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This study uses Shannon information theory to optimize multi-agent decision-making by modeling agent interactions and power allocation. Mathematical theorems reveal how combining agents impacts channel capacity for improved team performance.

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Area of Science:

  • Information Theory
  • Decision Science
  • Networked Systems

Background:

  • Multi-agent systems require robust decision-making frameworks.
  • Optimizing information flow is critical for team performance.
  • Shannon information theory provides tools for quantifying information transfer.

Purpose of the Study:

  • To apply Shannon information theory and its entropic terms to multi-agent decision-making.
  • To model agent interactions, including power allocation, within a team scenario.
  • To analyze the impact of agent combination on channel capacity.

Main Methods:

  • Utilizing classical Shannon channel capacity as a metric for information transfer between agents.
  • Developing mathematical models to represent agent interactions and power allocation strategies.
  • Applying information-theoretic principles to derive theorems on combined agent influence.

Main Results:

  • Established mathematical theorems demonstrating the relationship between agent combination and channel capacity.
  • Quantified information transfer using Shannon channel capacity in a multi-agent context.
  • Provided insights into optimal power allocation strategies for enhanced team performance.

Conclusions:

  • Shannon information theory offers a powerful framework for optimizing decisions in multi-agent systems.
  • Agent interaction modeling and power allocation significantly influence overall channel capacity.
  • The derived theorems provide a theoretical basis for designing more efficient and effective multi-agent teams.