Together we can improve the Moons to Karma ratio [Charts; Predictions; Long post]

Executive summary

It is possible to use simple linear regression to accurately predict Moons to Karma ratio. This Moon Round 17 is yet another anomaly, where quality > quantity. As a collective force we can improve the ratio while obeying all sub rules. Currently, the ratio is estimated at 0.160, but we can change it.

Introduction

Using machine learning to predict Moons to Karma ratio started as a fun scientific project within this community for me.^\1]) I was initially inspired by u/IHaventEvenGotADog posts. To predict the ratios I've created a cross-validated linear model that works on 2 numeric inputs - Daily scores and Daily comments.

Lately, it became more obvious to me that we all as a community can learn a lot about the current state of the sub based on the ratio predictions.

Anomalies

During the previous Moon Round 16 I have noticed that sub's behavior changed significantly, focusing more on quality rather than quantity.^\2]) Specifically, while there were many more comments in Dailies, not all comments seemed to receive high enough scores.

This same trend is true for the current Moon Round 17. Take a look at the charts below:

[c01] In this colorful scatterplot we can observe that round 16 and round 17 are different compared to all previous rounds. E.g., there are lots of comments, but scores are pretty low.

Now let's group all scatter-points from above chart and calculate the totals:

[c02] This chart visualizes totals for every round. The round 17 is estimated based on the current average daily comments and scores. The area of each point is proportional to total earned karma (round 17 is an estimation). Last 5 rounds are enumerated for clarity. This chart clearly shows how unique the rounds 16 and 17 (this one) are.

Model tests

During round 15 I created my first predictive model. During round 16 I created what I thought an improved predictive model. Turned out, good old round 15 model was much more accurate and also correctly predicted the last round anomaly. So I'm using it for this round 17 as well. You can read more about round 15 model in reference [1].

The only change I made to said model is how I estimate total number of Daily comments and total Daily score. Specifically, I'm simply using the current rate of commenting and scoring to estimate the totals.

[c03] This line chart shows the performance of my best predictive model at any given day and round, when complete information is not fully available. I.e., the most accurate predictions are always closer to day 28. If the commenting and scoring rates stay similar to what we have observed during the last 12 days, then the final ratio for this round should also stay close to 0.160

Discussion

• Is the model smarter than me in estimating the ratios? Yes! I was fooled last round by its overly optimistic predictions, which turned out to be very accurate. From now on I trust my predictive model.
• How can we improve Moons to Karma ratio legally? This and previous rounds showed that more comments do not necessarily mean smaller ratio. If we study the sub rules, report sh*tty content, vote fairly, we can cancel the negative effects of spam. Let's do smart Moon farming!
• As you can see from chart [c03] - it's not my final prediction so far. With each Moon Round the model itself gets smarter, since more training data is available. Contrarily, more new anomalies in our collective behavior seem to emerge. Plus all the new and upcoming rule changes.
• If I were to rely only on the total number of comments, I'd be getting much more pessimistic predictions. E.g., 0.200 for previous round and 0.100 for this round. Clearly, last round ratio was not 0.200, which supports my hypothesis that number of comments is not the only important input. The model that relies purely on total number of comments would work till round 15, as can be seen in chart [c02].

References

[1] Prior ML tests and model description https://www.reddit.com/r/CryptoCurrency/comments/oghi7f/a_machine_learning_model_which_predicts_moons_to/

[2] How Moon Round 16 was an anomaly https://www.reddit.com/r/CryptoCurrency/comments/ouxvri/what_makes_this_moon_round_different_chart/