Information quality and uncertainty in resource allocation decisions
Date
1986
Authors
Sackmann, T.
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Abstract
As demands on a finite supply of natural resources have intensified, decision makers have found it increasingly difficult to maximize the benefits of resource developments while ensuring that the biophysical bases of resource uses are maintained. These problems are accentuated when decision makers cannot predict the outcomes of resource allocations because of a lack of information regarding existing resource uses. This thesis therefore examines the influence of data quality and conditions of uncertainty on decision makers' ability to maximize the benefits and minimize the negative impacts of resource allocations.
Decisions can be made under conditions of certainty. risk or uncertainty, with each condition reflecting the relative predictability of decision outcomes. Uncertainty can only be reduced by gathering "functional" information, which specifies the interrelationship between elements of systems potentially affected by a decision. Subjective attempts by decision makers to reduce uncertainty may lead to biased assessments of decision outcomes.
Discharge and spawning habitat data for selected Vancouver Island salmon spawning streams was found to be completely "descriptive" in quality; this data, which merely outlines discharge and spawning habitat conditions as unrelated entities, does not allow resource decision makers to predict possible effects of small developments on downstream spawning habitats.
Thus, hydro streamflow allocations for small hydro plants on these streams would be made under conditions of uncertainty. Decision makers will not be able to maximize the benefits of small hydro developments while minimizing negative impacts on spawning habitats.
To facilitate effective resource allocations, decision makers should be made aware that 1) information quality and uncertainty are linked, and 2) subjective methods of uncertainty reduction will lead to unforeseen or undesirable outcomes to resource decisions. Finally, the advantages of anticipating and collecting functional-quality data in advance of a resource decision process are stressed.