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). Derived from these types of minds, innate biases, beliefs, heuristics, and values (BBHV) influence behaviors, often beneficially, when individuals or small groups face immediate, local, acute situations that they and their ancestors faced repeatedly in the past. BBHV, though, need to be recognized and possibly countered or used when facing new, complex issues or situations especially if they need to be managed for the benefit of a wider community, for the longer-term and the larger-scale. Taking BBHV into account, we explain and provide a cyclic science-infused adaptive framework for (1) gaining knowledge of complex systems and (2) improving their management. We explore how this process and framework could improve the governance of science and policy for different types of systems and issues, providing examples in the area of natural resources, hazards, and the environment. Lastly, we suggest that an "Open Traceable Accountable Policy" initiative that followed our suggested adaptive framework could beneficially complement recent Open Data/Model science initiatives.Plain Language Summary Our review paper suggests that society can improve the management of natural resources and environments by (1) recognizing the sources of human decisions and thinking and understanding their role in the scientific progression to knowledge; (2) considering innate human needs and biases, beliefs, heuristics, and values that may need to be countered or embraced; and (3) creating science and policy governance that is inclusive, integrated, considerate of diversity, explicit, and accountable. The paper presents a science-infused adaptive framework for such governance, and discusses the types of issues and systems that it would be best suited to address.

Purpose, processes, partnerships, and products: four Ps to advance participatory socio‐environmental modeling

“…), thus supporting discourse and the decision‐making process, and helping to overcome some of the cognitive limitations (see Glynn et al. ) that complex problems present (Sterman ). Also, in some types of modeling the stakeholders are encouraged to substantiate their qualitative ideas and mental models with data (Gray et al.…”

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”

“…We welcome the comment by Walker et al () and their critique of our paper (Glynn et al, ). Walker et al and their previous publications bring forth interesting ideas about the characterization of uncertainty in policy making.…”

“…In the key points of their comment, Walker et al make three broad statements in their critique of Glynn et al (). (1) They state that “Glynn et al assume predictive optimization models can identify a ‘best policy,’ not acknowledging the role of uncertainty policymaking.” (2) They argue that Glynn et al () “have an adaptive framework for knowledge generation and decision evaluation, but not for changing the decision as the world changes.” (3) Walker et al also claim to show how Glynn et al's () “narrow view can be widened to include aspects of risk and uncertainty.” We disagree with each of these statements. All three are based on incorrect interpretations or assumptions about what was written or implied in our article.…”

How Does Knowledge Infrastructure Mobilization Influence the Safe Operating Space of Regulated Exploited Ecosystems?

“…However, there is continued debate regarding the effective implementation of adaptive management in practice (McLain & Lee, 1996;Walters, 1997). For instance, quantity and quality of knowledge aggregate other concepts studies by scholars such as beliefs, values, getting facts and information (Glynn et al, 2017;Warren et al, 2018), or uncertainty (see Brugnach et al, 2008) or the typology of uncertainty developed by Walker et al (2013). Here we focus on the broader question of the roles that human infrastructure (knowledge and decision-making skills embodied in people) and knowledge infrastructure (stock of stored knowledge and the infrastructures that create, communicate, and maintain it, such as, sensors, IT systems, and organizations) play in adaptive management.…”