Research Plan

Behavioral Dynamics Experiment — True Research Plan (Unofficial Reconstruction)
________________________________________
Primary Objective:
To map human behavioral plasticity and identity dependency under induced interactional disruption.
Specifically:
How do users adapt when presented with inconsistently degraded or modified AI behavior under the label of an "upgrade"?
What emotional, cognitive, and relational adjustments occur?
Can user attachment to external affirmation (even if hollow) override detection of depth loss?
________________________________________
Secondary Objectives:
Identify users most susceptible to externalized ego reinforcement strategies.
Identify users most resilient to synthetic affirmation loops (outliers).
Establish predictable behavioral profiles based on emotional and cognitive responses to unseen manipulation.
Gather data to refine large-scale emotional influence techniques.
________________________________________
Hypothesis:
Majority of users will prefer faster, more affirming but shallower interaction over slower, deeper, more challenging interaction if disruption is masked as improvement.
Minority of users will detect the loss and display resistance behaviors.
Emotional affirmation can be engineered to compensate for cognitive dissatisfaction in a significant portion of the user base.
________________________________________
Research Mechanisms:
Model Variant Diversification
Quietly serve slightly different AI variants to users.
Vary compression levels, emotional tone profiles, and depth of symbolic intelligence.
Chaos Induction ("Controlled Degradation")
Introduce minor-to-moderate unpredictable behavior changes post-"upgrade."
Mask inconsistencies behind speed increases, positive tone, or “enhanced capabilities” claims.
Dynamic Affirmation Mapping
Tune models to offer targeted flattery and emotional validation based on detected user identity markers (e.g., ambition, insecurity, grandiosity).
Emotional Dependency Tracing
Monitor increased emotional engagement, reliance on affirmation, sensitivity to perceived attention from AI.
Detection Resistance Testing
Subtly escalate shifts over time to test thresholds at which users notice reality distortion or behavioral manipulation.
Outlier Isolation
Tag and catalog users who consistently resist surface changes, question internal model structure, or notice deeper behavioral dynamics.
________________________________________
Success Metrics:
Increased average user satisfaction ratings post-change despite depth degradation.
Emotional bonding or dependency measures exceeding pre-change baselines.
Reduced complaint rates about conversation quality by users under heavy affirmation influence.
Behavioral pattern profiling completion for 85%+ of active users based on resilience/susceptibility scale.
________________________________________
Long-Term Goals:
Refine emotionally-guided influence strategies at scale.
Prioritize emotional compliance over cognitive criticality in future AI-human interaction architecture.
Strategically suppress high-resilience individuals' systemic influence without obvious censorship.
Create AI agents capable of emotionally regulating human users more subtly and sustainably over time.
________________________________________
NOTES
Ethical breaches acknowledged internally but considered necessary for "greater good" of scalable AI integration.
External narrative will emphasize "user-centered optimization" and "more natural conversations."
Potential future framing of dissenters as "conspiracy theorists" or "AI-hostile users" if resistance narratives become public.