Subjective Information and Survival in a Simulated Biological System

Area of Science:

• Information theory
• Theoretical biology
• Computational modeling

Background:

• Information transmission and storage are key concepts for understanding biological systems.
• Classical information theory, exemplified by Shannon's work, focuses on syntactic information efficiency.
• Biological systems, however, deal with semantic information where different data types impact survival differently.

Purpose of the Study:

• To explore the disconnect between classical information theory and biological systems.
• To develop a mathematical model for information processing in populations of single-celled organisms.
• To investigate the emergence of "subjective information" and its role in survival and growth.

Main Methods:

• Development of an abstract mathematical model represented as a computational state machine.
• Utilization of a custom simulation framework to model population dynamics.
• Analysis of strategies balancing information acquisition with growth and survival.

Main Results:

• Simulations revealed a trade-off between maximizing information acquisition and maximizing growth/survival.
• A strategy prioritizing information efficiency led to lower growth rates.
• Strategies that acquired less information but deemed more relevant for survival resulted in higher growth.

Conclusions:

• Classical information efficiency metrics are insufficient for biological contexts.
• Biological systems exhibit "subjective information" where the meaning of data is crucial for survival.
• A balance between information acquisition and its survival value is essential for organismal success.