Rule-based decision making

Phase IV Rule-Based Decision-Making System... 

Rule-based decision making is well supported by training and job aids for complex rules (rules 2 and 3) or for lengthy lists of rules of type 1. Errors can be due to invoking the wrong rule. 

With rule-based decision making, the clinician, pilot or operator has to identify the situation they are facing, but is then able to identify a learned set of rules or procedures that should be adopted. Pilots, for instance, are trained in a wide variety of both routine and emergency procedures that, once initiated, follow a standard pattern. Pilots of commercial airliners are expected to have memorized all standard emergency procedures, for which they will have rehearsed mentally and trained in practice in simulators where time allows, they are also expected to actually check the flight manual to ensure they follow the procedure to the letter. 

From my discussion of what these criteria mean for technology, I suggest the following as principles on which more concrete policies to inform decisions about technology could be based. These are not rules for decision making, but indicators of what should be considered in deciding whether a technology helps to make the world a fit home for human life on earth. 

Tactical In tactical decisions, the human will begin one of several tasks based on the branch with the highest value. This could be used to model the many types of rule-based decisions that humans make, as illustrated in Figure 7. 

Alarm systems shonld be developed to support both rule-based and knowledge-based behavior. During rnle-based decision making, operators shonld have the ability to short-cut the response process to allow them to act npon previous experience and mental models. To snpport the operator in knowledge-based behavior, the system shonld provide the operator with suggested causes for the situation, as well as information on higher order effects of any action taken to retnm the system to a desired state. 

One variation of rule-based systems are fuzzy logic systems. These programs use statistical decision-making processes in which they can account for the fact that a specific piece of data has a certain chance of indicating a particular result. All these probabilities are combined in order predict a final answer. 

The Stroke-Thrombolytic Predictive Instrument (Stroke-TPI) has recently been developed in order to provide patient-specific estimates of the probability of a more favorable outcome with rt-PA, and has been proposed as a decision-making aid to patient selection for rt-PA." The estimates from this tool should, however, be treated with caution. The prediction rule is dependent on post hoc mathematical modeling, uses clinical trial data from subjects randomized beyond 3 hours who are not rt-PA-eligible according to FDA labeling and current best practice, and has not been externally validated. It is, therefore, not appropriate to exclude patients from rt-PA treatment based solely on Stroke-TPI predictions. 

The value chain strategy process focuses on long-term strategic business design and business rule decisions. Decisions are based on yearly and quarterly buckets with a horizon of multiple years. This process is conducted or updated yearly with a new or an updated strategy in case of very dynamic markets, a review could also be quarterly. In decision making a... 

An expert system has been written which helps the agricultural chemist develop formulations for new biologically active chemicals. The decision making process is segmented into two parts. The first is which type of formulation to use. The second is how to make a formulation of that tyrpe with the chemical of interest. The knowledge base currently contains rules to determine which formulation type to try and how to make an emulsifiable concentrate. The next phase will add rules on how to make other types of formulations. The program also interfaces to several FORTRAN programs which perform calculations such as solubilities. 

Expert systems are computer programs that simulate the decision-making process of human experts. The hallmark of expert systems is that decisions are based on heuristics (rules of thumb) when data is incomplete or there is not enough time to consider all possibilities [19]. An expert system can be a set of IF-THEN rules in FORTRAN, or it can be a written in one of the languages designed for expert systems, such as LISP. Expert systems can be used off-line to aid in cure cycle selection, or they can be used as real-time advisers or controllers. 

The proposed systems framework for EU decision-making under REACH seeks to counterbalance the weaknesses and to draw on the strengths of the national approaches while addressing the points shown in Table 7.1. Specifically, the framework would fuse hazard, technical and risk-benefit approaches to risk management (Table 7.2). Compared with the current process of chemical legislation and the recent REACH proposal, the systems framework would avoid a linear substance-by-substance approach by applying a set of decision-making rules based on hazard and use to all chemicals (Section 5.3.1). 

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