Collaborative conceptual modelling

Purpose: To help a research or policy-making group come to terms with the feedback dynamics of the system which characterises the problem they are interested in. The intent is to learn just enough about the way that feedback operates in the system-of-interest, that they can identify and explain its main effects. The aim is to avoid unwanted policy outcomes, despite having to work with constant change and significant levels of ignorance and uncertainty.

Description: The activities, which aim at understanding rather than prediction, are divided into two phases.

Phase I helps a group progress in developing a new, shared understanding through focused dialogue and conceptual integration.
It comprises three activities:

  1. Discuss problem or situation of concern
    Focus Question: What is the challenge?
  2. Gather historical data to reveal patterns of change over space and time
    Focus Question: What is the story?
  3. Integrate individuals’ mental models of cause and effect
    Focus Question: Can I see how you think?

Phase II is designed to support the group’s efforts to (a) develop a better understanding of the dominant dynamics of their system-of-interest, (b) identify leverage points for management interventions that can lead to effective adaptation, and (c) construct scenarios or other narratives that describe possible futures and that can guide policy making.
These correspond to the following three activities:

  1. Identify dominant stock-and-flow structures
    Focus Question: What drives system behaviour?
  2. Identify opportunities for effective adaptation
    Focus Question: Where are the leverage points?
  3. Use improved understanding of system behaviour to develop ‘memories of the future’
    Focus Question: Can we have new eyes?

Phase II is more challenging than Phase I and requires a greater commitment of time and some level of modelling expertise.

The process is based on system dynamics, plus insights from applied history, complex adaptive systems, resilience thinking and cognitive science. It aims to balance strong guiding principles with flexibility of application. There are a number of ways to implement each activity in the process.


References:
Newell, B., and Proust, K., 2012, Introduction to Collaborative Conceptual Modelling, Working Paper, ANU Open Access Research. https://digitalcollections.anu.edu.au/handle/1885/9386

Newell, B., and Proust, K., 2018, Escaping the complexity dilemma, in A. König and J Ravetz (eds.), Sustainability Science: Key Issues. London: Earthscan/Routledge, pp. 96-112. (This has an updated version of the framework.)

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Posted: January 2015
Last modified: November 2019