Computers, decision making

Many sophisticated models and correlations have been developed for consequence analysis. Millions of dollars have been spent researching the effects of exposure to toxic materials on the health of animals the effects are extrapolated to predict effects on human health. A considerable empirical database exists on the effects of fires and explosions on structures and equipment. And large, sophisticated experiments are sometimes performed to validate computer algorithms for predicting the atmospheric dispersion of toxic materials. All of these resources can be used to help predict the consequences of accidents. But, you should only perform those consequence analysis steps needed to provide the information required for decision making. 

Fullwood, R. and W. Shier, 1990, PRA Using Event Tables and the Brookhaven Event Tree Analyzer (BETA), The Role and Use of Personal Computers in Probabilistic Safety Assessment and Decision Making, Elsevier, NY, ISBN 1-85166-501-3, pp 79-92/... 

Bellamy, L. J., Geyer, T. A. W. (1988). Addressing Human Factors Issues in the Safe Design and Operation of Computer Controlled Process Systems. In B. A. Sayers (Ed.), Proceedings ofSARSS 88. Human Factors and Decision Making Their Influence on Safety and Reliability. 19-20 October, Altrincham, Manchester, U.K. London Elsevier Applied Science. 

In the context of drug discovery, computational methods do not add value unless they can achieve practical results. Results must be produced quickly enough so that they can influence decision making in chemical synthesis. Most importantly, computational methods must be accurate enough to maintain the trust of the medicinal chemist. Without this trust, computational predictions will rarely be tested in the laboratory, which will then prevent the generation of critical data useful for improving the original predictions. 

COMPUTERS IN DEVELOPMENT DECISION MAKING, ECONOMICS, AND MARKET ANALYSIS... 

Within the pharmaceutical industry we have progressed from the point where computers in the laboratory were rarely present or used beyond spreadsheet calculations. Now computers are ubiquitous in pharmaceutical research and development laboratories, and nearly everyone has at least one used in some way to aid in his or her role. It should come as no surprise that the development of hardware and software over the last 30 years has expanded the scope of computer use to virtually all stages of pharmaceutical research and development (data analysis, data capture, monitoring and decision making). Although there are many excellent books published that are focused on in-depth discussions of computer-aided drug design, bioinformatics, or other related individual topics, none has addressed this broader utilization of... 

Horn, J. (1997) Multicriteria Decision Making. In Back, T., Fogel, D.B., Ichalewicz, Z. (eds) Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol, UK. 

In a hit triage decision making process that blends the use of experimental data with expected general property trends and principles, there are situations where it is not feasible to obtain sufficient data to identify experimentally property trends for ADME or safety endpoints (either due to a small number of hit compounds in a series, or due to limited experimental capacity). Computational models for these parameters may provide some useful information when integrated with other known information [101],... 

On the technical side, many different model building techniques are being explored and utilized. A fundamental constraint on the application of any model is the quality and availability of the data that it is built upon. In drug discovery, where the true data of interest (human in vivo parameters) are difficult to obtain and scarce, in vitro or preclinical in vivo experimental models are used to generate larger data sets and to guide decision-making. Most commonly, computational models are then used to predict these in vitro or preclinical endpoints. 

Furthermore, there can be identified two opposing trends in model development. One is a trend toward more detailed models with higher fidelity to the real system, driven by the availability of highly resolved environmental data, increases in computer power, and progress in atmospheric and earth sciences. The other trend is toward models that are tailor-made to specific scientific questions or decision-making problems, driven by the philosophy of parsimony and the increase in the need for scientific results as a basis for decision-making in modem society. 

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