Brigitte Burgemeestre

Value-based argumentation for justifying compliance

Compliance is often achieved ‘by design’ through a coherent system of controls consisting of information systems and procedures. This system-based control requires a new approach to auditing in which companies must demonstrate to the regulator that they are ‘in control’. They must determine the relevance of a regulation for their business, justify which set of control measures they have taken to comply with it, and demonstrate that the control measures are operationally effective. In this paper we show how value-based argumentation theory can be applied to the compliance domain. Corporate values motivate the selection of control measures (actions) which aim to fulfil control objectives, i.e. adopted norms (goals). In particular, we show how to formalize the audit dialogue in which companies justify their compliance decisions to regulators using value-based argumentation. The approach is illustrated by a case study of the safety and security measures adopted in the context of EU customs regulation.

Agent Architectures for Compliance

A Normative Multi-Agent System consists of autonomous agents who must comply with social norms. Different kinds of norms make different assumptions about the cognitive architecture of the agents. For example, a principle-based norm assumes that agents can reflect upon the consequences of their actions; a rule-based formulation only assumes that agents can avoid violations. In this paper we present several cognitive agent architectures for self-monitoring and compliance. We show how different assumptions about the cognitive architecture lead to different information needs when assessing compliance. The approach is validated with a case study of horizontal monitoring , an approach to corporate tax auditing recently introduced by the Dutch Customs and Tax Authority.

Norm Emergence in Regulatory Compliance

In their daily activities, people and businesses are subject to all kinds of governmental regulations. There is a great variety in the setup of regulative systems that aim to enforce a subjects’ compliance with these regulations (Burgemeestre, Hulstijn, & Tan, 2009b). A trend in regulatory compliance is to formulate legislation as the so-called open norms: norms which leave room for contextual interpretation about their implementation (Gribnau, 2008). Open norms are considered to be flexible and adaptable to changing and varying circumstances, whereas rules require constant amendment to meet changing circumstances (Ford, 2008). Instances of open norms are principles or regulatory objectives. Unlike rules, open norms do not describe in detail what is permissible or prohibited; they indicate what behavior is expected by abstract principles or general objectives. For example, a recipe may prescribe to add salt “according to taste.” How much salt must be added depends on the cook and his guests. Through experience a cook learns what reasonable quantities of salt are appropriate for different kinds of dishes. So in general, open norms need to be interpreted and operationalized for a specific context. Interestingly, this interpretation and operationalization process itself is a normative process.

Towards an architecture for self-regulating agents: a case study in international trade

Norm-enforcement models applied in human societies may serve as an inspiration for the design of multi-agent systems. Models for norm-enforcement in multi-agent systems often focus either on the intra- or interagent level. We propose a combined approach to identify objectives for an architecture for self-regulating agents. In this paper we assess how changes on the inter-agent level affect the intra-agent level and how a generic BDI architecture IRMA can be adapted for self-regulation. The approach is validated with a case study of AEO certification, a European wide customs initiative to secure the supply chain while facilitating international trade.