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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17489 |
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| _version_ | 1866915462806241280 |
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| author | Finanser, Avital Talmon, Nimrod |
| author_facet | Finanser, Avital Talmon, Nimrod |
| contents | We introduce a model for collaborative text aggregation in which an agent community coauthors a document, modeled as an unordered collection of paragraphs, using a dynamic mechanism: agents propose paragraphs and vote on those suggested by others. We formalize the setting and explore its realizations, concentrating on voting mechanisms that aggregate votes into a single, dynamic document. We focus on two desiderata: the eventual stability of the process and its expected social welfare. Following an impossibility result, we describe several aggregation methods and report on agent-based simulations that utilize natural language processing (NLP) and large-language models (LLMs) to model agents and their contexts. Using these simulations, we demonstrate promising results regarding the possibility of rapid convergence to a high social welfare collaborative text. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Dynamic Approach to Collaborative Document Writing (Full Version) Finanser, Avital Talmon, Nimrod Computer Science and Game Theory We introduce a model for collaborative text aggregation in which an agent community coauthors a document, modeled as an unordered collection of paragraphs, using a dynamic mechanism: agents propose paragraphs and vote on those suggested by others. We formalize the setting and explore its realizations, concentrating on voting mechanisms that aggregate votes into a single, dynamic document. We focus on two desiderata: the eventual stability of the process and its expected social welfare. Following an impossibility result, we describe several aggregation methods and report on agent-based simulations that utilize natural language processing (NLP) and large-language models (LLMs) to model agents and their contexts. Using these simulations, we demonstrate promising results regarding the possibility of rapid convergence to a high social welfare collaborative text. |
| title | A Dynamic Approach to Collaborative Document Writing (Full Version) |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2508.17489 |