"
- "- Spectrum auctions"
question: In a Vickrey auction, why is bidding your true value the dominant strategy?
tokens_for_ai: |
Key insight: You pay 2nd price, not your bid.
Overbidding risks paying more than value.
Underbidding risks losing profitable wins.
True value bidding is optimal.
Categorize: excellent_explanation, understands_truthful, partial, set_language, off_topic
buckets: [excellent_explanation, understands_truthful, partial, set_language, off_topic]
transitions:
excellent_explanation:
ai_feedback: {tokens_for_ai: "Perfect! Since you pay 2nd price, not your bid, bidding true value is dominant. Can't improve by lying! This is mechanism design genius!"}
metadata_add: {score: "n+2", activity_completed: "true"}
understands_truthful:
ai_feedback: {tokens_for_ai: "Exactly! Paying 2nd price means truthful bidding is optimal. Over/under bidding can only hurt you. Brilliant design!"}
metadata_add: {score: "n+1", activity_completed: "true"}
partial:
ai_feedback: {tokens_for_ai: "Right idea! Key: you pay 2nd price. Bidding true value dominates - lying can't help, might hurt. This is mechanism design!"}
metadata_add: {activity_completed: "true"}
set_language: {metadata_add: {language: "the-users-response"}, counts_as_attempt: false, next_section_and_step: "mechanism_design:step_1"}
off_topic: {metadata_add: {activity_completed: "true"}}