The Symbiotic Merit Model (SMM): Cognitive Autonomy, Attention Economy, and a Three-Tier Framework for Qualified Human Contribution in the AI Ecosystem
Year: 2026
Type: article
Abstract: This record contains two documents: 1. SyMM v5.0 — Main Paper This study analyzes the risks that current usage patterns in the artificial intelligence (AI) ecosystem pose to human cognitive capacity and societal evolution, and presents a three-tier system architecture called the Symbiotic Merit Model (SyMM) as a structural response to these risks. Central thesis: Artificial intelligence is, for the first time in human history, a technology that makes possible not merely access to knowledge, but the externalization of the thinking process itself. This leads to the systematic erosion of cognitive autonomy through cognitive offloading (Risko & Gilbert, 2016) and automation bias (Goddard et al., 2012). SyMM complements the existing two-tier structure (consumer + professional) with an Intellectual Contribution Layer that measures, rewards, and generates social status from qualified contribution. Merit is tied not to purchasing power but to demonstrated cognitive depth. 2. SyMM: Mathematical Foundations — Companion Paper This document is the official mathematical derivation and rationale companion to SyMM v5.0. It explains why the formulas presented in the main paper take their specific form. Four core architectural blocks are addressed: operational definitions and weight-selection rationale for each CCS vector component; a closed-loop stability proof for the Trust(t) feedback mechanism (loop gain ≤ 0.071); calibration logic of the Entry Engine sigmoid threshold (θ = 3.0); and derivation of the β and α coefficients from CCS components. A full 36-month simulation conducted across four user archetypes is also presented, including methodology and results. Keywords: Cognitive Credit Score (CCS), Institutional Merit Score (IMS), Entry Engine, Trust(t), symbiotic coefficient, attention economy, cognitive autonomy, Ostrom governance, epistemic resistance, AI ecosystem
Author: kılıç, veysel
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7152108683
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