81 research outputs found

    The Survival of the Conformist: Social Pressure and Renewable Resource Management

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    This paper examines the role of pro-social behavior as a mechanism for the establishment and maintenance of cooperation in resource use under variable social and environmental conditions. By coupling resource stock dynamics with social dynamics concerning compliance to a social norm prescribing non-excessive resource extraction in a common pool resource (CPR), we show that when reputational considerations matter and a sufficient level of social stigma affects the violators of a norm, sustainable outcomes are achieved. We find large parameter regions where norm-observing and norm-violating types coexist, and analyze to what extent such coexistence depends on the environment.Cooperation, Social Norm, Ostracism, Common Pool Resource, Evolutionary Game Theory, Replicator Equation, Agent-based Simulation, Coupled Socio-resource Dynamics

    The evolution of cooperative versus non-cooperative governance of small-scale fisheries

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    Small-scale fisheries (SSF) around the world are often organized in patron-client relationships (PC), where fishers deliver fish to a fishbuyer who sells it at regional markets. PCs have recently been criticized as being unequal, exploitative and unsustainable. Many governments have thus developed measures to promote fishing cooperatives. Despite these efforts, PC relationships appear to be increasing globally. The aim of our work is to shed light on why this social organization is very persistent. To this end we combine in depth empirical research on fishbuyers and fishers in an SSF in Mexico with agent-based modeling. This allows us to test hypotheses about the role of actor characteristics, such as their social skills, and the resources, as well as the institutional environment in which these interactions take place, for the establishment and persistence of PC relationships versus cooperatives

    Modelling cooperation in Bali irrigation

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    In social-ecological systems research the use of natural resources is typically studied on either a conceptual (theory) or a detailed level (case studies). We use agent-based modelling to take an approach that is situated in between. With this we aim to generate understanding that goes beyond the case, while being sensitive to contextual aspects of a given social dilemma situation. Our model combines a theoretical model of norm-driven cooperation with a case-specific model of an irrigation dilemma. The theoretical model is contextualised by using case empirics to investigate the role of cooperation for the performance of a rice growing community. Particularly, for this conference, we focus on the effect of introducing ecological complexity by embedding empirical based resource dynamics

    Robustness of norm-driven cooperation in the commons

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    Sustainable use of common-pool resources such as fish, water or forests depends on the cooperation of resource users that restrain their individual extraction to socially optimal levels. Empirical evidence has shown that under certain social and bio-physical conditions self-organized cooperation in the commons can evolve. Global change, however, may drastically alter these conditions. We assess the robustness of cooperation to environmental variability in a stylised model of a community that harvests a shared resource. Community members follow a norm of socially optimal resource extraction, which is enforced through social sanctioning. Our results indicate that both resource abundance and a small increase in resource variability can lead to collapse of cooperation observed in the no-variability casewhile either scarcity or large variability have the potential to stabilize it. The combined effects of changes in amount and variability can reinforce or counteract each other depending on their size and the initial level of cooperation in the community. If two socially separate groups are ecologically connected through resource leakage, cooperation in one candestabilize the other. These findings provide insights into possible effects of global changeand spatial connectivity, indicating that there is no simple answer as to their effects on cooperation and sustainable resource use

    A diagnostic procedure for applying the social-ecological systems framework in diverse cases

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    The framework for analyzing sustainability of social-ecological systems (SES) framework of Elinor Ostrom is a multitier collection of concepts and variables that have proven to be relevant for understanding outcomes in diverse SES. The first tier of this framework includes the concepts resource system (RS) and resource units (RU), which are then further characterized through lower tier variables such as clarity of system boundaries and mobility. The long-term goal of framework development is to derive conclusions about which combinations of variables explain outcomes across diverse types of SES. This will only be possible if the concepts and variables of the framework can be made operational unambiguously for the different types of SES, which, however, remains a challenge. Reasons for this are that case studies examine other types of RS than those for which the framework has been developed or consider RS for which different actors obtain different kinds of RU. We explore these difficulties and relate them to antecedent work on common-pool resources and public goods. We propose a diagnostic procedure which resolves some of these difficulties by establishing a sequence of questions that facilitate the step-wise and unambiguous application of the SES framework to a given case. The questions relate to the actors benefiting from the SES, the collective goods involved in the generation of those benefits, and the action situations in which the collective goods are provided and appropriated. We illustrate the diagnostic procedure for four case studies in the context of irrigated agriculture in New Mexico, common property meadows in the Swiss Alps, recreational fishery in Germany, and energy regions in Austria. We conclude that the current SES framework has limitations when applied to complex, multiuse SES, because it does not sufficiently capture the actor interdependencies introduced through RS and RU characteristics and dynamics

    Towards representing human behavior and decision making in Earth system models. An overview of techniques and approaches

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    Today, humans have a critical impact on the Earth system and vice versa, which can generate complex feedback processes between social and ecological dynamics. Integrating human behavior into formal Earth system models (ESMs), however, requires crucial modeling assumptions about actors and their goals, behavioral options, and decision rules, as well as modeling decisions regarding human social interactions and the aggregation of individuals’ behavior. Here, we review existing modeling approaches and techniques from various disciplines and schools of thought dealing with human behavior at different levels of decision making. We demonstrate modelers’ often vast degrees of freedom but also seek to make modelers aware of the often crucial consequences of seemingly innocent modeling assumptions. After discussing which socioeconomic units are potentially important for ESMs, we compare models of individual decision making that correspond to alternative behavioral theories and that make diverse modeling assumptions about individuals’ preferences, beliefs, decision rules, and foresight. We review approaches to model social interaction, covering game theoretic frameworks, models of social influence, and network models. Finally, we discuss approaches to studying how the behavior of individuals, groups, and organizations can aggregate to complex collective phenomena, discussing agent-based, statistical, and representative-agent modeling and economic macro-dynamics. We illustrate the main ingredients of modeling techniques with examples from land-use dynamics as one of the main drivers of environmental change bridging local to global scales

    Implications of poverty traps across levels

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    Recent research has demonstrated the multidimensional nature of poverty and the multi-level organization of social-ecological systems that display poverty traps. The traps on these different levels can reinforce each other, and therefore multi-level traps pose particular challenges for poverty alleviation. Yet, poverty trap models rarely consider more than one level of organization and only a few attributes of the system at each level. These limitations constrain our understanding of the mechanisms that generate poverty traps and may hinder or even mislead development efforts. Here, we present a series of two-level dynamical system models of poverty traps and use these models to investigate the combined influences of biophysical and economic factors, farmers’ habits and community decisions on creating and alleviating persistent poverty. Our results indicate that neglecting key interactions can lead to incorrect assessments and potentially inadequate alleviation strategies. Moreover, we obtain necessary conditions for the existence of fractal poverty traps, and show that (i) cross-level interactions can open possibilities for escaping from poverty, (ii) that farmers’ behavioral changes may create or impede a way out of poverty, and (iii) that the effectiveness of development interventions depends on the combined influences of biophysical and economic dynamics, farmers’ behavior and community spending on agricultural and social activities.The research leading to these results received funding from the Sida-funded GRAID program at the Stockholm Resilience Centre, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 682472 – MUSES), the Swedish Research Council Formas (project grant 2014-589), and the Swedish Research Council Vetenskapsr?det (project grant 2018-06732)

    Resilience offers escape from trapped thinking on poverty alleviation

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    The poverty trap concept strongly influences current research and policy on poverty alleviation. Financial or technological inputs intended to “push” the rural poor out of a poverty trap have had many successes but have also failed unexpectedly with serious ecological and social consequences that can reinforce poverty. Resilience thinking can help to (i) understand how these failures emerge from the complex relationships between humans and the ecosystems on which they depend and (ii) navigate diverse poverty alleviation strategies, such as transformative change, that may instead be required. First, we review commonly observed or assumed social-ecological relationships in rural development contexts, focusing on economic, biophysical, and cultural aspects of poverty. Second, we develop a classification of poverty alleviation strategies using insights from resilience research on social-ecological change. Last, we use these advances to develop stylized, multidimensional poverty trap models. The models show that (i) interventions that ignore nature and culture can reinforce poverty (particularly in agrobiodiverse landscapes), (ii) transformative change can instead open new pathways for poverty alleviation, and (iii) asset inputs may be effective in other contexts (for example, where resource degradation and poverty are tightly interlinked). Our model-based approach and insights offer a systematic way to review the consequences of the causal mechanisms that characterize poverty traps in different agricultural contexts and identify appropriate strategies for rural development challenges.The research leading to these results received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement 283950 SES-LINK, a core grant to the Stockholm Resilience Centre by Mistra, the Sida-funded GRAID program at the Stockholm Resilience Centre, the Swedish Research Council Formas (project grant 2014-589), the Ragnar Söderberg Foundation, and the Erling-Persson Family Foundation

    Capturing emergent phenomena in social-ecological systems: an analytical framework

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    Social-ecological systems (SES) are complex adaptive systems. Social-ecological system phenomena, such as regime shifts, transformations, or traps, emerge from interactions among and between human and nonhuman entities within and across scales. Analyses of SES phenomena thus require approaches that can account for (1) the intertwinedness of social and ecological processes and (2) the ways they jointly give rise to emergent social-ecological patterns, structures, and dynamics that feedback on the entities and processes that generated them. We have developed a framework of linked action situations (AS) as a tool to capture those interactions that are hypothesized to have jointly and dynamically generated a social-ecological phenomenon of interest. The framework extends the concept of an action situation to provide a conceptualization of SES that focusses on social-ecological interactions and their links across levels. The aim of our SE-AS (social-ecological action situations) framework is to support a process of developing hypotheses about configurations of ASs that may explain an emergent social-ecological phenomenon. We suggest six social-ecological ASs along with social and ecological action situations that can commonly be found in natural resource or ecosystem management contexts. We test the ability of the framework to structure an analysis of processes of emergence by applying it to different case studies of regime shifts, traps, and sustainable resource use. The framework goes beyond existing frameworks and approaches, such as the SES framework or causal loop diagrams, by establishing a way of analyzing SES that focuses on the interplay of social-ecological interactions with the emergent outcomes they produce. We conclude by discussing the added value of the framework and discussing the different purposes it can serve: from supporting the development of theories of the emergence of social-ecological phenomena, enhancing transparency of SES understandings to serving as a boundary object for interdisciplinary knowledge integration
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