'Datafication': Making sense of (big) data in a complex world

This is a pre-print of an article published in European Journal of Information Systems. The definitive publisher-authenticated version is available at the link below. Copyright @ 2013 Operational Research Society Ltd.No abstract available (Editorial

The College News, 1941-05-14, Vol. 27, No. 24

Bryn Mawr College student newspaper. Merged with The Haverford News in 1968 to form the Bi-college News (with various titles from 1968 on). Published weekly (except holidays) during the academic year

Diagramming social practice theory:An interdisciplinary experiment exploring practices as networks

Achieving a transition to a low-carbon energy system is now widely recognised as a key challenge facing humanity. To date, the vast majority of research addressing this challenge has been conducted within the disciplines of science, engineering and economics utilising quantitative and modelling techniques. However, there is growing awareness that meeting energy challenges requires fundamentally socio-technical solutions and that the social sciences have an important role to play. This is an interdisciplinary challenge but, to date, there remain very few explorations of, or reflections on, interdisciplinary energy research in practice. This paper seeks to change that by reporting on an interdisciplinary experiment to build new models of energy demand on the basis of cutting-edge social science understandings. The process encouraged the social scientists to communicate their ideas more simply, whilst allowing engineers to think critically about the embedded assumptions in their models in relation to society and social change. To do this, the paper uses a particular set of theoretical approaches to energy use behaviour known collectively as social practice theory (SPT) - and explores the potential of more quantitative forms of network analysis to provide a formal framework by means of which to diagram and visualize practices. The aim of this is to gain insight into the relationships between the elements of a practice, so increasing the ultimate understanding of how practices operate. Graphs of practice networks are populated based on new empirical data drawn from a survey of different types (or variants) of laundry practice. The resulting practice networks are analysed to reveal characteristics of elements and variants of practice, such as which elements could be considered core to the practice, or how elements between variants overlap, or can be shared. This promises insights into energy intensity, flexibility and the rootedness of practices (i.e. how entrenched/ established they are) and so opens up new questions and possibilities for intervention. The novelty of this approach is that it allows practice data to be represented graphically using a quantitative format without being overly reductive. Its usefulness is that it is readily applied to large datasets, provides the capacity to interpret social practices in new ways, and serves to open up potential links with energy modeling. More broadly, a significant dimension of novelty has been the interdisciplinary approach, radically different to that normally seen in energy research. This paper is relevant to a broad audience of social scientists and engineers interested in integrating social practices with energy engineering

Nudging down theft from insecure vehicles. A pilot study

This report presents the preliminary findings of a pilot study to reduce thefts from cars committed against insecure vehicles, using the behavioural insights or ‘nudge approach’. The recipients of the ‘nudges’ were potential victims of theft from insecure vehicles living in high rate areas for this crime, where a bespoke leaflet campaign was developed to nudge vehicle owners into thinking more carefully when leaving their vehicles unattended, particularly when left on their driveways overnight. Although somewhat tentative at this stage, the preliminary findings indicate that the percentage of thefts committed against insecure vehicles in the two treatment areas was reduced significantly when compared with the two control group areas where no nudge interventions were introduced. This demonstrates that if appropriate nudges (grounded in psychological theory) are coupled with and delivered by appropriate messengers, the prosocial behavioural change can be encouraged which can lead to a reduction in criminal behaviour and opportunities for crim

A Note on the Shape of the Probability Weighting Function

The focus of this contribution is on the transformation of objective probability, which in Prospect Theory is commonly referred as probability weighting. Empirical evidence suggests a typical inverse-S shaped function: decision makers tend to overweight small probabilities, and underweight medium and high probabilities; moreover, the probability weighting function is initially concave and then convex. We apply different parametric weighting functions proposed in the literature to the evaluation of derivative contracts and to insurance premium principles

Comparison of governance approaches for the control of antimicrobial resistance: Analysis of three European countries

Policy makers and governments are calling for coordination to address the crisis emerging from the ineffectiveness of current antibiotics and stagnated pipe-line of new ones – antimicrobial resistance (AMR). Wider contextual drivers and mechanisms are contributing to shifts in governance strategies in health care, but are national health system approaches aligned with strategies required to tackle antimicrobial resistance? This article provides an analysis of governance approaches within healthcare systems including: priority setting, performance monitoring and accountability for AMR prevention in three European countries: England, France and Germany. Advantages and unresolved issues from these different experiences are reported, concluding that mechanisms are needed to support partnerships between healthcare professionals and patients with democratized decision-making and accountability via collaboration. But along with this multi-stakeholder approach to governance, a balance between regulation and persuasion is needed

‘20 tins of Stella for a fiver’: The making of class through Labour and Coalition government alcohol policy

Alcohol use in the UK has been a key concern to both the Labour and Coalition governments, and commands considerable attention in the media and academic discussions. This article analyses how recent government policy discussions have defined particular forms of drinking as problematic, and how these definitions and associated policy initiatives can be seen as part of a wider symbolic economy through which people come to be valued differently, incorporating ideas of economic, cultural and social capital. Therefore, I argue that government policies and discussions of drinking are a key way in which class is constituted in contemporary Britain

Energy end-use flexibility of the next generation of decision-makers in a smart grid setting: an exploratory study

Demand Response (DR) mechanisms have been developed to reshape consumption patterns in face of price signals, enabling to deal with the increasing penetration of intermittent renewable resources and balance electricity demand and supply. Although DR mechanisms have been in place for some time, it is still unclear to what extent end-users are ready, or willing, to embrace DR programs that can be complex and imply adjustments of daily routines. This work aims to understand how the next generation of Portuguese decision makers, namely young adults in higher education, are prepared to deal with energy decisions in the context of the challenges brought by the smart grids. Results demonstrate that cost savings and the contribution to environmental protection are found to be important motivating factors to enroll into DR programs, which should be further exploited in future actions for the promotion of end-user engagement. Moreover, DR solutions are well-accepted by higher education students, although with limited flexibility levels. In addition, there is room to exploit the willingness to adopt time-differentiated tariffs, yet savings should be clearer and more attractive to end-users. Also, the framing effect should be considered when promoting this type of time-differentiated tariffs.This work was partially supported by project grants UID/MULTI/00308/2013 and UID/CEC/00319/2013 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT—Portuguese Foundation for Science and Technology with in projects ESGRIDS (POCI-01-0145-FEDER-016434), Learn2Behave (02/SAICT/2016-023651), MAnAGER (POCI-01-0145-FEDER-028040), and POCI-01-0145-FEDER-007043, as well as by the Energy for Sustainability Initiative of the University of Coimbra

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating