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).