Current treatments of uncertainty in environmental research embody several myths about the causes and consequences of imperfect knowledge, namely: (1) the dominant role of environmental factors in controlling uncertainty, such as system complexity, non-linearity and space-time variability, rather than social and psychological factors; (2) the primacy of observations in locating, quantifying, and reducing uncertainty; and (3) the value of technical assessments of uncertainty in 'risk-based decision-making'. While the identification and treatment of specific sources of uncertainty remain impractical in some areas of environmental research, a source-based approach is increasingly used in environmental modeling. Here, selected sources of uncertainty are quantified with probability distributions and propagated to model outputs (a forward problem), while data are used to calibrate these estimates and reduce uncertainty (an inverse problem). More generally, current treatments of uncertainty and risk are dominated by attempts to quantify, minimize, and control uncertainty. Uncertainty is viewed as an 'information deficit' to be resolved, rather than an inherent product of conducting research. This paper argues for more open treatments of uncertainty in environmental research. Such openness requires an appreciation of the social and psychological causes of uncertainty, the role of observations as imperfect and contingent expressions of visible events, and the myriad ways in which scientific information can be misinterpreted, misused, or sidelined in environmental decision-making. The paper begins with a discussion of the nature and causes of uncertainty in environmental research. A review of current treatments of uncertainty is followed by an analysis of the source-based approach to assessing uncertainty. Prospects for the open treatment of uncertainty are then discussed in terms of circumventing the three 'myths of uncertainty' that characterize recent work in environmental research.
