Really good lecture John, thank you for explaining so clearly.
I believe we may have missed a step at point B, we should have deducted 2m from 4.59 = 2.59. So the decision would have been 5m & 2.59 m return. It doesn’t impact the final decision in this example as 5m is still a better option.
Who ever thought this technique is worth learning and should be added to the Syllabus should be fired from their job. imagine advising your boss to take particular decision based on this childish drawings he’ll fire you on the spot.
anyhow thank you mr john for making it simple and understandable
But most decisions in practice have uncertain outcomes and expected values is one approach to dealing with the uncertainty that might be worth considering. (The tree itself is not a requirement, but does help sort out the problem when there are several decisions to be made depending on the outcomes. The problem is not the tree but whether or not expected values are a sensible way of dealing with the uncertainty.)
Skrypnyk says
thank you very much.
With your explanations, the task was so simple
khushbooo7 says
Hello Sir,
Hope all is well.
Sir in 6th part you’ve taken the value 5 instead of 4.59.
please explain sir.
Thank You!
John Moffat says
Because it is our choice whether to shut or to continue with cheap refurbishment. Shutting gives us a bigger return,
Vjhajharia says
Well explained but as for remote exams with no rough sheet allowed, will it be possible to solve such questions?
John Moffat says
You cannot be asked to draw a tree, but you are expected to understand it and be able to do relevant calculations on it.
(Although there is no rough paper, there is a scratch pad on the computer)
adekjarin says
Thank you Mr. Moffat, your lecture is quite helpful as you explained a complex concept with simplicity yet informative
John Moffat says
Thank you for your comment 馃檪
abokor says
Hello john
i have struggled to find the value of perfect information in example two in the case of the decision three; could you help please.
i would like thank u in advance.
your are the best accounting lecturer i have ever saw.
John Moffat says
But example 2 does not ask for the value of perfect information 馃檪
abokor says
I know, but i wonder if we could find out the value of perfect information in the case of the decision three.
Thanks john.
alhassankamara says
This is the best lecture I have ever got on decision tree. Thanks for the clear and concise explanation.
Respectfully!!!
John Moffat says
Thank you for your comment 馃檪
naholom1990 says
Thank you very much for this presentation. It has indeed cleared alot of my confusion regarding the decision tree theory.
John Moffat says
Thank you for your comment 馃檪
peppyalways says
Really good lecture John, thank you for explaining so clearly.
I believe we may have missed a step at point B, we should have deducted 2m from 4.59 = 2.59. So the decision would have been 5m & 2.59 m return. It doesn’t impact the final decision in this example as 5m is still a better option.
sushanth12 says
Fantastic lecture.
John Moffat says
Thank you for your comment 馃檪
yaya700 says
What happens to the 200K for market research?
yaya700 says
Please ignore, sorry
John Moffat says
No problem 馃檪
charlamagne says
Who ever thought this technique is worth learning and should be added to the Syllabus should be fired from their job.
imagine advising your boss to take particular decision based on this childish drawings he’ll fire you on the spot.
anyhow thank you mr john for making it simple and understandable
John Moffat says
But most decisions in practice have uncertain outcomes and expected values is one approach to dealing with the uncertainty that might be worth considering.
(The tree itself is not a requirement, but does help sort out the problem when there are several decisions to be made depending on the outcomes. The problem is not the tree but whether or not expected values are a sensible way of dealing with the uncertainty.)
Thanks for the final comment 馃檪
alie2018 says
Excellent presentation. Thanks