Scientific Modeling
All attendees should complete this survey about plans for the Word Learning model extension.
This workshop will provide introductory training in the important and broadly applicable skill of scientific modeling. We will work together on the foundational concepts of modeling, as well as the relevant programming skills, and then will break into smaller groups to work on applying these concepts to more specific areas of interest. A tentative schedule is listed below:
Monday 1/12: Why Model?
Explore how making predictions can be thought of as formalizing the behavior of a system. Place computational modeling in the context of the scientific process.
Tuesday 1/13: Examining Assumptions
Guest presenter: Richard Prather (HDQM)
Explore typical modeling assumptions, compare analytical and operational assumptions and illuminate core relationships between assumptions and the purpose of the model.
Wednesday 1/14: Evaluating a Model of Word Learning
Guest presenter: Naomi Feldman (LING/CMSC)
Discuss the model of word learning as outlined in ”Defusing the Childhood Vocabulary Explosion” by Bob McMurray. Explore the model interactively using provided code in R. http://www.r-project.org/ Source code can be downloaded here and here.
Thursday 1/15: Resources for Modeling
Guest presenter: Philip Resnik (LING/CMSC)
With so many different tools, how do you choose the right one for the job? Demystifying the computational modeler’s toolbox and understanding the importance of sharing data.
Friday 1/16: Designing an extension
Participants split into smaller project groups for the remainder of the workshop and begin formalizing questions to be answered by a new iteration of the word learning model.
Monday 1/19: No sessions (MLK Day)
Tuesday 1/20 - Thursday 1/22: Implementing word learning model extension
Participants work in groups to implement extensions to the McMurray word learning model.
Friday 1/23: Presentations
Each group gives a brief presentation outlining their model extension, to include: the changes they made to the original model, their predictions for how these changes would alter the model’s performance, the basis for these predictions, and an evaluation of the model based on the results of relevant simulations.
