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Decision Models and Intelligent Systems

Decision Models and Intelligent Systems. Introduction to Managerial Support Systems. Learning Objectives. Describe managerial roles and understand why they require computerized support for decision making

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Decision Models and Intelligent Systems

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  1. Decision Models andIntelligent Systems Introduction to Managerial Support Systems

  2. Learning Objectives • Describe managerial roles and understand why they require computerized support for decision making • Define a decision support system (DSS) and the types of problems with which they are associated • Describe decision models and the benefits of computer supported decision making and experimentation • Describe artificial intelligence (AI) • Identify capabilities for natural (human) intelligence versus artificial intelligence • Define an expert system and identify its components • Discuss intelligent system examples that illustrate various forms of problem representation and reasoning

  3. Managers and Decision Making • Managers have three basic roles: • Interpersonal roles – figurehead, leader, liaison • Informational roles – monitor, disseminator, spokesperson • Decisional roles – disturbance handler, resource allocator, negotiator • Early information systems primarily supported the informational roles • In this discussion we focus on more recent developments where IT supports decisional roles

  4. Why Managers Need IT Support • It is difficult to make good decisions without valid and relevant information • Despite widespread information availability, making decisions is becoming increasingly difficult due to the following trends: • Number of alternatives is increasing • Time pressure • Increased uncertainty • Need to rapidly access remote information, consult with experts, or conduct a group decision-making session • Different IT solutions are needed depending on the problem structure and the nature of decision

  5. Decision Support Systems • Decision support systems (DSS) combine models and data in an attempt to solve semi-structured and some unstructured problems with extensive user involvement • Models are simplified representations, or abstractions, of reality

  6. Decision Model Examples • Models are representations of problems • Some examples include: • Mathematical (quantitative) models • Geographic information systems (GIS) • A GIS is a computer-based system for capturing, integrating, manipulating, and displaying data using digitized maps • Its most distinguishing characteristic is that every record or digital object has an identified geographical location • Virtual reality (VR) • The most common definitions usually describe VR as interactive, computer-generated, three-dimensional graphics delivered to the user through a head-mounted display • In VR, a person “believes” that what they are doing is real even though it is artificially created

  7. Benefits of Computer Supported Decision Systems • Cost of virtual experimentation is lower • Compresses time • Manipulations are easier • Cost of mistakes is lower • Can evaluate risk and uncertainty • Can compare a large number of alternatives • Can be used for training

  8. Intelligent Systems • Intelligent systems is a term that best describes the various commercial applications of artificial intelligence • Artificial intelligence (AI) is a subfield of computer science that is concerned with studying the thought processes of humans and re-creating the effects of those processes via machines, such as computers and robots • AI’s ultimate goal is to build machines that will mimic human intelligence • An interesting test to determine whether a computer exhibits intelligent behavior was designed by Alan Turing (the Turing test)

  9. Comparison of the Capabilities of Natural versus Artificial Intelligence

  10. Expert Systems • When an organization has a complex decision to make or problem to solve, it often turns to experts for advice • Expert systems (ESs) are computer systems that attempt to mimic human experts by applying expertise in a specific domain • The transfer of expertise from an expert to a computer and then to the user involves four activities: • Knowledge acquisition • Knowledge representation • Knowledge inferencing • Knowledge transfer

  11. Expert System Structure

  12. Question? • What makes a system “intelligent”?

  13. Answer • Intelligent systems include one, or more, of the following capabilities: • Reasoning • Deductive • Inductive • Analogical • Rationality • Efficient search for answers • Learning • Incorporate knowledge learned from past experience to improve decision making over time

  14. Intelligent System Examples • Rule-based expert systems • Machine (concept) learning • Case-based reasoning • Natural language processing (NLP) • Decision trees • Other applications?

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