Putting humans into the equations
Better representation of human behaviour needed in models of social-ecological systems
• Social-ecological modelling approaches tend to neglect or oversimplify human behaviours
• Study encourages modellers to make use of the diversity of human decision-making theories from the social sciences
• It provides a tool for mapping and comparing theories of human decision making from the social sciences for implementation in SES models
Studies of integrated systems of humans and nature (SES, social-ecological systems) often use modelling as a tool to advance theory and inform policy making. However, most modelling approaches in the field have tended to neglect or oversimplify human behaviours.
A study published in Ecological Economics which is co-authored by centre researchers Maja Schlüter, Kirill Orach and Nanda Wijermans provides help for modellers and other researchers to find and formalize relevant theories of human behaviour for modelling of SES.
There is an abundance of theories in the social sciences (psychology, economics, anthropology, sociology, political sciences, etc.) that describe and test how people behave in various contexts. The main challenge in accounting for human behaviour in models, however, is that they are scattered across disciplines, often covering only one aspect of decision making and not specifying causal mechanisms.
Making better use of social-science theory
In the article lead author Maja Schlüter and her colleagues, which also includes researchers from Sweden, USA, Germany, Australia and the Netherlands, introduce a new framework for modelling human behaviour called “MoHuB”, or Modelling Human Behaviour, and apply it to a number of well-established social-science theories.
“We encourage modellers to think more systematically about the implementation of human decision-making in their models and make use of the diversity of human decision-making theories from the social sciences, where possible”
Maja Schlüter, lead author
Fisheries are used as an example to illustrate how the MoHuB framework can be applied. Six different types of agents following six well-established social science theories of human behaviour are described:
A “rational fisher” calculates the net benefit from fishing based on presumed perfect knowledge of fish, costs, prices, and the behaviour of all other actors.
A “bounded rational fisher”, on the other hand, rather tries to maximize its utility on the basis of the knowledge that is actually available to it. A fisher behaving according to the Theory of Planned Behaviour has attitudes, perceived control and knowledge about (and value) the approval of his fishing behaviour by other fishers.
“A habitual fisher agent” will go fishing in the same manner as long as the net returns satisfy its needs.
A “descriptive norm fisher” will observe the behaviour of others, e.g. where and how they fish, to identify the dominant behaviour.
Finally, a “prospect theory fisher” will do its best to maximize its own utility, but based on risk attitude.
MoHuB can help go beyond the rational actor
Existing models have advanced our understanding of the dynamics of natural resources, including how they respond to management interventions and environmental change. This has informed policy decision on for example harvest quotas, management of biological invasions and the location of biodiversity hotspots. While the shortcomings of the rational actor model are well known and the drivers and implications of diverse human behaviour in natural resource management are increasingly being recognized, the integration of such conceptualizations into formal models is still a major challenge.
When turning to alternative theories of human behaviour modellers face the task of “identifying and transforming relevant theories on human decision-making into crisp causal relationships, while the best available knowledge is fragmented, context dependent and descriptive,” Schlüter and colleagues write.
In the paper they propose the MoHuB framework as a way to support mapping, describing, organizing and comparing different behavioural theories. This can act as a first step towards choosing and implementing them in more formal SES models. The mapping helps clarify the focus and underlying assumptions of a theory and develop a joint vocabulary for comparison and communication, they say. It also alerts researchers to consider the different elements and processes relevant for decision making, namely perception, evaluation, the state of the actor, its perceived behavioural options, as well as the selection of a behaviour and the actual behaviour itself which may change the social and ecological environment of the agent.
In future research Maja Schlüter and her colleagues’ will continue their work on implementing a diversity of behavioural theories into models of different social-ecological systems. This will help understand the implications of the diversity of human behaviour for sustainable natural resource management and the effectiveness of policies. This is particularly important in the many social-ecological systems around the world where actors are not rational and selfish, but make decisions according to different motivations.
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M.A., McAllister, R.R.J., Müller, B., Oracha, K., Schwarzh, N., and Wijermans, N. 2017. A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological Economics: 131: 21–35 Available online 28 August 2016
Maja Schlüter is a researcher at Stockholm Resilience Centre studying the co-evolution of social-ecological systems (SES) resulting from interactions between actors, institutions and ecosystems over time.
Kirill Orach is a PhD student studying cooperation of state and non-state actors in use and management of social-ecological system of the Baltic Sea.
Nanda Wijermans is a postdoctoral researcher. Her work ocuses on feedbacks between human (cooperative) behaviour and ecological dynamics in social-ecological systems (SES) as well as their implication for SES resilience and governance.
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