Specifying and Reasoning About Multiple Institutions

Cliffe, Owen; Vos, Marina De; Padget, Julián

doi:10.1007/978-3-540-74459-7_5

Cited by 39 publications

(36 citation statements)

References 16 publications

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“…The issue of multiple interacting institutions has been addressed [8,35], but these works do not consider the concepts addressed here: norm-governed institutions, Ostrom's institutional design principles, and most signicantly, inter-institutional social capital. However, recent work on teams, team structures and team coordination [16] could oer some useful insights into structuration (the duality of agency and structure, in that structures are made up of agents, and agents have memory of structures [20]).…”

Section: Related Workmentioning

confidence: 99%

Electronic Social Capital for Self-Organising Multi-Agent Systems

Petruzzi

Pitt

Busquets

2017

ACM Trans. Auton. Adapt. Syst.

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It is a recurring requirement in open systems, such as networks, distributed systems and socio-technical systems, that a group of agents must coordinate their behaviour for the common good. In those systemswhere agents are heterogeneous -unexpected behaviour can occur due to errors or malice. Agents whose practices free-ride the system can be accepted to a certain level; however, not only do they put the stability of the system at risk, but they also compromise the agents that behave according to the system's rules.In social systems, it has been observed that social capital is an attribute of individuals that enhances their ability to solve collective action problems. Sociologists have studied collective action through human societies and observed that social capital plays an important role in maintaining communities though time as well as in simplifying the decision making in them. In this work, we explore the use of Electronic Social Capital for optimising self-organised collective action.We developed a context-independent Electronic Social Capital framework to test this hypothesis. The framework comprises a set of handlers that capture events from the system and update three dierent forms of social capital: trustworthiness, networks and institutions. Later, a set of metrics are generated by the forms of social capital and used for decision-making. The framework was tested in dierent scenarios such as 2-player games, n-player games and public goods games. The experimental results show that social capital optimises the outcomes (in terms of long-term satisfaction and utility), reduces the complexity of decision-making and scales with the size of the population.This work proposes an alternative solution using Electronic Social Capital to represent and reason with qualitative, instead of traditional quantitative, values. This solution could be embedded into socio-technical systems to incentivise collective action without commodifying the resources or actions in the system. Additional Key Words and Phrases: social capital, nested self-organising systems, electronic institutions, resource allocation, common-pool resource ACM Reference format:

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Section: Related Workmentioning

confidence: 99%

Electronic Social Capital for Self-Organising Multi-Agent Systems

Petruzzi

Pitt

Busquets

2017

ACM Trans. Auton. Adapt. Syst.

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“…Norms have been widely advocated as a means of coordinating multi-agent systems and several approaches have been proposed in the literature, including state-based norms (where norms are defined in terms of states that should or should not occur), e.g., [10], and event-based norms (where norms are defined in terms of what agents should or should not do), e.g., [6,4]. Similarly, various approaches to the implementation of norms have been proposed, including enforce-ment (where sanctions are imposed on norm-violating states and behaviours) and regimentation (where norm-violating states and behaviours are eliminated).…”

Section: Problem Formalisationmentioning

confidence: 99%

“…Consider the following example. An agent driving a car enters a city in a foreign city, 4 which has streets and traffic dynamics that are known to the agent, and the agent has a goal to drop off a passenger as close as possible to the passenger's desired destination. The agent is unaware of the meaning of the signs in this city and of the frequency with which traffic wardens patrol the streets, and must make a decision as to where to drop off the passenger, knowing that traffic may force it to stop at undesirable locations.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning of Normative Monitoring Intensities

Meneguzzi

Fagundes

et al. 2016

Lecture Notes in Computer Science

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Abstract. Choosing actions within norm-regulated environments involves balancing achieving one's goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased.

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“…The language supports a simple set-based type system and syntax for the declaration of fluents, events, and institutions (bearing in mind the model also supports multiinstitutional models as discussed in [6]). Normative fluents are pre-defined for power, permission and obligation.…”

Section: Aspmentioning

confidence: 99%

“…Rather than give a formal syntax specification, for which there is not room here, we put forward and extended example in section 4 to illustrate the language features in a use-case. A detailed discussion of the InstAL language can be found in [6,4]. An InstAL reasoning problem consists of the following:…”

Section: Aspmentioning

confidence: 99%

Embedding Landmarks and Scenes in a Computational Model of Institutions

Cliffe

Vos

Padget

2008

Coordination, Organizations, Institutions, and Norms in Agent Systems III

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Abstract. Over the last decade, institutions have demonstrated that they are a powerful mechanism to make agent interactions more effective, structured, coordinated and efficient. Different authors have tackled the problem of designing and verifying institutions from different angles. In this paper we propose a formalism that is capable of unifying and extending some of these approaches, as well as providing the necessary tools to assist in the design and verification processes. We demonstrate our approach with a non-trivial case-study.

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Specifying and Reasoning About Multiple Institutions

Cited by 39 publications

References 16 publications

Electronic Social Capital for Self-Organising Multi-Agent Systems

Electronic Social Capital for Self-Organising Multi-Agent Systems

Reinforcement Learning of Normative Monitoring Intensities

Embedding Landmarks and Scenes in a Computational Model of Institutions

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