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📚 Enhance documentation with new insights on decentralized systems, mechanism design, and data culture
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Data/Data Culture.md
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Data/Data Culture.md
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- Progress in data isn't linear. As a research discipline, you might spend hours making no progress and then have a breakthrough. Or worse, prove your entire approach won't work.
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- [Apply a research mindset to data](https://jxnl.co/writing/2024/10/25/running-effective-ai-standups). Focus on input metrics, build scientific intuition, and embrace uncertainty.
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- [How can science – loosely, the production of facts – do more to "steer" the outcomes of these processes?](https://jscaseddon.co/2024/02/science-for-steering-vs-for-decision-making/)
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- Data is not superior or inferior to intuition or qualitative sensemaking; it is a third sense for operators. Effective decision-making uses all three: intuition, qualitative sensemaking, and data. [Data is just an added sense](https://commoncog.com/data-is-an-added-sense/). Treat data as a tool for building and verifying intuition, not as a replacement for it. Over-reliance on any single sense—data, intuition, or qualitative feedback—limits understanding.
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## Tools
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Decentralized Protocols.md
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Decentralized Protocols.md
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- Keep it simple. Keeping the protocol simple brings a number of benefits (e.g: makes the protocol simpler to reason about, increasing the number of people who understand and can participate, decreases the cost of creating new infrastructure, reduces the risk of catastrophic bugs, reduces the social attack surface).
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- Don't change it too often.
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- Cryptoeconomics is about trying to reduce social trust assumptions by creating systems where we introduce explicit economic incentives for good behavior and economic penalties for bad behavior.
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- [Decentralized systems accelerate innovation by maximizing the greatest number of possibilities and variations that are considered](https://faintsignal.org/decentralized-systems-accelerate-innovation/). Centralized systems are efficient, not disruptively innovative.
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## Types of Decentralization
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Incentives.md
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Incentives.md
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Mechanism design is the study of how incentives are created to achieve desired outcomes. It focuses on the design of [[Systems]] and [[Processes]] to achieve desired outcomes.
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- Software is eating Mechanism Design. Incentives can be encoded in [[blockchain|blockchains]].
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- The simpler a mechanism is, and the fewer parameters a mechanism has, the less space there is to insert hidden privilege for or against a targeted group. If a mechanism has fifty parameters that interact in complicated ways, then it’s likely that for any desired outcome you can find parameters that will achieve that outcome.
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- The simpler a mechanism is, and the fewer parameters a mechanism has, the less space there is to insert hidden privilege for or against a targeted group. If a mechanism has fifty parameters that interact in complicated ways, then it's likely that for any desired outcome you can find parameters that will achieve that outcome.
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- The best engineering designs are those that remove things and make them implicit.
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- Remember to keep fast [[Feedback Loops]] in mind when designing mechanisms.
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- Mechanism design flips game theory: choose rules (outcomes & payments) so strategic agents reach desired outcomes.
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- An agent's "type" is their private information that determines how much they value each possible outcome (e.g: a bidder's valuation for an item).
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- In quasilinear settings (utility = value − payment), mechanisms map reported types to decisions and transfers.
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- The Revelation Principle lets us focus on direct, truth-telling mechanisms: DSIC (dominant strategies) or BIC (Bayes-Nash).
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- Gibbard–Satterthwaite impossibility: with three or more options and unrestricted preferences, only dictatorial DSIC choice functions exist.
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- Top Trading Cycles yields Pareto-efficient, individually rational, strategyproof allocations in exchange problems.
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### Examples
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- Bitcoin block rewards.
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- [Vickrey–Clarke–Groves auction](https://en.wikipedia.org/wiki/Vickrey%E2%80%93Clarke%E2%80%93Groves_auction) or [Second-price auction](https://en.wikipedia.org/wiki/Generalized_second-price_auction).
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### Impact Evaluators
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Personal Handbooks.md
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Personal Handbooks.md
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Systems.md
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Systems.md
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Evolution is easier than revolution. A good approach to incrementally change a system (similar to [[Evolution|natural selection]]) is to:
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1. Start by identifying the highest-leverage level to optimize at: Ask whether you're optimizing the machine or a cog within it. Complex systems might change in unexpected ways (butterfly effects). Minor differences in starting points make big differences on future states.
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2. Begin optimizing the system by following the [Theory of Constraints](https://en.wikipedia.org/wiki/Theory_of_constraints): At any time, just one of a system's inputs is constraining its other inputs from achieving a greater total output. Make incremental changes. Alter the incentive landscape. [If you can make your system less miserable, make your system less miserable!](https://astralcodexten.substack.com/p/book-review-the-cult-of-smart)
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2. Begin optimizing the system by following the [Theory of Constraints](https://en.wikipedia.org/wiki/Theory_of_constraints): At any time, just one of a system's inputs is constraining its other inputs from achieving a greater total output. Make incremental changes. [Understand how the inputs affect the outputs of the system](https://faintsignal.org/pressure-to-meet-a-target-value-changes-the-system-or-the-data/#fn2). Alter the incentive landscape. [If you can make your system less miserable, make your system less miserable!](https://astralcodexten.substack.com/p/book-review-the-cult-of-smart)
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3. Re-examine the system from the ground up. Get data. Take nothing but the proven, underlying principles as given. Work up from there to create something better.
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These are places within a complex system (a corporation, an economy, a living body, a city, an ecosystem) where a small shift in one thing can produce big changes in everything. These are [the places to intervene in a system](https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/) (in increasing order of effectiveness):