Response to Comment by Walker et al. on “From Data to Decisions: Processing Information, Biases, and Beliefs for Improved Management of Natural Resources and Environments”

Abstract

Our different kinds of minds and types of thinking affect the ways we decide, take action, and cooperate (or not). The comment by Walker et al. (2018, https://doi.org/10.1002/2017EF000750) illustrates several points made by Glynn et al. (2017, https://doi.org/10.1002/2016EF000487) and many other articles. Namely, biases and beliefs often drive scientific reasoning, and scientists, just like other humans, are intimately attached to their values and heuristics. Scientists, just like many other people, also tend to read and interpret text in ways that best match their individual perceptions of a problem or issue: in many cases paraphrasing and changing the meaning of what they read to better match their initial ideas. Walker et al. are doing interesting and important research on uncertainty. Nonetheless, they misinterpret the work, assumptions, and conclusions brought forth by Glynn et al. (2017, https://doi.org/10.1002/2016EF000487).

Value of Information and Decision Pathways: Concepts and Case Studies

“…discussion and references in Glynn et al, 2022, this issue). BBHV and other behavioral and social factors mentioned in this paper affect everyone: information generators, consumers, funders, evaluators, decision makers, and even the most objective economists and scientists (Glynn, 2014;Glynn, 2017;Glynn et al, 2017;Glynn et al, 2018). Improving incorporation of these factors in VOI studies could provide a more holistic assessment of how and to what degree information influences decisions and resulting actions.…”

Black Boxes and the Role of Modeling in Environmental Policy Making

“…While scientific advice may be, in the future, increasingly based on complex models, the environmental policies benefiting from these tools should head into the direction of more transparent and collaborative processes. It may be difficult to communicate the computational details of a black box model, but it is still necessary to communicate the logic behind the results, as well as the policy process that uses the models (Röckmann et al, 2012;Glynn et al, 2018;Jordan et al, 2018). To keep the participatory processes up with the advancements of the fast-developing computation capacity and modeling technology, substantial effort should be spent on developing tools and skills to support stakeholder participation and engagement.…”

Twelve Questions for the Participatory Modeling Community

“…In particular, we do not know how much disagreement on how the system under study functions can persist without making it impossible for a group to agree on a course of action. We also do not know when joint, collaborative thinking, which can be quite helpful for group dynamics, becomes group thinking (Glynn et al, 2018) and turns into an obstacle for critical thinking and effective decision making.…”