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The Role of Artificial Intelligence and Machine Learning in Simulations
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Author(s): T. S. Srilalitha (Dayanada Sagar Business Academy, India), Vidya Sunil Gavekar (Suryadatta Institute of Management and Mass Communication, Pune, India), Yuvraj Lahoti (Vishwakarma University, India), P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), Puneet Kumar Gupta (The ICFAI University, Dehradun, India)and Manjunath T. C. (Rajarajeswari College of Engineering, India)
Copyright: 2026
Pages: 28
Source title:
Navigating Simulations in Marketing for Strategic Success
Source Author(s)/Editor(s): Andreas Masouras (Neapolis University Pafos, Cyprus)and Marcos Komodromos (University of Nicosia, Cyprus)
DOI: 10.4018/979-8-3373-3141-6.ch012
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Abstract
Simulation methods have long been instrumental in advancing scientific and engineering knowledge by offering valuable insights into complex systems and phenomena. However, these methods are not without their limitations, particularly in terms of computational complexity and accuracy. As simulation techniques have evolved over the years, they are increasingly yet several challenges persist. These challenges can impact the reliability, scalability, and efficiency of traditional simulation approaches. Primary limitations traditionally focus on issues related to computational complexity, accuracy, scalability, and other key factors that hinder their effectiveness. Another limitation of traditional simulation methods is the potential for reduced accuracy due to simplifying assumptions and approximations inherent in the models. Simulations rely on mathematical models to represent real-world systems, and the accuracy of the simulation results is only as good as the model itself.
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