More Like this

1. Noe: Norm Emergence and Robustness Based on Emotions in Multiagent Systems

   Tzeng, ST. (January 2022, International Workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems)

   Social norms characterize collective and acceptable group conducts in human society. Furthermore, some social norms emerge from interactions of agents or humans. To achieve agent autonomy and make norm satisfaction explainable, we include emotions into the normative reasoning process, which evaluates whether to comply or violate a norm. Specifically, before selecting an action to execute, an agent observes the environment and infers the state and consequences with its internal states after norm satisfaction or violation of a social norm. Both norm satisfaction and violation provoke further emotions, and the subsequent emotions affect norm enforcement. This paper investigates how modeling emotions affect the emergence and robustness of social norms via social simulation experiments. We find that an ability in agents to consider emotional responses to the outcomes of norm satisfaction and violation (1) promotes norm compliance; and (2) improves societal welfare. 

   more » « less
2. Fleur: Social Values Orientation for Robust Norm Emergence

   Tzeng, ST. (January 2022, 15th International Workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems)

   By regulating agent interactions, norms facilitate coordination in multiagent systems. We investigate challenges and opportunities in the emergence of norms of prosociality, such as vaccination and mask wearing. Little research on norm emergence has incorporated social preferences, which determines how agents behave when others are involved. We evaluate the influence of preference distributions in a society on the emergence of prosocial norms. We adopt the Social Value Orientation (SVO) framework, which places value preferences along the dimensions of self and other. SVO brings forth the aspects of values most relevant to prosociality. Therefore, it provides an effective basis to structure our evaluation. We find that including SVO in agents enables (1) better social experience; and (2) robust norm emergence. 

   more » « less
3. 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. 

   more » « less
4. Socially Intelligent Genetic Agents for the Emergence of Explicit Norms

   https://doi.org/10.24963/ijcai.2022/2  

   Agrawal, Rishabh; Ajmeri, Nirav; Singh, Munindar (July 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence)

   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. 

   more » « less
5. Prosocial Norm Emergence in Multi-agent Systems

   https://doi.org/10.1145/3540202  

   Mashayekhi, Mehdi; Ajmeri, Nirav; List, George F.; Singh, Munindar P. (June 2022, ACM Transactions on Autonomous and Adaptive Systems)

   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. 

   more » « less