A partnership approach to research that equitably involves, for example, community members, organizational representatives, and researchers in all aspects of the research process and in which all partners contribute expertise and share decision making and ownership (Israel et al. 1998)
Participatory research can (1) ensure culturally and logistically appropriate research, (2) enhance recruitment capacity, (3) generate professional capacity and competence in stakeholder groups, (4) result in productive conflicts followed by useful negotiation, (5) increase the quality of outputs and outcomes over time, (6) increase the sustainability of project goals beyond funded time frames and during gaps in external funding, and (7) create system changes and new unanticipated projects and activities. Negative examples illustrated why these outcomes were not a guaranteed product of PR partnerships but were contingent on key aspects of context (Jagosh et al. 2012)
Participatory research, through community involvement can be helpful to other methods as it can:
1. Remove ignorance, provide new information
2. Confirm prior knowledge.
3. Remove irrelevant information.
4. Remind us of important information to include.
5. Remove erroneous information.
Process based models
Population models: a class of mathematical models which study the dynamics of populations such as changes in the size and age composition, and the processes affecting these changes.
Agent Based Models: a class of mathematical models relying on computational resources to modelling systems composed of autonomous, interacting agents. “Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions .
Compared to ABMs, population models are usually based on a parsimonious set of assumptions on the underlying mechanism. In general, this results in a more transparent interpretation of the predictions. They are often based on a set of differential equations (which can be stochastic) allowing well-established further analytical approaches (e.g. stability analysis). They tend to require little computational resources.
In contrast, ABMs are in-silico experiments able to incorporate comprehensive and detailed biological, physical, environmental and behavioral factors. Compared to analytical approaches, they require a minor level of abstractions, which might be ad advantage for integration with participatory modeling.