I have investigated the issues you highlighted, diagnosed the underlying errors, and revised the model accordingly. The root of the problem was that I had sourced some of the estimates of the frequency of prosocial behavior from studies on social behavior under special, unrepresentative conditions, such as infants interacting with adults for 10 min while being observed by researchers and prosocial behavior in TV series. I have removed those biased estimates of the frequency of prosocial behavior in the real world. As a consequence, the predicted lifetime increase in the number of kind acts per person reached by the intervention dropped from 1600 to 64. The predicted cost-effectiveness of the research dropped from 110 times the cost-effectiveness of StrongMinds to 7.5 times the cost-effectiveness of StrongMinds.
In producing this revised version, I also made a few additional improvements. The most consequential of those was to base the estimated cost of deploying the intervention on empirical data on the effectiveness of online advertising in $ per install.
I am currently using Squiggle to program a much more rigorous version of this analysis. That version will include additional improvements and rigorously document and justify each of the model’s assumptions.
Thank you for your feedback, Michael, and thank you very much for making me aware of those specialized prediction platforms. I really like your suggestion. I think making predictions about the likely results of replication studies would be helpful for me. It would push me to critically examine and quantify how much confidence I should put in the studies my models rely on. Obtaining the predictions of other people would be a good way to make that assessment more objective. We could then incorporate the aggregate prediction into the model. Moreover, we could use prediction markets to obtain estimates or forecasts for quantities for which no published studies are available yet. I think it might be a good idea to incorporate those steps into our methodology. I will discuss that with our team today.
I have investigated the issues you highlighted, diagnosed the underlying errors, and revised the model accordingly. The root of the problem was that I had sourced some of the estimates of the frequency of prosocial behavior from studies on social behavior under special, unrepresentative conditions, such as infants interacting with adults for 10 min while being observed by researchers and prosocial behavior in TV series. I have removed those biased estimates of the frequency of prosocial behavior in the real world. As a consequence, the predicted lifetime increase in the number of kind acts per person reached by the intervention dropped from 1600 to 64. The predicted cost-effectiveness of the research dropped from 110 times the cost-effectiveness of StrongMinds to 7.5 times the cost-effectiveness of StrongMinds.
In producing this revised version, I also made a few additional improvements. The most consequential of those was to base the estimated cost of deploying the intervention on empirical data on the effectiveness of online advertising in $ per install.
I am currently using Squiggle to program a much more rigorous version of this analysis. That version will include additional improvements and rigorously document and justify each of the model’s assumptions.
Thanks for following up! Those sound like good changes.
Another thing you might do (if it’s feasible) is list the studies you’re using on something like Replication Markets.
Thank you for your feedback, Michael, and thank you very much for making me aware of those specialized prediction platforms. I really like your suggestion. I think making predictions about the likely results of replication studies would be helpful for me. It would push me to critically examine and quantify how much confidence I should put in the studies my models rely on. Obtaining the predictions of other people would be a good way to make that assessment more objective. We could then incorporate the aggregate prediction into the model. Moreover, we could use prediction markets to obtain estimates or forecasts for quantities for which no published studies are available yet. I think it might be a good idea to incorporate those steps into our methodology. I will discuss that with our team today.