Minds In Depth: Economics
This post is part of a series of posts that explains Minds in more detail to those who want to deepen their understanding of the platform. Please consult the series introductory post Minds - In Depth [Introduction] to find the other posts in this series. The present post will explain some economic aspects of the Minds platform and the Minds tokens.
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Economics
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The Economics, in comparison to other social networks is extremely user centered. What does that mean? In the case of Facebook, there are users, and there are advertisers. The user is the product, the advertisers are the consumers. The advertisers buy access to use your data, although in most cases, they do not see your data directly. They pay money to push their advertisements onto you, based on your interests and your demographic. Your browsing activity, and your interaction on Facebook is valuable data to advertisers. Facebook makes its money through advertisers. The benefit to the users is the use of the network, and hopefully, they get to see advertisements that they want to see.
With the present way Social Networks, and the World Wide Web itself is constructed, there is unfortunately always a playoff between targeted, more interesting advertising, and personal privacy. If you relax your privacy, advertisers can target you more directly and the advertisements you see are probably less likely to look spamy.
While Facebook and Google take the extreme of targeted, no privacy advertisements, Minds takes the other extreme of untargeted advertisements, but does not compromise your privacy. On Minds, the advertising comes in the form of Boosts. Boosts are an untargeted form of advertising.
To use Boosts, you pay Tokens to the platform, which then shows your posts to other people. The Mathematics of Boost advertising go something like in the image and explanation below.
Independent Variables
Total number of views per second. It represents the overall activity of the minds network. It depends on human action, but it is independent with respect to to the other variables. Human action is fundamentally unpredictable. This is why it can be said that value of a stock can never with 100% accuracy be predicted. However, if human action is quite sure, then you can make a good educated guess based on mathematical principles.
Boost Ratio represents the number of boosts shown for every subscription post shown. It is an independent variable currently set by the minds network to be 1/7.
Boosts in the Queue is an independent variable, it is unpredictable on any given day, but it has limits within the amount of tokens that are held by users. Whether a user chooses to save or spend tokens on any given day, and thereby adding them to the queue, is not known, human action is unpredictable.
Dependent Variables
Time per post(in units of posts). This is just the number of posts that must show before a Boost post which has been ordered begins to take effect. This is a mathematical certainty wholely determined by the number of boost posts in the queue, and the ratio at which boost posts are shown.
Time per post(in units of seconds). This is the same as above, but with time added. It is never obvious how many people will visit the network and view posts on any day. So this is dependent on an unpredictable variable.
Things to Note
If you increase the boost ratio(b_ratio), let's say, from 1/7 to 1/6 the time that it takes for your boosts to show up will decrease. Of course, the flipside of this is that you will see less posts from your subscribers.
Having more viewers and lurkers on the network will have the immediate consequence of decreasing the time for a boosted post to be shown, yet, if these users begin to actively boost their content, then the effect balances out. Therefore, having a greater number of users has no long term effect on the rate boost posts are seen.
Increasing the number of Rewards for likes, comments, etc is likely to increase the number of Boosts in the queue (theoretically users could just decide to accumulate points without spending them, but that's unlikely to happen). If the number of Boosts in th queue change, one of three things must happen.
Imagine that b_que increases, either
1) the number of viewers must increase(which isn't something one can control directly),
2) b_ratio must increase. Ie, the network must show boost posts every 1/6 subscriber posts instead of 1/7.
3) t_seconds. If neither of the above change, the time it takes for a Boost post to be shown will unavoidably get longer. If people have experienced longer Boost times recently, it might be because the rewards are too high.
4) Something that I have not yet taken into account in this model, there is a delay between the time tokens are earned, and the time when they are rewarded, and again the time when the boost post is delivered. This delay between an earned reward and the delivery of a boost post effectively reduces the size of the rewards. If they were delivered immediately, it's possible that rewards could be nearly infinite, so long as the interaction with boosted content is sufficient. It seems to me that my token rewards have decreased recently, but I still have lots of boosts queued up, which seems to support this conclusion. This is not necessarily a bad thing. If the rewards given by the network decrease when the size of the queue increases, then this prevents the queue from getting uncontrollably long. It provides a balancing check on the boost algorithm.