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1. Norm Enforcement with a Soft Touch: Faster Emergence, Happier Agents

   Tzeng, Sz-Ting; Ajmeri, Nirav; Singh, Munindar P (May 2024, IFAAMAS)

   A multiagent system is a society of autonomous agents whose interactions can be regulated via social norms. In general, the norms of a society are not hardcoded but emerge from the agents’ interactions. Specifically, how the agents in a society react to each other’s behavior and respond to the reactions of others determines which norms emerge in the society. We think of these reactions by an agent to the satisfactory or unsatisfactory behaviors of another agent as communications from the first agent to the second agent. Understanding these communications is a kind of social intelligence: these communications provide natural drivers for norm emergence by pushing agents toward certain behaviors, which can become established as norms. Whereas it is well-known that sanctioning can lead to the emergence of norms, we posit that a broader kind of social intelligence can prove more effective in promoting cooperation in a multiagent system. Accordingly, we develop Nest, a framework that models social intelligence via a wider variety of communications and understanding of them than in previous work. To evaluate Nest, we develop a simulated pandemic environment and conduct simulation experiments to compare Nest with baselines considering a combination of three kinds of social communication: sanction, tell, and hint. We find that societies formed of Nest agents achieve norms faster. Moreover, Nest agents effectively avoid undesirable consequences, which are negative sanctions and deviation from goals, and yield higher satisfaction for themselves than baseline agents despite requiring only an equivalent amount of information. 

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2. Designing Chatbots as Community-Owned Agents

   https://doi.org/10.1145/3405755.3406134  

   Luria, Michal; Seering, Joseph; Forlizzi, Jodi; Zimmerman, John (July 2020, Proceedings of the 2nd Conference on Conversational User Interfaces)

   null (Ed.)

   This work investigates how social agents can be designed to create a sense of ownership over them within a group of users. Social agents, such as conversational agents and chatbots, currently interact with people in impersonal, isolated, and often one-on-one interactions: one user and one agent. This is likely to change as agents become more socially sophisticated and integrated in social fabrics. Previous research has indicated that understanding who owns an agent can assist in creating expectations and understanding who an agent is accountable to within a group. We present findings from a three week case-study in which we implemented a chatbot that was successful in creating a sense of collective ownership within a community. We discuss the design choices that led to this outcome and implications for social agent design. 

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3. MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming

   https://doi.org/10.1145/3711032  

   Li, Yuan; Sun, Lichao; Zhang, Yixuan (April 2025, Proceedings of the ACM on Human-Computer Interaction)

   Significant advancements have occurred in the application of Large Language Models (LLMs) for social simulations. Despite this, their abilities to perform teaming in task-oriented social events are underexplored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behaviors and form efficient teams to solve tasks. To bridge this gap, we introduce MetaAgents, a social simulation framework populated with LLM-based agents. MetaAgents facilitates agent engagement in conversations and a series of decision making within social contexts, serving as an appropriate platform for investigating interactions and interpersonal decision-making of agents. In particular, we construct a job fair environment as a case study to scrutinize the team assembly and skill-matching behaviors of LLM-based agents. We take advantage of both quantitative metrics evaluation and qualitative text analysis to assess their teaming abilities at the job fair. Our evaluation demonstrates that LLM-based agents perform competently in making rational decisions to develop efficient teams. However, we also identify limitations that hinder their effectiveness in more complex team assembly tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations. 

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4. Local non-Bayesian social learning with stubborn agents

   https://doi.org/10.1109/TCNS.2022.3154679  

   Vial, Daniel; Subramanian, Vijay G. (February 2022, IEEE Transactions on Control of Network Systems)

   Altafini, Claudio; Como, Giacomo; Hendrickx, Julien M.; Olshevsky, Alexander; Tahbez-Salehi, Alireza (Ed.)

   We study a social learning model in which agents iteratively update their beliefs about the true state of the world using private signals and the beliefs of other agents in a non-Bayesian manner. Some agents are stubborn, meaning they attempt to convince others of an erroneous true state (modeling fake news). We show that while agents learn the true state on short timescales, they "forget" it and believe the erroneous state to be true on longer timescales. Using these results, we devise strategies for seeding stubborn agents so as to disrupt learning, which outperform intuitive heuristics and give novel insights regarding vulnerabilities in social learning. 

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5. Robotic Futures: Learning about Personally-Owned Agents through Performance

   https://doi.org/10.1145/3357236.3395488  

   Luria, Michal; Oden Choi, Judeth; Karp, Rachel Gita; Zimmerman, John; Forlizzi, Jodi (July 2020, DIS '20: Proceedings of the 2020 ACM Designing Interactive Systems Conference)

   null (Ed.)

   Agents that support spoken interaction (e.g., Amazon Echo) are designed for social spaces like the home, yet designers know little about how they should respond to social activity around them. We set out to reconsider current one-on-one interactions with agents, and explore the design space of future socially sophisticated agents. To do so, we use an iterative co-design process with designers and theatre experts to devise an immersive performance, "Robotic Futures." Theatre is a form of knowing through doing-by examining the interactions that persisted in the devising process and those that fell through, we conclude with a proposition for design considerations for future agents. Based on emerging research in this space, we focus on the characteristics of personally-owned agents in comparison to shared agents, and consider the roles and functions each introduce in their integration in the home. 

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