The path perspective on modelling projects

By Tuomas J. Lahtinen, Joseph H. A. Guillaume, Raimo P. Hämäläinen

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.

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.

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.

Taking a path perspective means engaging in reflection on the path taken, and awareness that the modelling path can matter. Even if a perfect path does not exist or cannot be found, a poor path or possibilities to improve a planned path can often be identified.

The problem solvers’ choices at decisions forks define the path. Paths are influenced by phenomena that:

1. influence the choices made at each fork,
2. give reasons to redirect the path from the route that was previously chosen,
3. make it difficult to change the path taken.

The phenomena can be classified according to their origin. The path might be taken:

1. through deliberate thinking (or lack thereof)
2. as a result of the processes, methods, and approaches used
3. as a reflection of preferences and motives (possibly hidden)
4. through intuitive reasoning, based on tacit knowledge or biases
5. as a result of the system of problem solving and system under study, and emergent phenomena arising from them.

The framework described in the table below covers a broad range of phenomena to be aware of when reflecting on a path. For more detail, including links to existing techniques to address the phenomena, see Lahtinen et al. (2017).

The path perspective challenges modellers to navigate their paths in a reflective mode. Critical and possibly hidden forks can easily exist in complex environmental problems.

Having explicit criteria for the success of the modelling project can help to keep track of the path. Resources should also be reserved to enable possible redirecting or restarting of the project. In important problems one could consider having two independent parallel problem solving processes.

Do you have any stories to share, where any of the phenomena described had an influence on the choices made, gave reason to redirect the path, or made it difficult to change the path taken?

Reference:
Lahtinen, T. J., Guillaume, J. H. A. and Hämäläinen, R. P. (2017). Why pay attention to paths in the practice of environmental modelling? Environmental Modelling and Software, 92: 74–81. Online (DOI): 10.1016/j.envsoft.2017.02.019

Biography: Tuomas Lahtinen is a doctoral student in the Systems Analysis Laboratory, Aalto University, Finland. He works on various topics related to Behavioural Operations Research, including path dependence, decision analysis, environmental portfolio decision making, behavioural experiments, and the practice of modelling. He is a board member in the Finnish Operations Research Society.

Biography: Joseph Guillaume is a Postdoctoral Researcher with the Water and Development Research Group at Aalto University, Finland. He is a transdisciplinary modeller with a particular interest in uncertainty and decision support. Application areas have focussed primarily on water resources, including rainfall-runoff modelling, hydro-economic modelling, ecosystem health, global water scarcity and global food security. Ongoing work involves providing a synthesis of the many ways we communicate about uncertainty, and their implications for modelling and decision support. He is member of the Core Modeling Practices pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

Biography: Raimo P. Hämäläinen is a professor emeritus in the Systems Analysis Laboratory, Aalto University, Finland. He has published extensively on decision and game theory, environmental decision making, as well as developed widely used decision support software. His recent interests include behavioural issues in modelling and systems intelligence in social interaction. He is the recipient of the Edgeworth Pareto Award of the International Society on Multiple Criteria Decision Making. He is the chair of the working group on Behavioural Operations Research of the Association of the European Operational Research Societies.