Phase 2 Conflict World Prescribe Challenge

Challenge Overview

Topcoder is working with a group of researchers organized by the University of Chicago that are competing to understand a series of simulated environments.  We are looking for simulation models that can predict the future states of our Conflict World.  The challenge builds on previous work in the following two challenges:  Causal Graph for Conflict World and Phase 2 Conflict World Prediction Challenge.  You be using data from the previous challenges in completing the task defined below.

The dataset to use can be downloaded here.

Here are links to the research request data and research request document itself.

Important Note:

Each University of Chicago Team has the ability to request additional information from the virtual world simulation teams beyond what is initially provided through a “Research Request” process.  Data files or folders that are denoted with an “RR” are the output of this process.  In the Code Document forum you’ll find a link to a Research Request document which provides the original request submitted by the University of Chicago researchers that can provide some context.  The requests have to include a plausible collection methodology (e.g. surveys or instruments that can collect data).  There may be additional data that is provided over the course of this challenge submission period.  You are encouraged to include this input into your analysis.

Final Submission Guidelines

The final submission must include the following items.

• A Jupyter notebook detailing:

  • How the data is prepared and cleaned, from the tsv files

  • How each model is created, trained, and validated

  • How individual predictions are created

  • How we can plug in new data into the model for validation purposes.

    • This will be part of the review, so please ensure the reviewer can easily put in the held-back data for scoring purposes.

  • Answers / Exploration of the counterfactual prediction questions detailed above.  This should be well documented and clearly described.
    
    
    Judging Criteria

    Winners will be determined based on the following aspects:

  • Model Usability (70%)

    • The research teams will be comparing your work with theirs in answering the questions above.  Your submission will receive a subjective evaluation from this team.  The more insightful and creative your model the more helpful it will be to this team that's preparing their own analysis.

  • Model Transparency (20%)

    • How easy is it to deploy your model?

    • Is your model’s training time-consuming?

    • How easy is it to understand your assumptions and analysis?  Can we understand your methods and conclusions?

  • Clarity of the Report (10%)

    • Do you explain your proposed method clearly?