I fully agree that pricing is a non-issue and that refining the system further is not worth the effort.
OTOH, it is fun speculating about the possibility of a "perfect" pricing system (even though one probably doesn't exist) and how it would work. That's probably why the topic generates so much discussion; not because players think the current system is detrimental to gameplay, but because brainstorming new ideas is enjoyable in itself.
And as long as we're all making up stuff that will never be implemented, here is my (almost completely impractical) approach to generating a near-perfect pricing scheme:
* Prior to the start of the testing period, randomly generate 1000 items (preferably more if you can get enough testers) to use as a baseline for gathering purchasing habits. These items will be saved to a file.
* Recruit LOTS of players to play test. The total number of games to be played should be on the order of 10x the number of baseline items, maybe more (ie 10,000 total games played if 1000 baseline items are used).
* At each visit to the town, stores are *ONLY* stocked with items chosen randomly from the list of baseline items.
* Each item is priced according to the current scheme, multiplied by a random factor between 0.1 and 10. The goal is to have nearly-random prices which are still within reason. Nobody's going to pay 1,000,000 AU for a sling, after all.
* Every time a player buys an item, record the price that was paid.
* At the end of the testing period, the "perfect" price of each baseline item would be set at 90th percentile of the prices paid for that particular item across all games (you want something close to the highest price paid, but ignoring outliers).
* Statistical analysis could then be used to conclude rough correlations between item abilities and value. Results of the analysis would be used to create the final pricing formula.
* OR (and I like this idea even better), ignore the previous step completely, and use the "perfect prices" as training data for a neural network. Information about an item (item type, abilities, weight, etc...) would be fed into the neural network as inputs, and the output would be an appropriate price for the item. All standard warnings apply regarding over-training the network.
OTOH, it is fun speculating about the possibility of a "perfect" pricing system (even though one probably doesn't exist) and how it would work. That's probably why the topic generates so much discussion; not because players think the current system is detrimental to gameplay, but because brainstorming new ideas is enjoyable in itself.
And as long as we're all making up stuff that will never be implemented, here is my (almost completely impractical) approach to generating a near-perfect pricing scheme:
* Prior to the start of the testing period, randomly generate 1000 items (preferably more if you can get enough testers) to use as a baseline for gathering purchasing habits. These items will be saved to a file.
* Recruit LOTS of players to play test. The total number of games to be played should be on the order of 10x the number of baseline items, maybe more (ie 10,000 total games played if 1000 baseline items are used).
* At each visit to the town, stores are *ONLY* stocked with items chosen randomly from the list of baseline items.
* Each item is priced according to the current scheme, multiplied by a random factor between 0.1 and 10. The goal is to have nearly-random prices which are still within reason. Nobody's going to pay 1,000,000 AU for a sling, after all.
* Every time a player buys an item, record the price that was paid.
* At the end of the testing period, the "perfect" price of each baseline item would be set at 90th percentile of the prices paid for that particular item across all games (you want something close to the highest price paid, but ignoring outliers).
* Statistical analysis could then be used to conclude rough correlations between item abilities and value. Results of the analysis would be used to create the final pricing formula.
* OR (and I like this idea even better), ignore the previous step completely, and use the "perfect prices" as training data for a neural network. Information about an item (item type, abilities, weight, etc...) would be fed into the neural network as inputs, and the output would be an appropriate price for the item. All standard warnings apply regarding over-training the network.
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