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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. 

Abstract It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples’ stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing affects user satisfaction and polarization in a social network. We investigate the influence of varying tolerance in users and selectively exposing users to congenial views. We find that (1) higher user tolerance slows down polarization and leads to lower user satisfaction; (2) higher selective exposure leads to higher polarization and lower user reach; and (3) both higher tolerance and higher selective exposure lead to a more homophilic social network. 

Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit. We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms.We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity. 

Synchronous, face-to-face interactions such as brainstorming are considered essential for creative tasks (the old normal). However, face-to-face interactions are difficult to arrange because of the diverse locations and conflicting availability of people—a challenge made more prominent by work-from-home practices during the COVID-19 pandemic (the new normal). In addition, face-to-face interactions are susceptible to cognitive interference. We employ crowdsourcing as an avenue to investigate creativity in asynchronous, online interactions. We choose product ideation,a natural task for the crowd since it requires human insight and creativity into what product features would be novel and useful. We compare the performance of solo crowd workers with asynchronous teams of crowd workers formed without prior coordination. Our findings suggest that, first, crowd teamwork yields fewer but more creative ideas than solo crowdwork. The enhanced team creativity results when (1) team workers reflect on each other’s ideas, and (2) teams are composed of workers of reflective, as opposed to active or mixed, personality types. Second, cognitive interference, known to inhibit creativity in face-to-face teams, may not be significant in crowd teams. Third, teamwork promotes better achievement emotions for crowd workers. These findings provide a basis for trading off creativity, quantity, and worker happiness in setting up crowdsourcing workflows for product ideation. 

Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes.