CHAPTER 4

Complexity of Decision Making

1

The Principle of Choice

2

What criteria to use? Best solution? Good enough solution?

Selection of a Principle of Choice

3

Not the choice phase

A decision regarding the acceptability of a solution approach

NormativeDescriptive

Normative Models

4

The chosen alternative is demonstrably the best of all (normally a good idea)

Optimization process

Normative decision theory based on rational decision makers

Rationality Assumptions

5

Humans are economic beings whose objective is to maximize the attainment of goals; that is, the decision maker is rational

In a given decision situation, all viable alternative courses of action and their consequences, or at least the probability and the values of the consequences, are known

Decision makers have an order or preference that enables them to rank the desirability of all consequences of the analysis

Suboptimization

6

Narrow the boundaries of a system

Consider a part of a complete system

Leads to (possibly very good, but) non-optimal solutions

Viable method

Descriptive Models

7

Describe things as they are, or as they are believed to be

Extremely useful in DSS for evaluating the consequences of decisions and scenarios

No guarantee a solution is optimal Often a solution will be good enough Simulation: Descriptive modeling technique

Descriptive Models

8

Information flowScenario analysisFinancial planningComplex inventory decisionsMarkov analysis (predictions)Environmental impact analysisSimulationWaiting line (queue) management

Satisficing (Good Enough)

9

Most human decision makers will settle for a good enough solution

Tradeoff: time and cost of searching for an optimum versus the value of obtaining one

Good enough or satisficing solution may meet a certain goal level is attained

(Simon, 1977)

Why Satisfice?Bounded Rationality (Simon)

10

Humans have a limited capacity for rational thinking Generally construct and analyze a simplified model Behavior to the simplified model may be rational But, the rational solution to the simplified model may

NOT BE rational in the real-world situation Rationality is bounded by

limitations on human processing capacitiesindividual differences

Bounded rationality: why many models are descriptive, not normative

Developing (Generating) Alternatives

11

In Optimization Models: Automatically by the Model!

Not Always So!

Issue: When to Stop?

Predicting the Outcome of Each Alternative

12

Must predict the future outcome of each proposed alternative

Consider what the decision maker knows (or believes) about the forecasted results

Classify Each Situation as UnderCertaintyRiskUncertainty

13

Decision Making Under Certainty

14

Assumes complete knowledge available (deterministic environment)

Example: U.S. Treasury bill investment

Typically for structured problems with short time horizons

Sometimes DSS approach is needed for certainty situations

Decision Making Under Risk (Risk Analysis)

15

Probabilistic or stochastic decision situation Must consider several possible outcomes for each

alternative, each with a probability Long-run probabilities of the occurrences of the given

outcomes are assumed known or estimated

Assess the (calculated) degree of risk associated with each alternative

Risk Analysis

16

Calculate the expected value of each alternative

Select the alternative with the best expected value

Example: poker game with some cards face up (7 card game - 2 down, 4 up, 1 down)

Decision Making Under Uncertainty

17

Several outcomes possible for each course of action BUT the decision maker does not know, or cannot

estimate the probability of occurrence

More difficult - insufficient information Assessing the decision maker's (and/or the

organizational) attitude toward risk Example: poker game with no cards face up (5 card stud

or draw)

Measuring Outcomes

18

Goal attainment Maximize profit Minimize cost Customer satisfaction level (minimize number of

complaints) Maximize quality or satisfaction ratings (surveys)

Scenarios

19

Useful in

Simulation What-if analysis

Importance of Scenarios in MSS

20

Help identify potential opportunities and/or problem areas

Provide flexibility in planning Identify leading edges of changes that management

should monitor Help validate major assumptions used in modeling Help check the sensitivity of proposed solutions to

changes in scenarios

Possible Scenarios

21

Worst possible (low demand, high cost) Best possible (high demand, high revenue, low cost) Most likely (median or average values) Many more

The scenario sets the stage for the analysis

22

The Choice Phase

23

The CRITICAL act - decision made here!

Search, evaluation, and recommending an appropriate solution to the model

Specific set of values for the decision variables in a selected alternative

The problem is considered solved only after the recommended solution to the model is successfully implemented

Search Approaches

24

Analytical Techniques

Algorithms (Optimization)

Blind and Heuristic Search Techniques

25

26

27

Evaluation: Multiple Goals, Sensitivity Analysis, What-If,

and Goal Seeking

28

Evaluation (with the search process) leads to a recommended solution

Multiple goals Complex systems have multiple goals

Some may conflict

Typically, quantitative models have a single goal

Can transform a multiple-goal problem into a single-goal problem

Common Methods

29

Utility theory Goal programming Expression of goals as constraints, using linear

programming Point system

Computerized models can support multiple goal decision making

Sensitivity Analysis

30

Change inputs or parameters, look at model results

Sensitivity analysis checks relationships

Types of Sensitivity Analyses

Automatic Trial and error

Trial and Error

31

Change input data and re-solve the problem

Better and better solutions can be discovered

How to do? Easy in spreadsheets (Excel)What-ifGoal seeking

Goal Seeking

32

Backward solution approach Example: Figure 2.10

What interest rate causes an the net present value of an investment to break even?

In a DSS the what-if and the goal-seeking options must be easy to perform

Goal Seeking

33

The Implementation Phase

34

There is nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things

(Machiavelli, 1500s)*** The Introduction of a Change ***

Important Issues Resistance to change Degree of top management support Users’ roles and involvement in system development Users’ training

How Decisions Are Supported

35

Specific MSS technologies relationship to the decision making process (see Figure 2.11)

Intelligence: DSS, ES, ANN, MIS, Data Mining, OLAP, EIS, GSS

Design and Choice: DSS, ES, GSS, Management Science, ANN

Implementation: DSS, ES, GSS

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Alternative Decision Making Models

37

Paterson decision-making process Kotter’s process model Pound’s flow chart of managerial behavior Kepner-Tregoe rational decision-making approach Hammond, Kenney, and Raiffa smart choice method Cougar’s creative problem solving concept and model Pokras problem-solving methodology Bazerman’s anatomy of a decision Harrison’s interdisciplinary approaches Beach’s naturalistic decision theories

Naturalistic Decision Theories

38

Focus on how decisions are made, not how they should be made

Based on behavioral decision theory

Recognition models Narrative-based models

Recognition Models

39

Policy Recognition-primed decision model

Narrative-based Models (Descriptive)

Scenario model Story model Argument-driven action (ADA) model Incremental models Image theory

Other Important Decision- Making Issues

40

Personality types Gender Human cognition Decision styles

Personality (Temperament) Types

41

Strong relationship between personality and decision making

Type helps explain how to best attack a problem

Type indicates how to relate to other typesimportant for team building

Influences cognitive style and decision style http://www.humanmetrics.com/cgi-win/JTypes2.asp

Myers-Briggs Dimensions

42

Extraversion (E) to Intraversion (I) Sensation (S) to Intuition (N) Thinking (T) to Feeling (F) Perceiving (P) to Judging (J)

http://www.mypersonality.info/personality-types/

43

Gender

44

Sometimes empirical testing indicates gender differences in decision making

Results are overwhelmingly inconclusive

http://stumblingandmumbling.typepad.com/stumbling_and_mumbling/2009/08/gender-decisionmaking.html

http://www.ijpsy.com/volumen7/num3/176/factors-that-affect-decision-making-gender-EN.pdf

Cognition

45

Cognition: Activities by which an individual resolves differences between an internalized view of the environment and what actually exists in that same environment

Ability to perceive and understand information

Cognitive models are attempts to explain or understand various human cognitive processes

Cognitive Style

46

The subjective process through which individuals perceive, organize, and change information during the decision-making process

Often determines people's preference for human-machine interface

Impacts on preferences for qualitative versus quantitative analysis and preferences for decision-making aids

Affects the way a decision maker frames a problem

Cognitive Style Research

47

Impacts on the design of management information systems May be overemphasized

Analytic decision maker Heuristic decision maker

Decision Styles

48

The manner in which decision makers Think and react to problems Perceive their

Cognitive response Values and beliefs

Varies from individual to individual and from situation to situation

Decision making is a nonlinear process

The manner in which managers make decisions (and the way they interact with other people) describes their decision style

There are dozens

Some Decision Styles

49

Heuristic Analytic Autocratic Democratic Consultative (with individuals or groups) Combinations and variations

For successful decision-making support, an MSS must fit the Decision situation Decision style

50

should be flexible and adaptable to different usershave what-if and goal seeking have graphics have process flexibility

An MSS should help decision makers use and develop their own styles, skills, and knowledge

Different decision styles require different types of support

Major factor: individual or group decision maker

The system

51

Summary

52

Personality (temperament) influences decision making

Gender impacts on decision making are inconclusive

Human cognitive styles may influence human-machine interaction

Human decision styles need to be recognized in designing MSS