In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that you could obtain from gaining access to a sample of additional observations before making a decision. The additional information obtained from the sample may allow you to make a more informed, and thus better, decision, thus resulting in an increase in expected utility. EVSI attempts to estimate what this improvement would be before seeing actual sample data; hence, EVSI is a form of what is known as preposterior analysis.
Formulation
Let
It is common (but not essential) in EVSI scenarios for , and , which is to say that each observation is an unbiased sensor reading of the underlying state , with each sensor reading being independent and identically distributed.
The utility from the optimal decision based only on your prior, without making any further observations, is given by
If you could gain access to a single sample, , the optimal posterior utility would be:
In probability theory, the expected value of a random variable is intuitively the long-run average value of repetitions of the experiment it represents. For example, the expected value of a six-sided die roll is 3.5 because, roughly speaking, the average of an extremely large number of die rolls is practically always nearly equal to 3.5. Less roughly, the law of large numbers guarantees that the arithmetic mean of the values almost surely converges to the expected value as the number of repetitions goes to infinity. The expected value is also known as the expectation, mathematical expectation, EV, average, mean value, mean, or first moment.
More practically, the expected value of a discrete random variable is the probability-weighted average of all possible values. In other words, each possible value the random variable can assume is multiplied by its probability of occurring, and the resulting products are summed to produce the expected value. The same works for continuous random variables, except the sum is replaced by an integral and the probabilities by probability densities. The formal definition subsumes both of these and also works for distributions which are neither discrete nor continuous: the expected value of a random variable is the integral of the random variable with respect to its probability measure.
Decision Analysis 4 (Tree): EVSI - Expected Value of Sample Information
*Construct Decision Tree with Sample (Imperfect) Information
*Calculate Expected Value of Sample Information
*Use EVSI to determine the best decision strategy
To prevent it from being boring, this video is a bit fast paced.
Please pause the video as often as necessary to ensure you capture all the details.
Other videos:
Solved Problem: https://youtu.be/TyyNL5Xd0dk
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://youtu.be/NQ-mYn9fPag
Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret
https://youtu.be/ajkXzvVegBk
Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information
https://www.youtube.com/watch?v=tbv9E9D2BRQ
Decision Analysis...
published: 26 Jun 2015
Expected Value Of Sample Information (EVSI) Tutorial
In this video, we go over how to calculate the expected value of our sample information. If you enjoyed the video please Like, Comment, Subscribe and share with someone who you think will find this useful.
published: 17 Feb 2020
Unit 9: Value of Information, Video 3: Expected Value of Sample Information
MIT IDS.333 Risk and Decision Analysis, Fall 2021
Instructor: Richard de Neufville
View the complete course: https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62jwhTqp8_1kwrkDkxZhpQC
This video outlines the process valuing information for a particular test. It demonstrates the complexity of process and the difficulty of obtaining the needed data. It motivates the practical approach in next video.
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support O...
published: 28 Sep 2022
Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information
In this tutorial, we discuss Decision Making With Probabilities (Decision Making under Risk).
We calculate Expected Monetary Value (EMV) and Expected Value of Perfect Information (EVPI).
Other videos:
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://www.youtube.com/watch?v=NQ-mYn9fPag
Decision Analysis 2: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 4: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: EVSI - Expected Value of Sample Information
https://www.youtube.com/watch?v=FUY07dvaUuE
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
published: 05 Jun 2015
Decision Trees - Calculating EVSI and EVPI
Expected Value of Sample Information and Expected Value of Perfect Information are explained in this video, as applied to a decision tree. This video is based on the Operations Research textbook by Wayne Winston.
published: 16 Sep 2022
The expected value of sample information
This is a very basic example of how to value imperfect ("sample") information in a decision tree.
published: 04 Feb 2021
Understanding EVSI and EVPI in Decision Analysis
#analytics #simulation #riskanalysis #decisiontree #decisionmaking #decisionanalysis #expectedvalue
This video introduces EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information) which are important concepts in decision analysis.
💻 Decision Analysis videos
➡ Intro to Decision Analysis: https://youtu.be/LaLfnMTHbtY
➡ Single Stage Decision Rules: https://youtu.be/0DAcQoUffJo
➡ Walkthrough Expected Monetary Value EMV Calculation: https://youtu.be/ZVjMHSFHsfc
➡ Using Palisade's PrecisionTree: https://youtu.be/TckySTf_qKM
➡ Multistage Decision Problems: https://youtu.be/_EFlyFMv2xM
➡ Walkthrough Multistage Decision Problems using PrecisionTree: https://youtu.be/HoF0imQJd5Q
➡ EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Inf...
published: 20 Sep 2022
Understanding the EVSI formula
Understanding the EVSI formula
powerpoint file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41318
excel file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41319
published: 29 Jan 2021
Payoff Table: Expected Value and Perfect Information for Costs
This brief video shows how to make decision based on Expected Value & Expected Value of Perfect Information given a Payoff Table consisting of costs.
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
published: 06 Jun 2018
The expected value of sample information (EVSI) is the difference between:
QUESTION
The expected value of sample information (EVSI) is the difference between:
ANSWER:
A.) the posterior probabilities and the prior probabilities of the states of nature.
B.) the expected payoff with perfect information (EPPI) and the expected monetary value for the best decision (EMV*).
C.) the expected monetary value with additional information (EMV') and the expected monetary value for the best decision (EMV*).
D.) the expected value of perfect information (EVPI) and the smallest expected opportunity loss (EOL*).
Pay someone to do your homework, quizzes, exams, tests, assignments and full class at:
https://paysomeonetodo.com/
*Construct Decision Tree with Sample (Imperfect) Information
*Calculate Expected Value of Sample Information
*Use EVSI to determine the best decision strategy
...
*Construct Decision Tree with Sample (Imperfect) Information
*Calculate Expected Value of Sample Information
*Use EVSI to determine the best decision strategy
To prevent it from being boring, this video is a bit fast paced.
Please pause the video as often as necessary to ensure you capture all the details.
Other videos:
Solved Problem: https://youtu.be/TyyNL5Xd0dk
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://youtu.be/NQ-mYn9fPag
Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret
https://youtu.be/ajkXzvVegBk
Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information
https://www.youtube.com/watch?v=tbv9E9D2BRQ
Decision Analysis 3: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: Posterior Probability Calculations
https://youtu.be/FpKiHpYnY_I
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
*Construct Decision Tree with Sample (Imperfect) Information
*Calculate Expected Value of Sample Information
*Use EVSI to determine the best decision strategy
To prevent it from being boring, this video is a bit fast paced.
Please pause the video as often as necessary to ensure you capture all the details.
Other videos:
Solved Problem: https://youtu.be/TyyNL5Xd0dk
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://youtu.be/NQ-mYn9fPag
Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret
https://youtu.be/ajkXzvVegBk
Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information
https://www.youtube.com/watch?v=tbv9E9D2BRQ
Decision Analysis 3: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: Posterior Probability Calculations
https://youtu.be/FpKiHpYnY_I
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
In this video, we go over how to calculate the expected value of our sample information. If you enjoyed the video please Like, Comment, Subscribe and share with...
In this video, we go over how to calculate the expected value of our sample information. If you enjoyed the video please Like, Comment, Subscribe and share with someone who you think will find this useful.
In this video, we go over how to calculate the expected value of our sample information. If you enjoyed the video please Like, Comment, Subscribe and share with someone who you think will find this useful.
MIT IDS.333 Risk and Decision Analysis, Fall 2021
Instructor: Richard de Neufville
View the complete course: https://ocw.mit.edu/courses/ids-333-risk-and-decisi...
MIT IDS.333 Risk and Decision Analysis, Fall 2021
Instructor: Richard de Neufville
View the complete course: https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62jwhTqp8_1kwrkDkxZhpQC
This video outlines the process valuing information for a particular test. It demonstrates the complexity of process and the difficulty of obtaining the needed data. It motivates the practical approach in next video.
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
MIT IDS.333 Risk and Decision Analysis, Fall 2021
Instructor: Richard de Neufville
View the complete course: https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62jwhTqp8_1kwrkDkxZhpQC
This video outlines the process valuing information for a particular test. It demonstrates the complexity of process and the difficulty of obtaining the needed data. It motivates the practical approach in next video.
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
In this tutorial, we discuss Decision Making With Probabilities (Decision Making under Risk).
We calculate Expected Monetary Value (EMV) and Expected Value of P...
In this tutorial, we discuss Decision Making With Probabilities (Decision Making under Risk).
We calculate Expected Monetary Value (EMV) and Expected Value of Perfect Information (EVPI).
Other videos:
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://www.youtube.com/watch?v=NQ-mYn9fPag
Decision Analysis 2: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 4: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: EVSI - Expected Value of Sample Information
https://www.youtube.com/watch?v=FUY07dvaUuE
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
In this tutorial, we discuss Decision Making With Probabilities (Decision Making under Risk).
We calculate Expected Monetary Value (EMV) and Expected Value of Perfect Information (EVPI).
Other videos:
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://www.youtube.com/watch?v=NQ-mYn9fPag
Decision Analysis 2: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 4: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: EVSI - Expected Value of Sample Information
https://www.youtube.com/watch?v=FUY07dvaUuE
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
Expected Value of Sample Information and Expected Value of Perfect Information are explained in this video, as applied to a decision tree. This video is based o...
Expected Value of Sample Information and Expected Value of Perfect Information are explained in this video, as applied to a decision tree. This video is based on the Operations Research textbook by Wayne Winston.
Expected Value of Sample Information and Expected Value of Perfect Information are explained in this video, as applied to a decision tree. This video is based on the Operations Research textbook by Wayne Winston.
#analytics #simulation #riskanalysis #decisiontree #decisionmaking #decisionanalysis #expectedvalue
This video introduces EVSI (Expected Value of Sample Informa...
#analytics #simulation #riskanalysis #decisiontree #decisionmaking #decisionanalysis #expectedvalue
This video introduces EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information) which are important concepts in decision analysis.
💻 Decision Analysis videos
➡ Intro to Decision Analysis: https://youtu.be/LaLfnMTHbtY
➡ Single Stage Decision Rules: https://youtu.be/0DAcQoUffJo
➡ Walkthrough Expected Monetary Value EMV Calculation: https://youtu.be/ZVjMHSFHsfc
➡ Using Palisade's PrecisionTree: https://youtu.be/TckySTf_qKM
➡ Multistage Decision Problems: https://youtu.be/_EFlyFMv2xM
➡ Walkthrough Multistage Decision Problems using PrecisionTree: https://youtu.be/HoF0imQJd5Q
➡ EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information): https://youtu.be/d1lysX5D4Rc
💻 Other Business Analytics topics
➡ Introduction to Business Statistics: https://youtube.com/playlist?list=PLVfkLzKIXfrFjZMKNGPswQMXCZmScnRb6
➡ Linear Regression: https://youtube.com/playlist?list=PLVfkLzKIXfrGgGRi5lZeZSsOSiwaZjafd
➡ Business Simulation (using @Risk): https://www.youtube.com/playlist?list=PLVfkLzKIXfrFOcr-pw8DD-S4H7idNT2Rb
➡ Decision Analysis (using PrecisionTree): https://youtube.com/playlist?list=PLVfkLzKIXfrFXJqVsFgryneIn8eTGDFc3
➡ Linear Programming (using Lingo): https://youtube.com/playlist?list=PLVfkLzKIXfrHd2P9TXn-IVbgROhCWLIDz
➡ XLSTAT tutorials: https://youtube.com/playlist?list=PLVfkLzKIXfrENQu8c5QrsjcuS8JUuaww4
#analytics #simulation #riskanalysis #decisiontree #decisionmaking #decisionanalysis #expectedvalue
This video introduces EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information) which are important concepts in decision analysis.
💻 Decision Analysis videos
➡ Intro to Decision Analysis: https://youtu.be/LaLfnMTHbtY
➡ Single Stage Decision Rules: https://youtu.be/0DAcQoUffJo
➡ Walkthrough Expected Monetary Value EMV Calculation: https://youtu.be/ZVjMHSFHsfc
➡ Using Palisade's PrecisionTree: https://youtu.be/TckySTf_qKM
➡ Multistage Decision Problems: https://youtu.be/_EFlyFMv2xM
➡ Walkthrough Multistage Decision Problems using PrecisionTree: https://youtu.be/HoF0imQJd5Q
➡ EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information): https://youtu.be/d1lysX5D4Rc
💻 Other Business Analytics topics
➡ Introduction to Business Statistics: https://youtube.com/playlist?list=PLVfkLzKIXfrFjZMKNGPswQMXCZmScnRb6
➡ Linear Regression: https://youtube.com/playlist?list=PLVfkLzKIXfrGgGRi5lZeZSsOSiwaZjafd
➡ Business Simulation (using @Risk): https://www.youtube.com/playlist?list=PLVfkLzKIXfrFOcr-pw8DD-S4H7idNT2Rb
➡ Decision Analysis (using PrecisionTree): https://youtube.com/playlist?list=PLVfkLzKIXfrFXJqVsFgryneIn8eTGDFc3
➡ Linear Programming (using Lingo): https://youtube.com/playlist?list=PLVfkLzKIXfrHd2P9TXn-IVbgROhCWLIDz
➡ XLSTAT tutorials: https://youtube.com/playlist?list=PLVfkLzKIXfrENQu8c5QrsjcuS8JUuaww4
Understanding the EVSI formula
powerpoint file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41318
excel file: https://learn.bcit.ca/d2l/lor/...
Understanding the EVSI formula
powerpoint file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41318
excel file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41319
Understanding the EVSI formula
powerpoint file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41318
excel file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41319
This brief video shows how to make decision based on Expected Value & Expected Value of Perfect Information given a Payoff Table consisting of costs.
EMV and E...
This brief video shows how to make decision based on Expected Value & Expected Value of Perfect Information given a Payoff Table consisting of costs.
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
This brief video shows how to make decision based on Expected Value & Expected Value of Perfect Information given a Payoff Table consisting of costs.
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
QUESTION
The expected value of sample information (EVSI) is the difference between:
ANSWER:
A.) the posterior probabilities and the prior probabilities of the ...
QUESTION
The expected value of sample information (EVSI) is the difference between:
ANSWER:
A.) the posterior probabilities and the prior probabilities of the states of nature.
B.) the expected payoff with perfect information (EPPI) and the expected monetary value for the best decision (EMV*).
C.) the expected monetary value with additional information (EMV') and the expected monetary value for the best decision (EMV*).
D.) the expected value of perfect information (EVPI) and the smallest expected opportunity loss (EOL*).
Pay someone to do your homework, quizzes, exams, tests, assignments and full class at:
https://paysomeonetodo.com/
QUESTION
The expected value of sample information (EVSI) is the difference between:
ANSWER:
A.) the posterior probabilities and the prior probabilities of the states of nature.
B.) the expected payoff with perfect information (EPPI) and the expected monetary value for the best decision (EMV*).
C.) the expected monetary value with additional information (EMV') and the expected monetary value for the best decision (EMV*).
D.) the expected value of perfect information (EVPI) and the smallest expected opportunity loss (EOL*).
Pay someone to do your homework, quizzes, exams, tests, assignments and full class at:
https://paysomeonetodo.com/
*Construct Decision Tree with Sample (Imperfect) Information
*Calculate Expected Value of Sample Information
*Use EVSI to determine the best decision strategy
To prevent it from being boring, this video is a bit fast paced.
Please pause the video as often as necessary to ensure you capture all the details.
Other videos:
Solved Problem: https://youtu.be/TyyNL5Xd0dk
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://youtu.be/NQ-mYn9fPag
Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret
https://youtu.be/ajkXzvVegBk
Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information
https://www.youtube.com/watch?v=tbv9E9D2BRQ
Decision Analysis 3: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: Posterior Probability Calculations
https://youtu.be/FpKiHpYnY_I
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
In this video, we go over how to calculate the expected value of our sample information. If you enjoyed the video please Like, Comment, Subscribe and share with someone who you think will find this useful.
MIT IDS.333 Risk and Decision Analysis, Fall 2021
Instructor: Richard de Neufville
View the complete course: https://ocw.mit.edu/courses/ids-333-risk-and-decision-analysis-fall-2021/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62jwhTqp8_1kwrkDkxZhpQC
This video outlines the process valuing information for a particular test. It demonstrates the complexity of process and the difficulty of obtaining the needed data. It motivates the practical approach in next video.
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
In this tutorial, we discuss Decision Making With Probabilities (Decision Making under Risk).
We calculate Expected Monetary Value (EMV) and Expected Value of Perfect Information (EVPI).
Other videos:
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
Decision Analysis 1: Maximax, Maximin, Minimax Regret
https://www.youtube.com/watch?v=NQ-mYn9fPag
Decision Analysis 2: Equally Likely (Laplace) and Realism (Hurwicz)
https://www.youtube.com/watch?v=zlblUq9Dd14
Decision Analysis 4: Decision Trees 1
https://www.youtube.com/watch?v=ydvnVw80I_8
Decision Analysis 5: EVSI - Expected Value of Sample Information
https://www.youtube.com/watch?v=FUY07dvaUuE
Sensitivity Analysis in Decision Analysis: https://youtu.be/ybz5AAlyrLk
Expected Value of Sample Information and Expected Value of Perfect Information are explained in this video, as applied to a decision tree. This video is based on the Operations Research textbook by Wayne Winston.
#analytics #simulation #riskanalysis #decisiontree #decisionmaking #decisionanalysis #expectedvalue
This video introduces EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information) which are important concepts in decision analysis.
💻 Decision Analysis videos
➡ Intro to Decision Analysis: https://youtu.be/LaLfnMTHbtY
➡ Single Stage Decision Rules: https://youtu.be/0DAcQoUffJo
➡ Walkthrough Expected Monetary Value EMV Calculation: https://youtu.be/ZVjMHSFHsfc
➡ Using Palisade's PrecisionTree: https://youtu.be/TckySTf_qKM
➡ Multistage Decision Problems: https://youtu.be/_EFlyFMv2xM
➡ Walkthrough Multistage Decision Problems using PrecisionTree: https://youtu.be/HoF0imQJd5Q
➡ EVSI (Expected Value of Sample Information) and EVPI (Expected Value of Perfect Information): https://youtu.be/d1lysX5D4Rc
💻 Other Business Analytics topics
➡ Introduction to Business Statistics: https://youtube.com/playlist?list=PLVfkLzKIXfrFjZMKNGPswQMXCZmScnRb6
➡ Linear Regression: https://youtube.com/playlist?list=PLVfkLzKIXfrGgGRi5lZeZSsOSiwaZjafd
➡ Business Simulation (using @Risk): https://www.youtube.com/playlist?list=PLVfkLzKIXfrFOcr-pw8DD-S4H7idNT2Rb
➡ Decision Analysis (using PrecisionTree): https://youtube.com/playlist?list=PLVfkLzKIXfrFXJqVsFgryneIn8eTGDFc3
➡ Linear Programming (using Lingo): https://youtube.com/playlist?list=PLVfkLzKIXfrHd2P9TXn-IVbgROhCWLIDz
➡ XLSTAT tutorials: https://youtube.com/playlist?list=PLVfkLzKIXfrENQu8c5QrsjcuS8JUuaww4
Understanding the EVSI formula
powerpoint file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41318
excel file: https://learn.bcit.ca/d2l/lor/viewer/view.d2l?ou=6605&loIdentId=41319
This brief video shows how to make decision based on Expected Value & Expected Value of Perfect Information given a Payoff Table consisting of costs.
EMV and EVPI in Excel: https://youtu.be/WsHCbEAhSzU
QUESTION
The expected value of sample information (EVSI) is the difference between:
ANSWER:
A.) the posterior probabilities and the prior probabilities of the states of nature.
B.) the expected payoff with perfect information (EPPI) and the expected monetary value for the best decision (EMV*).
C.) the expected monetary value with additional information (EMV') and the expected monetary value for the best decision (EMV*).
D.) the expected value of perfect information (EVPI) and the smallest expected opportunity loss (EOL*).
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In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that you could obtain from gaining access to a sample of additional observations before making a decision. The additional information obtained from the sample may allow you to make a more informed, and thus better, decision, thus resulting in an increase in expected utility. EVSI attempts to estimate what this improvement would be before seeing actual sample data; hence, EVSI is a form of what is known as preposterior analysis.
Formulation
Let
It is common (but not essential) in EVSI scenarios for , and , which is to say that each observation is an unbiased sensor reading of the underlying state , with each sensor reading being independent and identically distributed.
The utility from the optimal decision based only on your prior, without making any further observations, is given by
If you could gain access to a single sample, , the optimal posterior utility would be: