SlideShare a Scribd company logo

Marakas-Ch04-Saif Week 04.ppt

Machine Learning

1 of 26
Download to read offline
Chapter 4:
Modeling Decision Processes
Decision Support Systems in the 21st
Century, 2nd Edition
by George M. Marakas
4-1: Modeling
Typical modeling process begins with identification
of a problem and analysis of the requirements of
the situation.
It is advisable to
 analyze the scope of the problem domain; and
 the forces and dynamics of the environment.
4-1: Modeling
The next step is to identify the variables for the
model. The identification of decision variables
and their relationships is very important.
One should always ask if using a model is
appropriate??
If a model is appropriate, then one asks what
variables and relationships need to be specified,
using an appropriate modeling tool.
4-1: Defining the Problem and Its Structure
A fully formed problem statement contains
three key components:
 The current state of affairs
 The desired state of affairs
 A statement of the central objective(s) that
distinguish the two
Problem Definition Errors
A common error: premature focus on the set
of solutions rather than the problem itself
The decision maker may be left with a solution
looking for a problem to solve
Failing to identify and define the problem fully may
result in a great solution that does not solve the
right problem
Problem Scope
The problem may be worth solving but the
scope is beyond the available resources or
time constraints
In such cases, the scope must be reduced to a
focus that allows a solution
One method to limit the scope is to identify its
breadth by asking questions about people
involved, cost and magnitude

Recommended

Lecture3 Modelling Decision Processes
Lecture3 Modelling Decision ProcessesLecture3 Modelling Decision Processes
Lecture3 Modelling Decision ProcessesKodok Ngorex
 
httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx
httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docxhttphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx
httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docxadampcarr67227
 
Forecasting
ForecastingForecasting
Forecastingsumit235
 
Six sigma
Six sigmaSix sigma
Six sigmakmsonam
 
QA_Chapter_01_Dr_B_Dayal_Overview.pptx
QA_Chapter_01_Dr_B_Dayal_Overview.pptxQA_Chapter_01_Dr_B_Dayal_Overview.pptx
QA_Chapter_01_Dr_B_Dayal_Overview.pptxTeshome62
 
Aon FI Risk Advisory - CCAR Variable Selection
Aon FI Risk Advisory - CCAR Variable SelectionAon FI Risk Advisory - CCAR Variable Selection
Aon FI Risk Advisory - CCAR Variable SelectionEvan Sekeris
 
Soporte a las Decisiones Computarizado
Soporte a las Decisiones ComputarizadoSoporte a las Decisiones Computarizado
Soporte a las Decisiones Computarizadoluzenith_g
 
Variable Selection for CCAR
Variable Selection for CCARVariable Selection for CCAR
Variable Selection for CCAREvan Sekeris
 

More Related Content

Similar to Marakas-Ch04-Saif Week 04.ppt

Business decision, resource mgt and cost benefit analysis
Business decision, resource mgt and cost benefit analysisBusiness decision, resource mgt and cost benefit analysis
Business decision, resource mgt and cost benefit analysisMohammed Jasir PV
 
Analysis in Action 21 September 2021
Analysis in Action 21 September 2021Analysis in Action 21 September 2021
Analysis in Action 21 September 2021IIBA UK Chapter
 
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docxfelicidaddinwoodie
 
6. Fundamentals of decision making
6. Fundamentals of decision making 6. Fundamentals of decision making
6. Fundamentals of decision making Sudhir Upadhyay
 
Jacobs Kiefer Bayes Guide 3 10 V1
Jacobs Kiefer Bayes Guide 3 10 V1Jacobs Kiefer Bayes Guide 3 10 V1
Jacobs Kiefer Bayes Guide 3 10 V1Michael Jacobs, Jr.
 
Establishing schedule margin using monte carlo simulation
Establishing schedule margin using monte carlo simulation Establishing schedule margin using monte carlo simulation
Establishing schedule margin using monte carlo simulation Glen Alleman
 
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk AnalysisAdopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk AnalysisRicardo Viana Vargas
 
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a
3rd  alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a3rd  alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'aMahmoud Bahgat
 
MIS 05 Decision Support Systems
MIS 05  Decision Support SystemsMIS 05  Decision Support Systems
MIS 05 Decision Support SystemsTushar B Kute
 
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...QuantUniversity
 
WHATs NEW IN RISK ASSESSMENT
WHATs NEW IN RISK ASSESSMENTWHATs NEW IN RISK ASSESSMENT
WHATs NEW IN RISK ASSESSMENTFred Travis
 
Fundamentals of Quantitative Analysis
Fundamentals of Quantitative AnalysisFundamentals of Quantitative Analysis
Fundamentals of Quantitative AnalysisJubayer Alam Shoikat
 
8 rajib chakravorty risk
8 rajib chakravorty risk8 rajib chakravorty risk
8 rajib chakravorty riskCCR-interactive
 
Introduction to decision making methods
Introduction to decision making methodsIntroduction to decision making methods
Introduction to decision making methodsDr Ghaiath Hussein
 
Risk And Decision Analysis Essay
Risk And Decision Analysis EssayRisk And Decision Analysis Essay
Risk And Decision Analysis EssayKaren Hennings
 

Similar to Marakas-Ch04-Saif Week 04.ppt (20)

Business decision, resource mgt and cost benefit analysis
Business decision, resource mgt and cost benefit analysisBusiness decision, resource mgt and cost benefit analysis
Business decision, resource mgt and cost benefit analysis
 
Analysis in Action 21 September 2021
Analysis in Action 21 September 2021Analysis in Action 21 September 2021
Analysis in Action 21 September 2021
 
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx
1PPA 670 Public Policy AnalysisBasic Policy Terms an.docx
 
6. Fundamentals of decision making
6. Fundamentals of decision making 6. Fundamentals of decision making
6. Fundamentals of decision making
 
MIS chap # 11.....
MIS chap # 11.....MIS chap # 11.....
MIS chap # 11.....
 
Jacobs Kiefer Bayes Guide 3 10 V1
Jacobs Kiefer Bayes Guide 3 10 V1Jacobs Kiefer Bayes Guide 3 10 V1
Jacobs Kiefer Bayes Guide 3 10 V1
 
Establishing schedule margin using monte carlo simulation
Establishing schedule margin using monte carlo simulation Establishing schedule margin using monte carlo simulation
Establishing schedule margin using monte carlo simulation
 
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk AnalysisAdopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis
Adopting the Quadratic Mean Process to Quantify the Qualitative Risk Analysis
 
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a
3rd  alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a3rd  alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'a
 
MIS 05 Decision Support Systems
MIS 05  Decision Support SystemsMIS 05  Decision Support Systems
MIS 05 Decision Support Systems
 
Risk Management
Risk ManagementRisk Management
Risk Management
 
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...
 
WHATs NEW IN RISK ASSESSMENT
WHATs NEW IN RISK ASSESSMENTWHATs NEW IN RISK ASSESSMENT
WHATs NEW IN RISK ASSESSMENT
 
Fundamentals of Quantitative Analysis
Fundamentals of Quantitative AnalysisFundamentals of Quantitative Analysis
Fundamentals of Quantitative Analysis
 
CBAM
 CBAM CBAM
CBAM
 
8 rajib chakravorty risk
8 rajib chakravorty risk8 rajib chakravorty risk
8 rajib chakravorty risk
 
Introduction to decision making methods
Introduction to decision making methodsIntroduction to decision making methods
Introduction to decision making methods
 
Solution Thinking
Solution ThinkingSolution Thinking
Solution Thinking
 
Risk And Decision Analysis Essay
Risk And Decision Analysis EssayRisk And Decision Analysis Essay
Risk And Decision Analysis Essay
 
Relationship Forecasting
Relationship ForecastingRelationship Forecasting
Relationship Forecasting
 

More from ShujatHussainGadi

More from ShujatHussainGadi (7)

Decision Tree Assignment.pdf
Decision  Tree Assignment.pdfDecision  Tree Assignment.pdf
Decision Tree Assignment.pdf
 
Lec-01.ppt
Lec-01.pptLec-01.ppt
Lec-01.ppt
 
GA.pptx
GA.pptxGA.pptx
GA.pptx
 
CS767_Lecture_03.pptx
CS767_Lecture_03.pptxCS767_Lecture_03.pptx
CS767_Lecture_03.pptx
 
CS767_Lecture_02.pptx
CS767_Lecture_02.pptxCS767_Lecture_02.pptx
CS767_Lecture_02.pptx
 
CS767_Lecture_04.pptx
CS767_Lecture_04.pptxCS767_Lecture_04.pptx
CS767_Lecture_04.pptx
 
CS767_Lecture_05.pptx
CS767_Lecture_05.pptxCS767_Lecture_05.pptx
CS767_Lecture_05.pptx
 

Recently uploaded

mean stack mean stack mean stack mean stack
mean stack mean stack  mean stack  mean stackmean stack mean stack  mean stack  mean stack
mean stack mean stack mean stack mean stackNuttavutThongjor1
 
2.15.24 The Birmingham Campaign and MLK.pptx
2.15.24 The Birmingham Campaign and MLK.pptx2.15.24 The Birmingham Campaign and MLK.pptx
2.15.24 The Birmingham Campaign and MLK.pptxMaryPotorti1
 
DISCOURSE: TEXT AS CONNECTED DISCOURSE
DISCOURSE:   TEXT AS CONNECTED DISCOURSEDISCOURSE:   TEXT AS CONNECTED DISCOURSE
DISCOURSE: TEXT AS CONNECTED DISCOURSEMYDA ANGELICA SUAN
 
Practical Research 1: Nature of Inquiry and Research.pptx
Practical Research 1: Nature of Inquiry and Research.pptxPractical Research 1: Nature of Inquiry and Research.pptx
Practical Research 1: Nature of Inquiry and Research.pptxKatherine Villaluna
 
UniSC Sunshine Coast library self-guided tour
UniSC Sunshine Coast library self-guided tourUniSC Sunshine Coast library self-guided tour
UniSC Sunshine Coast library self-guided tourUSC_Library
 
Website Fixer-Upper Series to Boost your Online Presence
Website Fixer-Upper Series to Boost your Online PresenceWebsite Fixer-Upper Series to Boost your Online Presence
Website Fixer-Upper Series to Boost your Online PresenceSamantha Russell
 
Grantseeking Solo- Securing Awards with Limited Staff PDF.pdf
Grantseeking Solo- Securing Awards with Limited Staff  PDF.pdfGrantseeking Solo- Securing Awards with Limited Staff  PDF.pdf
Grantseeking Solo- Securing Awards with Limited Staff PDF.pdfTechSoup
 
UniSC Moreton Bay Library self-guided tour
UniSC Moreton Bay Library self-guided tourUniSC Moreton Bay Library self-guided tour
UniSC Moreton Bay Library self-guided tourUSC_Library
 
Evaluation and management of patients with Dyspepsia.pptx
Evaluation and management of patients with Dyspepsia.pptxEvaluation and management of patients with Dyspepsia.pptx
Evaluation and management of patients with Dyspepsia.pptxgarvitnanecha
 
D.pharmacy Pharmacology 4th unit notes.pdf
D.pharmacy Pharmacology 4th unit notes.pdfD.pharmacy Pharmacology 4th unit notes.pdf
D.pharmacy Pharmacology 4th unit notes.pdfSUMIT TIWARI
 
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)Rabiya Husain
 
Introduction of General Pharmacology PPT.pptx
Introduction of General Pharmacology PPT.pptxIntroduction of General Pharmacology PPT.pptx
Introduction of General Pharmacology PPT.pptxRenuka N Sunagad
 
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...EduSkills OECD
 
2.20.24 The March on Washington for Jobs and Freedom.pptx
2.20.24 The March on Washington for Jobs and Freedom.pptx2.20.24 The March on Washington for Jobs and Freedom.pptx
2.20.24 The March on Washington for Jobs and Freedom.pptxMaryPotorti1
 
Overview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfOverview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfChristalin Nelson
 
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTS
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTScatch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTS
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTSCarlaNicolas7
 
Shapley Tech Talk - SHAP and Shapley Discussion
Shapley Tech Talk - SHAP and Shapley DiscussionShapley Tech Talk - SHAP and Shapley Discussion
Shapley Tech Talk - SHAP and Shapley DiscussionTushar Tank
 

Recently uploaded (20)

mean stack mean stack mean stack mean stack
mean stack mean stack  mean stack  mean stackmean stack mean stack  mean stack  mean stack
mean stack mean stack mean stack mean stack
 
2.15.24 The Birmingham Campaign and MLK.pptx
2.15.24 The Birmingham Campaign and MLK.pptx2.15.24 The Birmingham Campaign and MLK.pptx
2.15.24 The Birmingham Campaign and MLK.pptx
 
DISCOURSE: TEXT AS CONNECTED DISCOURSE
DISCOURSE:   TEXT AS CONNECTED DISCOURSEDISCOURSE:   TEXT AS CONNECTED DISCOURSE
DISCOURSE: TEXT AS CONNECTED DISCOURSE
 
Practical Research 1: Nature of Inquiry and Research.pptx
Practical Research 1: Nature of Inquiry and Research.pptxPractical Research 1: Nature of Inquiry and Research.pptx
Practical Research 1: Nature of Inquiry and Research.pptx
 
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdfCapter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
Capter 5 Climate of Ethiopia and the Horn GeES 1011.pdf
 
UniSC Sunshine Coast library self-guided tour
UniSC Sunshine Coast library self-guided tourUniSC Sunshine Coast library self-guided tour
UniSC Sunshine Coast library self-guided tour
 
Website Fixer-Upper Series to Boost your Online Presence
Website Fixer-Upper Series to Boost your Online PresenceWebsite Fixer-Upper Series to Boost your Online Presence
Website Fixer-Upper Series to Boost your Online Presence
 
Grantseeking Solo- Securing Awards with Limited Staff PDF.pdf
Grantseeking Solo- Securing Awards with Limited Staff  PDF.pdfGrantseeking Solo- Securing Awards with Limited Staff  PDF.pdf
Grantseeking Solo- Securing Awards with Limited Staff PDF.pdf
 
UniSC Moreton Bay Library self-guided tour
UniSC Moreton Bay Library self-guided tourUniSC Moreton Bay Library self-guided tour
UniSC Moreton Bay Library self-guided tour
 
Evaluation and management of patients with Dyspepsia.pptx
Evaluation and management of patients with Dyspepsia.pptxEvaluation and management of patients with Dyspepsia.pptx
Evaluation and management of patients with Dyspepsia.pptx
 
D.pharmacy Pharmacology 4th unit notes.pdf
D.pharmacy Pharmacology 4th unit notes.pdfD.pharmacy Pharmacology 4th unit notes.pdf
D.pharmacy Pharmacology 4th unit notes.pdf
 
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)
HOW TO DEVELOP A RESEARCH PROPOSAL (FOR RESEARCH SCHOLARS)
 
Time-Honored Wisdom: African Teachings for VUCA Leaders
Time-Honored Wisdom: African Teachings for VUCA LeadersTime-Honored Wisdom: African Teachings for VUCA Leaders
Time-Honored Wisdom: African Teachings for VUCA Leaders
 
Introduction of General Pharmacology PPT.pptx
Introduction of General Pharmacology PPT.pptxIntroduction of General Pharmacology PPT.pptx
Introduction of General Pharmacology PPT.pptx
 
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...
Andreas Schleicher - 20 Feb 2024 - How pop music, podcasts, and Tik Tok are i...
 
2.20.24 The March on Washington for Jobs and Freedom.pptx
2.20.24 The March on Washington for Jobs and Freedom.pptx2.20.24 The March on Washington for Jobs and Freedom.pptx
2.20.24 The March on Washington for Jobs and Freedom.pptx
 
Overview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdfOverview of Databases and Data Modelling-1.pdf
Overview of Databases and Data Modelling-1.pdf
 
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTS
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTScatch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTS
catch-up-friday-ARALING PNLIPUNAN SOCIAL JUSTICE AND HUMAN RIGHTS
 
Shapley Tech Talk - SHAP and Shapley Discussion
Shapley Tech Talk - SHAP and Shapley DiscussionShapley Tech Talk - SHAP and Shapley Discussion
Shapley Tech Talk - SHAP and Shapley Discussion
 
first section physiology laboratory.pptx
first section physiology laboratory.pptxfirst section physiology laboratory.pptx
first section physiology laboratory.pptx
 

Marakas-Ch04-Saif Week 04.ppt

  • 1. Chapter 4: Modeling Decision Processes Decision Support Systems in the 21st Century, 2nd Edition by George M. Marakas
  • 2. 4-1: Modeling Typical modeling process begins with identification of a problem and analysis of the requirements of the situation. It is advisable to  analyze the scope of the problem domain; and  the forces and dynamics of the environment.
  • 3. 4-1: Modeling The next step is to identify the variables for the model. The identification of decision variables and their relationships is very important. One should always ask if using a model is appropriate?? If a model is appropriate, then one asks what variables and relationships need to be specified, using an appropriate modeling tool.
  • 4. 4-1: Defining the Problem and Its Structure A fully formed problem statement contains three key components:  The current state of affairs  The desired state of affairs  A statement of the central objective(s) that distinguish the two
  • 5. Problem Definition Errors A common error: premature focus on the set of solutions rather than the problem itself The decision maker may be left with a solution looking for a problem to solve Failing to identify and define the problem fully may result in a great solution that does not solve the right problem
  • 6. Problem Scope The problem may be worth solving but the scope is beyond the available resources or time constraints In such cases, the scope must be reduced to a focus that allows a solution One method to limit the scope is to identify its breadth by asking questions about people involved, cost and magnitude
  • 7. Problem Structure Design of problem structure is similar to design of many other entities  What is the final appearance?  What are the elemental details?  What are the relationships between those elements? Regardless of context, a problem structure can be described in terms of choices, uncertainties and objectives
  • 8. Problem Structure (cont.) Choices: there are always at least two alternatives (one is “do nothing”) Uncertainties: situations beyond the direct control of the decision maker; their individual probability of occurrence is only estimable within a certain range Objectives: methods of establishing the criteria used to measure the value of the outcome
  • 9. Structuring Tools Influence diagram: a simple method of graphing the components of a decision and linking them to show the relationships between them Decision Objective Uncertainty
  • 10. Structuring Tools Influence diagram: Relevance Arrows in Influence diagram B Event A outcome is relevant to probability of Event B outcome A B A Outcome of Event A is known when making decision B
  • 11. Structuring Tools Influence diagram: Relevance Arrows in Influence diagram A Decision A is necessary to estimate probability of Event B B B Decision A is made prior to decision B A
  • 12. Structuring Tools (cont.) Decision tree: another diagram that models choices and uncertainties and can be extended to include multiple, sequential decisions Decision Uncertainty
  • 13. Common Decision Structures Basic Risky Decision: Decision maker takes a choice in the face of uncertainty. . Success is a function of the choice and outcome
  • 14. Common Decision Structures Certainty A multiple-objective decision with little risk (risk is not significant). Multi-objective/multiple approach, no risk- decision Success is a function of the trade-off between objectives.
  • 15. Common Decision Structures Sequential: Decision process do not always present themselves in a way that shows a clear beginning and ending. Conditions change over the time & choice made earlier may no longer appropriate Several risky decisions over time. Earlier outcomes may affect later choices.
  • 16. 4-2: Decision Models Decision models can be classified in a number of ways:  Is time a factor? Models that do not include time are “static” versus “dynamic”  What is the technique’s mathematical focus?  Some abstract model types are deterministic, stochastic, simulation and domain specific
  • 17. Model Classification Examples Deterministic: linear programming, production planning Stochastic: queuing theory, linear regression analysis
  • 18. Model Classification Examples Simulation: production modeling, transportation analysis Domain-specific: EOQ, technology diffusion, meteorological models
  • 19. Conceptual Models A formal mathematical approach is not always appropriate Conceptual models are formulated under the notion that even though all problems are unique, no problem is completely new Decision makers can recall and combine a variety of past experiences to create an accurate model of the current situation
  • 20. 4-3: Types of Probability Three requirements of probability: 1. All probabilities are in the range 0 to 1 2. The probabilities of all outcomes of an event must add up to the probability of their union 3. The total probability of a complete set of outcomes must equal 1
  • 21. How Are Probabilities Generated? Long-run frequency: with enough “history”, you can estimate an event’s probability by its relative frequency Subjective: probability represents an individual’s “degree of belief” that an event will occur Logic: a probability may be derivable, but its accuracy may not be acceptable
  • 22. 4-4: Techniques for Forecasting Probabilities Direct probability forecasting — an expert is simply asked to estimate the chance that an outcome will occur Odds forecasting — a series of bets are proposed to determine how strongly the bettor feels an event will occur Comparison forecasting — similar to odds forecasting except that one game has known probabilities
  • 23. Decomposing Complex Probabilities Probabilities for complex events may be more easily generated by using conditional probabilities within subsets of the events For example, it may be easier to forecast sales of a weather-related product by forecasting sales under good weather, then bad weather and then considering the probability of bad weather
  • 24. 4-5: Calibration and Sensitivity A decision maker is said to be well calibrated if his probability forecasts are correct at about the same rate as his confidence in them (9 out of 10 times his 90% confidence intervals should be correct). Calibration requires years of experience and feedback to develop. Most of us are too optimistic and our intervals are too tight.
  • 25. Sensitivity Analysis A method for testing the degree to which a set of assumptions affects the results from a model. If a small change in the value of a variable yields a measurable change in output, that variable is said to be highly sensitive. Variables that are not sensitive may be treated as fixed, reducing the model’s complexity.
  • 26. Value Analysis We always need to be concerned that enough reliable information is available to make a successful decision. We can determine how much we are willing to pay for better info by computing its expected value. This involves a comparison of the expected return with the info to the expected return without the info.

Editor's Notes

  1. Although the ID is an excellent tool for modelling the structure of a particular decision context, it does not allow for depiction of many of the details associated with the decision at hand.
  2. Deterministic models – no variable can take more than one values at any given time Stiochastic – in this model atleast one variable is uncertain and must be described by some probability function Simulation - it combines both of the deterministic and stochastics models` Domain Specific – the advances in the Sc n Tech promote the needs for highly specific types of decision making techniques n context =
  3. EOQ – economic order quantity
  4. EOQ – economic order quantity