Publications
Selected Papers
The diversity bonus in pooling local knowledge about complex problems
Groups can collectively achieve an augmented cognitive capability that enables them to effectively tackle complex problems. Importantly, researchers have hypothesized that this group property—frequently known as collective intelligence—may be improved in functionally more diverse groups. This paper illustrates the importance of diversity for representing complex interdependencies in a social-ecological system. In an experiment with local stakeholders of a fishery ecosystem, groups with higher diversity—those with well-mixed members from diverse types of stakeholders—collectively produced more complex models of human–environment interactions which were more closely matched scientific expert opinions. These findings have implications for advancing the use of local knowledge in understanding complex sustainability problems, while also promoting the inclusion of diverse stakeholders for increasing management success.
Wisdom of stakeholder crowds in complex social–ecological systems
Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations. We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders.
Harnessing the collective intelligence of stakeholders for conservation
Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans. Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations.
Combining fuzzy cognitive maps with agent-based modeling
Agent-based modeling (ABM) is an established technique to capture human-environment interactions in socio-ecological systems. As a micro-model, it explicitly represents each agent, such that heterogeneous decision-making processes (e.g. based on the beliefs and experiences of stakeholders) can anticipate the socio-environmental consequences of aggregated individual behaviors. In contrast to ABM, Fuzzy Cognitive Mapping takes a macro-level view of the world that represents causal connections between concepts rather than individual entities. Researchers have expressed interest in reconciling the two, i.e. taking a hybrid approach and drawing of the strengths of each to more accurately model socio-ecological interactions. The intuition is to take FCMs, which can be quickly developed using participatory modeling tools and use them to create a virtual population of agents with sophisticated decision-making processes. In this paper, we detail two ways in which this combination can be done, and highlight the key questions that modelers need to be mindful of.
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The Spine Journal, 2019, Volume 19, Issue 6, Pages 1029-1040
Assessing (Social-Ecological) Systems Thinking by evaluating cognitive maps
Sustainability, 2019, Volume 11, Issue 20
Building a Collaborative Model ...to Understand the Diverse Perspectives of Experts
PM&R, 2019, Volume 11, Issue S1, Pages S11-S23
Perspectives of scholars on the nature of sustainability: a survey study
International Journal of Sustainability in Higher Education, 2019, Volume 21, Issue 1
iEMSs,9th International Congress on Environmental Modelling and Software, 2018
AgriEngineering, 2018, Volume 1 Issue 1