SIMULATING FEAR RESPONSES IN EVOLUTIONARY COMPUTATION (EC): A FEARISM APPROACH TO ADAPTIVE AI SYSTEMS

Abstract

In computer science, evolutionary computation (EC) is
a research area miming biological evolution through various
evolutionary algorithms (EA). Fearism is a philosophical
framework of recent origin developed predominantly by R.
Michael Fisher and Desh Subba that emphasizes the crucial role
of fear in shaping human behaviour, culture and social structures.
This research attempts to combine these two areas of study, EC
and fearism, to enhance the adaptability and decision-making of
artificial intelligence (AI) systems. By studying the theoretical
foundations of EC and fearism, the work proposes a new
approach to simulating fear responses within adaptive AI systems
that can respond to dynamic and unexpected situations of life in
a
human-like manner. The study finds that a nuanced
understanding of the ethical implications of fear in the context of
AI can help AI designers use fear as a constructive force in the
evolutionary processes. The study, however, does not claim to
provide any empirical models but a philosophical approach.