Improving and maintaining prediction accuracy in agent based modeling systems under dynamic environment

The dynamic and complex nature of real world systems makes it difficult to build an accurate artificial simulation. Agent Based Modeling Simulations used to build such simulated models are often oversimplified and not realistic enough to predict reliable results. In addition to this, the validation of such Agent Based Model (ABM) involves great difficulties thus putting a question mark on their effective usage and acceptability. One of the major problems affecting the reliability of ABM is the dynamic nature of the environment. An ABM initially validated at a given time stamp is bound to become invalid with the inevitable change in the environment over time. Thus, an ABM not learning regularly from its environment cannot sustain its validity over a longer period of time. This thesis describes a novel approach for incorporating adaptability and learning in an ABM simulation, in order to improve and maintain its prediction accuracy under dynamic environment. In addition, it also intends to identify and study the effect of various factors on the overall progress of the ABM, based on the proposed approach