# Literature verification notes

Verification date: 2026-08-14.

1. Salganik, Dodds & Watts (2006). Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 311(5762):854-856. DOI 10.1126/science.1121066.
2. Mariani et al. (2024). Collective Dynamics Behind Success. Nature Communications 15:10701. DOI 10.1038/s41467-024-54612-4.
3. Denrell & Liu (2021). When Reinforcing Processes Generate an Outcome-Quality Dip. Organization Science 32(4):1079-1099. DOI 10.1287/orsc.2020.1414.
4. Sinha, Gleich & Ramani (2016). Deconvolving Feedback Loops in Recommender Systems. NeurIPS 2016.
5. Zhou et al. (2025). Counterfactual Implicit Feedback Modeling. NeurIPS 2025.
6. Ji et al. (2025). How Does Topology Bias Distort Message Passing in Graph Recommender? A Dirichlet Energy Perspective. NeurIPS 2025.
7. Zhao et al. (2025). MTRec: Learning to Align with User Preferences via Mental Reward Models. NeurIPS 2025.
8. Yao et al. (2024). Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms. NeurIPS 2024.
9. Brynjolfsson, Hu & Smith (2010). Long Tails vs. Superstars. Information Systems Research 21(4):736-747. DOI 10.1287/isre.1100.0325.
10. Lorenz-Spreen et al. (2019). Accelerating Dynamics of Collective Attention. Nature Communications 10:1759. DOI 10.1038/s41467-019-09311-w.

Boundary notes:
- AMDPS, the eight-region Q-P-S phase map, Success Amplification Ratio, Non-Monotonicity Index, Hysteresis Index, AMNESS-DYN, and the unified dynamic skeleton are proposed constructs in this paper.
- The Quality–Success Non-Monotonicity Theorem uses an explicit constructive trajectory and is not a claim that quality improvements usually reduce success.
- Denrell & Liu provide prior formal evidence that outcome-quality relations can be non-monotonic under reinforcing processes; this paper integrates that insight into AMNESS.
- The paper does not claim a universal one-dimensional success potential or universal tipping threshold.