The Whole Elephant: Reconciling the Motivations and Implications of Systems Models
Modeling is a means to produce facsimiles of both human-controlled and non-human-controlled systems in a way that can be manipulated. Charting the extent of the global impact of human activities requires researchers to identify the key elements of systems, resulting in a variety of models ranging from predictive economics to agricultural simulation.
In today’s human-shaped world, a perspective on the frameworks by which we understand our surroundings grants insight into the functional process of many natural and social sciences. A controversy arises, however, when one considers that models are inherently simplifications of broader systems. When this happens, we must address the conflict of complexity versus practical use.
In this session, we, with the help of current scholars, will attempt to bring clarity to the following questions:
- How have spatial and behavioral models contributed to analysis of current environmental issues? Which issues see the most variance between models, and which show the most significant differences in how they are bounded?
- How do models prioritize between issues of the system complexity and interconnectedness? How are values assigned to inputs within different systems of modeling?
How does use of endogenous vs. exogenous variables affect the outcome of modeling projects?
- In what situations is modelling most valuable, and when is it best supplemented by other forms of analysis? What limitations does this suggest?