Informational parasites in code evolution
Andres C. Burgos, Daniel Polani

TL;DR
This paper models how code evolution in populations can be influenced by informational parasites that spread disinformation, leading to adaptive code changes that eventually make the parasite's information accessible.
Contribution
It introduces a model of informational parasites in code evolution, showing how populations adapt their codes to counter disinformation and inadvertently reveal the parasite's information.
Findings
Populations adapt their codes to avoid parasite messages.
The parasite's information becomes accessible after population adaptation.
Code drift occurs as populations respond to disinformation.
Abstract
In a previous study, we considered an information-theoretic model of code evolution. In it, agents obtain information about their (common) environment by the perception of messages of other agents, which is determined by an interaction probability (the structure of the population). For an agent to understand another agent's messages, the former must either know the identity of the latter, or the code producing the messages must be universally interpretable. A universal code, however, introduces a vulnerability: a parasitic entity can take advantage of it. Here, we investigate this problem. In our specific setting, we consider a parasite to be an agent that tries to inflict as much damage as possible in the mutual understanding of the population (i.e. the parasite acts as a disinformation agent). We show that, after introducing a parasite in the population, the former adopts a code such…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Evolutionary Algorithms and Applications · Computability, Logic, AI Algorithms
