Robust Adaptive Planning: A New Decisions Sciences
for Complex Systems
Robert Lempert
lempert@rand.org/ lempert@evolvinglogic.com / My RAND address <lempert@rand.org>
Models of complex systems can capture much useful information,
but can be difficult to apply to real-world decision-making because the type
of information they contain is often inconsistent with that required for
traditional decision analysis. In particular, complex systems models may
be most useful under conditions of deep uncertainty, that is, where the emergent
behavior of the system makes accurate points forecasts or probabilistic predictions
-- and thus traditional decision analysis -- difficult. New approaches,
such as Computer Assisted Reasoning (CAR), which use inductive reasoning
over large ensembles of computational experiments, now make possible systematic
comparison of alternative policy options under conditions of deep uncertainty.
Such CAR approaches enable two key analytical steps important to decision
analysis with complex systems: 1) the use of ensembles of plausible models,
rather than any single best guess, as the best description of the available
information about the future, and 2) the use of criteria such as robustness
and satisficing, rather than optimality and efficiency, to compare the performance
of alternative decisions.
This talk describes the Computer Assisted Reasoning approach
to decision-making under conditions of deep uncertainty which is ideally
suited to applying complex systems to policy analysis. In particular, this
talk will describe the use of CAR to support robust, adaptive planning (RAP).
The talk will draw examples from a variety of decision problems, including
the policy problem of global climate change, with a particular focus on the
role of technology policies in a robust, adaptive strategy for greenhouse
gas abatement.