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1. A Normative Approach for Resilient Multiagent Systems: A Summary

   Mahal, Geeta; Kafali, Ozgur; Dam, Hoa; Ghose, Aditya; Singh, Munindar P (May 2024, IFAAMAS)

   We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. We express stakeholder needs to ensure that a MAS demonstrates resilience, allowing it to recover effectively from failures within a brief timeframe. This extended abstract presents a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. An important contribution of the framework is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. This paper facilitates specification refinement through methodological guidelines, emphasizing joint modeling of social and technical layers. We demonstrate our framework using a manufacturing scenario with competing public, industrial, and environmental requirements. This is an extended abstract of our JAAMAS paper available online. 

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2. Stakeholders in the cloud computing value-chain : A socio-technical review of data breach literature

   https://doi.org/10.1109/ISTAS50296.2020.9462169  

   Kolevski, David; Michael, Katina; Abbas, Roba; Freeman, Mark (January 2020, 2020 IEEE International Symposium on Technology and Society (ISTAS))

   null (Ed.)

   This paper is about stakeholders in the cloud computing value-chain. Early cloud computing literature focused on the technical aspect of the technology and viewed the provider and customer as essential value-chain stakeholders. The more users that use cloud services, the potential for data breaches increases. The review of the literature was carried out using a social-technical approach. Socio-technical theory encapsulates the social, technical and environmental dimensions of a system. The outcomes of the search indicated that there are two pertinent stakeholder types: operational and non-operational. Operational stakeholders include cloud providers, customers, enablers, resellers and third-party providers. Non-operational stakeholders include regulators, legislators, courts, non-government organisations, law enforcement, industry-standard bodies and end-users. The end-users are critically important in the cloud value-chain in that they rely on online services for everyday activities and have their data compromised. The cloud value-chain presents that cloud services encapsulate more than just technology services. The paper considers the complex stakeholder relationships and data breach issues, indicating the need for a better socio-technical response from the stakeholders within the value-chain. 

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3. Incentive-Compatible Mechanisms for Norm Monitoring in Open Multi-Agent Systems

   https://doi.org/10.1613/jair.1.11214  

   Alechina, Natasha; Halpern, Joseph Y.; Kash, Ian A.; Logan, Brian (May 2018, Journal of Artificial Intelligence Research)

   We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents–the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state. 

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4. Incentive Compatible Mechanisms for Norm Monitoring

   Alechina, N.; Halpern, J.Y.; Kash, I. A.; Logan, B. (June 2018, Journal of artificial intelligence research)

   We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from \emph{scrip systems}, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents---the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state. 

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5. 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. 

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