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DR. DANIEL J. BERLEANT
Heuristic Perspectives for Making and Discussing Predictions
said
Here is a list of heuristic perspectives that we have composed to
facilitate foresight analyses that are both systematic and
comprehensive.
1. Identify factors in the physical and biotic world that may be
relevant. These factors are neither technical or societal. Examples
of
such factors include natural resource availability, land cover, climate
trends, and risks of volcanism or earthquake.
2. Identify relevant societal, cultural, and behavioral
patterns.
Numerous aspects of technology, the physical world, and the biotic
world are not separable from human activity, and require consideration
of societal, cultural, and psychological factors. Natural resources,
for example, exist only so far as human groups define them as useful
and only in so far as technology, politics, and economics can make them
available (Steward 1972).
Dr. Daniel J. Berleant was the author of the above and
is Associate Professor at the
Department of Information Science, University of Arkansas at
Little Rock. He is project leader for
1)
PubMed Assistant, a biologist friendly
interface for enhanced PubMed searches; 2)
PathBinderH,
a large bioinformatics
text repository; 3)
MetNet PathBinder, a plant metabolomics
text repository currently being integrated into the overall MetNet
system; and of 4)
Statool, a system for arithmetic on
partially specified probability distributions.
He is a member of the Task Force on the Relation Between Interval
and Fuzzy
Techniques, organized by the Fuzzy Technical Committee of the IEEE
Neural Networks Society (IEEE-NNS).
Dan coauthored
Corpus Properties of Protein Interaction Descriptions in
MEDLINE,
Unimodality, independence lead to NP-hardness of interval
probability
problems,
Equivalence of methods for uncertainty propagation of real-valued
random variables,
Using Pearson correlation to improve envelopes around the
distributions
of functions,
Representation and problem solving with Distribution Envelope
Determination (DEnv),
Qualitative and quantitative simulation: bridging the gap,
and
Dependable handling of uncertainty.
Read his
full list of publications!
He earned his B.S. in Computer Science and Engineering at the
Massachusetts Institute of Technology (MIT) in 1982, his M.S. in
Computer Sciences at the University of Texas at Austin in 1990 and his
Ph.D. in Computer Sciences at the University of Texas at Austin in
1991. He is a member of
ACM, IEEE (Senior Member), and the International Society for
Computational Biology (ICSB).
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