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📝 Update incentives and learning resources
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Incentives.md
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Incentives.md
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- A good mechanism is also a mechanism that actually does solve the problems that we care about. If it can't be done completely neutrally, it doesn't mean it should not be done at all.
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- Any mechanism that can help genuinely under-coordinated parties coordinate will, without the right safeguards, also help already coordinated parties (such as many accounts controlled by the same person) [over-coordinate](https://vitalik.eth.limo/general/2019/04/03/collusion.html) with potential ways to "do wrong" (e.g: extract money from the system).
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- You can increase mechanism complexity if you trade it off for identity or collusion resistance. If you figure out a way to make it the mechanism identity resistant then, it'll support more complex setups.
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- [Truthtelling games](https://jonathanwarden.com/truthtelling-games/) can incentivize honesty through coordination games where participants win by giving the same answer as others, with truth serving as a powerful Schelling point (truthtelling is the winning strategy only if everybody else tells the truth). Information elicitation mechanisms can get people to reveal private/subjective information truthfully even without verification.
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### Examples
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- Auctions. The input is bids, the output is who gets the item being sold, and how much the buyer must pay.
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- [Vickrey–Clarke–Groves auction](https://en.wikipedia.org/wiki/Vickrey%E2%80%93Clarke%E2%80%93Groves_auction).
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- [Second-price auction](https://en.wikipedia.org/wiki/Generalized_second-price_auction).
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- Quadratic voting and funding as a way of coming to agreement on matters of governance and public goods.
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### Impact Evaluators
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It's hard to fund important things like public goods, open-source software, research, etc. that don't have a clear, immediate financial return, especially high-risk/high-reward projects.
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- Quadratic voting and funding as a way of coming to agreement on matters of governance and [[Public Goods Funding]].
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Traditional funding often fails here. Instead of just giving money upfront (prospectively), Impact Evaluators create systems that look back at what work was actually done and what impact it actually had (retrospectively). The setup is similar to Control Theory. Based on measuring and evaluating this impact against predefined goals, the system then distributes rewards (e.g: similar to how BitCoin block rewards do it).
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### Resources
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- The Impact Evaluator goal is to **create strong incentives for people/teams to work on valuable, uncertain things** by promising a reward if they succeed in creating demonstrable impact.
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- They work well on concrete things that you can turn into measurable stuff. They are powerful and will always overfit. When the goal is not exactly aligned, they can be harmful. Eg. Bitcoin wasn't created to maximize the energy consumption.
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- They should be flexible as it's hard to predict ways the evaluation metrics will be gamed.
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- Allow Community Feedback Mechanisms. Implement robust feedback systems that allow participants to report and address concerns about the integrity of the metrics or behaviors in the community. This feedback can be used to refine and improve the system continuously.
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- Use hard to game metrics. Metrics that are hard, or expensive, to game, will be resistant (but not immune) to goodharts law.
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- Use diversified metrics. Rather than relying on a single metric or indicator, use a diverse set of metrics to assess performance or impact.
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- Dynamic and Adaptive Metrics. Implement mechanisms that allow for metrics to be adjusted or replaced as the system evolves. This adaptability can help prevent the gaming of static metrics and ensure that measures continue to align with the underlying goals of the IE.
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- [Jonathan Warden's blog](https://jonathanwarden.com)
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- [Sam Harsimony's Substack](https://substack.com/@splittinginfinity)
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- [Victor Sint Nicolaas' blog](https://victorsintnicolaas.com/)
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- [Allocation Mechanisms](https://www.allo.expert/mechanisms)
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Learning.md
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Learning.md
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- We all have a web of concepts in our minds, our [[Knowledge Graphs]]. The collection of all the concepts we understand, all of our existing knowledge and intuitions, connected together. And you have learned something when you can convert it to concepts and **connect it to your existing understanding**. This means not just understanding the concept itself, but understanding where it fits into the bigger picture, where to use it, etc.
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- Knowledge is not as a hunt for a single, ultimate and universal truth. [Is similar to the spirit of ecology: a gradual evolution of stably coexisting diversity that speciates and complexifies as it develops](https://www.radicalxchange.org/media/blog/why-i-am-a-pluralist/).
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- T[he world is full of things that merely appear good but aren't really](https://sarahconstantin.substack.com/p/what-goes-without-saying), and that it's important to vigilantly sift out the real from the fake. Concepts like Goodhart's Law, cargo-culting, greenwashing, hype cycles, Sturgeon's Law, ... are all pointing at the basic understanding that it's easier to seem good than to be good.
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## Learning Soft Skills
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Public Goods Funding.md
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Public Goods Funding.md
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# Public Goods Funding
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Public goods are defined as goods that are both nonexcludable (it's infeasible to prevent someone from consuming the good) and nonrivalrous (one person's consumption can not reduce another person's consumption of the good). They benefit everyone but are difficult to fund through traditional market mechanisms due to their non-excludable and non-rivalrous nature. Examples include open-source software, scientific research, environmental protection, and community infrastructure.
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- The fact that public goods are not excludable creates a "free-rider problem", where people are incentivized to enjoy the benefits of the good without paying for it.
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- [What's the point of contributing to a public park if non-contributors can use it too](https://splittinginfinity.substack.com/p/the-public-goods-funding-landscape)?
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- All funding mechanisms involve trade-offs between simplicity, robustness, and theoretical optimality. There is no mechanism that [can simultaneously achieve four desirable criteria](https://www.jstor.org/stable/2298018)-
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- [[Plurality|Different public goods require different funding approaches based on their characteristics and communities]].
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- Mathematical optimality matters less than perceived fairness and historical precedent. Ideal funding methods that don't work in practice are not ideal.
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- Mechanisms which satisfy different constraints have already been discovered, and it seems unlikely that a different approach will radically change the landscape. Instead, the **bottleneck seems to be in popularizing and scaling existing mechanisms in the real world**.
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## Desirable Criteria
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- Pareto Efficiency. The outcome achieved by the mechanism maximizes the overall welfare or some other desirable objective function.
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- Incentive Compatibility. Designing mechanisms so that participants are motivated to act truthfully, without gaining by misrepresenting their preferences.
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- Individual Rationality. Ensuring that every participant has a non-negative utility (or at least no worse off) by participating in the mechanism.
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- Budget Balance. The mechanism generates sufficient revenue to cover its costs or payouts, without running a net deficit.
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- Coalition-Proofness. Preventing groups of participants from conspiring to manipulate the mechanism to their advantage.
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- Provable Participation. Even if spending should be kept private, users may want to prove their participation in a funding mechanism in order to boost their reputation or as part of an agreement.
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- Identity and Reputation. To prevent sybil attacks, some form of identity is needed. If reputation is important, a public identity is preferred. If anonymity is required, zero-knowledge proofs or re-randomizable encryption may be necessary. Reputation is an important incentive to fund public goods. Some form of reputation score or record of participation can be useful for repeated games. These scores can help identify bad actors or help communities coalesce around a particular funding venue. [Identity-free mechanism can also be used](https://victorsintnicolaas.com/funding-public-goods-in-identity-free-systems/).
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- Verifiable Mechanisms. Users may want certain guarantees about a mechanism before or after participation, especially if the mechanism being used is concealed. Ex-ante, they may want to upper-bound their amount of spending towards the good, ex-post, they may require proof that a sufficient number of individuals contributed.
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- Anti-Collusion Infrastructure. Like secure voting systems, there is a threat of buying votes in a funding mechanism. Collusion can be discouraged by making it impossible for users to prove how they reported their preferences. This infrastructure must be extended to prevent collusion between the 3rd party and the users.
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## Resources
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- [Public Goods Funding Landscape](https://splittinginfinity.substack.com/p/the-public-goods-funding-landscape)
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- [Publig Goods Funding Mechanism List](https://docs.google.com/document/d/1n8fP3tWfLBIa-w4wC0rbOw2wo7odWvwseWptbuBdg6c/edit?tab=t.0)
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- [List of Public Goods Funding Mechanisms](https://harsimony.wordpress.com/2022/02/10/list-of-public-goods-funding-mechanisms/)
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- [Funding public goods using the Nash product rule](https://victorsintnicolaas.com/funding-public-goods-using-the-nash-product-rule/)
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## Impact Evaluators
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It's hard to fund important things like public goods, open-source software, research, etc. that don't have a clear, immediate financial return, especially high-risk/high-reward projects.
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Traditional funding often fails here. Instead of just giving money upfront (prospectively), Impact Evaluators create systems that look back at what work was actually done and what impact it actually had (retrospectively). It's much easier to judge the impact in a retrospective way!
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- The setup is similar to Control Theory. Based on measuring and evaluating this impact against predefined goals, the system then distributes rewards (e.g: similar to how BitCoin block rewards do it).
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- The Impact Evaluator goal is to **create strong incentives for people/teams to work on valuable, uncertain things** by promising a reward if they succeed in creating demonstrable impact.
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- They work well on concrete things that you can turn into measurable stuff. They are powerful and will always overfit. When the goal is not exactly aligned, they can be harmful. Eg. Bitcoin wasn't created to maximize the energy consumption.
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- They should be flexible as it's hard to predict ways the evaluation metrics will be gamed.
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- Allow Community Feedback Mechanisms. Implement robust feedback systems that allow participants to report and address concerns about the integrity of the metrics or behaviors in the community. This feedback can be used to refine and improve the system continuously.
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- Use hard to game metrics. Metrics that are hard, or expensive, to game, will be resistant (but not immune) to goodharts law.
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- Use diversified metrics. Rather than relying on a single metric or indicator, use a diverse set of metrics to assess performance or impact.
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- Dynamic and Adaptive Metrics. Implement mechanisms that allow for metrics to be adjusted or replaced as the system evolves. This adaptability can help prevent the gaming of static metrics and ensure that measures continue to align with the underlying goals of the IE.