Modeling Hyper-Complex Historical Systems as Incomplete Information Game-Theoretic Frameworks Using Tools from Artificial Intelligence and Machine Theory
محورهای موضوعی : Artificial Intelligence Tools in Software and Data Engineering
1 - دانش آموخته دکتری تخصصی تاریخ اسلام ، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
کلید واژه: Historical Systems, Incomplete Information Game Theory, Artificial Intelligence, Machine Theory, Hyper-Complex Systems,
چکیده مقاله :
The aim of this study is to present a theoretical and computational framework for modeling hyper-complex historical systems using tools from machine theory and artificial intelligence. Historical systems, due to their multi-agent nature, temporal dynamics, and incomplete information, exhibit high levels of complexity that often render traditional analytical methods ineffective or limited. The significance of this approach lies in its ability to more accurately represent human behavior, simulate historical decision-making processes, and forecast future scenarios—particularly in domains such as public policy, international security, and strategic studies, where decision-making under uncertainty is critical.
In this research, historical decision-making structures are mathematically modeled through finite state machines, learning machines, and decision-making automata. Additionally, by employing machine learning algorithms, neural networks, and reinforcement models, the study enables the analysis of historical data, discovery of hidden patterns, and optimization of decision-making processes. The findings indicate that integrating machine theory with artificial intelligence is not only effective in reconstructing past behaviors but also highly efficient in analyzing counterfactual scenarios and designing forward-looking policies.
This approach provides a platform for testing historical hypotheses, analyzing multi-agent interactions, and simulating complex behaviors—marking a significant step toward the development of computational historiography and bridging the gap between the humanities and data sciences. Ultimately, the study demonstrates that modeling historical systems with AI tools contributes not only to the theoretical enrichment of historical research but also offers practical instruments for intelligent decision-making in the face of contemporary complex challenges.
The aim of this study is to present a theoretical and computational framework for modeling hyper-complex historical systems using tools from machine theory and artificial intelligence. Historical systems, due to their multi-agent nature, temporal dynamics, and incomplete information, exhibit high levels of complexity that often render traditional analytical methods ineffective or limited. The significance of this approach lies in its ability to more accurately represent human behavior, simulate historical decision-making processes, and forecast future scenarios—particularly in domains such as public policy, international security, and strategic studies, where decision-making under uncertainty is critical.
In this research, historical decision-making structures are mathematically modeled through finite state machines, learning machines, and decision-making automata. Additionally, by employing machine learning algorithms, neural networks, and reinforcement models, the study enables the analysis of historical data, discovery of hidden patterns, and optimization of decision-making processes. The findings indicate that integrating machine theory with artificial intelligence is not only effective in reconstructing past behaviors but also highly efficient in analyzing counterfactual scenarios and designing forward-looking policies.
This approach provides a platform for testing historical hypotheses, analyzing multi-agent interactions, and simulating complex behaviors—marking a significant step toward the development of computational historiography and bridging the gap between the humanities and data sciences. Ultimately, the study demonstrates that modeling historical systems with AI tools contributes not only to the theoretical enrichment of historical research but also offers practical instruments for intelligent decision-making in the face of contemporary complex challenges.
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