The path perspective on modelling projects

Community member post by Tuomas J. Lahtinen, Joseph H. A. Guillaume, Raimo P. Hämäläinen

tuomas-lahtinen
Tuomas J. Lahtinen (biography)

How can we identify and evaluate decision forks in a modelling project; those points where a different decision might lead to a better model?

Although modellers often follow so called best practices, it is not uncommon that a project goes astray. Sometimes we become so embedded in the work that we do not take time to stop and think through options when decision points are reached.

Joseph H. A. Guillaume (biography)

One way of clarifying thinking about this phenomenon is to think of the path followed. The path is the sequence of steps actually taken in developing a model or in a problem solving case. A modelling process can typically be carried out in different ways, which generate different paths that can lead to different outcomes. That is, there can be path dependence in modelling.

raimo-hamalainen
Raimo P. Hämäläinen (biography)

Recently, we have come to understand the importance of human behaviour in modelling and the fact that modellers are subject to biases. Behavioural phenomena naturally affect the problem solving path. For example, the problem solving team can become anchored to one approach and only look for refinements in the model that was initially chosen. Due to confirmation bias, modelers may selectively gather and use evidence in a way that supports their initial beliefs and assumptions. The availability heuristic is at play when modellers focus on phenomena that are easily imaginable or recalled. Moreover particularly in high interest cases strategic behaviour of the project team members can impact the path of the process. Continue reading

Making predictions under uncertainty

Community member post by Joseph Guillaume

Joseph Guillaume (biography)

Prediction under uncertainty is typically seen as a daunting task. It conjures up images of clouded crystal balls and mysterious oracles in shadowy temples. In a modelling context, it might raise concerns about conclusions built on doubtful assumptions about the future, or about the difficulty in making sense of the many sources of uncertainty affecting highly complex models.

However, prediction under uncertainty can be made tractable depending on the type of prediction. Here I describe ways of making predictions under uncertainty for testing which conclusion is correct. Suppose, for example, that you want to predict whether objectives will be met. There are two possible conclusions – Yes and No, so prediction in this case involves testing which of these competing conclusions is plausible. Continue reading