To ensure best action for all platform users is to provide and receive genuine pricing estimates, the protocol is designed to be economically impractical for malicious actors.

Types of Malicious Actors

  • Type A Malicious Actors Maliciously high estimates would place Type A Malicious Actors’ estimates further away from the mean estimate unless the malicious submission carries so much RP that substantially outweighs all other submissions combined and becomes the mean. Given RP can only be earned and given the Staking Limits control, attacks by Type A Malicious Actors are highly unlikely. Reward Mechanism allocates rewards among Price Experts based on Z Score of their price estimates and their relative stakes. In particular, Price Experts with Z Score outside of ±0.1 will not be entitled to any reward regardless of their stake. Boosters based on Z Score further allocates increasing share in the reward pools to Price Experts with Z Score closer to 0. Because of these controls, the optimal strategy for Type A Malicious Actors would be to stake just enough RP and maximum LITH with estimates as close to the expected mean as possible.

Preventive Measures

Key preventative measures are:

  • Reputation Must be Earned Although LITH together with precision of estimates determine reward distribution, LITH carries no weight towards the output price estimate. The weight or influence of any input pricing estimate is determined solely by RP or the reputation staked towards a submission.

  • Encrypted Submission All input pricing estimates will be encrypted prior to submission in order to prevent cheating and late submission advantages. No other Price Expert will have any information about other submissions until the Reveal Stage.

  • Staking Limits Price Experts will only be entitled to stake RP after achieving and maintaining the RP Threshold. Additional LITH Staking Limit and RP Staking Limit controls are also in place to prevent sybil attacks and any attempts to maintain multiple accounts for distorting outputs.

  • Positive Reinforcement Once market consensus answer is uncovered, malicious actors' reputation in RP will be slashed, providing positive feedback that will continuously improve the protocol integrity.

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