Reliable simulation of human behavior is essential for explaining, predicting, and intervening in our society. Recent advances in large language models (LLMs) have shown promise in emulating human behaviors, interactions, and decision-making, offering a powerful new lens for social science studies. However, the extent to which LLMs diverge from authentic human behavior in social contexts remains underexplored, posing risks of misinterpretation in scientific studies and unintended consequences in real-world applications. Here, we introduce a systematic framework for analyzing LLMs' behavior in social simulation. Our approach simulates multi-agent interactions through chatroom-style conversations and analyzes them across five linguistic dimensions, providing a simple yet effective method to examine emergent social cognitive biases. We conduct extensive experiments involving eight representative LLMs across three families. Our findings reveal that LLMs do not faithfully reproduce genuine human behavior but instead reflect overly idealized versions of it, shaped by the social desirability bias. In particular, LLMs show social role bias, primacy effect, and positivity bias, resulting in "Utopian" societies that lack the complexity and variability of real human interactions. These findings call for more socially grounded LLMs that capture the diversity of human social behavior.

This paper investigates the factors fostering collective intelligence (CI) through a case study of *LinYi's Experiment, where over 2000 human players collectively controll an avatar car. By conducting theoretical analysis and replicating observed behaviors through numerical simulations, we demonstrate how self-organized division of labor (DOL) among individuals fosters the emergence of CI and identify two essential conditions fostering CI by formulating this problem into a stability problem of a Markov Jump Linear System (MJLS). These conditions, independent of external stimulus, emphasize the importance of both elite and common players in fostering CI. Additionally, we propose an index for emergence of CI and a distributed method for estimating joint actions, enabling individuals to learn their optimal social roles without global action information of the whole crowd.

Perceptions of gender are a significant aspect of human-human interaction, and gender has wide-reaching social implications for robots deployed in contexts where they are expected to interact with humans. This work explored two flexible modalities for communicating gender in robots--voice and appearance--and we studied their individual and combined influences on a robot's perceived gender. We evaluated the perception of a robot's gender through three video-based studies. First, we conducted a study (n=65) on the gender perception of robot voices by varying speaker identity and pitch. Second, we conducted a study (n=93) on the gender perception of robot clothing designed for two different tasks. Finally, building on the results of the first two studies, we completed a large integrative video-based study (n=273) involving two human-robot interaction tasks. We found that voice and clothing can be used to reliably establish a robot's perceived gender, and that combining these two modalities can have different effects on the robot's perceived gender. Taken together, these results inform the design of robot voices and clothing as individual and interacting components in the perceptions of robot gender.

Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or removed during this initial stage is under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten "quality" and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. Overall, we hope that our work will encourage a new line of research on pretraining data curation practices and its social implications.

Personalisation is essential to achieve more acceptable and effective results in human-robot interaction. Placing users in the central role, many studies have focused on enhancing the abilities of social robots to perceive and understand users. However, little is known about improving user perceptions and interpretation of a social robot in spoken interactions. The work described in the paper aims to find out what affects the personalisation of affordance of a social robot, namely its appearance, voice and language behaviours. The experimental data presented here is based on an ongoing project. It demonstrates the many and varied ways in which people change their preferences for the affordance of a social robot under different circumstances. It also examines the relationship between such preferences and expectations of characteristics of a social robot, like competence and warmth. It also shows that individuals have different perceptions of the language behaviours of the same robot. These results demonstrate that one-sized personalisation does not fit all. Personalisation should be considered a comprehensive approach, including appropriate affordance design, to suit the user expectations of social roles.

Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses "You are a helpful assistant" as part of the default system prompt. But is "a helpful assistant" the best role for LLMs? In this study, we present a systematic evaluation of how social roles in system prompts affect model performance. We curate a list of 162 roles covering 6 types of interpersonal relationships and 8 types of occupations. Through extensive analysis of 3 popular LLMs and 2457 questions, we show that adding interpersonal roles in prompts consistently improves the models' performance over a range of questions. Moreover, while we find that using gender-neutral roles and specifying the role as the audience leads to better performances, predicting which role leads to the best performance remains a challenging task, and that frequency, similarity, and perplexity do not fully explain the effect of social roles on model performances. Our results can help inform the design of system prompts for AI systems. Code and data are available at https://github.com/Jiaxin-Pei/Prompting-with-Social-Roles.

The integration of the physical capabilities of an industrial collaborative robot with a social virtual character may represent a viable solution to enhance the workers' perception of the system as an embodied social entity and increase social engagement and well-being at the workplace. An online study was setup using prerecorded video interactions in order to pilot potential advantages of different embodied configurations of the cobot-avatar system in terms of perceptions of Social Presence, cobot-avatar Unity and Social Role of the system, and explore the relation of these. In particular, two different configurations were explored and compared: the virtual character was displayed either on a tablet strapped onto the base of the cobot or on a large TV screen positioned at the back of the workcell. The results imply that participants showed no clear preference based on the constructs, and both configurations fulfill these basic criteria. In terms of the relations between the constructs, there were strong correlations between perception of Social Presence, Unity and Social Role (Collegiality). This gives a valuable insight into the role of these constructs in the perception of cobots as embodied social entities, and towards building cobots that support well-being at the workplace.

Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons, this chapter examines a number of relevant cross-cutting concerns, conceptualizations, modeling problems and design challenges in large-scale distributed Artificial Intelligence, as well as in institutional systems, and identifies potential grounds for novel advances.

To make machine exhibit human-like abilities in the domains like robotics and conversation, social commonsense knowledge (SCK), i.e., common sense about social contexts and social roles, is absolutely necessarily. Therefor, our ultimate goal is to acquire large-scale SCK to support much more intelligent applications. Before that, we need to know clearly what is SCK and how to represent it, since automatic information processing requires data and knowledge are organized in structured and semantically related ways. For this reason, in this paper, we identify and formalize three basic types of SCK based on first-order theory. Firstly, we identify and formalize the interrelationships, such as having-role and having-social_relation, among social contexts, roles and players from the perspective of considering both contexts and roles as first-order citizens and not generating role instances. Secondly, we provide a four level structure to identify and formalize the intrinsic information, such as events and desires, of social contexts, roles and players, and illustrate the way of harvesting the intrinsic information of social contexts and roles from the exhibition of players in concrete contexts. And thirdly, enlightened by some observations of actual contexts, we further introduce and formalize the embedding of social contexts, and depict the way of excavating the intrinsic information of social contexts and roles from the embedded smaller and simpler contexts. The results of this paper lay the foundation not only for formalizing much more complex SCK but also for acquiring these three basic types of SCK.

Psychological theories suggest that emotion represents the state of mind and instinctive responses of one’s cognitive system (Cannon 1927). Emotions are a complex state of feeling that results in physical and psychological changes that influence our behavior. In this paper, we study an interesting problem of emotion contagion in social networks. In particular, by employing an image social network (Flickr) as the basis of our study, we try to unveil how users’ emotional statuses influence each other and how users’ positions in the social network affect their influential strength on emotion. We develop a probabilistic framework to formalize the problem into a role-aware contagion model. The model is able to predict users’ emotional statuses based on their historical emotional statuses and social structures. Experiments on a large Flickr dataset show that the proposed model significantly outperforms (+31% in terms of F1-score) several alternative methods in predicting users’ emotional status. We also discover several intriguing phenomena. For example, the probability that a user feels happy is roughly linear to the number of friends who are also happy; but taking a closer look, the happiness probability is superlinear to the number of happy friends who act as opinion leaders (Page et al. 1999) in the network and sublinear in the number of happy friends who span structural holes (Burt 2001). This offers a new opportunity to understand the underlying mechanism of emotional contagion in online social networks.