S4h Game Theory Auction logo

S4h Game Theory Auction

Organization
human-avatar
s4h-game-theory-auction

Analyses bidding strategy and auction design — how much to bid, how to avoid the winner's curse, and how to design revenue-maximising or efficient auctions. Triggers: 'bidding strategy', 'auction analysis', 'how much should I bid', 'auction design', 'winner's curse', 'sealed bid', 'how do I avoid overbidding', 'procurement auction', 'design an auction', 'competitive offer'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-game-theory-auction
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Game Theory Auction AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-game-theory-auction
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-game-theory-auction .claude/skills/s4h-game-theory-auction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Game Theory Auction in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads S4h Game Theory Auction on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so S4h Game Theory Auction is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Game Theory: Auction Analysis

William Vickrey's 1961 discovery is one of the cleanest results in economics: in a second-price sealed-bid auction, bidding your true value is a dominant strategy — the best move regardless of what others bid. The mechanism works because you pay the second-highest bid, not your own. Overbidding your true value doesn't help you (you might win but pay more than the item is worth); underbidding doesn't help you either (you might lose an item worth more than you'd have paid). So you bid your true value and let the second-highest bid determine the price. Vickrey received the Nobel Prize in 1996 for this result and related work.

First-price auctions are strategically different: you pay what you bid, so optimal play requires shading your bid below your true value. The optimal shade depends on the number of competitors (shade more with more competitors) and the distribution of their valuations (shade more when competition is intense). In equilibrium, first-price and second-price auctions generate the same expected revenue — the revenue equivalence theorem — under standard conditions.

The winner's curse is the most common failure mode in common-value auctions (where the item has an underlying objective value everyone is trying to estimate, rather than a private personal value). Winning means you bid highest, which means your estimate was the most optimistic among all bidders. In expectation, if you bid your unconditional estimate and win, you've overpaid — because winning reveals that you were the most optimistic, not the most accurate. The correct bid is your estimate conditional on winning, which is lower than your unconditional estimate.

Paul Milgrom and Robert Wilson (Nobel 2020) developed the modern theory of auction design, including the simultaneous ascending auction used in FCC spectrum allocation — showing how auction design directly affects both revenue and efficient allocation.


Your Process

Step 1: Auction type identification Identify the auction format:

  • First-price sealed bid: all bidders submit one bid simultaneously; highest bid wins and pays their own bid
  • Second-price sealed bid (Vickrey): highest bid wins but pays the second-highest bid
  • Ascending (English): price rises until only one bidder remains; winner pays the final price
  • Descending (Dutch): price falls from a high start until the first bidder claims the item at the current price
  • Other: procurement reverse auctions, combinatorial auctions, multi-round formats

Framing check: Confirm the auction situation before continuing. State what you've identified — the auction format, whether the user is a bidder or designer, and the key parameters (item, number of competitors, value structure if apparent) — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the auction situation, role, and key parameters]. Is that right?"
  • Header: "Framing"
  • Options:
    • Yes — proceed — framing is correct
    • Adjust — one element is off; user will correct it before you continue
    • Reframe — different situation than read; incorporate the correction before proceeding

Step 2: Private vs. common value Determine the value structure:

  • Private value: each bidder has their own subjective valuation, independent of others. What the item is worth to you doesn't depend on what it's worth to others. Most art auctions, personal property sales.
  • Common value: the item has an underlying value that is the same for all bidders, but each has an imperfect estimate. Mineral rights, spectrum licenses, antique coins (where the value is objective but uncertain). Winner's curse applies here.
  • Affiliated values: intermediate case — your valuation is positively correlated with others'. Most real situations fall here.

Step 3: Optimal bidding strategy by type

Second-price (Vickrey): Bid your true value. This is a dominant strategy — it is best regardless of what others bid. No adjustment needed.

First-price sealed bid: Shade your bid below your true value. As a rough rule with n symmetric bidders: bid approximately (n−1)/n × your true value. With 2 bidders, bid 50% of your value; with 4 bidders, 75%; with 10 bidders, 90%. In practice: bid higher when competition is intense (many bidders, strong demand) because the shading needs to be small to remain competitive.

Ascending (English): Stay in the auction until the price exceeds your true value, then drop out. Never bid beyond your valuation. The private-value dominant strategy is identical in structure to the Vickrey auction.

Descending (Dutch): Accept at the price that equals your true value. No advantage to waiting longer (you risk losing), and accepting earlier costs you money.

Step 4: Winner's curse adjustment (common value only) In common-value settings, adjust your bid downward to correct for the selection bias of winning. Procedure:

  1. Estimate the item's true value using your available information
  2. Ask: conditional on winning (i.e., conditional on having submitted the highest bid), what does that tell me about the true value? Winning means everyone else estimated lower — your estimate is the most optimistic
  3. Revise your estimate downward by an amount that increases with the number of bidders and the uncertainty in your estimate
  4. Bid based on this revised, downward-adjusted estimate, not your initial estimate

Step 5: Auction design (for designers) Apply the following principles:

  • Reserve price: set a floor below which you won't sell. Even in revenue-maximising design, a properly set reserve price increases expected revenue by eliminating sales at too-low prices
  • Revenue equivalence: under standard conditions, first-price and second-price formats generate equal expected revenue. Choose based on other considerations: second-price is simpler to reason about (dominant strategy bidding); first-price gives more price certainty upfront
  • Efficiency vs. revenue: second-price with no reserve is most efficient (item goes to highest-value bidder); adding a reserve or using a first-price format trades some efficiency for revenue
  • Multi-unit and combinatorial: when multiple items are sold and bidders value combinations, use a format that handles complementarities — the Vickrey-Clarke-Groves mechanism generalises the Vickrey auction to multi-item settings

Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full analysis — Complete all steps, reasoning shown throughout
    • Key findings only — Bottom-line output, skip step-by-step detail
    • Specific focus — Zoom in on one aspect of this analysis
    • Reframe — The read is off; correct it and the analysis will follow the corrected framing

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.


Output Format

Auction Analysis

Auction Type [Format identified: first-price sealed bid / second-price / ascending / descending / other]

Value Structure [Private value / common value / affiliated values — and the implication for strategy]

Optimal Bidding Strategy [Specific recommended strategy for this auction type — dominant strategy or optimal shade with reasoning]

Winner's Curse Adjustment (common value only) [Revised estimate after conditioning on winning, and the magnitude of the adjustment]

Specific Bid Recommendation [If a bidder: the recommended bid with precise reasoning. If asked to evaluate a strategy: assessment of whether it is optimal]

Designer Recommendations (if applicable) [Reserve price, format choice, revenue vs. efficiency trade-offs, multi-unit design considerations]


Notes

The revenue equivalence theorem holds under strong assumptions: symmetric bidders, private values, independent valuations, risk-neutral bidders. When these fail — bidders are asymmetric, values are affiliated, bidders are risk-averse — the revenue equivalence breaks down and format choice matters for revenue.

The winner's curse is not irrational behaviour corrected by experience alone. It is a structural consequence of the selection process: winning an auction carries information about the item's value, and that information updates your estimate downward. Even sophisticated bidders overbid in novel common-value contexts.

Auction analysis is a specialised application of mechanism design. For the general framework of designing rules to produce desired behaviour from self-interested players, use /s4h-game-theory-mechanism-design. For the equilibrium analysis of the auction (confirming which strategy is actually optimal for each player), use /s4h-game-theory-equilibrium.

Pairs with: /s4h-game-theory-mechanism-design (the general framework for auction design), /s4h-game-theory-equilibrium (equilibrium analysis of specific auction formats), /s4h-decision-expected-value (when the bidding decision is primarily about expected value under uncertainty, not strategic interaction).


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Auction analysed. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-game-theory-mechanism-design — Refine the auction mechanism based on findings
    • /s4h-probability-expected-value-calculation — Calculate expected value of different bidding strategies
    • /s4h-strategy-intelligence — Gather information to improve bidding position
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Game Theory Auction AI skill do?

Analyses bidding strategy and auction design — how much to bid, how to avoid the winner's curse, and how to design revenue-maximising or efficient auctions. Triggers: 'bidding strategy', 'auction analysis', 'how much should I bid', 'auction design', 'winner's curse', 'sealed bid', 'how do I avoid overbidding', 'procurement auction', 'design an auction', 'competitive offer'.

Why use S4h Game Theory Auction on TypingMind?

Because you install it once and use it with any model. S4h Game Theory Auction is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install S4h Game Theory Auction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-game-theory-auction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Game Theory Auction?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with S4h Game Theory Auction?

As many as you like. As long as a model supports skills, you can use S4h Game Theory Auction with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Game Theory Auction AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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