# CogNarr Ecosystem: Facilitating Group Cognition at Scale

arXiv: https://arxiv.org/abs/2407.18945 · HTML: https://mlsanity.com/paper/2407.18945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 111 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 30 of 43)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Hypothesis Search: Inductive Reasoning with Language Models](https://mlsanity.com/paper/2309.05660/) (2023)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Language Model Cascades](https://mlsanity.com/paper/2207.10342/) (2022)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Structural Adapters in Pretrained Language Models for AMR-to-text Generation](https://mlsanity.com/paper/2103.09120/) (2021)
- [Deep Active Inference](https://mlsanity.com/paper/1709.02341/) (2017)
- [TreeBERT: A Tree-Based Pre-Trained Model for Programming Language](https://mlsanity.com/paper/2105.12485/) (2021)
- [StructCoder: Structure-Aware Transformer for Code Generation](https://mlsanity.com/paper/2206.05239/) (2022)
- [Dimensions of Commonsense Knowledge](https://mlsanity.com/paper/2101.04640/) (2021)
- [CodeKGC: Code Language Model for Generative Knowledge Graph Construction](https://mlsanity.com/paper/2304.09048/) (2023)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [Designing Ecosystems of Intelligence from First Principles](https://mlsanity.com/paper/2212.01354/) (2022)
- [Towards a Theory of Evolution as Multilevel Learning](https://mlsanity.com/paper/2110.14602/) (2021)
- [PyReason: Software for Open World Temporal Logic](https://mlsanity.com/paper/2302.13482/) (2023)
- [Exploring Neural Methods for Parsing Discourse Representation Structures](https://mlsanity.com/paper/1810.12579/) (2018)
- [PolicyKit: Building Governance in Online Communities](https://mlsanity.com/paper/2008.04236/) (2020)
- [Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem](https://mlsanity.com/paper/2210.11694/) (2022)
- [Reason Against the Machine: Future Directions for Mass Online Deliberation](https://mlsanity.com/paper/2107.12711/) (2021)
- [Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems](https://mlsanity.com/paper/2111.05364/) (2021)
- [A World unto Itself: Human Communication as Active Inference](https://mlsanity.com/paper/1906.10538/) (2019)
- [Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation](https://mlsanity.com/paper/2306.00124/) (2023)
- [Solving Probability and Statistics Problems by Program Synthesis](https://mlsanity.com/paper/2111.08267/) (2021)
- [Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference](https://mlsanity.com/paper/2203.04857/) (2022)
- [Interactive inference: a multi-agent model of cooperative joint actions](https://mlsanity.com/paper/2210.13113/) (2022)

## Abstract (arXiv)

Human groups of all sizes and kinds engage in deliberation, problem solving, strategizing, decision making, and more generally, cognition. Some groups are large, and that setting presents unique challenges. The small-group setting often involves face-to-face dialogue, but group cognition in the large-group setting typically requires some form of online interaction. New approaches are needed to facilitate the kind of rich communication and information processing that are required for effective, functional cognition in the online setting, especially for groups characterized by thousands to millions of participants who wish to share potentially complex, nuanced, and dynamic perspectives. This concept paper proposes the CogNarr (Cognitive Narrative) ecosystem, which is designed to facilitate functional cognition in the large-group setting. The paper's contribution is a novel vision as to how recent developments in cognitive science, artificial intelligence, natural language processing, and related fields might be scaled and applied to large-group cognition, using an approach that itself promotes further scientific advancement. A key perspective is to view a group as an organism that uses some form of cognitive architecture to sense the world, process information, remember, learn, predict, make decisions, and adapt to changing conditions. The CogNarr ecosystem is designed to serve as a component within that architecture.