We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.

Models are used to inform policymaking and underpin large amounts of government expenditure. Several authors have observed a discrepancy between the actual and potential use of models in government. While there have been several studies investigating model acceptance in government, it remains unclear under what conditions models are accepted. In this paper, we address the question ''What criteria affect model acceptance in policymaking?'', the answer to which will contribute to the wider understanding of model use in government. We employ a thematic coding approach to identify the acceptance criteria for the eight models in our sample. Subsequently, we compare our findings with existing literature and use qualitative comparative analysis to explore what configurations of the criteria are observed in instances of model acceptance. We conclude that model acceptance is affected by a combination of the model's characteristics, the supporting infrastructure and organizational factors.

Optimising policy choices to steer social/economic systems efficiently towards desirable outcomes is challenging. The inter-dependent nature of many elements of society and the economy means that policies designed to promote one particular aspect often have secondary, unintended, effects. In order to make rational decisions, methodologies and tools to assist the development of intuition in this complex world are needed. One approach is the use of agent-based models. These have the ability to capture essential features and interactions and predict outcomes in a way that is not readily achievable through either equations or words alone.In this paper we illustrate how agent-based models can be used in a policy setting by using an example drawn from the biowaste industry. This example describes the growth of in-vessel composting and anaerobic digestion to reduce food waste going to landfill in response to policies in the form of taxes and financial incentives. The fundamentally dynamic nature of an agent-based modelling approach is used to demonstrate that