Abstract:Despite improved knowledge and stricter regulations, numerous fish stocks remain overharvested. Previous research has shown that fisheries management may fail when the models and assessments used to inform management are based on unrealistic assumptions regarding fishers' decision‐making and responses to policies. Improving the understanding of fisher behaviour requires addressing its diversity and complexity through the integration of social science knowledge into modelling. In our paper, we review and synthe… Show more
“…One strand of this work has focused on deviations from standard rational actor models and largely draws on the group of theories we have identified as 'focused on constraints.' Examples of this approach include descriptions of behavior that focus on bounded rationality and satisficing, imitation and mimicry, or social norms as shortcuts or heuristics (Libre et al 2015;e.g., Dressler et al 2018;Wijermans et al 2020). These are largely quantitative, experimental or model-based SES studies that use, for instance, prospect theory (e.g.…”
Section: Human Behavior In Ses: Contributions To An Emerging Research Frontiermentioning
The complex, context-dependent, and dynamic nature of human behavior is increasingly recognized as both an important cause of sustainability problems and potential leverage for their solution. Human beings are diverse, as are the social, ecological, and institutional settings in which they are embedded. Despite this recognition and extensive knowledge about human decision-making in the behavioral sciences, empirical analysis, formal models, and decision support for sustainability policy in natural resource management often either neglect human behavior or are based on narrow and overly simplistic assumptions. Integrating insights from behavioral sciences into sustainability research and policy remains a challenge. This is in part due to the abundance and fragmentation of theories across the social sciences and in part the challenges of translating research across disciplines. We provide a set of tools to support the integration of knowledge about human behavior into empirical and model-based sustainability research. In particular, we (i) develop a process-oriented framework of embedded human cognition (Human Behavior-Cognition in Context or HuB-CC), (ii) select an initial set of 31 theories with the potential to illuminate behavior in natural resource contexts and map them onto the framework, and (iii) suggest pathways for using the framework and mapping to encourage trans-disciplinary investigations, identify and compare theories, and facilitate their integration into empirical research, formal models, and ultimately policy and governance for sustainability. Our theory selection, framework, and mapping offer a foundation—a “living” platform—upon which future collaborative efforts can build to create a resource for scholars and practitioners working at the intersection of social sciences and natural resource management.
“…One strand of this work has focused on deviations from standard rational actor models and largely draws on the group of theories we have identified as 'focused on constraints.' Examples of this approach include descriptions of behavior that focus on bounded rationality and satisficing, imitation and mimicry, or social norms as shortcuts or heuristics (Libre et al 2015;e.g., Dressler et al 2018;Wijermans et al 2020). These are largely quantitative, experimental or model-based SES studies that use, for instance, prospect theory (e.g.…”
Section: Human Behavior In Ses: Contributions To An Emerging Research Frontiermentioning
The complex, context-dependent, and dynamic nature of human behavior is increasingly recognized as both an important cause of sustainability problems and potential leverage for their solution. Human beings are diverse, as are the social, ecological, and institutional settings in which they are embedded. Despite this recognition and extensive knowledge about human decision-making in the behavioral sciences, empirical analysis, formal models, and decision support for sustainability policy in natural resource management often either neglect human behavior or are based on narrow and overly simplistic assumptions. Integrating insights from behavioral sciences into sustainability research and policy remains a challenge. This is in part due to the abundance and fragmentation of theories across the social sciences and in part the challenges of translating research across disciplines. We provide a set of tools to support the integration of knowledge about human behavior into empirical and model-based sustainability research. In particular, we (i) develop a process-oriented framework of embedded human cognition (Human Behavior-Cognition in Context or HuB-CC), (ii) select an initial set of 31 theories with the potential to illuminate behavior in natural resource contexts and map them onto the framework, and (iii) suggest pathways for using the framework and mapping to encourage trans-disciplinary investigations, identify and compare theories, and facilitate their integration into empirical research, formal models, and ultimately policy and governance for sustainability. Our theory selection, framework, and mapping offer a foundation—a “living” platform—upon which future collaborative efforts can build to create a resource for scholars and practitioners working at the intersection of social sciences and natural resource management.
“…In fact, it is common for fishers to be willing to pay for the opportunity to fish (Cantrell et al 2004, Johnston et al 2006. For many fishers who continue to fish despite economic loss, fishing can be a form of recreation and a way to preserve self-image (Wijermans et al 2020). Satisfaction was chosen as our model variable because it is flexible enough to capture these non-economic behavioral motivators.…”
Marine fisheries represent a social-ecological system driven by both complex ecological processes and human interactions. Ecosystem-based fisheries management requires an understanding of both the biological and social components, and management failure can occur when either are excluded. Despite the significance of both, most research has focused on characterizing biological uncertainty rather than on better understanding the impacts of human behavior because of the difficulty of incorporating human behavior into simulation models. In this study, we use the fisheries in Narragansett Bay (Rhode Island, USA) as a case study to demonstrate how coupled modeling can be used to represent interactions between the food web and fishers in a social-ecological system. Narragansett Bay holds both a commercial fishery for forage fish, i.e., Atlantic menhaden (Brevoortia tyrannus) and a recreational fishery for their predators, i.e. striped bass (Morone saxatilis) and bluefish (Pomatomus saltatrix). To explore trade-offs between these two fisheries, we created a food web model and then coupled it to a recreational fishers' behavior model, creating a dynamic socialecological representation of the ecosystem. Fish biomass was projected until 2030 in both the stand-alone food web model and the coupled social-ecological model, with results highlighting how the incorporation of fisher behavior in modeling can lead to changes in the ecosystem. We examined how model outputs varied in response to three attributes: (1) the forage fish commercial harvest scenario, (2) the predatory (piscivorous) fish abundance-catch relationship in the recreational fishery, and (3) the rate at which recreational fishers become discouraged (termed "satisfaction loss"). Higher commercial harvest of forage fish led to lower piscivorous fish biomass but had minimal effects on the number of piscivorous fish caught recreationally or recreational fisher satisfaction. Both the abundancecatch relationship and satisfaction loss rate had notable effects on the fish biomass, the number of fish caught recreationally, and recreational fisher satisfaction. Currently, the lack of spatial and location-specific fisher behavior data limits the predictive use of our model. However, our modeling framework shows that fisher behavior can be successfully incorporated into a coupled social-ecological model through the use of agent-based modeling, and our results highlight that its inclusion can influence ecosystem dynamics. Because fisher decision making and the ecosystem can influence one another, social responses to changing ecosystems should be explicitly integrated into ecosystem modeling to improve ecosystem-based fisheries management efforts.
“…The FIBE model (Wijermans et al, 2020) is a fishery model that represents the diversity of fisher behaviours as observed and classified in the (Swedish) Baltic Sea fishery. The purpose of the FIBE model is to explore the consequences of behavioural diversity for the sustainability and management of the fishery.…”
Section: Theory Of Planned Behaviourmentioning
confidence: 99%
“…Visualisation of a decision tree in the FIBE model: an archipelago fisher deciding whether to fish or not (modified afterWijermans et al 2020).…”
Incorporating representations of human decision-making that are based on social science theories into social-ecological models is considered increasingly important – yet choosing and formalising a theory for a particular modelling context remains challenging. Here, we reflect on our experiences of selecting, formalising and documenting psychological and economic theories of human decision-making for inclusion in different agent-based models (ABMs) of natural resource use. We discuss the challenges related to four critical tasks: How to select a theory? How to formalise a theory and how to translate it into code? How to document the formalisation? In this way, we present a systematic overview of the choices researchers face when including theories of human decision-making in their ABMs, reflect on the choices we made in our own modelling projects and provide guidance for those new to the field. Also, we highlight further challenges regarding the parameterisation and analysis of such ABMs and suggest that a systematic overview of how to tackle these challenges contributes to an effective collaboration in interdisciplinary teams addressing socio-ecological dynamics using models.
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