Significant role of mathematical metamodels in analyzing snaking ladders and laddering snakes in real world data

Abstract

Appearances of data need not always demonstrate the real facts in many real-world situations. Immediate outcomes need not exhibit the real nature of ultimate outcomes. Initial assumptions about data models may get proved to be illogical in the long run. In effect, certain rewarding elements can ultimately perform like punishments and even punishing ones may perform the role of rewards. Certain ladders silently perform the role of snakes and vice versa in the context of gaming and gamification models of Snakes and Ladders. The machine learning techniques like Q-Learning are helpful to analyze the ultimate underlying nature of complex data models by collaborating mathematical and statistical techniques. With the help of meta-models from gaming, gamification and real world instances, this study demonstrates the scope for intelligent software system models with self-learning and self-improving capabilities, in discovering the real and ultimate nature of data through repeated explorative learning epochs, which is highly relevant in the context of analyzing complex data models in many real world situations, as in an epidemic situation. Also, this paper points out the significant role of experts and expertise in Mathematics and Statistics to associate with domain experts and technologists in modeling intelligent software solutions.