Cheater Detection Arms Race
When planning long-term fraud prevention strategy, budgeting for ongoing detection investment, or evaluating the sustainability of current detection approaches.
Framework for managing the ongoing evolutionary arms race between detection systems and cheater tactics, recognizing that perfect detection is impossible and continuous investment is required.
When to Use Cheater Detection Arms Race
When planning long-term fraud prevention strategy, budgeting for ongoing detection investment, or evaluating the sustainability of current detection approaches.
How to Apply
Invest to Keep Cheating Below Trust-Destruction Threshold
Identify the fraud rate that causes cooperators to stop participating
Questions to Ask
- What fraud rate causes user defection?
- What detection investment maintains acceptable levels?
Outputs
- Target fraud rate and required investment level
Accept Imperfect Detection as Optimal
99% detection may cost 10× what 95% costs - diminishing returns apply
Questions to Ask
- Where does marginal cost exceed marginal trust benefit?
- What's the cost curve for additional detection?
Outputs
- Optimal detection level (not maximum)
Use Multiple Mechanisms for Redundancy
Reputation + automation + third-party + market punishment
Questions to Ask
- Do mechanisms reinforce or substitute for each other?
- Can cheaters who defeat one mechanism be caught by another?
Outputs
- Multi-mechanism detection architecture
Update Detection as Cheating Evolves
Detection must improve as fast as cheating sophistication
Questions to Ask
- Is detection adapting to new cheating tactics?
- ML auto-updating or manual rule updates needed?
Outputs
- Continuous improvement process for detection