I think it would probably take a while to figure out the specific cruxes of our disagreements.
On your “aesthetic disagreement”, I’d point out that there are, say, three types of forecasting work with respect to organizations.
Organization-specific, organization-unique questions.
These are questions such as, “Will this specific initiative be more successful than this other specific initiative?” Each one needs to be custom made for that organization.
Organization-specific, standard questions.
These are questions such as, “What is the likelihood that employee X will leave in 3 months”; where this question can be asked at many organizations and compared as such. A specific instance is unique to an organization, but the more general question is quite generic.
Inter-organization questions.
These are questions such as, “Will this common tool that everyone uses get hacked by 2020?”. Lots of organizations would be interested.
I think right now organizations are starting traditional judgemental forecasting for type (1), but there are several standard tools already for type (2). For instance, there are several startups that help businesses forecast key variables; like engineering timelines, sales, revenue, and HR issues.
https://www.liquidplanner.com/
I think type (3) is most exciting to me; that’s where PredictIt and Metaculus are currently. Getting the ontology right is difficult, but possible. Wikipedia and Wikidata are two successful (in my mind) examples of community efforts with careful ontologies that are useful to many organizations; I see many future public forecasting efforts in a similar vein. That said, I have a lot of uncertainty, so would like to see everything tried more.
I could imagine, in the “worst” case, that the necessary team for this could just be hired. You may be able to do some impressive things with just 5 full time equivalents, which isn’t that expensive in the scheme of things. The existing forecasting systems don’t seem to have that many full time equivalents to me (almost all forecasters are very part time)
I think it would probably take a while to figure out the specific cruxes of our disagreements.
On your “aesthetic disagreement”, I’d point out that there are, say, three types of forecasting work with respect to organizations.
Organization-specific, organization-unique questions. These are questions such as, “Will this specific initiative be more successful than this other specific initiative?” Each one needs to be custom made for that organization.
Organization-specific, standard questions. These are questions such as, “What is the likelihood that employee X will leave in 3 months”; where this question can be asked at many organizations and compared as such. A specific instance is unique to an organization, but the more general question is quite generic.
Inter-organization questions. These are questions such as, “Will this common tool that everyone uses get hacked by 2020?”. Lots of organizations would be interested.
I think right now organizations are starting traditional judgemental forecasting for type (1), but there are several standard tools already for type (2). For instance, there are several startups that help businesses forecast key variables; like engineering timelines, sales, revenue, and HR issues. https://www.liquidplanner.com/
I think type (3) is most exciting to me; that’s where PredictIt and Metaculus are currently. Getting the ontology right is difficult, but possible. Wikipedia and Wikidata are two successful (in my mind) examples of community efforts with careful ontologies that are useful to many organizations; I see many future public forecasting efforts in a similar vein. That said, I have a lot of uncertainty, so would like to see everything tried more.
I could imagine, in the “worst” case, that the necessary team for this could just be hired. You may be able to do some impressive things with just 5 full time equivalents, which isn’t that expensive in the scheme of things. The existing forecasting systems don’t seem to have that many full time equivalents to me (almost all forecasters are very part time)