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    Area of Science:

    • Distributed Systems
    • Multi-Agent Systems
    • Computational Economics

    Background:

    • Coordination in distributed systems is computationally intensive.
    • Load balancing is crucial for efficient task completion.
    • Autonomous agents require mechanisms for cooperation and resource sharing.

    Purpose of the Study:

    • To introduce a novel framework for distributed agents to manage externally generated and volunteered tasks.
    • To reduce coordination overhead in distributed load balancing.
    • To incentivize task sharing among agents through a fictitious trading economy.

    Main Methods:

    • Agents adjust volunteering propensity asynchronously within a fictitious trading economy.
    • Analysis of diminishing marginal returns and network topology effects.
    • Development of a decentralized distributed gradient-ascent algorithm.

    Main Results:

    • The trading economy incentivizes volunteering for remote tasks, enabling load sharing.
    • Competitive equilibria lead to task reallocations similar to explicit coordination.
    • The algorithm guarantees convergence to equilibrium without exceeding task-processing rates.

    Conclusions:

    • The proposed framework enables efficient distributed load balancing via a virtual economy.
    • It provides theoretical conditions for equilibrium existence and uniqueness.
    • The approach is applicable to autonomous systems and sheds light on cooperation evolution.