2018 Nobel prize go to Yale and NYU, where one of the recipients, Laureate William Nordhaus’ research on the impacts of climate change on economic growth is based on simulations.  The world has spoken that simulation is a useful tool to predict uncertain future events, such as the impacts of student loans on economic growth.  The answer to this question is pretty straight forward–negatively affect not just the economic growth, but also income inequality.  The politicians love to use the DOW to measure how well the US current economy has grown in recent years.  This positive growth is welcomed mostly by the investors in the Wall Street.  Or few companies such as Apple or Amazon, and their shareholders are enjoying it too.  What about the working class?  Have their wages or take-home pay going up, parallel to the DOW increases?.  What about those who still have to pay their student loans?  Given the real problem facing the nation and the world, this is a life time opportunity for anyone to study this issue, and chances may not be that bad (stated with disclaimer) for those researchers to potentially be the future of Nobel prize winner.  The American public is eager to see which economist(s) represents, either the Freshwater of the Saltwater will win this important race.

It may not be a coincident why AAEA has just published an article on September 21, 2018 which urged the analytics community to think ahead of the curve, ie., a way from IRI V.1 to IRI V.2. Most people are amazed how predictive analytics can solve their business problems, especially in the education industry. That is obsolete–that was the things of five years ago.  Many months ago, the Association of American Education Analytics and Data Scientists has proposed to move away from IRI V.1, which is statistical based approach toward stochastic simulation, that combined the estimates from statistics, feed into mathematical programming to find optimal solutions.

Decision makers cannot just make strategies based only on one option, but need to see results of different scenarios, before the final decisions are made.  Statistical based business analytics just gives what the optimal solution, given the past events that have occurred.  In other words, a data scientist makes inference based on past or historical data.  The question is what next?  A college decision maker can make strategic decision based on the results, assuming the “world” that produced the data did not change.  In reality the world changes every nanosecond. The environments where the organizations are operating, changing constantly.  For example, the CEOs of any companies who are exporting their products to the world market, need to anticipate what is the impact of stronger US dollars and what is going to happen when the administrators ignite the trade war?  Past data cannot answer this question in totality, because there is no data that have simultaneously capture these two events.  The impacts can be accessed with n possibilities using simulation–IRI V.2.