Participatory Bayesian Networks for uncovering reflexive unknowns in strategic environmental risk management

Abstract

Strategic environmental risk management and planning must account for uncertainty and complexity, necessitating methods that facilitate scenario development under incomplete knowledge. This paper introduces a participatory modelling (PM) -based knowledge co-production and strategic planning approach utilizing one type of AI tool - Bayesian Networks (BN) - for systemic scenario development, analysis and resilience-building. The developed method integrates diverse perspectives and expertise of participants through a structured BN model, enabling co-imagination and -construction of causal pathways, translating them into probabilistic dependencies, and diagnostically identifying potential leverage points for strategic resilience-increasing actions. We illustrate and test this approach using a case study of a chemical transportation accident in an urban environment, documenting the participatory process and the algorithm to translate the participants’ thinking to a computational BN. Through content analysis of transcribed audio recordings, we demonstrate how the exercise helped uncover “reflexive unknowns” – previously unrecognized threats that became apparent and thinkable only through the collaborative modelling process. An example of such a reflexive unknown in our case exercise is the prospect of toxic rainfall following the accident and its short- and long-term implications for the built and natural environment. This was a blind spot in the thinking of the participants, and it appeared and became a scenario to be acted upon only as a result of the process of collective cross-sectoral causal thought represented with a BN model. The paper provides a detailed description of the developed participatory BN approach and methodology, enabling their applicability in various contexts. Through a qualitative analysis of the exercise's implementation, the article also demonstrates how the approach fostered collective, iterative reflection, generating new insights to socio-environmental resilience.