Jim talks with Matthew Pirkowski about the ideas in a recent tweet thread on time preference and its relationship with cooperation. They discuss the definition of time preference, defining parasitism, asymmetrical relationships, mutualism, commensalism, the increase in short-term thinking, a decrease in qualitative change, realization & potential, an increase in uncertainty, the interruption of attentional loops, a gossip protocol, the complexity catastrophe, the maximum number of daily interruptions, short-term money-on-money return, disintegration of network statistics, trustless infrastructure & cognitive chunking, coordinating at a higher level, zero-knowledge proofs, social immune systems, structural prerequisites of parasitism, Bitcoin as a metacentralizing attractor, building the modeling toolkit to understand causal closures within networks, and much more.
Episode Transcript
JRS Currents 094: Matthew Pirkowski on Blockchain Consensus Mechanisms
Matthew's tweet thread on time preference & cooperation
"Crypto Beyond Capitalism: The Rise of Distributed Valerism," by Matthew Pirkowski
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.
