TrashCash is a hypothetical name for a blockchain-based reward scheme that basically incentivizes people to do any combination of three related and extremely valuable tasks:
- report waste/hazards (such as broken glass bottles on a beach; motor oil dumped behind a gas station; etc)
- analyze/verify existing reports
- respond to the object/condition by cleaning it up or otherwise materially mitigating the risk posed by that object/condition
Here are the three steps, visually:
Although TrashCash is hypothetical, there are many existing players in this space, which is growing rapidly.
Walking through the TrashCash use case, we hope you see the radical transformative potential of distributed consensus and glocal economic coordination.
1. TrashCash Basics
Let’s imagine TrashCash as a lightweight iOS/Android mobile app. After downloading, the user has the option of adding her existing blockchain address (eg AliceSmith.eth) or generating a new one.
If Alice generates a new address, she would be promoted to write down or copy her public/private key pair.
In theory, this app could work with many different crypto instruments. But for the sake of simplicity, let’s imagine our hypothetical TrashCash is an ERC-20 token on the Ethereum blockchain — $TRSH.
To illustrate some of the problems with existing token issuance frameworks, let’s also add a hard 1,000,000 $TRSH issuance limit. Further, let’s imagine a linear issuance rate: 1 $TRSH transferred to reporter’s address per submitted report.
2. Alice Can Now #EarnCrypto!
Now, Alice can start earning crypto simply by reporting waste or a hazard that she finds is a problem. For instance, going about her day, Alice sees an improperly discarded tire.
She opens TrashCash, which goes directly to the camera app. She clicks a green button on the bottom, as though taking a photo. A green confirmation flash appears, telling Alice she’s earned 1 $TRSH.
The app automatically closes and Alice continues about her day. Because the photo has been sent to dCloud storage, it was instantly deleted from her camera roll. Alice is happy because she doesn’t have photos of trash clogging up her phone.
3. Alice-the-Analyst & Alice-the-Cleaner
Alice likes how easy it is to earn crypto for doing socially-useful work, so she digs deeper and learns there are two related (but different) ways she can earn:
- validate/analyze existing reports (eg, by adding tags, suggesting levels of urgency, estimating mass/volume, notify responsible party, etc.)
- respond to existing reports (by picking up reported objects & properly disposing them; by cleaning a hazardous condition, such as broken glass or cigarette butts on the playground)
Alice does all three activities and sees she gets the same 1 $TRSH in exchange for 2 minutes of reporting work; 20 seconds of validation work; or 10 minutes of response work.
4. Linear TrashCash Problems:
The problems with linear issuance and a hard issuance cap are immediately apparent:
- linear issuance (1 $TRSH / report is fair for late adopters, but does not incentivize early adoption)
- hard issuance cap (as conceived, the system can only process ~1,000,000 reports + validations + responses, at which point, there is no more $TRSH to incentivize reporting)
There’s a more foundational problem with this version of $TRSH: there is a clear misalignment in reward value between (1) reporting; (2) analytics; (3) response processes.
If someone gets the same 1 $TRSH for 20s of validator work as for 10m of response work, most everyone will gravitate towards the easier task.
5. Existing Tools to Optimize Token-Based Coordination Games
To solve the hard-cap problem (running out of $TRSH after ~1M reports), and the early-adopter incentive problem, $TRSH issuers could simply change a few issuance parameters. For instance:
- incentivize early adopters with higher rewards (eg, t1 early adopters get 8x rewards; t2 adopters get 4x rewards; t3 adopters get 2x rewards, etc.)
- move from arbitrary hard-cap to a log-based infinite supply
$TRSH issuers can also try to manually solve (yikes!) the disparity-in-reward problem by assigning largely arbitrary token values to what they (the token-issuers) consider more-or-less equivalent amounts of labor.
Under new hypothetical guidelines put out by the $TRSH team, reporting is still rewarded at 1 $TRSH, but each validation produces .2 $TRSH, each 10m response task produces 5 $TRSH.
The new model may appear more ‘efficient’ and more fair, but it really isn’t.
Users intuitively know that the 1, .2, and 5 $TRSH amounts are more or less irrational — they’re just numbers plucked from the issuer’s mind to create the appearance of some future rational equilibrium. In many ways, the system was more fair when users got exactly 1 $TRSH per unit of TrashCash economic activity — reporting; analytics; response.
Now, Alice in Thailand has to wonder if Bob is earning 5x more than she is for every cigarette butt that Bob picks up on a playground in Chicago.
These feelings of perceived inequity will quickly destroy a global coordination project.
6. Augmented Bonding Curve Approach
How can augmented bonding curves (ABCs) help $TRSH developers prior to token issuance and at crucial implementation stages? What is the concrete value proposition of ABCs for projects like TrashCash?
Answers: dynamic modeling, modularity, support.
Here’s what this actually means in the context of, say, CommonsStack:
- CommonsStack is a nonprofit whose mission is to deliver order-of-magnitude efficiency gains in how humanity uses its material and immaterial resources ( → CSA needs you to succeed in order for the mission to succeed);
- cadCAD is an industry-leading tokenomic modeling tool that allows teams to simulate (and potentially change) issuance parameters even amidst an existing issuance;
- CommonsStack templates and cadCAD are open-source tools that invite adaptation, customization, and extension. CommonsStack developers and Trusted Seed members are directly incentivized to support your integration because it maximizes the likelihood of more efficient solutions for everyone, us included.
Please note this does NOT solve the more difficult disparity-in-reward problem. But dynamic modeling allows experimentation to find optimal solutions in specific contexts.
At global scales, there may still be misalignment in reward value between (1) reporting; (2) analytics; (3) response processes. But in specific local contexts, the seemingly arbitrary values may actually effectively align reporters, analysts, and responders.