Environmental Information Saturation and Measurement-Induced Stable Localization A Testable Phenomenological Model Based on Effective Mass and Open-System Dynamics
Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Quantum Mechanics and Applications
Abstract
AbstractThis study treats quantum measurement not as a process that must produce an instantaneous ontological collapse, but as an open-system process that spatially narrows a specified measurement record and stabilizes it against subsequent spreading. The proposed phenomenological model combines system-environment mutual information, the effective number of distinguishable record modes, and the independently calibrated effective collective mass of the pointer coordinate in a single localization-rate ansatz. Conditional continuous measurement reduces the position variance while producing momentum backaction; a large effective mass slows kinetic dispersion; and confinement and damping permit a finite stationary width. The model distinguishes screen detection from which-path recording and produces no additional decoherence in the zero-which-path-information limit. For Gaussian states, a closed covariance system, stationary width, energy balance, visibility law, and a post-measurement free-expansion test are provided. The claim is not an ontological solution to the collapse problem, but a new, operational, and falsifiable scaling proposal for the formation of stable spatial records. In addition, the ansatz is matched to the position-diffusion coefficient obtained from a linear system-environment interaction in the Born-Markov limit, and a quantitative record-information test is identified that distinguishes the proposal from GRW, CSL, Diosi-Penrose, and standard continuous-measurement scalings.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.