The wisdom of crowds: What researchers can learn from local communities of natural resources stakeholders
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EmailCan natural resource stakeholders accurately estimate fish populations and human pressures on the environment?
How many elephants are in Tanzania? How many trees are in the Amazon? How many tuna are in the Atlantic? While we probably wouldn’t know the answers to these questions on an individual level, new research indicates that if we collectively consider all our wrong answers, as a group we would probably arrive at the right answer.
“In elementary school, you may remember guessing how many jelly beans were in a jar - and while all individual guesses were unlikely to be correct, averaging a bunch of wrong guesses turns out to be surprisingly accurate. My colleagues and I wanted to see if, rather than estimating jelly beans, can a group of natural resource stakeholders accurately guess fish population estimates or estimate human pressures on the environment? And it turns out they can,” says Dr. Steven Gray, associate professor at Michigan State University.
Drawing on a well-established phenomenon known as “wisdom of the crowd,” popularized by Francis Galton in 1907, the theory has been applied to many situations ranging from predicting stock market changes to guessing the winner of sporting events. In a new study recently published in the journal Frontiers in Ecology and the Environment, Gray and his colleagues used so-called “collective intelligence” approaches to see if the wisdom of the crowd effect can also be applied to complex sustainability issues.
In this study, recreational anglers in Massachusetts participated in two estimation tasks in a striped bass fishery to investigate if there are differences between two modes of collective intelligence, “wisdom of the crowds” and “swarm intelligence.” To test this idea, Gray and colleagues asked 33 recreational anglers to answer a survey about their personal knowledge of the striped bass fishery including how many recreational fishers there are, the frequency of different lengths of bass caught during the season, and the environmental factors they thought were impacting the bass population. The researchers could then average the responses to create a “wisdom of the crowds” estimate for these elements of the fishery.
The researchers then had a sub-group of the original respondents participate in a live “swarm” event where participants responded to the same questions collectively in the online Swarm AI interface. The online swarming exercise encouraged individuals to converge on a solution through real-time collaboration, allowing participants’ responses to be influenced by opinions other than their own.
The researchers found that both the “wisdom of the crowds” and “swarm intelligence” approaches were able to correctly identify the distribution of striped bass of various sizes, the size of fish caught most and least frequently, and prioritize the importance of environmental factors impacting the striped bass fishery as compared to expert analysis. However, the “wisdom of the crowds” approach was generally more accurate in numerical estimates such as the number of recreational fishers in the system.
“Researchers and wildlife managers are often strapped for resources when trying to address local natural resource issues. There often is not enough time, money, or personnel to collect all the data necessary to help inform management of these resources. Our research indicates that we can cheaply and effectively mine the existing knowledge of hunters and fishers who interact with the environment for not only fisheries, but for all kinds of sustainability issues,” says Payam Aminpour, a co-author on the study.
The researchers recommend using the principle of collective intelligence more systematically in the management of natural resources. This applies in particular to cases where knowledge and data are limited, and thus there are insufficient resources to achieve a deeper scientific understanding. In these cases, harnessing the collective intelligence of diverse and knowledgeable natural resource stakeholders can be a reliable way of addressing social and ecological data uncertainty and scarcity, while also encouraging stakeholder participation.
The authors argue that collective intelligence technologies, such as Swarm AI, can act as a middleman between resource users and managers by providing a venue for more inclusive forms of decision making and improving methods of incorporating stakeholder inputs. And while no one person likely knows the answer to how to become more sustainable, collectively we might just figure it out.
Additional scientists contributing to the study include Rebecca Jordan from Department of Community Sustainability at MSU, Caitie Reza from Department of Integrative Biology at MSU, Steven Scyphers and Jonathan Grabowski from Northeastern University, Robert Murphy Jr. from Alaska Pacific University, Alison Singer from Northern Arizona University, David Baltaze from Unanimous AI, Antonie Jetter from Portland State University, and Joshua Introne from Syracuse University.
Citation: Steven Gray et al. (2020). Harnessing the collective intelligence of stakeholders for conservation. Frontiers in Ecology and the Environment. DOI: 10.1002/fee.2232
(Note for media: Please include a link to the original paper in online coverage: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/fee.2232)
