How do you train a trader to take cognizance of fluctuations in markets, changes in currencies and discern geo-political context before deciding to buy or sell? Training your learners for such real-world tasks poses several challenges – context, relevance and engagement.
A dynamic learning solution to the above quandary is a simulation. Simulations focus on contextual decision making, encourage learning in a low-risk environment, and offer the autonomy to explore various options.
But what do you do when the environment you seek to simulate is highly complex? What if it involves interplay of numerous related factors ranging from business to leadership to geo-political influences? The solution lies in a model that replicates aspects of the intended environment.
This article by Nikhil Mishra, Senior Consultant, Math Modelling at TIS explains the concept of a ‘model’, how modelling works, model unboxing, and development of a model. It further talks about deployment and mode of delivery of the final model-based simulation.