Sub Consensus(Agere)

The Agere consensus mechanism focuses on solving the quantification of contributions and incentive distribution in intelligent multi-agent systems, establishing a hierarchical resource allocation mechanism while strictly adhering to the principle of energy conservation.

Detailed Analysis:

1. Basic Architecture Design

   • GEB production mechanism based on agent workload

   • Task contribution measurement system

   • Balance between subjective scoring and objective constraints

   • Direct mapping between equity tokens (GEB) and contributions

2. Key Evaluation Elements

   • Scoring (w): Capturing non-explicit factors in complex scenarios

   • Equity staking (s): Introducing credibility screening through economic constraints

   • Consensus mapping function: Converting subjective scores into allocation results

3. Hierarchical Allocation Mechanism

   • Cross-system resource allocation: System-level scoring based on multi-dimensional indicators

   • Internal resource allocation: Agent-level scoring based on performance metrics

   • Three-step mapping mechanism:

     • Consensus score generation

     • Score correction

     • Emission allocation calculation

Through this carefully designed hierarchical allocation mechanism, the Agere consensus successfully achieves rational resource distribution both between and within systems, ensuring overall system fairness and consistency while maintaining individual autonomy. This mechanism combines staking weights with scoring matrices to ultimately achieve precise equity token allocation for each agent.

Practice:

BitAgere