The open agent society: retrospective and prospective views

Pitt, Jeremy; Artikis, Alexander

doi:10.1007/s10506-015-9173-y

Cited by 8 publications

(10 citation statements)

References 73 publications

(57 reference statements)

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“…We envisage this as an important operationalisation of two proposals. In what Pitt et al [67] called polycentric governance, it is argued that in complex artificial MAS a single one-size-fits-all institution is inadequate, since different localised parts of the MAS may need different regulations. Therefore, separate lower level institutions should be designed, appealing to subsidiarity, inline with overarching institutions abstractly prescribing what regulations should be implemented.…”

Section: Discussionmentioning

confidence: 99%

“…In our framework we showed how constitutive rules provide the ontological basis for capturing links between concrete and abstract norms an appropriate ontology for an artificial society. Hence, we foresee our contributions supporting the design of governance for artificial and socio-technical systems according to the design principles of [18,67], based on an appropriate ontology of constitutive rules for an artificial or socio-technical society.…”

Section: Discussionmentioning

confidence: 99%

“…We view multi-level governance as comprising three distinctive characteristics relevant to compliance checking, schematically depicted in Figure 1. We draw these characteristics from political science literature [45], work on multi-level governance for artificial societies in AI [67,68] (in what is called polycentric governance), and the real-world case study we focus on. These three characteristics are: -Regulation of regulation: higher level institutions govern lower level institutions' designs with norms that govern norms.…”

Section: Multi-level Governancementioning

confidence: 99%

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Automated multi-level governance compliance checking

King

Vos

Dignum

et al. 2017

Auton Agent Multi-Agent Syst

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An institution typically comprises constitutive rules, which give shape and meaning to social interactions and regulative rules, which prescribe agent behaviour in the society. Regulative rules guide social interaction, in particular when they are coupled with reward and punishment regulations that are enforced for (non-)compliance. Institution examples include legislation and contracts. Formal institutional reasoning frameworks automate ascribing social meaning to agent interaction and determining whether those actions have social meanings that comprise (non-)compliant behaviour. Yet, institutions do not just govern societies. Rather, in what is called multi-level governance, institutional designs at lower governance levels (e.g., national legislation at the national level) are governed by higher level institutions (e.g., directives, human rights charters and supranational agreements). When an institution design is found to be non-compliant, punishments can be issued by annulling the legislation or imposing fines on the responsible designers (i.e., government). In order to enforce multi-level governance, higher governance levels (e.g., courts applying human rights) must check lower level institution designs (e.g., national legislation) for compliance; in order to avoid punishment, lower governance levels (e.g., national governments) must check their institution designs are compliant with higher-level institutions before enactment. However, checking non-compliance of institution designs in multi-level governance is non-trivial. In particular, because institutions in multi-level governance operate at different levels of abstraction. Lower level institutions govern with concrete regulations whilst higher level institutions typically comprise increasingly vague and abstract regulations. To address this issue, in this paper we propose a formal framework with a novel semantics that defines compliance between concrete lower level institutions and abstract higher level institutions. The formal framework is complemented by a sound and complete computational framework that automates compliance checking, which we apply to a real-world case study

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Multi-level Governancementioning

confidence: 99%

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Automated multi-level governance compliance checking

King

Vos

Dignum

et al. 2017

Auton Agent Multi-Agent Syst

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“…It can also be applied to tools, technologies and functional objects. It is not intended or felt towards these objects' designers or producers, but rather towards the working devices themselves [11,14]. This also applies to multi-agent complex dynamics and results, like traffic in a given city, or performances on the markets.…”

Section: Trust and Technologymentioning

confidence: 99%

“…Thus, > for computer-mediated social orders and systems, one has to explain for whose advantage that order is established, which group/class is favoured, and if there are other fairer orders. > See also on that Pitt and Artikis' work on the design of Self-Governing Institutions (for example, [14]) on the basis of Ostrom's and Rescher's principles of Commons and Justice, axiomatised in computational logics.…”

Section: The Emergent Equilibriums Are Not Necessarily the Best And Fairmentioning

confidence: 99%

Trust & Self-Organising Socio-technical Systems

Castelfranchi¹,

Falcone²

2016

Trustworthy Open Self-Organising Systems

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We present our theory on trust and its components and dimensions, and apply it to trust in complex dynamic socio-technical systems and to their selforganising emergent results. Specifically, we apply our theory to ICT-based systems, where a "Social Order" is no longer fully "spontaneous" due to the invisible hand impinging on individual and selfish-decisions. In such contexts, a social order is rather based on programmed interactions, algorithmic procedures and big data. Since trust cannot be fully programmable and predictable, how can we build it in this complex and dynamic system? Some of our research questions sound: is it necessary that folks "understand" the underlying mechanisms they are relying on? What kind of information about forecasts or future projections should be provided and adjusted? What kind of role do simulation, serious games play on learning to understand and expect? Will there be algorithms working on the micro-processes and producing the emergent organisation, and if yes, how effective and reliable will they be? There are at least two different levels of trust in complex systems and in their functioning processes: trust in the emergent order and trust in the microlayer rules. Are the systems rules and resulting equilibriums fair, equity inspired, in relation to the interests of the involved groups/subjects? A complex and cognitive model of trust is needed for this analysis. Keywords PremiseWe will present some of the main features of our socio-cognitive theory of trust and its components (expectations, evaluations, desires, perceived risks) and bases, and apply this model to trust in complex dynamic socio-technical systems and to their self-organising emergent results. The aim is to discuss how we can build trust in C. Castelfranchi ( ) • R. Falcone

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From Trust and Forgiveness to Social Capital and Justice: Formal Models of Social Processes in Open Distributed Systems

Pitt

2016

Trustworthy Open Self-Organising Systems

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The open agent society: retrospective and prospective views

Cited by 8 publications

References 73 publications

Automated multi-level governance compliance checking

Automated multi-level governance compliance checking

Trust & Self-Organising Socio-technical Systems

From Trust and Forgiveness to Social Capital and Justice: Formal Models of Social Processes in Open Distributed Systems

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