Artificial intelligence has two roles in a decision support system (DSS). First, artificial intelligence can serve as a model type. Secondly, an application of artificial intelligence in a DSS can provide intelligent assistance to the users.
- How can designers, with the use of artificial intelligence, build into the DSS the expertise the decision maker lacks?
- Explain how to design and implement a system to address uncertainty in both information and relationships.
Outline your plan addressing these issues and other issues.
Sample Answer
Artificial intelligence (AI) can be used in decision support systems (DSS) in two ways:
- As a model type:Â AI can be used to create models that can be used to make decisions. For example, AI can be used to create models that predict customer behavior or that optimize production schedules.
- As intelligent assistance:Â AI can be used to provide intelligent assistance to users of DSS. For example, AI can be used to provide recommendations, to explain complex concepts, or to help users find information.
Full Answer Section
Designers can use AI to build into the DSS the expertise the decision maker lacks in two ways:
- By incorporating AI-generated models:Â AI-generated models can be used to provide decision makers with insights that they would not otherwise have. For example, an AI-generated model could be used to predict customer behavior, which could help a decision maker make better marketing decisions.
- By providing intelligent assistance:Â AI can be used to provide intelligent assistance to decision makers. For example, AI could be used to explain complex concepts or to help decision makers find information.
To design and implement a system to address uncertainty in both information and relationships, the following steps can be taken:
- Identify the sources of uncertainty:Â The first step is to identify the sources of uncertainty in the system. This includes uncertainty in the information, uncertainty in the relationships between the information, and uncertainty in the decision maker’s preferences.
- Quantify the uncertainty:Â Once the sources of uncertainty have been identified, they need to be quantified. This means assigning probabilities to the different possible outcomes.
- Develop a model:Â A model can then be developed that takes into account the uncertainty in the system. This model can be used to make decisions even in the face of uncertainty.
- Implement the model:Â The model can then be implemented in a DSS. The DSS can then be used to make decisions in the face of uncertainty.
In addition to the above steps, the following issues should also be addressed:
- Data quality:Â The quality of the data used in the DSS is critical. If the data is not accurate, then the results of the DSS will not be reliable.
- Model accuracy:Â The model used in the DSS should be accurate. If the model is not accurate, then the results of the DSS will not be reliable.
- User acceptance:Â The DSS should be user-friendly and easy to use. If the DSS is not user-friendly, then users will not use it.
By following these steps, a system can be designed and implemented that can address uncertainty in both information and relationships. This system can then be used to make decisions even in the face of uncertainty.
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