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Resurrecting deceased snooks
Payment Structure
The resurrection cost is proportional (not linear) to the number of Traits a recently deceased snook had. The total effective cost of resurrection is comprised of two types of payment:
- 1.Direct payment in $SNKDespite the fact that it costs the equivalent of $1.25USD to mint a snook-NFT, the floor/face-value of a Trait is not $1.25USD simply because not all Traits are “created equal”. In other words, some Traits are more common than others. The simple truth is that we don’t know what the exact face-value is and since that value, as we will explain, continues to change over time, this will remain the state of affairs.
- 2.Payment in-kind with Traits, that, as we mentioned before, serve as a vehicle for storing value. The implementation is simple:
- First resurrection of a given snook will bring the snook back minus one randomly selected Trait that was associated with it.
- Second resurrection of the same snook will incur a price of two Traits (on top of the direct payment of $SNK). You get the gist, it’s turtles all the way down.
Now you see why it's always good to collect and have spare Traits? Actually, that’s not the most important reason. Soon all will be revealed.
Resurrection fees and Fusion as well, all go directly to the Treasury. Further allocation is done through the Treasury.
Resurrecting deceased snooks and fusing
let:
- ($SNK) be the resurrection price
- be (:D) the number of Traits
- ($SNK) be the price for a single minted snook
- Letbe the level of rarity/difficulty of the snook
- Letbe the ranking of the snook. The value of is dynamic and measures the relative frequency of snooks with the same number of Traits in the general snook population. For a given snook that has for example 5 Traits, anmeans that 60% of all snooks have 5 or fewer Traits.
then
-
-
adjusts the resurrection price of the snook based on the number of Traits the snook has at the moment of death and its ranking relative to all other snooks. To calculate
we will use mock data for 10 different snooks as shown in the table below.
In the next table we can see that we have 5 snooks with 1 property (i.e., Trait), 4 snooks with 2 properties, and 1 snook with 4 properties.
Based on the above, we can build a probability density function:
with
being the number of snooks with
Traits and
in the maximum number of Traits.
In this example
therefore,
is:
Let
be a cumulative probability function, so, in our example:
The following tables show values of
for
= [1...4]
Last modified 1yr ago