Our methods draw on a combination of empirical analysis, participatory methods, fieldwork, agent-based modeling, and mechanism-based theorizing to develop a stylized model of coastal small-scale fisheries in Zanzibar that allow us to analyze under which conditions we observe sustainable or unsustainable outcomes according to varying levels of success as defined by different actors in the system. The modeling approach serves multiple purposes. First it is a way to bring different world views (or perspectives) together to arrive at a holistic understanding of the important processes leading up to successful outcomes. Second, it allows to simulate a small-scale fishery to understand when and why we could expect successful versus failed outcomes. To analyze the simulated outcomes we use a mechanism based approach and theorizing to understand the sequence of events leading up to success or failure. The fieldwork will help us to collect narratives to reach a in-depth understanding of why and how different actors view success and failure the way they do.

Data Collection


Liz conducting interviews at Mkokotoni, Zanzibar. Photo by Vitas.

A mixture of formal and informal social science data collection tools will be used infield Zanzibar. On the informal side direct and participant observation techniques will be employed during all field visits. In addition, field researchers will, together with Mwambao, ensure they live within or close to the fishing communities and make sure to build relationships and trust with the various actors involved. Rather then leaning on surveys with fisherfolk and market actors the field team will carry out focus groups and participatory dialogues for eliciting information. These interactions will be carefully designed with hierarchy, power and knowledge co-production in mind. Key informant interviews will be used at the higher levels with actors involved in the NGOs, governmental departments or businesses that have broader perceptions and knowledge on the entire system with respect to these closures.

Synthesizing data for the first prototype agent-based model. The prototype model will build on two data sources. First a synthesis of literature focused on marine protected areas and octopus closures, and second a mini survey (or workshop) with the participating researchers and our local collaborators to scope 1) critical mechanisms, i.e. factors and processes 2) important actors and 3) research questions to explore through the model. The prototype will serve as a boundary object to integrate different knowledge and a set of hypotheses to explore further in the field. It is also an important step in our interdisciplinary mix of scientists to scope different viewpoints and system understandings.

Analytical approaches

Agent-based modeling (ABM) is a computational method for the simulation of agents and their interactions with each other and their environment (Figure 1). ABMs are an increasingly common method in the social sciences such as political sciences, economics and sociology, and in social-ecological systems research to analyze the complex multi-level dynamics of social systems and identify causal mechanisms. We see agent-based modeling as particularly suitable for our aims because it allows us to model and explore small-scale fisheries in a dynamic setting accounting for important contextual factors. It also allows us to create a novel approach to co-develop the model, and thus refine our hypotheses, in an iterative process building on repeated participatory workshops and fieldwork data.


Figure 1. Conceptual model of an agent-based model “world”. In a co-evolving process, actions at the micro-level change the variables at the macro-level, which in turn influences the micro-level. This continues until the end of the simulation. (1) Agents can interact with other agents or fish resources, and act according to a goal while they are influenced by their environment. (2) The resulting micro-level interactions affect the macro-level endogenous to the model environment.  (3) The state of the macro-level variables in turn influence the behavior of the agents at the micro-level. (4) During and after the simulation, emergent phenomena may emerge (such as overfishing, poverty, degraded reefs, or increased tourism) can be observed and measured, or distributional patterns such as inequalities or local overfishing.

A mechanism-based approach. To analyze and identify observed interactions and their associated outcomes both from simulation experiments and empirical data we use a mechanism-based approach (Figure 2). A mechanism-based approach is focused on identifying causal explanatory models and is suggested to offer new pathways for researching sustainability. With its roots in sociology, a mechanismic thinking offers a scientific approach to explain cause–effect relations, i.e. what sequence of events lead to a certain outcome. It can also be useful for generalizing from case studies, one of the perennial issues in the methodology of the social sciences. Petri Ylikoski suggests that the idea of mechanism-based theorizing provides a fruitful basis for understanding how case studies contribute to general understanding of social phenomena. We will extend these ideas to look at social-ecological phenomena. (This is acted on through “CauSES” a second complementary project of Emilie and Maja where we will collaborate on this issue together with Petri and other experts in the area to explore causality in social-ecological systems)


Figure 2. An extended version of the Coleman diagram used for mechanism based theorizing (Adopted from Ylikoski 2018).

Complex adaptive systems theory

The main theory we build on is complexity theory. Complex adaptive systems are a class of dynamical systems characterized by diversity and heterogeneity of their components, adaptation and self-organization and emergence of macro-level features from micro-level interactions of components within interaction structures such as networks. They exhibit non-linear behavior such as sudden shifts or surprises. Examples of complex adaptive systems are economies, financial markets, the brain, ecosystems, fisheries or social-ecological systems. Their macroscopic properties or behaviors cannot be explained by the properties and actions of the constituents or the structure alone but involve interactions within and across micro and macro-levels.