Four Types of Decisions (p.128 - p.130)

• Structured vs. Nonstructured (Examples?)

– Structured: Follow rules and criteria. The right answer exists. No “feel” or “intuition”.

– Nonstructured: No rules or criteria. Several “right” answers but there is no precise way to get the right answer.

• Recurring vs. Nonrecurring (Examples?)

– Recurring: repeatedly or periodically

– Nonrecurring: infrequently, perhaps only once

Chapter 4 Decision Support and Artificial Intelligence

Information Technologies that Help Decision Making

• Decision support systems (DSS)• Group decision support systems (GDSS)• Geographic information systems (GIS)• Artificial Intelligence (AI)

– Expert systems– Neural networks– Genetic algorithms– Artificial Intelligence

Decision Support System and Its Components

DSS is an interactive IT system that creates new information on demand to help you make nonstructural decisions. (p.131)

Components of DSS: (p.132 Fig. 4.5)

• User interface

• Model base

• Database

Decision Support Systems

Group Decision Support System (p.135)

A GDSS is a type of DSS that facilitates the formulation of and solution to problems by a team. It integrates (Fig. 4.7)

• Groupware (email, conferencing systems, collaborative authoring systems, coordination systems)

• DSS capability

• telecommunications

Group Decision Support Systems

IT Supports Team Decision-Making Process (p.136)

• Brainstorming - GDSS allows team members to enter comments and suggestions anonymously.

• Issue categorization and analysis - GDSS sorts and classifies the team’s ideas into folders.

• Ranking and voting - GDSS calculates and displays the outcome.

Group Decision Support Systems

Advantages of GDSS Enhanced Meeting (p.138 - p.140)

• GDSS introduces independent thought and anonymity that reduce bias by influential members, conflict between members and groupthink.

• Different locations

• Different-time meetings by using an electronic bulletin board, a central database, or email.

Group Decision Support Systems

What is a GIS? (p.140)

• A computer system that records, stores, and analyses information about the earth’s surface.

• GIS can generate two or three dimensional images of an area, showing natural and man-made features.

• GIS databases consist of sets of information called layers. Each layer represents a particular type of geographic data such as roads, utilities, population, elevation, and so on. The GIS can combine these layers into one image. (continued)

Geographic Information Systems

What is a GIS? (continued)

• GIS sensors can scan some of geographic data directly from a variety of sources. GIS convert all data into a digital code and store in database.

• GIS provides an easy means of trying various “what if scenarios”.

• An example of GIS:

Massachusetts Geographic Information System.

Geographic Information Systems

What is artificial intelligence?

AI is the science of making machines imitate human thinking and behavior. IT techniques and software can enable computers to mimic human behavior in various ways.

What are major categories of AI uses by businesses?• Expert systems• Neural networks• Genetic algorithms• Intelligent agents

Artificial Intelligence (p.143)

1. Expert Systems (p.144)

• What is an expert system? – applies rules and reasoning capability to reach a conclusion. It’s typically for a specific field.

• What are components of an expert system? (p.146)

• What is knowledge representation and production rules? (p.147 – domain expertise captured as rules

by the knowledge engineer)

• Reading: Joseph Schmuller, “Expert Systems: A Quick Tutorial,” Journal of IS Education.

• An example of expert system.

2. Neural Networks (p.150)

What is a neural network (e-book)?

• Neural network is an information-processing system patterned after the human brain (neurons and

synapses).

• Neural network is an artificial intelligence system which is capable of learning to differentiate patterns.

• Neural network is capable of adaptive learning. It can be trained (attributes and weights).

• Example: Good stocks (p.150-p.151)

2. Neural Networks

For more details, read

Introduction to neural network

For applications, read

Synergistic Market Analysis with Neural Network

3. Genetic Algorithms (p.151)

What is GA? John Holland, 1960’s

• GA is an algorithm that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. GA is based on an evolution of random tries, not on logic as regular optimal algorithms.

• GA borrowed ideas from biological evolution: only the combination of different genomes can lead to the optimal or better solution.

(Continued)

3. Genetic Algorithms

What is GA? (Continued)

• GA search for better solution through iterations. Each cycle includes: (p.152)

– Selection: select survivors with objective function.

– Crossover: combine portions of outcomes in the hope of creating an even better outcomes.

– Mutation: randomly trying combinations and evaluating the outcomes.

3. Genetic Algorithms

An example of GA: Maze Solver

• The targets: red dot.

• Genes: alternate green-blue dots.

• Bombs: light red dots.

• Randomize button: generate new maze pattern.

• The GA completes when either the target is encountered or maximum number of generation steps is reached.

4. Intelligent Agents (p.152)

What is Intelligent Agent?

• An intelligent agent is an artificial intelligent system which can act as personal assistant to perform repetitive tasks independently, adapting itself to your preference.

• Four types of intelligent agents– Find-and-retrieve agents– User agents (help an individual, work in the

background, e.g., check email)– Monitor and surveillance agents (monitor large

network for potential problems).– Data-mining agents (identify new relationships and

patterns, and alert you)