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Blockchains Require Dramatic Innovations to Prosper

Yuxi Li
Sep 25, 2023
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Blockchains Require Dramatic Innovations to Prosper

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Blockchains calls for killer apps

  • Deep learning has revolutionized computer vision and natural language processing (NLP), with applications like face recognition and ChatGPT

  • Reinforcement learning has breakthroughs like AlphaGo, but so so in business

  • Blockchain: Crypto? NFT? DeFi? GameFi? Seems not quite revolutionary yet.

The current practice is misaligned with the goal. 

  • A codebase is controlled by a small group of people.

    • Vitalik Buterin is the King of Ethereum. 

  • Blockchains are built on the Internet, which is controlled by authorities.  

  • Blockchains are more or less, sooner or later regulated by some authorities.

    • The influential crypto exchanges (Binnance, Coinbase) are centralized.

    • A blockchain may encounter soft- and hard-forks. 

  • Matthew effect: The rich get richer and the poor get poorer.

  • No cryptocurrency is stable yet.

  • The current codebases are far from mature.

    • “Smart contracts” are neither “smart” nor “contracts”.

      • basically “stored procedures”

    • e.g., All Smart Contracts Are Ambiguous, J. of Law & Innovation, 2019

    • e.g., A Survey of Smart Contract Formal Specification and Verification, ACM Comput. Surveys, Jan 2021 

    • The current codebases are very vulnerable to attacks.

      • e.g., A Survey on Ethereum Systems Security: Vulnerabilities, Attacks, and Defenses, ACM Computing Surveys, June 2020

  • Tradeoffs are required due to the security-decentralization-scalability trilemma.

  • Blockchains are run by humans, with all potential good and evil, i.e., not ideal. 

    • See e.g., stories about FTX, Steemit, etc.

    • See e.g., a video Blockchain - Innovation or Illusion? 

  • Pragmatically, blockchains need to co-exist with other forms of institutions, like firms, markets, economies and nation states, for a long time, or FOREVER.

Blockchains are innovations, w.r.t.  

  • An information and computation technology

    • “world computer”, Internet 2.0, Web3

  • An organization and governance technology

    • competing with firms, markets, economies

  • e.g., Bitcoin’s academic pedigree, CACM, Dec 2017

  • e.g., Some Simple Economics of the Blockchain, CACM, July 2020

    • the cost of verification and the cost of networking

  • e.g., Blockchains and the economic institutions of capitalism, Journal of Institutional Economics, 2017

Blockchain design

  • Cross-disciplinary by nature

    • Computer science, economics, behavioral science, optimization, etc.  

    • e.g., Foundations of Cryptoeconomic Systems, 2020

  • Cryptography

    • e.g., a MOOC, Zero Knowledge Proofs (2023)

  • Distributed computing; consensus protocols. 

    • e.g. A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks, IEEE Access, Jan 2019

  • Mechanism design

    • e.g., a course, Incentives in Computer Science (2020?); videos (2021).

  • Token design 

    • a building block for all blockchains

  • Smart contract 

    • a building block for all (?) “Blockchain 2.0” applications (e.g., DAOs)

  • Desired properties: decentralized, secure, scalable, immutable, trustworthy, open, transparent, persistent, resilient, interoperable

Computational approaches

  • Too complex for theoretical analysis / modeling

    • usually too large theory-practice gap, due to too strong assumptions

  • Resort to computational approaches

    • Simulation, Reinforcement learning / AI

  • Gamification is relevant

    • A course on Coursera

  • A blockchain simulator?

    • e.g., Agent-based modeling in economics and finance: past, present, and future, Journal of Economic Literature, 2022

    • e.g., Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning, arXiv 2022

  • Adversarial training?

    • e.g., "Auction Learning as a Two Player Game": GANs (?) for Mechanism Design, ICLR 2022 Blog

Ongoing efforts (from the community)

  • Token engineering

    • Block Science

      • Complex Adaptive Dynamics Computer Aided design (cadCAD)

    • Token Engineering Commons

    • Token engineering academy

    • e.g., Token Economy: How the Web3 reinvents the Internet, 2020

  • Commons Stack

    • e.g., Beyond markets and states- Polycentric governance of complex economic systems, American Economic Review, 2010 (Nobel Lecture)

    • e.g., Automating Ostrom for Effective DAO Management, 2019

Epilogue

The above was basically written at the end of 2022. Everyone knows what happened then. It is hard for a person in AI to resist the big wave.

There may be good opportunities at the intersection of AI and blockchains. One example is credit assignment, e.g., for open source software, which is indispensable for further progress of AI, yet may lack of a proper incentive mechanism. See the recent Future of Decentralization, AI, and Computing Summit. I plan to spend more time on this promising direction.

Stay tuned!

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