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Recognition and protection: a conversation with Fatima Ahdash, Liam Davis, Claire Fenton-Glynn, Maebh Harding, Emily Jackson, Dafni Lima, Alice Margaria, Julie McCandless, Beth Tarleton, and Sarah Trotter
No full textAutonomy and its limits in ‘the good society’
No full textIn arguably his two major works, published more than a century ago, the social psychologist and co-founder of the London School of Economics and Political Science (LSE), Graham Wallas, argued first against utilitarian intellectualism for it being excessively reductionist in the face of complex human psychology, but then for a form of intellectualism to instil in people the reasoning abilities required for a large industrialised Great Society to also become a ‘Good Society’. In this essay, I share Wallas’s concern for over-intellectualising human motivation and at the same time believing that an intellectualism of sorts is needed for a social organisation that is tolerable for all. Specifically, I argue that the psychological affects that lie deep within human cognition may have evolved for good reason, and that even in the modern world it is not possible to determine when and where these tendencies lead people astray from their own personal desires. As such, individual autonomy over their choices and behaviours ought to be respected when people impose no substantive harms on others. However, in circumstances where autonomous actions cause substantive external harms, it may often be appropriate to intervene to curtail them. In short, we are faced with the delicate balancing of autonomy and harm when attempting to protect liberty for all. I conclude that in order to arrive at an appropriate balance, we might usefully turn to the writings of Joseph Raz, who intimated that the characteristics of autonomy are to extend people’s opportunities, to improve their agentic capabilities, and to protect them from coercion and manipulation. Raz’s arguments, I contend, offer up a framework for the Good Society, redux
Strengthening monitoring and metrics for just transitions in emerging economies
No full textIn emerging economies, for metrics to play an effective role in driving progress for both the net zero transition and social justice, monitoring needs to get closer to the ground. Here, Rob Macquarie and Judith Tyson explore what it takes to go beyond high-level talk about climate finance allocation and use national and domestic policy frameworks to track how climate transitions ‘walk the talk’ on just transition
Achieving a step change in monitoring companies’ just transitions and investment
No full textAs London Climate Action Week gets underway, rising uncertainty in economic and political outlooks is threatening effective and coordinated climate action, including by companies, investors, and rightsholders. Rob Macquarie and Judith Tyson outline insights from an interactive workshop that considered the channels and sources of information needed for successful just transition action
Advancing healthcare decision-making for the common good: a tribute to Professor Rovira Forns
No full textThis editorial introduces the special issue dedicated to commemorating the life and scholarly achievements of Professor Joan Rovira Forns, a distinguished health economist whose pioneering work continues to influence global health policy and research. We discuss why Professor Rovira was a prominent figure in the field and summarise some of his key contributions. Next, we highlight the collection of papers featured in this issue, explaining how they connect to his work and contribute to his lasting legacy by celebrating his interdisciplinary approach and dedication to societal impact
Firm markups and the economic value of innovation
No full textWe examine the relationship between firms' markups and the economic value of their innovation, including both the private value captured by the innovating firm and the knowledge spillovers that benefit other firms. Using a sample of over 14,500 EU firms and 2,400 US firms granted patents between 2005 and 2014, we find that innovation by high-markup firms is more valuable privately and also creates more external value. These associations are robust to controlling for the stock of past innovation and to estimating innovation value in various ways
Predicting negotiation behavior to support decision making in civil dispute resolution
No full textDespite the advocacy of leveraging data analytics to improve operational efficiency, there is a paucity of research on how analytical technologies afford professional service innovation and enhancement. We propose a data-driven decision support framework for civil litigation negotiation, which is a routine business activity in legal service firms. It is typically conducted in a traditional manner with the conflicting parties drawing on their past experiences and prior knowledge to guide decision-making. This model predicts human negotiation behavior based on historical records and incorporates the behavioral insights into the decision-making process. We introduce a sequential directed acyclic graph to characterize the causal relationships between offers and employ different approaches to predicting the opponent’s next moves. By integrating utility analysis, each player can decide whether to accept the opponent’s offer or counter back. The proposed framework is illustrated through a field experiment based on the UK MoJ Portal for handling low-cost injury claims and 88 cases with complete negotiation history. We find that better outcomes for both parties can be delivered by implementing the proposed model. The analysis result also represents convincing evidence that low-cost cases should ideally be settled out of the court via negotiation to maximize shared benefits. This paradigm could be easily generalized to other types of civil dispute resolutions negotiation to enhance both operational efficiency and service quality
Economic models and frameworks to guide climate policy
No full textReaching net-zero emissions will involve a structural transformation of the global economy. The transition is complicated by deep uncertainty about the new economic configurations that will emerge, coordination challenges, and non-linear dynamics amidst shifting political winds, where nation states are actively intervening to gain comparative advantage in key technologies. Here, we consider key economic questions about the net-zero transition that are of interest to finance ministries, based on a recent survey. Specifically, this paper asks: ‘What is the most effective way economic models and frameworks can help guide policy, given the complexity and uncertainty involved?’ We suggest five general criteria that models and frameworks should meet, and provide some guidance on how to select the right model for the question at hand—there is no single model to rule them all. A range of examples are offered to illustrate how models can be used and abused in the provision of economic advice to policy-makers. We conclude by noting that there are several gaps in our collective modelling capability that remain to be addressed
Generative data modelling for diverse populations in Africa: insights from South Africa
No full textStudies on the demography and health of racially diverse African populations are scarce, particularly due to lingering data challenges. Generative data modelling has emerged as a valuable solution to this burden. The study, therefore, examined the efficacy of Conditional Tabular GAN (CTGAN), CopulaGAN, and Tabula Variational Autoencoder (TVAE) for generating synthetic but realistic demographic and health data. This study employed the World Health Organisation stigy on global ageing and adult health survey (SAGE) Wave 1 South African data (n = 4227). Information missing from SAGE Wave 1, including demographic (e.g., race, age) and health (e.g., hypertension, blood pressure) indicators, were imputed using Generative Adversarial Imputation Nets (GAIN). CopulaGAN, CTGAN, and TVAE, sourced from the sdv 1.24.1 python library, generated 104,227 synthetic records based on the SAGE data constituents. The outcomes were accessed with similarity and machine learning (XGBoost) augmentation metrics (sourced from the sdmetrics 0.21.0 python library), including column shapes and overall and precision ratio scores. Generally, the GAIN imputations resulted in data with properties that were comparable to original and with no missing information. CTGAN’s (89.20%) overall quality of performance was above that of TVAE (86.50%) and CopulaGAN (88.45%). These findings underscore the usefulness of generative data modelling in addressing data quality challenges in diverse populations to enhance actionable health research and policy implementation
Evidence submission and comments on the London Plan consultation
No full textThis is a submission made to the Mayor of London’s consultation on the next London Plan, the strategic framework that guides and shapes London’s development. The consultation ran from 9 May to 22 June 2025