Carl D. Shapiro

From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments

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.

Economic Assessment of the Use Value of Geospatial Information

Geospatial data inform decision makers. An economic model that involves application of spatial and temporal scientific, technical, and economic data in decision making is described. The value of information (VOI) contained in geospatial data is the difference between the net benefits (in present value terms) of a decision with and without the information. A range of technologies is used to collect and distribute geospatial data. These technical activities are linked to examples that show how the data can be applied in decision making, which is a cultural activity. The economic model for assessing the VOI in geospatial data for decision making is applied to three examples: (1) a retrospective model about environmental regulation of agrochemicals; (2) a prospective model about the impact and mitigation of earthquakes in urban areas; and (3) a prospective model about developing private–public geospatial information for an ecosystem services market. Each example demonstrates the potential value of geospatial information in a decision with uncertain information.

An economic and geographic appraisal of a spatial natural hazard risk: a study of landslide mitigation rules

Natural hazards in the form of landslides are pervasive throughout the United States. Efficient mitigation of natural hazards requires a spatial representation of the risk, based upon the geographic distribution of physical parameters and man-related development activities. Through such a representation, the spatial probability of landslides based upon physical science concepts is estimated for Cincinnati, Ohio. Mitigation programs designed to reduce loss from landslide natural hazards are then evaluated. An optimum mitigation rule is suggested that is spatially selective and is determined by objective measurements of hillside slope and properties of the underlying soil.

Assessing the socioeconomic impact and value of open geospatial information

Panel-increased supply of geospatial information and expanded participatory processes in the production of data (such as crowd sourcing)