Rule-Based Decision Tools

What are they?

Rule-based decision tools are decision making models that help novices to make decisions like an expert.

It is generally accepted that having the relevant information is one of the factors that allows us to make more effective decisions. Having some experience in the area is another factor. Sometimes, however, relevant information may not be available for a variety of reasons. And there is no substitute for experience. Rule-based decision tools are used in these situations to overcome these limitations.

Experts make better decisions than beginners. One reason for this is that they have better pattern recognition in their area of expertise than the beginner. They utilize the available information in different ways because of the experiences they have had.

The idea behind rule-based decision tools is to take the knowledge available in literature and the skills that exists only in the heads of a few experts and make it available to others. Hence they are called expert systems or knowledge-based systems. This knowledge is then applied to the information in a particular situation so as to allow others to make more effective choices, and even how to make faster decisions.


The design

Initially, all potential decisions or outcomes that could reasonably occur in a situation are established. Then questions are set out about specific circumstances or conditions that may or may not be present. Finally, a set of rules is established with 'if this - then that' scenarios. The whole thing can then be mapped out in written form or as a decision tree.

The decision maker simply gathers information from his own experience and enters it to reach a rational and informed decision. It would also be consistent because many people putting in the same information would arrive at the same decision.


The pros

The advantages of rule-based decision tools include:

  • They are very structured
  • Allow people to learn and make decisions 'like the experts'.
  • No special skills required so are easy to use
  • Because it is documented, it's easy to keep records and this allows for accountability.


And the cons...

Difficulties include:

  • Reactive: It can be boring and repetitive filling in data with little real learning occurring
  • Inflexibility: As expertise develops, user may 'outgrow' the tool
  • Limiting: Only prescribed decisions are allowed
  • They are only as useful as the overall design and the questions asked.
  • They do not include the human being.


Where are they used?

Rule-based decision tools can be used for quantitative as well as qualitative decisions.

Quantitative examples include moneylenders and insurance companies. The rule-based decision tools allow them to quickly give you a quote over the phone after filling in your particular details, for example.

Qualitative situations in which knowledge-based systems have proved useful include biological systems such as managing lake systems, prescribed burning of forests, rotating livestock herds around fields and avalanche prediction.

Read more about decision making models and decision support systems

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