Herbert Simon (1947) observed that human learning and decision making does not strive for optimal solutions but merely ones that are good enough (known as Satisficing). However, the absence of a theory of what precisely counts as good enough has limited the utility of Satisficing in the social and behavioral sciences. We draw insights from recent work in cognitive science that provides a precise criterion for good enough learning (termed the “Tolerance Principle” or TP) which describes how humans form generalizations based on limited or inconsistent information. To date, TP has only been defined for individual learning. Here, we show how TP provides a precise model of how social norms emerge in a paradigmatic coordination game frequently used in research on cultural evolution and collective intelligence - i.e., the Wittgensteinian name game as designed by Centola & Baronchelli (2015). We compare different agent-based models in their ability to predict norm emergence in experimental runs of the name game involving human participants. TP considerably outperforms models which assume that agents leverage optimal rather than good enough predictions for social behavior. We also show how TP provides a more accurate model of tipping point dynamics in the name game. Altogether, we show how social coordination follows the same simple formula for “good enough” solutions as individual learning, providing a novel synthesis of psychological and sociological constructs. 

LIVE - Social satisficing: How a simple cognitive constraint governs norm emergence

Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning. We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word meaning both within and beyond extant language. In particular, randomly generated character n-grams lack meaning but contain primitive information based on the distribution of characters they contain. By studying the embeddings of a large corpus of garble, extant language, and pseudowords using CharacterBERT, we identify an axis in the model's high-dimensional embedding space that separates these classes of n-grams. Furthermore, we show that this axis relates to structure within extant language, including word part-of-speech, morphology, and concept concreteness. Thus, in contrast to studies that are mainly limited to extant language, our work reveals that meaning and primitive information are intrinsically linked.

Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

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Social Norms & Values

technical paper

Welcome to the Cultural Evolution Society (CES) Conference 2024, which was a hybrid conference held at Durham University (UK). The conference was hosted in Durham by the Durham Cultural Evolution Research Centre (DCERC) with administrative assistance from the CES Transformation Fund team.

**For more information about the CES Conference, please visit the [CES Website Home Page](https://ces2024.webspace.durham.ac.uk/).**

#### Durham Cultural Evolution Research Centre (DCERC) 
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**Interdisciplinary study of cultural change and diversity**

We are a highly interdisciplinary group of researchers using evolutionary models to study cultural change and diversity. We apply cultural evolutionary methods to a wide variety of topics including cognition, innovation, social learning, technology, art, music, narrative, religion, sociality, language and communication. We have a growing interest in applying cultural evolutionary theory to current societal challenges such as understanding the spread of prosocial health behaviours and health conspiracies. 
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Cultural Evolution Society Conference 2024

The field of cultural evolution is growing in size and scope; its theories, methods and applications - both academic and applied - are getting ever more robust. Do join us in what promises to be an exciting exchange of ideas and a celebration of our field.

Posters: Interpretability and Analysis of Models for NLP

poster

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Douglas Guilbeault

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Presentations

LIVE - Social satisficing: How a simple cognitive constraint governs norm emergence

Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

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