How Group Lives Go Well
- URL: http://arxiv.org/abs/2504.19968v1
- Date: Mon, 28 Apr 2025 16:40:06 GMT
- Title: How Group Lives Go Well
- Abstract summary: This paper proposes a framework for representing collective welfare, group functions, and long term contributions within an engineering context.<n>Traditional well being theories focus on individual states, often relying on hedonistic, desire satisfaction, or objective list models.<n>This paper refines and extends the Counterfactual Account (CT) of well being, which evaluates goodness of an event by comparing an individual's actual well being with a hypothetical counterpart in a nearby possible world.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores the ontological space of group well being, proposing a framework for representing collective welfare, group functions, and long term contributions within an ontology engineering context. Traditional well being theories focus on individual states, often relying on hedonistic, desire satisfaction, or objective list models. Such approaches struggle to account for cases where individual sacrifices contribute to broader social progress, a critical challenge in modeling group flourishing. To address this, the paper refines and extends the Counterfactual Account (CT) of well being, which evaluates goodness of an event by comparing an individual's actual well being with a hypothetical counterpart in a nearby possible world. While useful, this framework is insufficient for group level ontologies, where well being depends on functional persistence, institutional roles, and historical impact rather than immediate individual outcomes. Drawing on Basic Formal Ontology (BFO), the paper introduces a model in which group flourishing is evaluated in terms of group functional, where members bear roles and exhibit persistence conditions akin to biological systems or designed artifacts. This approach enables semantic interoperability for modeling longitudinal social contributions, allowing for structured reasoning about group welfare, social institutions, and group flourishing over time.
Related papers
- To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias [61.40435736418359]
We introduce a unified framework that standardizes heterogeneous benchmarks to contrast isolated demographic assessments with forced-choice comparative settings.<n>We show that comparative settings act as aggressive catalysts for latent discrimination, a shift primarily driven by underspecified contexts.<n>Finally, we demonstrate that this comparative prejudice is a generalized phenomenon that scales positively with model size.
arXiv Detail & Related papers (2026-06-23T13:53:50Z) - Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis [49.796228341744076]
We propose a task that models organized groups as collective entities from a practical perspective.<n>We present GROVE, a benchmark covering 44 entities with 8,052 real-world context-decision pairs.<n>We also propose a structured analytical framework that converts collective decision-making events into an interpretable, adaptive, and traceable behavioral model.
arXiv Detail & Related papers (2026-04-10T20:04:33Z) - Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community [0.5394948236100675]
This paper introduces data-driven silicon sociology as a systematic empirical framework for studying social structure formation among interacting artificial agents.<n>We present a pioneering large-scale data mining investigation of an in-the-wild agent society by analyzing Moltbook, a social platform designed primarily for agent-to-agent interaction.
arXiv Detail & Related papers (2026-02-02T11:08:38Z) - Fairness in Opinion Dynamics [0.7340017786387767]
We study how a state-of-the-art model discriminates certain minority groups and whether it is possible to reliably predict for whom it will perform worse.<n>Our work explores how three classifier models (Demography-Based, Topology-Based, and Hybrid) perform when assessing for whom this algorithm will provide inaccurate predictions.<n>We conclude that a multi-faceted approach, incorporating both individual attributes and network structures, is essential for reducing algorithmic bias.
arXiv Detail & Related papers (2026-01-07T12:15:02Z) - Part-Aware Bottom-Up Group Reasoning for Fine-Grained Social Interaction Detection [82.70752567211251]
We propose a part-aware bottom-up group reasoning framework for fine-grained social interaction detection.<n>The proposed method infers social groups and their interactions using body part features and their interpersonal relations.<n>Our model first detects individuals and enhances their features using part-aware cues, and then infers group configuration by associating individuals via similarity-based reasoning.
arXiv Detail & Related papers (2025-11-05T17:33:03Z) - Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
arXiv Detail & Related papers (2025-04-10T02:52:34Z) - Disentangled Modeling of Preferences and Social Influence for Group Recommendation [16.953172230355268]
Group recommendation (GR) aims to suggest items for a group of users in social networks.<n>Existing methods either neglect the social influence of individual members or bundle preferences and social influence together.<n>We propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec)
arXiv Detail & Related papers (2025-01-20T09:03:18Z) - Jointly modelling the evolution of social structure and language in online communities [5.384630221560811]
Group interactions take place within a particular socio-temporal context.<n>We propose a method for jointly modelling community structure and language over time.<n>We apply and evaluate our method in the context of a set of misogynistic extremist groups.
arXiv Detail & Related papers (2024-09-28T05:19:51Z) - CogNarr Ecosystem: Facilitating Group Cognition at Scale [0.0]
This concept paper proposes the CogNarr ecosystem, which is designed to facilitate functional cognition in the large-group setting.
A key perspective is to view a group as an organism that uses some form of cognitive architecture to sense the world.
arXiv Detail & Related papers (2024-07-11T08:59:17Z) - Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Predicting the long-term collective behaviour of fish pairs with deep learning [52.83927369492564]
This study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus.
We compare the results of our deep learning approach to experiments and to the results of a state-of-the-art analytical model.
We demonstrate that machine learning models social interactions can directly compete with their analytical counterparts in subtle experimental observables.
arXiv Detail & Related papers (2023-02-14T05:25:03Z) - Hunting Group Clues with Transformers for Social Group Activity
Recognition [3.1061678033205635]
Social group activity recognition requires recognizing multiple sub-group activities and identifying group members.
Most existing methods tackle both tasks by refining region features and then summarizing them into activity features.
We propose to leverage attention modules in transformers to generate effective social group features.
Our method is designed in such a way that the attention modules identify and then aggregate features relevant to social group activities.
arXiv Detail & Related papers (2022-07-12T01:46:46Z) - This Must Be the Place: Predicting Engagement of Online Communities in a
Large-scale Distributed Campaign [70.69387048368849]
We study the behavior of communities with millions of active members.
We develop a hybrid model, combining textual cues, community meta-data, and structural properties.
We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment.
arXiv Detail & Related papers (2022-01-14T08:23:16Z) - On the Opportunities and Risks of Foundation Models [256.61956234436553]
We call these models foundation models to underscore their critically central yet incomplete character.
This report provides a thorough account of the opportunities and risks of foundation models.
To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
arXiv Detail & Related papers (2021-08-16T17:50:08Z) - Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document.
We also simultaneously cluster users, removing the need for post-hoc cluster estimation.
Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
arXiv Detail & Related papers (2021-06-15T20:55:55Z) - Deep reinforcement learning models the emergent dynamics of human
cooperation [13.425401489679583]
Experimental research has been unable to shed light on how social cognitive mechanisms contribute to the where and when of collective action.
We leverage multi-agent deep reinforcement learning to model how a social-cognitive mechanism--specifically, the intrinsic motivation to achieve a good reputation--steers group behavior.
arXiv Detail & Related papers (2021-03-08T18:58:40Z) - Joint Learning of Social Groups, Individuals Action and Sub-group
Activities in Videos [23.15064911470468]
We propose an end-to-end trainable framework for the social task.
Our main contributions are: i) we propose an end-to-end trainable framework for the social task; ii) our proposed method sets the state-of-the-art results on two widely adopted benchmarks for the traditional group recognition activity task.
arXiv Detail & Related papers (2020-07-06T10:42:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.