Even if there is no acceptable way to share the data semi-anonymously outside of match group, the arguments for prediction markets still apply within match group. A well designed prediction market would still be a better way to distribute internal resources and rewards amongst competing data science teams within match group.
But I’m skeptical that the value of match group’s private data is dominant even in the fully private data scenario. People who actually match and meetup with another user will probably have important inside view information inaccessible to the algorithms of match group.
Manifold.Love’s lack of success is hardly much evidence against the utility of prediction markets for dating markets, any more or less than most startup’s failure at X is evidence against the utility of X.
a better way to distribute internal resources and rewards amongst competing data science teams within match group.
Yeah, additionally, a prediction market may be a good way to aggregate the predictions of heterogenous prediction models.
Manifold.Love’s lack of success
It’s important to note that manifold.love basically still has not been tried; has only existed for a few months and is still missing some core features.
Though I do expect most of its matchmaking strength wont come from its prediction features. Prediction markets could still be used in this space to appoint paid professional matchmakers (who would place bets like, “there will be a second date conditional on a first date”, or “if these two read my introductions, 8% probability they’ll eventually get married”), and to filter spam or abuse (to send a message, you have to bet that the recipient probably wont hate it).
This question can probably be operationalized as “how much richer will CupidBot be than all human matchmakers combined.”
Even if there is no acceptable way to share the data semi-anonymously outside of match group, the arguments for prediction markets still apply within match group. A well designed prediction market would still be a better way to distribute internal resources and rewards amongst competing data science teams within match group.
I used to think things like this, but now I disagree, and actually think it’s fairly unlikely this is the case.
Internal prediction markets have tried (and failed) at multiple large organisations who made serious efforts to create them
As I’ve explained in this post, prediction markets are very inefficient at sharing rewards. Internal to a company you are unlikely to have the right incentives in place as much as just subsidising a single team who can share models etc. The added frictions of a market are substantial.
The big selling points of prediction markets (imo) come from:
Being able to share results without sharing information (ie I can do some research, keep the information secret, but have people benefit from the conclusions)
Incentivising a wider range of people. At an orgasation, you’d hire the most appropriate people into your data science team and let them run. There’s no need to wonder if someone from marketing is going to outperform their algorithm.
People who actually match and meetup with another user will probably have important inside view information inaccessible to the algorithms of match group.
I strongly agree. I think people often confuse “market” and “prediction market”. There is another (arguably better) model of dating apps which is that the market participants are the users, and the site is actually acting as a matching engine. Since I (generally) think markets are great, this also seems pretty great to me.
Even if there is no acceptable way to share the data semi-anonymously outside of match group, the arguments for prediction markets still apply within match group. A well designed prediction market would still be a better way to distribute internal resources and rewards amongst competing data science teams within match group.
But I’m skeptical that the value of match group’s private data is dominant even in the fully private data scenario. People who actually match and meetup with another user will probably have important inside view information inaccessible to the algorithms of match group.
Manifold.Love’s lack of success is hardly much evidence against the utility of prediction markets for dating markets, any more or less than most startup’s failure at X is evidence against the utility of X.
Yeah, additionally, a prediction market may be a good way to aggregate the predictions of heterogenous prediction models.
It’s important to note that manifold.love basically still has not been tried; has only existed for a few months and is still missing some core features.
Though I do expect most of its matchmaking strength wont come from its prediction features. Prediction markets could still be used in this space to appoint paid professional matchmakers (who would place bets like, “there will be a second date conditional on a first date”, or “if these two read my introductions, 8% probability they’ll eventually get married”), and to filter spam or abuse (to send a message, you have to bet that the recipient probably wont hate it).
This question can probably be operationalized as “how much richer will CupidBot be than all human matchmakers combined.”
I used to think things like this, but now I disagree, and actually think it’s fairly unlikely this is the case.
Internal prediction markets have tried (and failed) at multiple large organisations who made serious efforts to create them
As I’ve explained in this post, prediction markets are very inefficient at sharing rewards. Internal to a company you are unlikely to have the right incentives in place as much as just subsidising a single team who can share models etc. The added frictions of a market are substantial.
The big selling points of prediction markets (imo) come from:
Being able to share results without sharing information (ie I can do some research, keep the information secret, but have people benefit from the conclusions)
Incentivising a wider range of people. At an orgasation, you’d hire the most appropriate people into your data science team and let them run. There’s no need to wonder if someone from marketing is going to outperform their algorithm.
I strongly agree. I think people often confuse “market” and “prediction market”. There is another (arguably better) model of dating apps which is that the market participants are the users, and the site is actually acting as a matching engine. Since I (generally) think markets are great, this also seems pretty great to me.