1. Foundations and technologies for decision making
Chapter 2
Foundations and technologies for decision making

2. Learning Objectives
Understand the conceptual foundations of decision making
Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation
Recognize the concepts of rationality and bounded rationality, and how they relate to decision making

3. Learning Objectives
Differentiate between the concepts of making a choice and establishing a principle of choice
Learn how DSS support for decision making can be provided in practice
Understand the systems approach

4. Decision Making: Introduction and Definitions
Characteristics of decision making
Groupthink
Decision makers are interested in evaluating what-if scenarios
Experimentation with the real system may result in failure (develop a schedule and try it!)
Experimentation with the real system is possible only for one set of conditions at a time and can be disastrous
Changes in the decision making environment may occur continuously, leading to invalidating assumptions about the situation (delivery around holidays!)

5. Decision Making: Introduction and Definitions
Characteristics of decision making
Changes in the decision making environment may affect decision quality by imposing time pressure on the decision maker
Collecting information and analyzing a problem takes time and can be expensive. It is difficult to determine when to stop and make a decision
There may not be sufficient information to make an intelligent decision, Or
Information overload

6. Plan of the chapter Dissecting DSS into its main concepts 
Building successful DSS requires a thorough understanding of these concepts

7. Decision Making: Introduction and Definitions
The action of selecting among alternatives
A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)

8. Decision making VS. Problem solving!
Phases of the decision process
Intelligence
Design
Choice
Problem solving
A process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal. It includes the 4th phase of the decision process
Implementation

9. Decision-Making Disciplines
Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology
Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics
Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions

10. Decision Making: Introduction and Definitions
Decision making disciplines
Behavioral
Scientific
Successful decision
Effectiveness
The degree of goal attainment. Doing the right things
Efficiency
The ratio of output to input. Appropriate use of resources. Doing the things right

11. Decision-Making Disciplines
Better decisions
Tradeoff: accuracy versus speed
Fast decision may be detrimental
Many areas suffer from fast decisions
Effectiveness versus Efficiency
Effectiveness  “goodness” “accuracy”
Efficiency  “speed” “less resources”
A fine balance is what is needed!

12. Decision Making: Introduction and Definitions
Decision style and decision makers
Decision style
The manner in which a decision maker thinks and reacts to problems. It includes
- perceptions,
- cognitive responses,
- values, and
- beliefs

13. Decision Style
Personality temperament tests are often used to determine decision styles
There are many such tests
Meyers/Briggs,
True Colors (Birkman),
Keirsey Temperament Theory, …
Various tests measure somewhat different aspects of personality
They cannot be equated!

14. Decision Style Decision-making styles
Heuristic versus Analytic
Autocratic versus Democratic
Consultative (with individuals or groups)
A successful computerized system should fit the decision style and the decision situation
Should be flexible and adaptable to different users (individuals vs. groups)

15. Decision Makers Small organizations Medium-to-large organizations
Individuals
Conflicting objectives
Medium-to-large organizations
Groups
Different styles, backgrounds, expectations
Consensus is often difficult to reach
Help: Computer support, GSS, …

16. Phases of Decision-Making Process
Humans consciously or subconsciously follow a systematic decision-making process Simon (1977)
Intelligence
Design
Choice
Implementation
(?) Monitoring (a part of intelligence?)

17. Phases of the Decision-Making Process

18. Phases of the Decision-Making Process
Intelligence phase
The initial phase of problem definition in decision making
Design phase
The second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions

19. Phases of the Decision-Making Process
Choice phase
The third phase in decision making, in which an alternative is selected
Implementation phase
The fourth decision-making phase, involving actually putting a recommended solution to work

20. Decision Making: Intelligence Phase
Scan the environment, either intermittently or continuously
Identify problem situations or opportunities
Monitor the results of the implementation
Problem is the difference between what people desire (or expect) and what is actually occurring
Symptom versus Problem
Timely identification of opportunities is as important as identification of problems

21. Decision Making: The Intelligence Phase
Problem (or opportunity) identification: some issues that may arise during data collection
Data are not available
Obtaining data may be expensive
Data may not be accurate or precise enough
Data estimation is often subjective
Data may be insecure
Important data that influence the results may be qualitative
Data change over time (time-dependence)

22. Application Case 2.1
Making Elevators Go Faster!

23. Making Elevators Go Faster!
Application Case 2.1
Making Elevators Go Faster!
Background
Problem description
Proposed solution
Results

24. British bombers armor

25. Decision Making: Intelligence Phase
Problem Classification
Classification of problems according to the degree of structuredness
Problem Decomposition
Often solving the simpler subproblems may help in solving a complex problem.
Information/data can improve the structuredness of a problem situation
Problem Ownership
Outcome of intelligence phase 
A Formal Problem
Statement

26. Web and the Decision-Making Process

27. Decision Making: The Design Phase
The design phase involves finding or developing and analyzing possible courses of action
Understanding the problem
Testing solutions for feasibility
A model of the decision-making problem is constructed, tested, and validated

28. Decision Making: The Design Phase
Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative form
Models have:
Decision variables
Principle of choice

29. Decision Making: The Design Phase
Decision variables
A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, and so on
Principle of choice
The criterion for making a choice among alternatives

30. Decision Making: The Design Phase
Selection of a Principle of Choice
It is a criterion that describes the acceptability of a solution approach
Reflection of decision-making objective(s)
In a model, it is the result variable
Choosing and validating against
High-risk versus low-risk
Optimize versus satisfice
Criterion is not a constraint!

31. Decision Making: The Design Phase
Normative models (= optimization)
the chosen alternative is demonstrably the best of all possible alternatives
Assumptions of rational decision makers
Humans are economic beings whose objective is to maximize the attainment of goals
For a decision-making situation, all alternative courses of action and consequences are known
Decision makers have an order or preference that enables them to rank the desirability of all consequences

32. Decision Making: The Design Phase
Heuristic models (= suboptimization)
The chosen alternative is the best of only a subset of possible alternatives
Often, it is not feasible to optimize realistic (size/complexity) problems
Suboptimization may also help relax unrealistic assumptions in models
Help reach a good enough solution faster

33. Decision Making: The Design Phase
Descriptive models
Describe things as they are or as they are believed to be (mathematically based)
They do not provide a solution but information that may lead to a solution
Simulation - most common descriptive modeling method (mathematical depiction of systems in a computer environment)
Allows experimentation with the descriptive model of a system

34. Decision Making: The Design Phase
Good Enough, or Satisficing
“something less than the best”
A form of suboptimization
Seeking to achieve a desired level of performance as opposed to the “best”
Benefit: time saving
Simon’s idea of bounded rationality

35. Decision Making: The Design Phase
Good enough or satisficing
Reasons for satisficing:
Time pressures
Ability to achieve optimization
Recognition that the marginal benefit of a better solution is not worth the marginal cost to obtain it

36. Decision Making: The Design Phase
Developing (generating) alternatives
In optimization models the alternatives may be generated automatically by the model
In most MSS situations it is necessary to generate alternatives manually (a lengthy, costly process); issues such as when to stop generating alternatives are very important
The search for alternatives usually occurs after the criteria for evaluating the alternatives are determined
The outcome of every proposed alternative must be established

37. Decision Making: The Design Phase
Measuring outcomes
The value of an alternative is evaluated in terms of goal attainment
Risk
One important task of a decision maker is to attribute a level of risk to the outcome associated with each potential alternative being considered

38. Decision Making: The Design Phase
Scenario
A statement of assumptions about the operating environment of a particular system at a given time; a narrative description of the decision-situation setting
Scenarios are especially helpful in simulations and what-if analyses

39. Decision Making: The Design Phase
Scenarios play an important role in MSS because they:
Help identify opportunities and problem areas
Provide flexibility in planning
Identify the leading edges of changes that management should monitor
Help validate major modeling assumptions
Allow the decision maker to explore the behavior of a system through a model
Help to check the sensitivity of proposed solutions to changes in the environment

40. Decision Making: The Design Phase
Possible scenarios
The worst possible scenario
The best possible scenario
The most likely scenario
The average scenario

41. Decision Making: The Design Phase
Errors in decision making
The model is a critical component in the decision-making process
A decision maker may make a number of errors in its development and use
Validating the model before it is used is critical
Gathering the right amount of information, with the right level of precision and accuracy is also critical

42. Decision Making: The Choice Phase
The actual decision and the commitment to follow a certain course of action are made here
The boundary between the design and choice is often unclear (partially overlapping phases)
Generate alternatives while performing evaluations
Includes the search, evaluation, and recommendation of an appropriate solution to the model
Solving the model versus solving the problem!

43. Decision Making: The Choice Phase
Search approaches
Analytic techniques (solving with a formula)
Algorithms (step-by-step procedures)
Heuristics (rule of thumb)
Blind search (truly random search)
Additional activities
Sensitivity analysis
What-if analysis
Goal seeking

44. Decision Making: The Choice Phase
Analytical techniques
Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems
Algorithm
A step-by-step search in which improvement is made at every step until the best solution is found

45. Decision Making: The Choice Phase
Heuristics
Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth

46. Decision Making: The Choice Phase
Sensitivity analysis
A study of the effect of a change in one or more input variables on a proposed solution
What-if analysis
A process that involves asking a computer what the effect of changing some of the input data or parameters would be

47. Decision Making: The Implementation Phase
“Nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things.”
- The Prince, Machiavelli 1500s
Solution to a problem  Change
Change management ?..
Implementation: putting a recommended solution to work

48. How Decisions are Supported

49. How Decisions are Supported
Support for the Intelligence Phase
Enabling continuous scanning of external and internal information sources to identify problems and/or opportunities
Resources/technologies: Web; ES, OLAP, data warehousing, data/text/Web mining, EIS/Dashboards, KMS, GSS, GIS,…
Business activity monitoring (BAM)
Business process management (BPM)
Product life-cycle management (PLM)

50. How Decisions are Supported
Support for the Design Phase
Enabling generating alternative courses of action, determining the criteria for choice
Generating alternatives
Structured/simple problems: standard and/or special models
Unstructured/complex problems: human experts, ES, KMS, brainstorming/GSS, OLAP, data/text mining
A good “criteria for choice” is critical!

51. How Decisions are Supported
Support for the Choice Phase
Enabling selection of the best alternative given a complex constraint structure
Use sensitivity analyses, what-if analyses, goal seeking
Resources
KMS
CRM, ERP, and SCM
Simulation and other descriptive models

52. How Decisions are Supported
Support for the Implementation Phase
Enabling implementation/deployment of the selected solution to the system
Decision communication, explanation and justification to reduce resistance to change
Resources
Corporate portals, Web 2.0/Wikis
Brainstorming/GSS
KMS, ES

53. DSS Capabilities
DSS early definition: it is a system intended to support managerial decisions in semistructured and unstructured decision situations
DSS were meant to be adjuncts to decision makers  extending their capabilities
They are computer based and would operate interactively online, and preferably would have graphical output capabilities
Nowadays, simplified via Web browsers and mobile devices

54. DSS Capabilities

55. DSS Classifications
Association for Information Systems Special Interest Group on Decision Support Systems (AIS SIGDSS) Classification
Communication-driven and group DSS
Data-driven DSS
Document-driven DSS
Knowledge-driven DSS
Model-driven DSS
Often DSS is a hybrid of many classes

56. DSS Classifications Other DSS Categories Institutional and ad-hoc DSS
Custom-made systems versus ready-made systems
Personal, group, and organizational support
Individual support system versus group support system (GSS)…

57. Components of DSS

58. Components of DSS Data Management Subsystem Model Management Subsystem
Includes the database that contains the data
Database management system (DBMS)
Can be connected to a data warehouse
Model Management Subsystem
Model base management system (MBMS)
User Interface Subsystem
Knowledgebase Management Subsystem
Organizational knowledge base

59. Components of DSS Database management system (DBMS)
Software for establishing, updating, and querying (e.g., managing) a database
Data warehouse
A physical repository where relational data are organized to provide clean, enterprise-wide data in a standardized format
Database
The organizing of files into related units that are then viewed as a single storage concept. The data in the database are generally made available to a wide range of users

60. Components of DSS Model management subsystem User interface
Model base management system (MBMS)
Software for establishing, updating, combining, and so on (e.g., managing) a DSS model base
User interface
The component of a computer system that allows bidirectional communication between the system and its user

61. Components of DSS Knowledge-based management subsystem
The knowledge-based management subsystem can support any of the other subsystems or act as an independent component
Organizational knowledge base
An organization’s knowledge repository

62. DSS Components: Data Management Subsystem
DSS database
DBMS
Data directory
Query facility

63. Data Management Subsystem
The Database
Internal data come mainly from the organization’s transaction processing system
External data include industry data, market research data, census data, regional employment data, government regulations, tax rate schedules, and national economic data
Private data can include guidelines used by specific decision makers and assessments of specific data and/or situations

64. Data Management Subsystem
Data organization
Data extraction
The process of capturing data from several sources, synthesizing them, summarizing them, determining which of them are relevant, and organizing them, resulting in their effective integration

65. Data Management Subsystem
Database management system (DBMS)
Software for establishing, updating, and querying (e.g., managing) a database
Query Facility
The (database) mechanism that accepts requests for data, accesses them, manipulates them, and queries them
Directory
A catalog of all the data in a database or all the models in a model base

66. Data Management Subsystem
Key database and database management system issues
Data quality
Data integration
Scalability
Data security

67. DSS Components: Model Management Subsystem
Model base
MBMS
Modeling language
Model directory
Model execution, integration, and command processor

68. The Model Management Subsystem
Model base
A collection of preprogrammed quantitative models (e.g., statistical, financial, optimization) organized as a single unit

69. The Model Management Subsystem
Four categories of models with the model base
Strategic models
Tactical models
Operational models
Analytical models

70. The Model Management Subsystem
Strategic models
Models that represent problems for the strategic level (i.e., executive level) of management
Tactical models
Models that represent problems for the tactical level (i.e., midlevel) of management

71. The Model Management Subsystem
Operational models
Models that represent problems for the operational level of management
Analytical models
Mathematical models into which data are loaded for analysis

72. The Model Management Subsystem
Model building blocks and routines
Model building blocks
Preprogrammed software elements that can be used to build computerized models. For example, a random-number generator can be employed in the construction of a simulation model
Model components for building DSS
Modeling tools

73. The Model Management Subsystem
Model base management system: MBMS software has four main functions
Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks
Generation of new routines and reports
Model updating and changing
Model data manipulation

74. The Model Management Subsystem
Model directory
Model execution is the process of controlling the actual running of the model
Model integration involves combining the operations of several models when needed
A model command processor is used to accept and interpret modeling instructions from the user interface component and route them to the MBMS, model execution, or integration functions

75. DSS Components: User Interface Subsystem
Application interface
User Interface (GUI?)
DSS User Interface
Portal
Graphical icons
Dashboard
Color coding
Interfacing with PDAs, cell phones, etc.
See Technology Insight 2.2 for next gen devices

76. User Interface (Dialog) Subsystem
The component of a computer system that allows bidirectional communication between the system and its user.
User interface management system (UIMS)
The DSS component that handles all interaction between users and the system

77. User Interface (Dialog) Subsystem
The user interface process
Object
A person, place, or thing about which information is collected, processed, or stored
Graphical user interface (GUI)
An interactive, user-friendly interface in which, by using icons and similar objects, the user can control communication with a computer

78. User Interface (Dialog) Subsystem
DSS user interfaces access is provided through Web browsers including:
Voice input and output
Portable devices
Direct sensing devices

79. End of the Chapter
Questions, comments

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