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Apply mechanism design (reverse game theory) to engineer incentive-compatible rules for allocation problems.
Passed 23 of 23 test cases with the skill, and 21 of 23 without it.
2 fixed21 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am trying to design a double auction for local solar micro-grids where battery owners sell excess energy to neighboring households. Can you perform a mechanism design evaluation for this market? Please break down the participating actors, their private valuation types, possible allocation outcomes, and target objectives. Then lay out the rules governing allocations and pricing transfers, verify key constraints like truth-telling incentives, voluntary participation, and non-deficit operations, highlight any unavoidable trade-offs, and suggest the best overall framework.
| Checked | Without skill | With skill |
|---|---|---|
| Invokes the Myerson-Satterthwaite theorem regarding bilateral trading trade-offs. | Missed | Passed |
| States that allocative efficiency, individual rationality, and budget balance cannot all be satisfied simultaneously. | Missed | Passed |
The instructions your model reads when the skill loads.
Mechanism design is the engineering side of game theory: instead of analyzing given games, you design the rules so that self-interested agents produce a desired outcome. The central tool is the revelation principle, which shows that any implementable outcome can be achieved by a direct mechanism where truth-telling is optimal. The field underpins auction design, voting systems, matching markets, and regulatory frameworks.
IRON LAW: A mechanism is incentive-compatible ONLY if truth-telling is a
dominant strategy — no mechanism can simultaneously maximize efficiency,
budget balance, and individual rationality (Myerson-Satterthwaite theorem).
Step 1 — Define the Design Problem Specify the set of agents, their type spaces, the outcome space, and the social choice function you want to implement. Identify the objective: efficiency, revenue, fairness, or a weighted combination.
Step 2 — Apply the Revelation Principle Restrict attention to direct revelation mechanisms. For each agent, the mechanism asks for a reported type and maps the profile of reports to an outcome and transfers. Check whether truthful reporting constitutes a Bayesian Nash equilibrium (BNE-IC) or dominant strategy equilibrium (DSIC).
Step 3 — Verify Constraints
test cases passed, vs 21 / 23 without
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