CHAPTER 2

Decision Making, Systems, Decision Making, Systems, Modeling, and SupportModeling, and Support

Decision Making, Systems, Modeling, and Support

Conceptual Foundations of Decision Making Conceptual Foundations of Decision Making The Systems ApproachThe Systems Approach How Support is ProvidedHow Support is Provided

Opening Vignette:Opening Vignette:

How to Invest $10,000,000How to Invest $10,000,000

Typical Business Decision Aspects

Decision may be made by a Decision may be made by a groupgroup Group member biasesGroup member biases GroupthinkGroupthink Several, possibly Several, possibly contradictory objectivescontradictory objectives Many Many alternativesalternatives Results can occur in the Results can occur in the futurefuture Attitudes towards Attitudes towards riskrisk Need informationNeed information Gathering information takes time and expenseGathering information takes time and expense Too much informationToo much information ““What-ifWhat-if” scenarios” scenarios Trial-and-errorTrial-and-error experimentation with the real system may result in experimentation with the real system may result in

a lossa loss ExperimentationExperimentation with the real system - only with the real system - only onceonce ChangesChanges in the environment can occur continuously in the environment can occur continuously Time pressureTime pressure

How are decisions made???How are decisions made???

What methodologies can be applied?What methodologies can be applied?

What is the role of information systems in supporting What is the role of information systems in supporting decision making?decision making?

DSSDSS DecisionDecision SupportSupport SystemsSystems

Decision Making

Decision MakingDecision Making: a : a processprocess of choosing among of choosing among alternative courses of action for the purpose of alternative courses of action for the purpose of attaining a goal or goalsattaining a goal or goals

Managerial Decision MakingManagerial Decision Making is synonymous with is synonymous with the whole process of managementthe whole process of management (Simon, 1977) (Simon, 1977)

Decision Making versus Problem Solving

Simon’s Simon’s 44 Phases of Decision Making Phases of Decision Making

11. . IntelligenceIntelligence22. . DesignDesign33. . ChoiceChoice44. . ImplementationImplementation

Decision makingDecision making and and problem solvingproblem solvingare interchangeableare interchangeable

Systems

A A SYSTEMSYSTEM is a collection of objects such as is a collection of objects such as people, resources, concepts, and procedures people, resources, concepts, and procedures intended to perform an identifiable function or to intended to perform an identifiable function or to serve a goalserve a goal

System Levels (Hierarchy)System Levels (Hierarchy): All systems are : All systems are subsystems subsystems interconnected through interconnected through interfacesinterfaces

The Structure of a System

Three Distinct Parts of Systems (Figure 2.1)Three Distinct Parts of Systems (Figure 2.1) InputsInputs ProcessesProcesses OutputsOutputs

SystemsSystems Surrounded by an environment Surrounded by an environment Frequently include feedbackFrequently include feedback

The The decision makerdecision maker is usually considered part of the is usually considered part of the systemsystem

InputsInputs are elements that enter the system are elements that enter the system

ProcessesProcesses convert or transform inputs into outputs convert or transform inputs into outputs

OutputsOutputs describe finished products or consequences of being in the describe finished products or consequences of being in the systemsystem

FeedbackFeedback is the flow of information from the output to the decision is the flow of information from the output to the decision maker, who may modify the inputs or the processes (closed loop)maker, who may modify the inputs or the processes (closed loop)

The The EnvironmentEnvironment contains the elements that lie outside but impact contains the elements that lie outside but impact the system's performance the system's performance

How to Identify the Environment?

Two Questions (Churchman, 1975)Two Questions (Churchman, 1975)

11. . Does the element matter relative to the system's goals? Does the element matter relative to the system's goals? [YES][YES]

22. . Is it possible for the decision maker to significantly Is it possible for the decision maker to significantly manipulate this element? [NO]manipulate this element? [NO]

Environmental Elements Can Be

SocialSocial PoliticalPolitical LegalLegal PhysicalPhysical EconomicalEconomical Often Other SystemsOften Other Systems

The Boundary Separates a System From Its Environment

Boundaries may be physical or nonphysical (by definition Boundaries may be physical or nonphysical (by definition of scope or time frame)of scope or time frame)

Information system boundaries are usually by definition!Information system boundaries are usually by definition!

Closed and Open Systems

Defining manageable boundaries is Defining manageable boundaries is closingclosing the system the system

A A Closed SystemClosed System is totally independent of other systems is totally independent of other systems and subsystemsand subsystems

An An Open SystemOpen System is very dependent on its environment is very dependent on its environment

An Information System

Collects, processes, stores, analyzes, and Collects, processes, stores, analyzes, and disseminates information for a specific purposedisseminates information for a specific purpose

Is often at the Is often at the heartheart of many organizations of many organizations

Accepts inputs and processes data to provide Accepts inputs and processes data to provide information to decision makers and helps decision information to decision makers and helps decision makers communicate their resultsmakers communicate their results

System Effectiveness and Efficiency

Two Major Classes of Performance MeasurementTwo Major Classes of Performance Measurement

EffectivenessEffectiveness is the degree to which goals are achieved is the degree to which goals are achievedDoing the right thing!Doing the right thing!

EfficiencyEfficiency is a measure of the use of inputs (or resources) is a measure of the use of inputs (or resources) to achieve outputsto achieve outputsDoing the thing right!Doing the thing right!

MSS emphasize MSS emphasize effectivenesseffectivenessOften: several non-quantifiable, conflicting goalsOften: several non-quantifiable, conflicting goals

Models

Major component of DSSMajor component of DSS Use models instead of experimenting on the real Use models instead of experimenting on the real

systemsystem

A A modelmodel is a simplified representation or is a simplified representation or abstraction of reality. abstraction of reality.

Reality is generally too complex to copy exactly Reality is generally too complex to copy exactly Much of the complexity is actually Much of the complexity is actually irrelevantirrelevant in in

problem solvingproblem solving

Degrees of Model Abstraction

(Least to Most)(Least to Most)

Iconic (Scale) ModelIconic (Scale) Model: Physical replica of a system: Physical replica of a system

Analog ModelAnalog Model behaves like the real system behaves like the real system butbut does does notnot look like it (symbolic representation) look like it (symbolic representation)

Mathematical (Quantitative) ModelsMathematical (Quantitative) Models use use mathematical relationships to represent complexitymathematical relationships to represent complexityUsed in most DSS analysesUsed in most DSS analyses

Benefits of Models

11. . Time compressionTime compression22. . Easy model manipulationEasy model manipulation33. . Low cost of constructionLow cost of construction44. . Low cost of execution (especially that of errors)Low cost of execution (especially that of errors)55. . Can model risk and uncertaintyCan model risk and uncertainty66. . Can model large and extremely complex systems with Can model large and extremely complex systems with

possibly infinite solutionspossibly infinite solutions77. . Enhance and reinforce learning, and enhance Enhance and reinforce learning, and enhance

training. training.

Computer graphicsComputer graphics advances: more iconic and advances: more iconic and analog models (visual simulation) analog models (visual simulation)

The Modeling Process--

A Preview How Much to Order for the Ma-Pa Grocery?How Much to Order for the Ma-Pa Grocery? Bob and Jan: How much bread to stock each day? Bob and Jan: How much bread to stock each day?

Solution ApproachesSolution Approaches Trial-and-ErrorTrial-and-Error SimulationSimulation OptimizationOptimization HeuristicsHeuristics

The Decision-Making Process

Systematic Decision-Making Process (Simon, 1977)Systematic Decision-Making Process (Simon, 1977)

IntelligenceIntelligence DesignDesign ChoiceChoice ImplementationImplementation

(Figure 2.2)(Figure 2.2)

Modeling is Modeling is EssentialEssential to the Process to the Process

Intelligence phaseIntelligence phase Reality is examined Reality is examined The problem is identified and definedThe problem is identified and defined

Design phaseDesign phase Representative model is constructedRepresentative model is constructed The model is validated and evaluation criteria are setThe model is validated and evaluation criteria are set

Choice phaseChoice phase Includes a proposed solution to the Includes a proposed solution to the modelmodel If reasonable, move on to theIf reasonable, move on to the

Implementation phaseImplementation phase Solution to the original problemSolution to the original problem

FailureFailure: Return to the modeling process: Return to the modeling processOften Backtrack / Cycle Throughout the Process Often Backtrack / Cycle Throughout the Process

The Intelligence Phase

Scan the environment to identify problem situations or Scan the environment to identify problem situations or opportunitiesopportunities

Find the Problem Find the Problem IdentifyIdentify organizational goals and objectives organizational goals and objectives DetermineDetermine whether they are being met whether they are being met Explicitly Explicitly definedefine the problem the problem

Problem Classification

Structured versus UnstructuredStructured versus Unstructured

Programmed versus Nonprogrammed Problems Programmed versus Nonprogrammed Problems Simon (1977)Simon (1977)

NonprogrammedNonprogrammed ProgrammedProgrammed

ProblemsProblems Problems Problems

Problem Decomposition:Problem Decomposition: Divide a complex problem into (easier to Divide a complex problem into (easier to solve) subproblemssolve) subproblemsChunkingChunking (Salami) (Salami)

Some seemingly poorly structured problems may have some highly Some seemingly poorly structured problems may have some highly structured subproblemsstructured subproblems

Problem OwnershipProblem Ownership

Outcome: Outcome: Problem StatementProblem Statement

The Design Phase

Generating, developing, and analyzingGenerating, developing, and analyzingpossible courses of actionpossible courses of action

Includes Includes Understanding the problem Understanding the problem Testing solutions for feasibilityTesting solutions for feasibility A model is constructed, tested, and validatedA model is constructed, tested, and validated

ModelingModeling Conceptualization of the problemConceptualization of the problem Abstraction to quantitative and/or qualitative formsAbstraction to quantitative and/or qualitative forms

Mathematical Model

Identify variables Identify variables Establish equations describing their relationshipsEstablish equations describing their relationships Simplifications through Simplifications through assumptionsassumptions Balance model simplification and the accurate Balance model simplification and the accurate

representation of realityrepresentation of reality

ModelingModeling: an art and science: an art and science

Quantitative Modeling Topics

Model ComponentsModel Components Model StructureModel Structure Selection of a Selection of a Principle of ChoicePrinciple of Choice

(Criteria for Evaluation)(Criteria for Evaluation) Developing (Generating) AlternativesDeveloping (Generating) Alternatives Predicting OutcomesPredicting Outcomes Measuring OutcomesMeasuring Outcomes ScenariosScenarios

Components of Quantitative Models

Decision VariablesDecision Variables Uncontrollable Variables (and/or Parameters)Uncontrollable Variables (and/or Parameters) Result (Outcome) VariablesResult (Outcome) Variables Mathematical RelationshipsMathematical Relationships

or or Symbolic or Qualitative Relationships Symbolic or Qualitative Relationships

(Figure 2.3) (Figure 2.3)

DecisionDecision Uncontrollable FactorsUncontrollable Factors Relationships among VariablesRelationships among Variables

Results of Decisions are Determined by the

Result Variables

Reflect the level of effectiveness of the systemReflect the level of effectiveness of the system DependentDependent variablesvariables Examples - Table 2.2Examples - Table 2.2

Decision Variables

Describe alternative courses of actionDescribe alternative courses of action The decision maker The decision maker controlscontrols them them Examples - Table 2.2Examples - Table 2.2

Uncontrollable Variables or Parameters

Factors that affect the result variablesFactors that affect the result variables Not under the controlNot under the control of the decision maker of the decision maker Generally part of the environmentGenerally part of the environment Some constrain the decision maker and are called Some constrain the decision maker and are called

constraintsconstraints Examples - Table 2.2Examples - Table 2.2

Intermediate Result Variables Intermediate Result Variables Reflect intermediate outcomesReflect intermediate outcomes

The Structure of Quantitative Models

Mathematical expressions (e.g., equations or Mathematical expressions (e.g., equations or inequalities) connect the componentsinequalities) connect the components

Simple financial model Simple financial model P = R - CP = R - C

Present-value modelPresent-value modelP = F / (1+i)P = F / (1+i)nn

LP Example

The Product-Mix Linear Programming ModelThe Product-Mix Linear Programming Model MBI Corporation MBI Corporation Decision: How many computers to build next month?Decision: How many computers to build next month? Two types of computersTwo types of computers Labor limitLabor limit Materials limitMaterials limit Marketing lower limitsMarketing lower limits

ConstraintConstraint CC7CC7 CC8CC8 RelRel LimitLimitLabor (days)Labor (days) 300300 500500 <=<= 200,000 / mo200,000 / moMaterials $Materials $ 10,00010,000 15,00015,000 <=<= 8,000,000/mo8,000,000/moUnitsUnits 11 >=>= 100100UnitsUnits 11 >=>= 200200Profit $Profit $ 8,0008,000 12,00012,000 MaxMax

ObjectiveObjective: Maximize Total Profit / Month: Maximize Total Profit / Month

Linear Programming Model

ComponentsComponents Decision variablesDecision variables Result variableResult variable Uncontrollable variables (constraints)Uncontrollable variables (constraints)

SolutionSolution XX11 = 333.33 = 333.33

XX2 2 = 200= 200

Profit = $5,066,667 Profit = $5,066,667

Optimization Problems

Linear programming Goal programming Network programming Integer programming Transportation problem Assignment problem Nonlinear programming Dynamic programming Stochastic programming Investment models Simple inventory models Replacement models (capital budgeting)

The Principle of Choice

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

Selection of a Principle of Choice

Not the choice phaseNot the choice phase

A A decision regarding the acceptability decision regarding the acceptability of a solution approachof a solution approach

NormativeNormative DescriptiveDescriptive

Normative Models

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

OptimizationOptimization process process

Normative decision theory based on Normative decision theory based on rationalrational decision makers decision makers

Rationality Assumptions

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

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

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

Suboptimization

Narrow the boundaries of a systemNarrow the boundaries of a system

Consider a part of a complete systemConsider a part of a complete system

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

Viable methodViable method

Descriptive Models

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

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

No guarantee a solution is optimalNo guarantee a solution is optimal Often a solution will be good enoughOften a solution will be good enough SimulationSimulation: Descriptive modeling technique: Descriptive modeling technique

Descriptive Models

Information flowInformation flow Scenario analysisScenario analysis Financial planningFinancial planning Complex inventory decisionsComplex inventory decisions Markov analysis (predictions)Markov analysis (predictions) Environmental impact analysisEnvironmental impact analysis SimulationSimulation Waiting line (queue) managementWaiting line (queue) management

Satisficing (Good Enough)

Most human decision makers will settle for a good Most human decision makers will settle for a good enough solutionenough solution

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

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

(Simon, 1977)(Simon, 1977)

Why Satisfice?Bounded Rationality (Simon)

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

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

limitations on human processing capacitieslimitations on human processing capacities individual differencesindividual differences

Bounded rationality: why many models are descriptive, Bounded rationality: why many models are descriptive, not normativenot normative

Developing (Generating) Alternatives

In Optimization Models: Automatically by the Model!In Optimization Models: Automatically by the Model!

Not Always So!Not Always So!

Issue: When to Stop?Issue: When to Stop?

Predicting the Outcome of Each Alternative

Must predict the future outcome of each proposed Must predict the future outcome of each proposed alternativealternative

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

Classify Each Situation as UnderClassify Each Situation as Under CertaintyCertainty RiskRisk Uncertainty Uncertainty

Decision Making Under Certainty

AssumesAssumes complete knowledge available complete knowledge available (deterministic environment)(deterministic environment)

Example: U.S. Treasury bill investmentExample: U.S. Treasury bill investment

Typically for structured problems with short Typically for structured problems with short time horizonstime horizons

Sometimes DSS approach is needed for certainty Sometimes DSS approach is needed for certainty situationssituations

Decision Making Under Risk (Risk Analysis)

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

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

given outcomes are assumed known or estimatedgiven outcomes are assumed known or estimated

Assess the (Assess the (calculatedcalculated) ) degree of riskdegree of risk associated with associated with each alternative each alternative

Risk Analysis

Calculate the expected value of each alternative Calculate the expected value of each alternative

Select the alternative with the best expected value Select the alternative with the best expected value

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

Decision Making Under Uncertainty

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

estimate the probability of occurrence estimate the probability of occurrence

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

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

stud or draw)stud or draw)

Measuring Outcomes

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

complaints)complaints) Maximize quality or satisfaction ratings (surveys)Maximize quality or satisfaction ratings (surveys)

Scenarios

Useful inUseful in

Simulation Simulation What-if analysisWhat-if analysis

Importance of Scenarios in MSS

Help identify potential opportunities and/or Help identify potential opportunities and/or problem areasproblem areas

Provide flexibility in planningProvide flexibility in planning Identify leading edges of changes that management Identify leading edges of changes that management

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

changes in scenarioschanges in scenarios

Possible Scenarios

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

The scenario sets the stage for the analysisThe scenario sets the stage for the analysis

The Choice Phase

The CRITICAL act - decision made here!The CRITICAL act - decision made here!

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

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

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

Search Approaches

Analytical TechniquesAnalytical Techniques

Algorithms (Optimization)Algorithms (Optimization)

Blind and Heuristic Search TechniquesBlind and Heuristic Search Techniques

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

Goal Seeking Evaluation (with the search process) leads to a Evaluation (with the search process) leads to a

recommended solutionrecommended solution Multiple goals Multiple goals Complex systems have multiple goalsComplex systems have multiple goals

Some may conflictSome may conflict

Typically, quantitative models have a single goalTypically, quantitative models have a single goal

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

Common Methods

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

programmingprogramming Point system Point system

Computerized models can support multiple Computerized models can support multiple goal decision makinggoal decision making

Sensitivity Analysis

Change inputs or parameters, look at model resultsChange inputs or parameters, look at model results

Sensitivity analysis checks relationshipsSensitivity analysis checks relationships

Types of Sensitivity AnalysesTypes of Sensitivity Analyses

Automatic Automatic Trial and errorTrial and error

Trial and Error

Change input data and re-solve the Change input data and re-solve the problemproblem

Better and better solutions can be Better and better solutions can be discovereddiscovered

How to do? Easy in spreadsheets How to do? Easy in spreadsheets (Excel)(Excel) What-ifWhat-if Goal seekingGoal seeking

Goal Seeking

Backward solution approachBackward solution approach Example: Figure 2.10Example: Figure 2.10

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

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

Goal Seeking

The Implementation Phase

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

(Machiavelli, 1500s)(Machiavelli, 1500s)

*** The Introduction of a *** The Introduction of a ChangeChange *** ***

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

How Decisions Are Supported

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

IntelligenceIntelligence: DSS, ES, ANN, MIS, Data Mining, : DSS, ES, ANN, MIS, Data Mining, OLAP, EIS, GSSOLAP, EIS, GSS

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

ImplementationImplementation: DSS, ES, GSS: DSS, ES, GSS

Alternative Decision Making Models

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

Naturalistic Decision Theories

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

Based on behavioral decision theoryBased on behavioral decision theory

Recognition modelsRecognition models Narrative-based modelsNarrative-based models

Recognition Models

PolicyPolicy Recognition-primed decision modelRecognition-primed decision model

Narrative-based Models (Descriptive)

Scenario modelScenario model Story modelStory model Argument-driven action (ADA) modelArgument-driven action (ADA) model Incremental modelsIncremental models Image theoryImage theory

Other Important Decision- Making Issues

Personality typesPersonality types GenderGender Human cognitionHuman cognition Decision stylesDecision styles

Personality (Temperament) Types

Strong relationship between personality and Strong relationship between personality and decision makingdecision making

Type helps explain how to best attack a Type helps explain how to best attack a problemproblem

Type indicates how to relate to other typesType indicates how to relate to other types important for team buildingimportant for team building

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

Myers-Briggs Dimensions

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

Gender

Sometimes empirical testing indicates Sometimes empirical testing indicates gender differences in decision makinggender differences in decision making

Results are overwhelmingly inconclusiveResults are overwhelmingly inconclusive

Cognition

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

Ability to perceive and understand informationAbility to perceive and understand information

Cognitive models are attempts to Cognitive models are attempts to explainexplain or or understandunderstand various human cognitive processes various human cognitive processes

Cognitive Style

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

Often determines people's preference for human-machine Often determines people's preference for human-machine interfaceinterface

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

Affects the way a decision maker Affects the way a decision maker framesframes a problem a problem

Cognitive Style Research

Impacts on the design of management information systems Impacts on the design of management information systems May be overemphasizedMay be overemphasized

Analytic decision makerAnalytic decision maker Heuristic decision makerHeuristic decision maker

Decision Styles

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

Cognitive responseCognitive response Values and beliefs Values and beliefs

Varies from individual to individual and from situation to situationVaries from individual to individual and from situation to situation Decision making is a Decision making is a nonlinearnonlinear process process

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

There are dozensThere are dozens

Some Decision Styles

HeuristicHeuristic AnalyticAnalytic Autocratic Autocratic Democratic Democratic Consultative (with individuals or groups)Consultative (with individuals or groups) Combinations and variationsCombinations and variations

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

The system The system should be flexible and adaptable to different usersshould be flexible and adaptable to different users have what-if and goal seeking have what-if and goal seeking have graphics have graphics have process flexibilityhave process flexibility

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

Different decision styles require different types of supportDifferent decision styles require different types of support

Major factor: individual or group decision maker Major factor: individual or group decision maker

The Decision Makers

IndividualsIndividuals GroupsGroups

Individuals

May still have conflicting objectivesMay still have conflicting objectives Decisions may be fully automatedDecisions may be fully automated

Groups

Most major decisions made by Most major decisions made by groupsgroups Conflicting objectivesConflicting objectives are common are common Variable sizeVariable size People from different departmentsPeople from different departments People from different organizationsPeople from different organizations The The group decision-making processgroup decision-making process can be very complicated can be very complicated Consider Consider Group Support SystemsGroup Support Systems (GSS) (GSS)

Organizational DSSOrganizational DSS can help in enterprise-wide decision-making can help in enterprise-wide decision-making situationssituations

Summary

Managerial decision making is the whole process of Managerial decision making is the whole process of managementmanagement

Problem solving also refers to opportunity's evaluationProblem solving also refers to opportunity's evaluation A system is a collection of objects such as people, A system is a collection of objects such as people,

resources, concepts, and procedures intended to perform resources, concepts, and procedures intended to perform an identifiable function or to serve a goalan identifiable function or to serve a goal

DSS deals primarily with open systemsDSS deals primarily with open systems A model is a simplified representation or abstraction of A model is a simplified representation or abstraction of

realityreality Models enable fast and inexpensive experimentation with Models enable fast and inexpensive experimentation with

systemssystems

Modeling can employ optimization, heuristic, or Modeling can employ optimization, heuristic, or simulation techniquessimulation techniques

Decision making involves four major phases: Decision making involves four major phases: intelligence, design, choice, and implementationintelligence, design, choice, and implementation

What-if and goal seeking are the two most What-if and goal seeking are the two most common sensitivity analysis approachescommon sensitivity analysis approaches

Computers can support all phases of decision Computers can support all phases of decision making by automating many required tasksmaking by automating many required tasks

Summary

Personality (temperament) influences decision Personality (temperament) influences decision makingmaking

Gender impacts on decision making are Gender impacts on decision making are inconclusiveinconclusive

Human cognitive styles may influence human-Human cognitive styles may influence human-machine interactionmachine interaction

Human decision styles need to be recognized in Human decision styles need to be recognized in designing MSSdesigning MSS