Announcement of Opportunity
Biocomplexity in the Environment, Coupled Natural and Human Systems
NSF Directorate for Engineering
Wednesday, 3 December 2003
For more information please go to:
http://www.eng.nsf.gov/be/be-2-cnh.htm
This is a reminder for human dimensions researchers and those interested
in human dimensions research in the Arctic that the Biocomplexity in the
Environment, Coupled Natural and Human Systems' announcement of
opportunity is out and has a 3 December 2003 deadline.
CNH "focuses on the complex interactions among human and natural systems
at diverse spatial, temporal, and organizational scales. To be
competitive for support, teams of investigators drawn from natural and
human sciences must examine the dynamics of appropriate natural and
human systems as well as the interactions that link those human and
natural systems."
Engineers could make critical contributions in one or more of three key
"pieces" of the CNH effort:
(1) improved modeling of natural systems, based on unique knowledge of
chemical processes or other relevant processes linked to the use of
technology;
(2) improved understanding of present or future human impacts on the
environment, based on unique knowledge of present and future technology
options and the forces which affect their deployment and results in the
real world; and
(3) greater use of "systems technology" to address the key questions of
how to integrate this information, in assessing uncertainty, robustness,
stability, vulnerability, and value assessment in novel ways.
As an example, economists and decision theorists have developed good
theoretical tools for thinking about stability and valuation, which are
mathematically equivalent to the tools used in engineering, under many
conditions; however, the ability to approximate value measures (like the
Bellman or Pontryagin value measures) in large systems, under conditions
of noise, numerically, may provide unique opportunities for engineers as
part of cross-disciplinary teams.
Likewise, tools for analyzing stability and robustness and vulnerability
of energy systems, and future energy technologies, may be relevant here
as well. Tools for deriving approximately optimal decision strategies,
in addressing complex stochastic systems, may be applied to testbed
challenges in this area. Adaptive or robust data-driven system
identification techniques may also provide new opportunities, above and
beyond more traditional modeling techniques.
For further information go to:
http://www.eng.nsf.gov/be/be-2-cnh.htm