Participatory modelling

Tool authored by: Saša Raicevich and Steven Mackinson; ISPRA, Italy & CEFAS, UK

Tool Description

Models are simplified (but not simplistic) representations of complex systems that allow us to summarize the relationships between systems’ components and predict system’s behaviour under definite conditions. For instance they are intensively used in fisheries and natural resource management to describe stock status and (socio) ecological processes, according to a set of input data and scenarios. Participatory modelling relies on the integration of stakeholder knowledge into the process of model construction and hypothesis testing. In this sense participatory modelling differs from modelling itself in that stakeholders may play a role both in the model definition (e.g. select relevant variables, provide quali-quantitative information on variables, setting the relationships among them, setting the general conditions) and in the selection of scenarios to be investigated (e.g. defining possible management regimes to be compared).20140415_162805 Moreover, stakeholders contribute to the evaluation and interpretation of models’ outputs. Several typologies of models have been applied in the context of stakeholder engagement (for an extensive review related to the field of environmental assessment, see Voinov and Bousquet, 2010). However, rather than focussing on the typology of models adopted, we emphasise the relevance of the process emebedded in participatory modeling.

Indeed, participatory modeling establishes an interaction with stakeholders that can be summarised according to three different typologies: i) extractive use, in which knowledge, values or preferences are synthesized by the extracting group and passed on as a diagnosis to a decision-making process; ii) co-learning, in which syntheses are developed jointly and the implications are passed to a decision-making process; iii) co-management, in which the participants perform the syntheses and include them in a joint decision-making process. According to Röckmann et al. (2012) “participatory modelling (in fisheries managemen) has the potential to facilitate and structure discussions between scientists and stakeholders about uncertainties and the quality of the knowledge base. It can also contribute to collective learning, increase legitimacy, and advance scientific understanding.

However, when approaching real-life situations, modelling should not be seen as the priority objective. Rather, the crucial step in a science–stakeholder collaboration is the joint problem framing in an open, transparent way”.

Expected Outcomes 

Participatory modelling can provide several-fold outcomes:

  • increase and share knowledge and understanding of a system and its dynamics under various conditions;
  • identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management;
  • increase legitimacy, transparency and understanding of management process (Haapasaari et al., 2009); facilitate compliance;
  • empower stakeholder participants and mutual collaboration between scientists, stakeholders and policy makers.

What is needed

Participatory modelling needs the definition of a goal of common interest between scientists and stakeholders, as well as the selection of the most appropriate model to be used for the exploration of hypothesis. Most often the selection of the model falls to the scientists. However, input data and model structure can be discussed, revised, integrated and challenged within scientist-stakeholder dialogue. Thus expertise both on model construction and the system to be investigated are necessary. Moreover, due to the participatory nature of the approach, experience in group facilitation is necessary to allow an effective mutual collaboration and flow of knowledge.

How it works

  • Identify goals and establish the group. These two items are inherently linked, since the goals of a modelling exercise should be defined according to the interest of people (scientists, stakeholders, and possibly policy makers) to be involved. Keep the process open and transparent.
  • Discuss system and choose the modelling tool. The generic futures of the system to be modelled, relevant variables and conditions should be defined, integrating scientific and stakeholder knowledge. The modelling tool should be defined according to the goal previously identified and the systems features to be modelled.
  • Collect and process data. This implies not only research based knowledge to be used and embedded in the model, but also experience based knowledge from stakeholders. Stakeholders could also comment on/assess the quality of scientific data according to their experience. This could lead to a definition of uncertainty of data input in the model.
  • Run model, discuss results, and discuss improvement. An iterative process of modelling application, results analysis, and revision of the model should be established until models outputs are considered to be representative of the investigated processes and consistent to the definition of the models goals.
  • Present results to other stakeholders and decision makers. Results should be presented to other stakeholders and policy makers, including the description of the collaborative work, decision taken, knowledge base, hypothesis definitions. Uncertainty in the model output should be also highlighted and discussed.


  • Models are a tool, not the only objective of participatory modelling.
  • The process should be open and transparent.
  • All relevant stakeholders and policy makers should be included from the  early stages.
  • The presence of facilitators is very beneficial to support the process and establish mutual trust and collaboration between participants.
  • Accept and assess uncertainty; be sure that everyone understand models limitations and embedded hypothesis.

Examples from GAP2 and beyond

Participatory modelling is being used within three GAP2 case studies.

In the UK, in the Crab fisheries, Paul Hart and Emma Pearson are developing an Individual Based Model to integrate crabs migration and catches to assess the sustainability of brown crabs fisheries in the UK.Crabs a plenty © Emma Pearson Local fishermen have contributed 10 years+ of catch records and taken observers to sea to collect data on catch and discards. This data will then be used to model the sustainability of the study area. Fishermen have also been involved in seminars set up to discuss the parameters of the model. Learn more at: studies/case-study-1/

In Denmark, Lotte Worsøe Clausen, is developing a tool which both industry, stock-assessors and managers can use to predict the behaviour of the Western Baltic Herring stock, under a range of management scenarios. The tool can predict the migration, growth and ‘production’ of the Western Baltic Spawning Stock (WBSS) of herring and is built into the assessment model, and in turn used in the Management Strategy Evaluations (MSEs). The results of the scenarios defined by the industry and managers forms the basis for a Long Term Management Plan (LTMP) for WBSS in the Western Baltic and Skagerrak-Kattegat. Stakeholders are mainly involved in the definitions of models features, hereunder management objectives, and the possible management scenarios, as well as the establishment of proposals for LTMP according to model outputs. Learn more at: studies/case-study-4/

In the UK, Steve Mackinson is working in collaboration with the NSRAC to develop ecosystem models. The work focuses on the North Sea mixed demersal fisheries for cod, haddock and whiting and uses ecosystem models to identify and examine the ecological and management trade-offs of alternative plausible management approaches identified by the NSRAC. Management strategy evaluation procedures will help inform management decisions and yield scientific and policy relevant outcomes. Learn more at: studies/case-study-13/

Other examples where participatory modelling for fisheries management has been carried out can be found, for instance, in the JACKFISH project (see Röckmann et al., 2012).

What people say about this tool

“We, as fishermen, will need all the help we can get because of what’s coming around the corner, with these MCZ, MPA and all that…and someone like you and Paul with all this knowledge and the model. It’s essential to be honest” (South Devon Fisherman)

“I have used a similar approach (but a simpler model) before and it worked okay, although the important pre-discussions of management objectives and policy issues clearly was missing at that point. Thus participatory modelling is a useful tool, but only if there is a mutually agreed and shared approach to what actually is going to be modelled” (Lotte Worsøe Clause).

“Even though the tool is yet far from finalised, I feel that the process of setting up the evaluation points (output) and agreeing upon management objectives (what to model) has worked very successfully ­but  as I understand it, also  made the modelling part increasingly advanced and difficult” (Christian Olesen, Chair of DPPO).

References and resources

Haapasaari P., Mäntyniemi S., Kuikka S. (2009). Participatory modeling to enhance understanding and consensus within fisheries management: the Baltic herring case. ICES CM 2009/O:13.

Voinov A., Bousquet F. (2010). Modelling with stakeholders. Environmental Modelling & Software, 25: 1268-1281.

Röckmann C., Ulrich C., Dreyer M. et al., (2012). The added value of participatory modelling in fisheries management–what has been learnt. Marine Policy, 36: 1072–1085.

Web resources

Visual references and resources 

Participatory modelling process from Voionov

Participatory modelling process from Voionov