Name
Title | ||
|---|---|---|
Small increases in agent-based model complexity can result in large increases in required calibration data |
Abstract | ||
|---|---|---|
Agent-based models (ABMs) are widely used to analyze coupled natural and human systems. Descriptive models require careful calibration with observed data. However, ABMs are often not calibrated in a formal sense. Here we examine the impact of data record size and aggregation on the calibration of an ABM for housing abandonment in the presence of flood risk. Using a perfect model experiment, we examine (i) model calibration and (ii) the ability to distinguish a model with inter-agent interactions from one without. We show how limited data sets may not adequately constrain a model with just four parameters and relatively minimal interactions. We also illustrate how limited data can be insufficient to identify the correct model structure. As a result, many ABM-based inferences and projections rely strongly on prior distributions. This emphasizes the need for utilizing independent lines of evidence to select sound and informative priors. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.envsoft.2021.104978 | Environmental Modelling & Software |
Keywords | DocType | Volume |
Agent-based modeling,Statistical calibration,Model selection | Journal | 138 |
ISSN | Citations | PageRank |
1364-8152 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vivek Srikrishnan | 1 | 0 | 0.34 |
| Klaus Keller | 2 | 30 | 4.37 |