We always wonder what will happen in the future? and what will be the effects of future events? Every one of us has estimated a future event at some point in our life, where the domain of the event might be political, personal, science or technology. Imagine being rewarded for your prediction simply because you were right! That’s the idea of prediction market, participants predict and get rewarded if the prediction is right. The decision or prediction market is a collection of people contemplating about a future event. The idea behind prediction market is very simple and it is good to have an estimate of what’s to come. Even though the prediction markets are interesting, they are still limited. In 2016, before the US election, the prediction markets gave an estimation that there is 35% likelihood of Trump winning the election. This proves that the prediction markets could use a better system for forecasting. The question is how can we improve the system of forecasting? An approach to answer this question would be to study the existing prediction markets and make a comparison of current prediction markets to see if there any better methods to improve the accuracy of the prediction markets. This project deals with a prediction market and trying to find approaches that help forecast better than existing ones.
The prediction market associated with this project is SciCast. SciCast was a prediction market run by George Mason University and sponsored by IARPA (Intelligence Advanced Research Projects Activity) to forecast the outcomes of key issues in science and technology. Hundreds of participants made over 100K forecasts on hundreds of science & tech topics like vaccination & disease spread, space missions, tech announcements, ice levels, bee deaths, and robotic soccer kicks.
Objective of the project: The objective is to improvise the probability estimate of current claims by analyzing the data from market participants. The analytical approach to the model uses weighted average based on two factors: timeline for a question to be resolved and the accuracy of the user based on his/her historical trades. The results are based on absolute error as a metric to compare the results of the weighted average with the baseline models. The mean absolute error of all the models is compared at the end.