This document discusses decision theory and provides examples of how to analyze decisions using various decision making criteria. It defines decision theory as providing a rational approach to dealing with problems involving uncertainty. Some key decision making criteria discussed include expected monetary value (EMV), expected value of perfect information (EVPI), Maximax, Maximin, Laplace criterion, Hurwicz criterion, opportunity loss matrix, and Savage principle. Worked examples applying these criteria to hypothetical business decisions are provided.
this ppt is helpful for BBA/B.tech//MBA/M.tech students.
the ppt is on simulation topic...its covers -
Meaning
Advantages & Disadvantages
Uses
Process
Monte Carlo SImulation
Advantages & Disadvantages
Its example
this ppt is helpful for BBA/B.tech//MBA/M.tech students.
the ppt is on simulation topic...its covers -
Meaning
Advantages & Disadvantages
Uses
Process
Monte Carlo SImulation
Advantages & Disadvantages
Its example
An Introduction to Bayesisan Decision Analysis Medgate Inc.
Exposure assessments are central to any Industrial Hygiene program as Hygienists spend huge amounts of time classifying exposure profiles. However, it can be difficult to be sure when the right classification has been made, particularly when a hygienist’s individual judgment is required. This challenge is further exacerbated when working with small data sets.
Fortunately, Bayesian decision analysis (BDA) is a form of statistical analysis of occupational exposure data that allows hygienists to select the most appropriate exposure category, even with limited data. BDA results in easy to interpret “decision charts”, permits the user to mathematically incorporate prior information and professional judgment into the analysis, and can handle non-detects. It can also be used to select the most appropriate level of respiratory protection for those difficult to control exposure scenarios.
Presentation covers the basic methodology, assumptions and cautions, and will include several examples drawn from the chemical and pharmaceutical industries.
Watch this webinar and learn:
A basic understanding of BDA methodology.
Ability to apply BDA, and use the resulting “decision charts” to make IH decisions.
An understanding of the relationship between BDA and the AIHA exposure assessment model.
An introduction to quantifying “professional judgment” using a Prior Decision Chart.
An appreciation of the advantage that BDA has over the calculation of standard statistics and confidence intervals.
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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An Introduction to Bayesisan Decision Analysis Medgate Inc.
Exposure assessments are central to any Industrial Hygiene program as Hygienists spend huge amounts of time classifying exposure profiles. However, it can be difficult to be sure when the right classification has been made, particularly when a hygienist’s individual judgment is required. This challenge is further exacerbated when working with small data sets.
Fortunately, Bayesian decision analysis (BDA) is a form of statistical analysis of occupational exposure data that allows hygienists to select the most appropriate exposure category, even with limited data. BDA results in easy to interpret “decision charts”, permits the user to mathematically incorporate prior information and professional judgment into the analysis, and can handle non-detects. It can also be used to select the most appropriate level of respiratory protection for those difficult to control exposure scenarios.
Presentation covers the basic methodology, assumptions and cautions, and will include several examples drawn from the chemical and pharmaceutical industries.
Watch this webinar and learn:
A basic understanding of BDA methodology.
Ability to apply BDA, and use the resulting “decision charts” to make IH decisions.
An understanding of the relationship between BDA and the AIHA exposure assessment model.
An introduction to quantifying “professional judgment” using a Prior Decision Chart.
An appreciation of the advantage that BDA has over the calculation of standard statistics and confidence intervals.
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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2. Decision Theory
•“an analytic and systematic approach to
the study of decision making”
Bad decisions:
•not based on reasoning
•do not consider all available data and
possible alternatives
•do not employ a quantitative approach
Decision Theory Good decisions:
•based on reasoning
•consider all available data and possible
alternatives
•employ a quantitative approach
–A good decision may occasionally result in an unexpected outcome; it is still a good
decision if made properly
–A bad decision may occasionally result in a good outcome if you are lucky; it is still a
bad decision
3. What is Decision Theory?
Decision theory provides a rational approach to
the managers in dealing with problems confronted
with partial, imperfect or uncertain future
conditions.
Ex: A manufacturing company has several
alternative methods to increase its production to
meet the increasing market demand may have the
following options :
4. Decision Theory
Ex: A manufacturing company has several alternative methods to increase
its production to meet the increasing market demand may have the
following options :
List all the viable alternatives
Expand the present plant
Construct a new plant
Subcontract production for extra demand
5. Table 1 : 12 possible payoffs for the
manufacturing company’s expansion decision
Events/Outcomes
States of
Nature(Demand)
(E)
Acts or Alternatives or Options(A)
Expand Diversify Subcontract
High 50000 70000 30000
Moderate 25000 30000 15000
Low -25000 -40000 -1000
6. Problem
Determine EMV, EVPI, Maximax, Maximin, Laplace
Criterion, Hurwicz Criterion (α = 0.6), Opportunity Loss
Matrix, Savage Principle for the following payoffs:
Product
Acceptance
Probabilities Sales of Product Lines in lakhs
Soap Detergent Tooth Paste
High 0.30 30 45 20
Medium 0.50 60 30 40
Low 0.20 25 10 70
7. Solution
EMV(Expected Monetary Value)
The EMV for each decision-making alternative is obtained by
multiplying the conditional profits by assigned probabilities and
adding the resulting conditional values.
EMV [Soap] = 0.30 * 30 + 0.5 * 60 + 0.20 * 25 = 44
EMV [Detergent] = 0.30 * 45 + 0.5 * 30 + 0.20 * 10 = 30.5
EMV [Tooth Paste] = 0.30 * 20 + 0.5 * 40 + 0.20 * 70 = 40
Conclusion
The alternative that yields the highest EMV is selected. The highest
EMV is Soap which yields the highest sales.
8. EVPI (Expected Value of Perfect Information )
EVPI is obtained by multiplying with the associated
probabilities and the highest value in each row
In the first row of the pay-off table, the highest value is
45. similarly it is multiplied with the other values
EVPI = 0.30 * 45 + 0.5 * 60 + 0.20 * 70 = Rs. 57.5
Conclusion
The decision-maker spends Rs. 57.5 to obtain the perfect
information about the best alternative among the product
lines that yields the highest sales.
9. Maximax
Select the decision that results in the maximum of the maximum rewards
A very optimistic decision criterion
–Decision maker assumes that the most favorable state of nature for each action will
occur
Most risk prone agent
Maximax Calculation
Identify the highest value in each column and select the highest value
Soap = 60, Detergent = 45, Tooth Paste = 70
Conclusion
Tooth Paste is considered as an alternative having highest value
10. Maximin
Select the decision that results in the maximum of the minimum rewards
A very pessimistic decision criterion
–Decision maker assumes that the minimum reward occurs for each decision alternative
–Select the maximum of these minimum rewards
Most risk averse agent
Maximin Calculation
Identify the smallest value in each column and select the highest value
Soap = 25, Detergent = 10, Tooth Paste = 20
Conclusion
Soap is considered as an alternative having highest value
11. Laplace Criterion
1/n[ P1 + P2 + .. + Pn] [where n is the number of events, P1, P2 are the
expected payoff’s]
Product Lines Mean (Expected) Payoff
Soap (30 + 60 + 25)/3 = 38.33
Detergent (45 + 30 + 10)/3 = 28.33
Tooth Paste (20 + 40 + 70)/3 = 83.33
Conclusion
Tooth Paste is considered as an alternative having highest value.
12. Hurwicz Criterion (α = 0.6)
Product Lines Max Min Criterion Value
= α . maximum + ( 1 – α ) .
minimum
Soap 60 25 0.6 * 60 + 0.4 * 25 = 46
Detergent 45 10 0.6 * 45 + 0.4 * 10 = 31
Tooth Paste 70 20 0.6 * 70 + 0.4 * 20 = 50
Conclusion
Tooth Paste is considered as an alternative having highest value.
13. Opportunity Loss or Regret Table
The Opportunity Loss or Regret table can be obtained by subtracting from the
highest profit value in each row with the remaining elements
In the first row of the Payoff-table, the highest element is 45. In the second
row of the Payoff-table, the highest element is 60. In the third row of the
Payoff-table, the highest element is 70.
Product Acceptance
Sales of Product Lines in lakhs
Soap Detergent Toothpaste
High 15 0 25
Medium 0 30 20
Low 55 60 0
14. Savage Principle
From the Opportunity Loss Table or Regret Table, the maximum value in each
column is identified. From all the maximum regret values in each of the
columns the minimum among the maximum regret is selected.
In the first column it is 55, in the second column it is 60 and in third column,
it is 25.
From all the maximum values, the minimum value is selected.
The minimum value is 25.
Conclusion
Tooth Paste is considered as an alternative having smallest value
15. Practice Problems in Decision Theory
Q.1
Determine EMV, EVPI, Maximax, Maximin, Laplace Criterion, Hurwicz Criterion
(α = 0.3), Opportunity Loss Matrix, Savage Principle for the following payoffs
Demand
Probabilities Profit of Product Lines in lakhs
T. V Refrigerator Washing Machine
High 0.35 120 75 40
Medium 0.25 50 80 20
Low 0.40 55 30 30
16. Q.2
Determine EMV, EVPI, Maximax, Maximin, Laplace Criterion, Hurwicz Criterion
(α = 0.7), Opportunity Loss Matrix, Savage Principle for the following payoffs
Demand
Probabilities Sales in lakhs
Expand Diversify Sub Contract
High 0.60 30 80 25
Medium 0.15 65 70 90
Low 0.25 35 15 40