"Try to learn something about
everything and everything about something."
- Thomas H. Huxley
ACTIVE PROJECTS
Bayesian
networks of society-environment interactions are being developed to aid management and decision-making under
conditions of environmental variability and uncertainty. By succinctly and
effectively translating causal assertions between variables into patterns of
probabilistic dependence, Bayesian networks (BNs)
facilitate logical and holistic reasoning in complex systems. Such reasoning is necessary for accurate
analysis, synthesis, prediction, diagnosis, and decision-making. In society-environment systems, BNs are useful because the predictive link that we want to
model is often a complex causal chain, the entirety of which rarely falls
within a single, coordinated research project.
BNs allow this causal chain to be factored
into an articulated sequence of conditional relationships, each of which can
then be quantified independently using an approach suitable for the type and
scale of information available.
Social and
biological indicators of sustainability can be used to encourage individual and organizational stakeholders in
a natural resource to act in ways that promote ecological, economic, and social
health. We hypothesize that regular
monitoring and reporting of such indicators may improve resilience in the
complex human-environment system by improving stakeholder perception of
ecological change, enhancing learning, and facilitating the process of adaptive
management over time. This expectation
arises for two reasons related to psychological framing: First, an explicit link between pollutant
controls and measurable indicators will frame the sustainability issue in terms
of “property rights.” Second, well-designed indicators are more
easily remembered and processed and may link more easily to personal
aspirations. There is evidence to
suggest that both effects are likely to motivate stakeholders to act in ways
that promote sustainability. We
anticipate that the results of our work in this area will be used to design
regulatory frameworks, especially in the energy domain, that ensure
environmental protection while exploiting economic efficiencies and addressing
social justice concerns.
Applied
robust Bayesian analysis adds the
dimension of probabilistic imprecision to standard model-based uncertainty
analyses. The robust Bayesian approach employs sets of probability measures to
describe uncertainty, rather than precise values. This concept has the
potential to capture ambiguity or disagreement in probability specification in
a variety of ways and therefore can provide a more realistic portrayal of
scientific knowledge. The objective of our research in this area is to bring
the strengths of the robust Bayesian approach to bear on the problem of
separating sources of uncertainty in environmental model-based assessments.
Linking predictive
climate models to economic assessments under conditions of uncertainty can lead to methodological complications that have
only recently been recognized. In
particular, appropriate methods and rates of discounting future benefits change
under uncertainty relative to situations in which the future is assumed to be
known. The appropriate discount rate may be substantially higher or lower than
values commonly used, depending on the specific case and preference axioms
employed. For an issue such as climate change, in which the benefits of policy
decisions extend over a long time horizon, small changes in discount rates can
have dramatic implications for the optimal choice of policy. Clarifying and
demonstrating the ramifications of model uncertainty for linked economic
analyses is a major objective of this interdisciplinary research effort.
Decision
analytic evaluation of emerging technologies will ensure that engineering developments address the wide range of
interests and concerns that users have regarding modern technology. Multiattribute
decision theory can be used to balance economic, environmental, cultural,
political, and social objectives in developing, producing, and marketing new
products. It can also help to identify
novel implementation strategies that encourage productive action today while
still allowing for transition periods, learning, or technology development in
the future.
Representation
of model inputs and parameters using stochastic, time-dependent parameters can improve the accuracy of model predictions and
representations of uncertainty. This is
because, in deterministic models of environmental systems, systematic
discrepancies between model simulations and measured data typically occur. These
systematic deviations may be indicative of true indeterminism, or they might be
the consequence of model aggregation and simplification. In either case, the
implausibility of typical statistical assumptions implies that parameter
uncertainty estimates or model extrapolations based on common techniques are
likely to be unreliable. To address
these discrepancies, we propose making selected parameters in the model
time-variable by treating them as continuous-time stochastic processes. We have
developed a Markov chain
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Last Revised: August 2007 by
MEB