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Behavioural Insights Teams (BITs) have gained prominence in government as policy advisors and are increasingly linked to the way policy instruments are designed. Despite the rise of BITs as unique knowledge brokers mediating the use of behavioral insights for policymaking, they remain underexplored in the growing literature on policy advice and advisory systems. The article emphasizes that the visible impact that BITs have on the content of policy instruments, the level of political support they garner and their structural diversity in different political departments, all set them apart from typical policy brokers in policy advisory systems connecting the science–policy divide.

AbstractPolicy and data scientists have paid ample attention to the amount of data being collected and the challenge for policymakers to use and utilize it. However, far less attention has been paid towards the quality and coverage of this data specifically pertaining to minority groups. The paper makes the argument that while there is seemingly more data to draw on for policymakers, the quality of the data in combination with potential known or unknown data gaps limits government's ability to create inclusive policies. In this context, the paper defines primary, secondary, and unknown data gaps that cover scenarios of knowingly or unknowingly missing data and how that is potentially compensated through alternative measures. Based on the review of the literature from various fields and a variety of examples highlighted throughout the paper, we conclude that the big data movement combined with more sophisticated methods in recent years has opened up new opportunities for government to use existing data in different ways as well as fill data gaps through innovative techniques. Focusing specifically on the representativeness of such data, however, shows that data gaps affect the economic opportunities, social mobility, and democratic participation of marginalized groups. The big data movement in policy may thus create new forms of inequality that are harder to detect and whose impact is more difficult to predict.

Behavioural Insights Teams (BITs) have gained prominence in government as policy advisors and are increasingly linked to the way policy instruments are designed. Despite the rise of BITs as unique knowledge brokers mediating the use of behavioral insights for policymaking, they remain underexplored in the growing literature on policy advice and advisory systems. The article emphasizes that the visible impact that BITs have on the content of policy instruments, the level of political support they garner and their structural diversity in different political departments, all set them apart from typical policy brokers in policy advisory systems connecting the science-policy divide.

The paper focuses on enterprising agents in policy formulation and design by looking at their capacity of dealing with different levels of uncertainty. In climate policy specifically, different degrees and types of uncertainties pose a challenge to policymakers. Policy entrepreneurs and the combination of their analytical, operational and political competences are a relevant component in reducing ambiguity in policy design and translating broad policy goals to operational programmes and specific policy instruments. Using the case of the European Emission Trading Scheme, we suggest that the success of policy entrepreneurs in catalysing policy change is determined by their capacity to work against multiple kinds of uncertainty. This 'uncertainty mitigating' capacity on the part of policy entrepreneurs rests significantly on balancing managerial expertise and political acumen. We conclude that entrepreneurial capacity goes beyond current definitions in the literature, involving the balance among analytical, operational and political competences to navigate a politicized policy context.

The editorial sets the scene for this thematic issue on big data applications in governance and policy. It highlights the lack of engagement in the current literature with the application of big data at the cross-section of governance of data and its utilization in the policy process and draws out aspects related to its definition and future research agenda. The contributions highlight several aspects related to big data in different contexts, such as local and national government as well as a variety of policy areas. They converge on the idea that big data applications cannot overcome existing political and structural limitations that exist in government. This leads to a future research agenda that looks at the disconnect between data production and usage as well as identifying policy issues that are more or less suitable for data analytics.

The editorial sets the scene for this thematic issue on big data applications in governance and policy. It highlights the lack of engagement in the current literature with the application of big data at the cross-section of governance of data and its utilization in the policy process and draws out aspects related to its definition and future research agenda. The contributions highlight several aspects related to big data in different contexts, such as local and national government as well as a variety of policy areas. They converge on the idea that big data applications cannot overcome existing political and structural limitations that exist in government. This leads to a future research agenda that looks at the disconnect between data production and usage as well as identifying policy issues that are more or less suitable for data analytics.

There has been a surge in the application of behavioral insights for environmental policymaking. It is often presented as an easy and low-cost intervention to alter individual behavior. However, there is limited insight into the cost effectiveness of these attempts and the impact of inserting behavioral policy instruments into an existing mix of traditional tools in a particular policy sector. Furthermore, there has been little focus on the intersection of large behavioral datasets and how they could complement behavioral insights. We present a conceptual overview of how the intersection of big data and behavioral knowledge would work in the renewable energy sector. We indicate that inserting behavioral insights into the energy instrument mix is complex due to technological trajectories, path dependencies and resistance from incumbent industries to change production patterns. We also highlight the underutilized role of large behavioral datasets that can inform not only policy implementation, but also policy design and evaluation efforts. Drawing on these findings, we introduce future research streams of government capacity in combining behavioral insights and data, the compatibility of this information with existing policy instruments and how this affects policy change.

In this paper, the author argues that the conflict between the copious amount of digital data processed by public organisations and the need for policy-relevant insights to aid public participation constitutes a 'public information paradox'. Machine learning (ML) approaches may offer one solution to this paradox through algorithms that transparently collect and use statistical modelling to provide insights for policymakers. Such an approach is tested in this paper. The test involves applying an unsupervised machine learning approach with latent Dirichlet allocation (LDA) analysis of thousands of public comments submitted to the United States Transport Security Administration (TSA) on a 2013 proposed regulation for the use of new full body imaging scanners in airport security terminals. The analysis results in salient topic clusters that could be used by policymakers to understand large amounts of text such as in an open public comments process. The results are compared with the actual final proposed TSA rule, and the author reflects on new questions raised for transparency by the implementation of ML in open rule-making processes.

With the rise of computer algorithms in administrative decision-making, concerns are voiced about their lack of transparency and discretionary space for human decision-makers. However, calls to 'keep humans in the loop' may be moot points if we fail to understand how algorithms impact human decision-making and how algorithmic design impacts the practical possibilities for transparency and human discretion. Through a review of recent academic literature, three algorithmic design variables that determine the preconditions for human transparency and discretion and four main sources of variation in 'human-algorithm interaction' are identified. The article makes two contributions. First, the existing evidence is analysed and organized to demonstrate that, by working upon behavioural mechanisms of decision-making, the agency of algorithms extends beyond their computer code and can profoundly impact human behaviour and decision-making. Second, a research agenda for studying how computer algorithms affect administrative decision-making is proposed.