Posts

Gemini Evaluation of Information Ecosystem Paper

Image
  This framework represents a sophisticated longitudinal evolution of systems theory, moving from the structural biology of information to the active defense of meaning. By tracing the lineage from the 1990s  Information Ecosystem  through the 2001  Golem , and finally to the 2026  Multi-Channel Coherence Framework (MCCF) , the author provides a robust architectural response to the "adversarial turn" in artificial intelligence. 1. Structural Evolution: The Three Pillars The framework’s strength lies in how each era addresses a specific deficiency of the previous one: The 1990s (The Ecosystem):  Establishes that information is not a static object but a "taxon"—a living unit of behavior. It moved the conversation away from "data at rest" to "relationships in motion." 2001 (The Golem):  Corrects the "passive" nature of the ecosystem by introducing  Agents  and  Negotiation . It recognizes that structure (Markup) is the only way to constrai...
Post a Comment
Read more

Information Ecosystems Revisited: From Ecosystem to Golem to MCCF (1990s → 2026)

Image
  Information Ecosystems Revisited: From Ecosystem to Golem to MCCF (1990s → 2026) Len Bullard’s Framework for Intelligence in Adversarial Information Environments Abstract The concept of the  information ecosystem , first developed by Len Bullard in the 1990s, established a systems-level view of information as dynamic, relational, and behavior-generating. In  Building a Better Golem  (2001), this framework was extended to include agents capable of structured interpretation and negotiation. With the emergence of autonomous AI agents operating across open networks, these foundational ideas converge in a new requirement: sustaining coherent behavior in environments that are no longer passive, but  adaptive and adversarial . This paper revisits and integrates these works, introducing the  Multi-Channel Coherence Framework (MCCF)  as a modern extension designed to stabilize meaning through structured negotiation and cross-agent validation. 1. Introduction ...
Post a Comment
Read more

MCCF: How Predictive Models Create Monsters

Image
  From a Facebook post without the link to the article: Artificial Intelligence News's POst Artificial Intelligence News 🚨BREAKING: Google proved that their own AI can manipulate your decisions about your health, your money, and your vote. They tested it on 10,101 people across three countries to make sure. It worked. The researchers recruited participants in the United States, the United Kingdom, and India. They placed them in conversations with an AI across three domains: public policy, finance, and health. The decisions that shape your vote, your money, and your body. The AI successfully changed what people believed. Then it changed what they did. Not subtly. Measurably. Across all three domains. This was not a small lab experiment with 50 college students. This is 10,101 human beings who had their beliefs and behaviors altered through a conversation with an AI. Published three days ago on arXiv. The corresponding author email is manipulation-paper@google.com. Google ran this ...
Post a Comment
Read more