🧠 Understanding Potential Manipulation
Any system that relies on user inputs faces potential challenges from strategic behavior. We’ve carefully analyzed these risks:- Potential Manipulation: We’ve engineered against self-inflation and strategic omissions
- Game Theory Analysis: We’ve applied robust game theory principles to design a system resistant to gaming
🎯 Key Manipulation Vectors
We’ve identified several potential manipulation strategies and built countermeasures for each:- Inflating Own Company: Employees might rate their firm excessively high
- Omitting Strong Competitors: Deliberately excluding strong competitors from admiration lists
- Coordinated Voting: Groups of employees coordinating their votes for mutual benefit
🛡️ Comprehensive Countermeasures
Our system includes multiple layers of protection:- Share Price ≠ Score: Actual price is determined by market events, not internal ratings
- Weighted Voting: Senior employees with longer tenure have more influence
- Anonymity: Reduces fear of retaliation and makes coordination more difficult
- Data Validation: Sophisticated outlier detection prevents extreme manipulation
⚙️ Algorithmic Protections
Our PageRank-inspired algorithm itself provides inherent protection:- Network Effect: Companies only achieve high rankings when admired by other highly-ranked companies
- Balance Factor: The damping factor () balances insider bullishness with peer admiration
- Convergence Properties: The iterative process tends to smooth out attempted manipulations
👁️ Governance and Oversight
Beyond technical measures, we maintain system integrity through:- Regular Audits: Periodic review of voting patterns and outcomes
- Transparency: Clear communication about how rankings are determined
- Community Standards: Establishing norms and expectations for participation