A strategic disclosure and capital-allocation experiment
Computational Microeconomics
Welcome to the Game
You are participating in a simplified financial market where
firms disclose information to an AI valuation system and investors
use the AI's valuation when deciding how much to bid.
FirmDisclosure
→
AIValuation
→
InvestorBid
→
AllocationCapital
→
FirmPerformance
Research question:
When firms know how an AI evaluator scores their disclosures,
can a history-aware evaluator and an auction-based allocation
mechanism reduce strategic disclosure while preserving informative pricing?
Game Setup
First choose the role you want to study.
Investor Decision
Round 1
Utility: 0
AI Evaluator
AI Valuation
Your Private Signal
Available Shares1
Your Decision
You observe the AI valuation and your private signal.
Submit your bid for the share.
Firm Decision
Round 1
Total profit: 0
True Firm Quality
AI Evaluator
Manipulation Cost
Choose Your Disclosure Strategy
You may strategically increase your disclosed quality.
Higher manipulation may increase the AI valuation,
but manipulation is costly.
0.0
AI Evaluator Design
Choose how the AI uses historical information.
Game Summary
Research Interpretation
The experiment studies whether strategic disclosure can be
rewarded by an AI-assisted capital-allocation system.
It also tests whether an evaluator that learns from historical
outcomes can accidentally learn from outcomes partly caused by
its own previous allocation decisions.