Modeling Retailism

by Jango – originally shared on jango.eth.limo

Retailist networks are deterministic, meaning all networks configured the same way will have the same trajectory.

Each network is defined by fixed rules that change in fixed ways over a fixed time frame known as a “generation”.

Rules

Configuring a retailist network requires making three choices, and allows for five additional optional choices:

Currency (optional)

In which currency will the network store its value? ETH is used by default.

Initial issuance rate (optional)

At the network’s onset, how many $TOKENs will be issued when it receives 1 ETH? The default is 1.

Generation duration (required)

How long is a generation? Everyone who joins the network during a generation will receive the same amount of its $TOKENs when they pay ETH.

Generational tax rate (required)

By what percent will the network’s $TOKEN issuance rate be reduced between generations?

Exit tax rate (required)

How much less does a $TOKEN holder receive when accessing the network’s ETH immediately before any another $TOKEN holder does?
This is specified as a percent that’ll be input into the formula t / s * ((1-r) + r / s), where t is the network’s ETH balance, s is the total $TOKEN supply, and r is the tax rate. 0% suggests no tax, 100% suggests $TOKEN holders can never access the treasury because they’re taxed entirely.

Deploy tax (optional)

The amount of $TOKENs to mint at the time of deployment for the deployer.

Boost duration (optional)

The amount of time to apply a boost for, which pre-allocates a percentage of newly issued tokens to some beneficiary as the network receives ETH.

Boost rate (optional)

The percentage of newly issued $TOKENs to pre-allocate when the network receives ETH during the boost period.

Tensions

As it unfolds, each network will have a generation-over-generation growth rate which describes how much ETH a network receives over time, and an exit rate which describes what percentage of $TOKEN holders leave during each generation.

Tensions arise when considering how a network’s rules will affect these two tendencies:

networks with longer generations or a flatter generational tax rate leave room for more equal access over time to entice newcomers, but may lack energy from incumbent participants whose contributions are less recognized.
networks with a larger exit tax reward more committed network participants, but may dissuade someone from making a commitment to the network in the first place since they’ll have less freedom to leave.
networks with a deploy tax or boost can acknowledge the non-financial forms of participation which are often the subjects around which they revolve, but may also create governance burdens and spoil fair launch narratives.

Each network should consider its own unique circumstances when navigating these trade-offs, keeping in mind that each new $TOKEN holder will eventually become an elder one.

Playground

Play around with these levers in this spreadsheet. Adjust the rules and set a few projections at the top to see how the outcome changes.

the first graph shows the amount of ETH each $TOKEN can be redeemed for at any point in time. Notice how this number can only go up or stay still, depending on how much motion (new entries, new exists) there is in the network.
the second graph shows the amount of ETH that guarentees to back the last $TOKEN left in the network after everyone else has exited, at any point in time.
the third graph shows the percent rebate that all payments into the network over time would yield if the $TOKENs the payment produced were immediately redeemed. Notice how the amount converges on a percentage, approaching from the bottom as preminted $TOKENs get diluted or from the top if no preminted $TOKENs are specified.
the last shows the total value of all the $TOKENs allocated to boosting over time, if none were redeemed. Notice how even after boosting stops, the potential value of the allocated $TOKENs continues to rise as their redemption value rises.
the table gives all context and calculations from which the graphs are derived. It shows how the network’s dynamics change over time given the provided projections.

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