Thank you for the suggestion. I agree that we canāt extrapolate the conclusion about predicted follow-through rate based on what percentage of companies have followed through on the commitment so far. I looked at it again, and I think that if analyzed correctly it still provides valuable information, so I will leave it in the model, but Iāll move it to the section on updates based on qualitative data and change the values based on the information below. I think that a good proxy for the information is whether the top 20 biggest companies are making progress and will eventually switch is if they responded to EggTrack. We can break it down to: * Companies that passed their deadline: Whole Foods (2004) ā 100% follow-through Costco (2018) - as of July 2017, 78% and 100% converted to liquid *Those that didnāt respond to EggTrack are Walmart, Albertsons, McDonaldās, Target, Sysco, ALDI, Burger King, Tim Hortons, Southeastern, and Wendyās. * Those that did respond and reported progress: Kroger (21% as of 2017), Publix (50%), SUPERVALU (as of 2016, 12%) * Those in 20 that I do not have information on US Foods, IGA, Inc., Associated Grocers of Florida.
I will estimate the value in this cell based on this information unless you have info that could fill in the gaps.
I generally think that with all very uncertain estimates, whatever the result, it should be only treated as a cautious update and be combined with prior estimates of value.
As a meta comment, I think Iām less concerned about an error in one of the parameters than you seem to be because of the different goals of the research. My goal is to reach broadly good conclusions about which intervention should be executed from a given list of options given a limited amount of time, rather than get the right answer to a specific question, even if it takes me an extremely large amount of time. I think that using cluster approach is superior in such cases. If you are using cluster approach, the more perspectives you take into account the lower the odds of your decision being wrong, and so I trade other aspects (eg. number of interventions compared in a given time frame and how accurate a single estimate need to be) differently. One contradicting factor also cannot overpower the whole decision, etc. A completely different method should be used when we are trying to have as accurate beliefs about the world as possible vs getting to a good decision.
I think Iām less concerned about an error in one of the parameters than you seem to be because of the different goals of the research.
Just to be clear, I wasnāt concerned about the error, I saw that deleting the cell and then making other appropriate changes increases the estimated probability by only 3%. I only commented about it because I thought that it is easy to fix. I agree that the approach you chose has its benefits.
Thank you for the suggestion. I agree that we canāt extrapolate the conclusion about predicted follow-through rate based on what percentage of companies have followed through on the commitment so far. I looked at it again, and I think that if analyzed correctly it still provides valuable information, so I will leave it in the model, but Iāll move it to the section on updates based on qualitative data and change the values based on the information below.
I think that a good proxy for the information is whether the top 20 biggest companies are making progress and will eventually switch is if they responded to EggTrack. We can break it down to:
* Companies that passed their deadline:
Whole Foods (2004) ā 100% follow-through
Costco (2018) - as of July 2017, 78% and 100% converted to liquid
*Those that didnāt respond to EggTrack are Walmart, Albertsons, McDonaldās, Target, Sysco, ALDI, Burger King, Tim Hortons, Southeastern, and Wendyās.
* Those that did respond and reported progress: Kroger (21% as of 2017), Publix (50%), SUPERVALU (as of 2016, 12%)
* Those in 20 that I do not have information on US Foods, IGA, Inc., Associated Grocers of Florida.
I will estimate the value in this cell based on this information unless you have info that could fill in the gaps.
I generally think that with all very uncertain estimates, whatever the result, it should be only treated as a cautious update and be combined with prior estimates of value.
As a meta comment, I think Iām less concerned about an error in one of the parameters than you seem to be because of the different goals of the research. My goal is to reach broadly good conclusions about which intervention should be executed from a given list of options given a limited amount of time, rather than get the right answer to a specific question, even if it takes me an extremely large amount of time. I think that using cluster approach is superior in such cases. If you are using cluster approach, the more perspectives you take into account the lower the odds of your decision being wrong, and so I trade other aspects (eg. number of interventions compared in a given time frame and how accurate a single estimate need to be) differently. One contradicting factor also cannot overpower the whole decision, etc. A completely different method should be used when we are trying to have as accurate beliefs about the world as possible vs getting to a good decision.
Just to be clear, I wasnāt concerned about the error, I saw that deleting the cell and then making other appropriate changes increases the estimated probability by only 3%. I only commented about it because I thought that it is easy to fix. I agree that the approach you chose has its benefits.