SIMULATING FEAR RESPONSES IN EVOLUTIONARY COMPUTATION (EC): A FEARISM APPROACH TO ADAPTIVE AI SYSTEMS - Ramala Sarma (Journal of Dharma, 2024)
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.