本期节目探讨了区块链共识机制的本质，以及其在维护区块链安全和完整性方面的关键作用。两位嘉宾深入讨论了主动推理、工作量证明与权益证明之间的关系，以及共识机制如何应对现代社会信息过载和符号失控的挑战。

🤔主动推理：主动推理是一种认知框架，它将生物体视为预测机器，通过最小化预测误差来最大化其生存机会。在区块链中，主动推理可以帮助理解参与者如何达成共识，并确保网络的稳定运行。

💪工作量证明与权益证明：工作量证明（PoW）通过计算资源消耗来验证交易，而权益证明（PoS）则通过持有代币数量来赋予验证权。两者各有优劣，PoW更安全，但能耗较高；PoS更节能，但安全性相对较低。

⏳信息熵与能量：信息熵是衡量信息无序程度的指标，而能量是驱动系统运行的动力。两者之间存在紧密联系，能量的消耗会导致信息熵的增加。在区块链中，共识机制需要平衡能量消耗与信息安全之间的关系。

🌐共识机制的挑战：随着信息过载和符号失控，共识机制面临着新的挑战。如何确保信息来源的可靠性，如何防止恶意操纵，都是需要解决的问题。

🧬演化与适应：共识机制需要不断演化，以适应不断变化的环境。通过学习和适应，共识机制可以更好地应对各种挑战，并确保区块链的长期稳定发展。

Jim talks with Matthew Pirkowski about the kinds of consensus mechanisms that can be used to secure blockchains. They discuss active inference, proof of work vs proof of stake & the relationship between them, auto-catalytic networks, proof of work in emergent nature, what consensus means & why it needs to be protected, integrity of the ledger, an analogy with clocks, accelerating entropy, photosynthesis, exploring vs exploiting tensions in emergent systems, coordinating central points of reference, energetic openness, the relationship between energy & information, resistance to manipulation, postmodernity & symbols untethered to reality, the evolution of evolvability, adaptive drift, a stable foundation for building infrastructure, the tight relationship between information theory & thermodynamics, whether existing cryptocurrencies exist in a Goldilocks zone vs an arbitrary spot in design space, bugs of global reserve currencies, whether investing in Bitcoin is an anti-social act, currency vs wealth, personal stores of abstract potential energy, and much more. Episode Transcript JRS Currents 066: Matthew Pirkowski on Emergence in Possibility Space "Dividend Money: An Alternative to Central Banker Managed Fractional Reserve Banking Money," by Jim Rutt (lecture)Matthew Pirkowski works at the intersection of software, psychology, and complex systems. These interests first took root while studying Evolutionary Psychology and assisting with Behavioral Economic research at Yale’s Comparative Cognition Laboratory. From there Matthew began a career in software engineering, where he applied these interests to the development of software interfaces used by millions around the world, most notably as a member of Netflix’s Television UI team, where he worked on experimental initiatives conceptualizing and prototyping the future of entertainment software.Presently, Matthew is building the underlying modeling architecture at Bioform Labs, a company focused on using the Active Inference toolkit to model organizations as emergent cybernetic organisms. He believes these models can help organizations manage their deployment of and interaction with AI-based agents, as well as more adaptively manage their own emergent complexity.