11–20 of 152 posts
Just change the epsilon = 0.1 (10%) higher or lower depending on your initial (personal) confidence, and if your guess was right, and your epsilon low, then the overall impact to 'optimal' solution is negligible, but you have built in a fail safe in case you were human after all.
With all metrics, it's important to understand what's actually going into the measure and where it might get tripped up.
A potential solution might be to add a decay factor, so that the older data carries less weight.
There are appropriate solutions to the multi-armed bandit problem, and a wealth of literature out there, however this is not one of those solutions. Here's a simple thought experiment to show that this will not 'beat A/B testing every time.' Imagine you have two designs, one has a 100% conversion rate, one has a 0% conversion rate. Simple A/B testing will allow you to pick the the winning example. Whereas this soluti…
Sure the OP advocates "set and forget", but it seems simply silly to do this indefinitely. Obviously, after a while, you're going to want to check in on the results and prune out low performers.
Is that not the entire point of all these different testing methodologies to begin with?
Also, if you were to follow the same "set and forget" strategy with A/B, you'd have a 50% chance of showing the 0% CR design, where-as it's only 10% with this method.
Seems like it wins out in both cases to me.
This is an interesting technique, but it too has flaws. If there is a period of buzz and excitement surrounding your app, whatever design was most popular at that time will be rewarded accordingly, and accrue a high click through rate with tens of thousands of case. If you introduce a new superior design after the period of buzz has gone away, the new design may take a very long time to catch up. Even though it is cu…
This is an interesting technique, but it too has flaws. If there is a period of buzz and excitement surrounding your app, whatever design was most popular at that time will be rewarded accordingly, and accrue a high click through rate with tens of thousands of case. If you introduce a new superior design after the period of buzz has gone away, the new design may take a very long time to catch up. Even though it is cu…
There are appropriate solutions to the multi-armed bandit problem, and a wealth of literature out there, however this is not one of those solutions. Here's a simple thought experiment to show that this will not 'beat A/B testing every time.' Imagine you have two designs, one has a 100% conversion rate, one has a 0% conversion rate. Simple A/B testing will allow you to pick the the winning example. Whereas this soluti…
Even your thought experiment is not conclusive. First, it will take some time to be convinced which variation is better. The lost revenue during the 50/50 experimentation phase may outweigh the long-run benefit if your discount rate is high. This can happen in markets where customer acquisition is slow and expensive (i.e. a niche targeted via AdWords). Second, except in the unrealistic 0% vs 100% case, you could get stuck on wrong choice forever if you pass a statistical significance test by chance. Epsilon-greedy will correct itself over time and is asymptotically guaranteed to make the correct choice most of the time. A/B testing only has this asymptotic guarantee if you spend asymptotically long experimenting, which defeats the whole exercise.
This is an interesting technique, but it too has flaws. If there is a period of buzz and excitement surrounding your app, whatever design was most popular at that time will be rewarded accordingly, and accrue a high click through rate with tens of thousands of case. If you introduce a new superior design after the period of buzz has gone away, the new design may take a very long time to catch up. Even though it is cu…