SAS24 – Modeling for policy will take place at HLRS from November 25th to 27th. The conference will feature a panel on AI & Society organized by the Gesellschaft für Wissenschaftsforschung on Monday.
Further information can be found in the booklet of abstracts. (Last minute changes are always possible, so please check back directly before the conference)
| 25.11. Monday | 26.11. Tuesday | 27.11. Wednesday | |
| 8:30 | Reception | Reception | Reception |
| 9:00 | Keynote Alyssa Bilinsky | Keynote Stephanie Harvard | Modeling for Policy |
| 10:00 | Coffee Break | Coffee Break | Coffee Break |
| 10:30 | Values in Modeling | Values in Climate Modeling | Trusting Models |
| 11:30 | Coffee break | Coffee break | Coffee break |
| 12:00 | Transdisciplinary model building | Climate Modeling | Energy Models and policy |
| 13:00 | Lunch break | Lunch break | Lunch break |
| 14:30 | GeWif Panel Keynote – Reinhard Kahle | Uncertainty in Modeling | Values in Modeling 2 |
| 15:30 | Coffee break | Coffee break | Coffee break |
| 16:00 | GeWif Session 1 | Assumptions in Modeling | Modeling for Policy 2 |
| 17:00 | Coffee break | End | Guided tours: CAVE and Computing Room |
| 17:30 | GeWif Session 2 | End | |
| 18:30 | Uni Thekle (self-pay, cash only!) |
Keynotes
From Napkin Math to Test-Driven Development: Why Simple Models Matter (More) in an Era of High-Performance Computing
Alyssa Bilinski (Brown University)
Over the past few decades, researchers have seen rapid advancements in computational power, allowing for development and dissemination of complex statistical and mechanistic models. Amidst this backdrop, I will argue that simple models remain critical for policy – both on their own and as complements to more complicated “black boxes.” I will begin by highlighting key features that differentiate policy modeling from other common prediction problems, including discrete (often binary) decisions; diverse, user-specific objectives; and asymmetric costs between false positives and false negatives. I will then illustrate, with examples from disease simulation modeling, several insights from simple models:
- “Aiming off” — Understanding what does (and does not) matter your decision
- “It’s all linear” – Knowing when complex models are simple models in disguise and why this helps us understand how (and whether) to use them
- “Test-driven development” – Applying simple models to improve complex ones
Moral Models: Policy-making in the Age of Computer Simulation
Stephanie Harvard (University of British Columbia)
What does it mean to say that models are “value-laden” and why does it matter? In this presentation, we will address these questions, along with philosophical proposals for how to appropriately manage value-laden decisions in science, such as those that arise in policy-oriented modelling. Using a case study in health economics modelling in the context of climate change, we will identify philosophical and practical challenges that complicate the idea of ‘values management’ in policy-oriented modelling and consider to what extent those challenges can be overcome. Finally, we will consider the goal of achieving ‘trustworthiness’ in policy-oriented modelling, and reflect on what responsibilities modellers must uphold in order to warrant public trust.