Adaptive workforce exploration in complex productivity landscapes

[2608.27656] Adaptive workforce exploration in complex productivity landscapes Skip to main content Search Submit Donate Log in Condensed Matter > Statistical Mechanics arXiv:2608.27656 (cond-mat) [Submitted on 27 Aug 2026] Title: Adaptive workforce exploration in complex productivity landscapes Authors: Mateus F. B. Granha , Igor V. G. de Oliveira , André L. M. Vilela , Chao Wang , Paulo R. A. Campos View PDF HTML (experimental) Abstract: Specialization and task allocation enhance efficiency and innovation across diverse systems, from biological organisms to socioeconomic institutions. The evolution of task distribution and its influence on organizational productivity encapsulate the dynamics between task dependencies and adaptive strategies. We explore the organizational division of labor, inspired by the NK model of rugged landscapes, which is widely applied in evolutionary biology, and incorporate interdependencies among the attributes of technical experts within an organization. Our model considers two types of employees characterized by their task allocation strategies: specialists, who are permanently assigned to a single task, and generalists, who stochastically select a task at each time step. We investigate how the ruggedness of the productivity landscape, shaped by task interdependency, affects the organization's capacity to optimize labor division and meet market demands. Using group selection algorithms, we reveal the emergence of nonlinear adaptive dynamics, providing insights into how companies can adapt their strategies to meet market demands and foster innovation. Comments: 17 pages, 9 figures, 1 table Subjects: Statistical Mechanics (cond-mat.stat-mech) MSC classes: 60K35, 68W50, 82-10 Cite as: arXiv:2608.27656 [cond-mat.stat-mech] (or arXiv:2608.27656v1 [cond-mat.stat-mech] for this version) https://doi.org/10.48550/arXiv.2608.27656 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Andre Luis Da Mota Vilela [ view email ] [v1] Thu, 27 Aug 2026 19:44:35 UTC (5,458 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.stat-mech < prev | next > new | recent | 2026-08 Change to browse by: cond-mat References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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