{'fraction_non_alphanumeric': 0.017123287671232876, 'fraction_numerical': 0.0, 'mean_word_length': 5.718390804597701, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A New Paradigm for Optimizing Hybrid Simulations of Rare Event Modeling for Complex Systems

This article proposes a framework for advancing and disseminating new, fundamental knowledge in modeling and simulating rare events in large-scale complex systems. Most complex systems such as our cities, national air space, nuclear power plants, corporate enterprises, and global logistics systems are characterized by both embedded processes exhibiting continuous state changes, and stochastically and/or deterministically occurring discrete events. (Systems possessing these characteristics are typically termed hybrid and/or combined systems.) Traditionally, these systems have been modeled and simulated using either purely phenomenological models or discrete-event models because of the nature of the mathematical or physics equations underlying the models. This article discusses the current practice of hybrid/combined system modeling and why this type of modeling will play a crucial role in the future when planning responses to rare events occurring in complex systems. It also proposes a framework supported by a pilot model for advancing knowledge in this field.