Day 1: Wednesday March 22, 2017
Moderators: Susan Volman (NIDA)
Liz Ginexi (OBSSR)
David Rodrick (AHRQ)
Coryse St Hillaire-Clarke (NIA)
- 10:10 - 10:30 am: Bruce Lee "Case Studies of Modeling for Policymakers"
- 10:50 - 11:10 am: Ross Hammond "Policy-oriented models in tobacco and obesity"
- 11:10 - 11:30 am: panel discussion
The objectives of this session are to:
- Describe and illustrate key examples of computational methods used to bridge multiple scales in development of the models
- Identify strategies required to successfully navigate barriers to the incorporation of multiscale models into informed decision-making about public policy
- Provide insight regarding the adoption of computational models by policy makers, and/or examples of when the complexity of multi-scale models may hinder adoption
- Provide examples of successes and failures of modeling being accepted for public policy
The specific charge to the speaker:
- How and why is modeling that traverses multiple scales (spatial, temporal or other definition) required for the success of policy-related models?
- What are the gaps and opportunities for future research in multi-scale modeling for policy, such as new modeling approaches, capitalization on the new trends in big data and open science, etc.?
- What can be learned from modeling in domains other than your own to facilitate and accelerate model development and implementation?
Comments/Questions (please identify yourself):
Back to MAIN AGENDA
What are best practices to
What are best practices to adequately explaining model assumptions and model functionlaity to policy makers to give them confidence in the models output?
Have any of you engaged the
Have any of you engaged the subject communities/community members in instantiating your behavioral rules? We are conisidering using a geospatial ABM of Chicago to evaluate epidemiology of urban violence, but there is a wide range of hypotheses about how people behave and what drives decision making. We would thiink about using such a model as a rhetorical device (this implies that all the models are inhernetly subjective).