AWRA Technical Director, friend and colleague Dick Engberg sent me this thoughtful paper from Pierre D. Glynn who is currently Branch Chief for the Eastern Branch of the USGS National Research Program in Reston, VA.
This paper is from a special volume form the Geological Society of London (hence the double 'l' in 'modeling'):
From: Riddick, A. T., Kessler, H. & Giles, J. R. A. (eds) Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges. Geological Society, London, Special Publications, 408, http://doi.org/10.1144/SP408.9
Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for mean- ingful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other sug- gestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.
"There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know." - Donald Rumsfeld