Significance Human memory is highly malleable. Because of this malleability, jointly remembering the past along with another individual often results in increased similarity between the conversational partners’ memories. We propose an approach that examines the conversation between a pair of participants as part of a larger network of social interactions that has the potential to reveal how human communities form collective memories. Empirical evidence indicating that dyadic-level conversational alignment processes give rise to community-wide shared memories is presented. We find that individual-level memory updating phenomena and social network structure are two fundamental factors that contribute to the emergence of collective memories. , The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.
Cognition in a Social Context: A Social-Interactionist Approach to Emergent Phenomena
The formation of collective memories, emotions, and beliefs is a fundamental characteristic of human communities. These emergent outcomes are thought to be the result of a dynamical system of communicative interactions among individuals. But despite recent psychological research on collective phenomena, no programmatic framework to explore the processes involved in their formation exists. Here, we propose a social-interactionist approach that bridges cognitive and social psychology to illuminate how microlevel cognitive phenomena give rise to large-scale social outcomes. It involves first establishing the boundary conditions of cognitive phenomena, then investigating how cognition is influenced by the social context in which it is manifested, and finally studying how dyadic-level influences propagate in social networks. This approach has the potential to (a) illuminate the large-scale consequences of well-established cognitive phenomena, (b) lead to interdisciplinary dialogues between psychology and the other social sciences, and (c) be more relevant for public policy than existing approaches.
LLMs and people both learn to form conventions -- just not with each other
Humans align to one another in conversation -- adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communication game. Both humans and LLMs display evidence of convention-formation (increasing the accuracy and consistency of their turns while decreasing their length) when communicating in same-type dyads (humans with humans, AI with AI). However, heterogenous human-AI pairs fail -- suggesting differences in communicative tendencies. In Experiment 2, we ask whether LLMs can be induced to behave more like human conversants, by prompting them to produce superficially humanlike behavior. While the length of their messages matches that of human pairs, accuracy and lexical overlap in human-LLM pairs continues to lag behind that of both human-human and AI-AI pairs. These results suggest that conversational alignment requires more than just the ability to mimic previous interactions, but also shared interpretative biases toward the meanings that are conveyed.
Shared Reality: Experiencing Commonality with others' Inner States about the World
Humans have a fundamental need to experience a shared reality with others. We present a new conceptualization of shared reality based on four conditions. We posit (a) that shared reality involves a (subjectively perceived) commonality of individuals' inner states (not just observable behaviors); (b) that shared reality is about some target referent; (c) that for a shared reality to occur, the commonality of inner states must be appropriately motivated; and (d) that shared reality involves the experience of a successful connection to other people's inner states. In reviewing relevant evidence, we emphasize research on the saying-is-believing effect, which illustrates the creation of shared reality in interpersonal communication. We discuss why shared reality provides a better explanation of the findings from saying-is-believing studies than do other formulations. Finally, we examine relations between our conceptualization of shared reality and related constructs (including empathy, perspective taking, theory of mind, common ground, embodied synchrony, and socially distributed knowledge) and indicate how our approach may promote a comprehensive and differentiated understanding of social-sharing phenomena.
remember
For groups The final step in organizing group conversations about computer use is to commit to remembering what has been said before. And to say it again....
Misplaced Divides? Discussing Political Disagreement With Strangers Can Be Unexpectedly Positive
Differences of opinion between people are common in everyday life, but discussing those differences openly in conversation may be unnecessarily rare. We report three experiments ( N = 1,264 U.S.-based adults) demonstrating that people’s interest in discussing important but potentially divisive topics is guided by their expectations about how positively the conversation will unfold, leaving them more interested in having a conversation with someone who agrees versus disagrees with them. People’s expectations about their conversations, however, were systematically miscalibrated such that people underestimated how positive these conversations would be—especially in cases of disagreement. Miscalibrated expectations stemmed from underestimating the degree of common ground that would emerge in conversation and from failing to appreciate the power of social forces in conversation that create social connection. Misunderstanding the outcomes of conversation could lead people to avoid discussing disagreements more often, creating a misplaced barrier to learning, social connection, free inquiry, and free expression.
Humans are social creatures. We crave bonding and social connections. In our digital world, our common social space is a campfire, which is the group chat.
The Emergence of Social Norms and Conventions
The utility of our actions frequently depends upon the beliefs and behavior of other agents. Thankfully, through experience, we learn norms and conventions that provide stable expectations for navigating our social world. Here, we review several distinct influences on their content and distribution. At the level of individuals locally interacting in dyads, success depends on rapidly adapting pre-existing norms to the local context. Hence, norms are shaped by complex cognitive processes involved in learning and social reasoning.
The Social Physics of Conversation: Why Communication Patterns Matter
Discover how communication patterns shape team performance, innovation, and collective intelligence. Learn why idea flow matters more than talent alone.
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their interactions can improve collective reasoning but may also produce herding, polarization, or amplify shared biases. Understanding and predicting these collective dynamics is therefore important for designing effective and aligned multi-agent systems. Here, we study over 10,000 communities of language-model agents that repeatedly exchange messages and revise their opinions across objective mathematics questions and subjective political statements. Despite substantial diversity in possible behavior, the individual and group dynamics can be represented by three characteristic regimes: indifference, polarization, and consensus. AI agents start indifferent and build conviction as they interact. On objective questions, communication improves collective accuracy, while on subjective questions it often drifts group opinions toward the right in the political spectrum. We explain these observations with a statistical-mechanics formalism in which agents stochastically favor lower social pressure. Given only initial opinions, our model predicts individual trajectories, outperforms all standard baselines, generalizes to unseen community graphs, and reproduces the observed group archetype distributions. Our fitted model parameters reveal the mechanics underlying our key observations: i) communities operate below the critical social temperature, which explains conviction buildup; ii) attractive ties outweigh repulsive ones, which favors consensus; and iii) agents holding the correct answer exert the strongest pull, which drives truth-seeking. Overall, our results demonstrate that collective behavior of AI agents, like that of other complex systems, follows compact and predictive dynamical laws.
Network dynamics of social influence in the wisdom of crowds
Significance Since the discovery of the wisdom of crowds over 100 years ago theories of collective intelligence have held that group accuracy requires either statistical independence or informational diversity among individual beliefs. Empirical evidence suggests that allowing people to observe the beliefs of others leads to increased similarity of individual estimates, reducing independence and diversity without a corresponding increase in group accuracy. As a result, social influence is expected to undermine the wisdom of crowds. We present theoretical predictions and experimental findings demonstrating that, in decentralized networks, social influence generates learning dynamics that reliably improve the wisdom of crowds. We identify general conditions under which influence, not independence, produces the most accurate group judgments. , A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
Roomy and community organizing for system change
Roomy is a group messaging app that gives online communities tools for autonomous collaboration — designable spaces not just for chat, but for cultivating a web of shared understanding together. We show how cheap, user-controlled, interoperable data on ATProto can enable online communities to channel impassioned mobilizing into sustained organizing in the shadow of Big Tech's empire.
How Online Mobs Act Like Flocks Of Birds
A growing body of research suggests human behavior on social media is strikingly similar to collective behavior in nature.
How Online Mobs Act Like Flocks Of Birds
A growing body of research suggests human behavior on social media is strikingly similar to collective behavior in nature.
The Majority Illusion in Social Networks
Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.
The network science of collective intelligence
In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively.
An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies
As artificial intelligence increasingly mediates public discourse, it becomes important to understand how human-AI collectives shape opinion formation, deliberation, and democratic outcomes. We present a novel experimental method for studying opinion dynamics in hybrid human-AI social networks. Participants, human or AI, were embedded in $5\times5$ grid lattice networks and iteratively asked to select and revise statements on a given polarizing topic over eight rounds. We compared three conditions: human-only, AI-only, and hybrid networks with equal proportions of human and AI participants. Hybrid human-AI networks achieved the lowest final polarization while, in contrast, human-only networks exhibited higher polarization with lower neighbor agreement. We also ran additional experiments varying Large Language Model (LLM) prompt framing to explore whether instruction design might influence convergence patterns. Although these early findings are preliminary and cannot yet support broad generalizations, they highlight the potential value of experimental social networks for understanding opinion dynamics in human-AI hybrid societies.