I'm going to spend a couple of posts talking about some techniques I've been using while building and calibrating models, as they seem to be less well known than they might be. The first in this series is Sensitivity Analysis.

What is Uncertainty and Sensitivity Analysis

UA and SA are techniques to analyse the robustness of model outputs; that is, given a model, and certain assumptions about its input parameters (and their distributions), we would like to know how much we can trust the output. In general terms, Uncertainty Analysis gives us the amount of uncertainty (or variance) in the model output, while Sensitivity Analysis tells us how much of that variance is due to each of the input factors.

For the second time in two days, I came across the terms emic and etic, so it's time to have a bit more of a look into what they mean. This was prompted by a paper on JASSS1, which I found following up on the links between Prospect Theory and ABM. (Not while looking for ways to model crack dealers.)

1. 1. Agents in Living Color: Towards Emic Agent-Based Models

So, it's a good time to be in the Agent Based Modelling (ABM) business. More and more attention is being given to the idea that while we have a certain level of understanding of the physical, chemical and biological processes around climate change, in order to change what's happening, we need to look at the social systems which are contributing.