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Measuring Fitness Effects of Agent-Environment Interactions.

Simon McGregor1, Pedro A M Mediano2

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Summary

We introduce a new way to measure adaptation, defining it as how well an agent adjusts to environmental changes. This quantitative measure assesses an agent's ability to maintain fitness through appropriate responses to new information.

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Area of Science:

  • Systems Biology
  • Theoretical Biology
  • Information Theory

Background:

  • Adaptation is crucial for agent survival and function.
  • Existing definitions of adaptation lack quantitative rigor.
  • Understanding adaptation requires measuring an agent's response to environmental stimuli.

Purpose of the Study:

  • To propose a novel quantitative measure for agent adaptation.
  • To extend Klyubin's (2002) definition of adaptation as "increased robustness to repeated perturbation."
  • To provide a computable framework for assessing adaptation in biological and artificial systems.

Main Methods:

  • Defined adaptation as the average fitness gain from environment-induced state changes.
  • Introduced a counterfactual comparison: agent fitness versus fitness in a world with disrupted agent-environment causal links.
  • Utilized a fitness function to quantify the value of state changes.

Main Results:

  • Developed a novel quantitative measure for adaptation.
  • Demonstrated the measure's applicability in a simple Markov chain model.
  • Validated the measure using a simulated protocell model exhibiting autopoietic agency.

Conclusions:

  • The proposed measure offers a robust framework for quantifying adaptation.
  • This quantitative approach enhances our understanding of how agents adapt to changing environments.
  • The measure is applicable across diverse systems, from simple models to complex simulated agents.