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:
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
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
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:
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