107159 research outputs found
Sort by
Development policy and legal persistence: evidence from India
No full textIt is widely believed that colonial-era institutions, including laws, persist well past independence. This is due to political vested interests or high switching costs. While these factors have surely mattered, the most important determinant of independent India’s legal trajectory has been its development strategy. With some important exceptions and delays, law has usually persisted or changed as needed by development policy
The urban dimension in spatial development: contributions from spatial economics
No full textThis editorial introduces the nine papers included in this issue of Spatial Economic Analysis (SEA). The papers in this issue study how urban features influence the spatial economy and the mechanisms taking place in cities. The starting point is the importance of agglomerations in the spatial economy and what happens in cities to influence economic and social outcomes at local and wider scales. Specifically, the papers focus on the growth impact of cities that depend on network externalities; how the shape of cities influences migration and growth; the factors that influence the liveability of neighbourhoods; the influence of the urban structure on migration decisions; how external shocks and the possibility to work from home can affect people’s dwelling decisions; and the factors determining the occupancy of multi-family apartments. Furthermore, novel methodologies are presented in this issue in the estimation of the factors determining rent, such as new machine learning algorithms and spatiotemporal hedonic modelling using distributional regression models and Bayesian estimators. In all urban contexts, neighbourhood spillover effects are considered and taken into account in the papers included in the issue
A primer on variational inference for physics-informed deep generative modelling
No full textVariational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantification and practical tractability. It excels at generative modelling and inversion tasks due to its built-in Bayesian regularization and flexibility, essential qualities for physics-related problems. For such problems, the underlying physical model determines the dependence between variables of interest, which in turn will require a tailored derivation for the central VI learning objective. Furthermore, in many physical inference applications, this structure has rich meaning and is essential for accurately capturing the dynamics of interest. In this paper, we provide an accessible and thorough technical introduction to VI for forward and inverse problems, guiding the reader through standard derivations of the VI framework and how it can best be realized through deep learning. We then review and unify recent literature exemplifying the flexibility allowed by VI. This paper is designed for a general scientific audience looking to solve physics-based problems with an emphasis on uncertainty quantification. This article is part of the theme issue ‘Generative modelling meets Bayesian inference: a new paradigm for inverse problems’
Learning to incentivise: using reinforcement learning for sustainable urban mobility
No full textTraffic management has traditionally focused on toll-based and road pricing solutions. However, road pricing often raises concerns about accessibility and public dissatisfaction, leading to its prohibition in some places, such as Finland. This study optimises the dynamic allocation of incentives to drivers, encouraging them to reroute onto alternative (potentially longer) paths to achieve greater societal benefit, reduced total travel time (TTT) and total emissions in the transportation network, contributing to sustainable urban mobility. We employ a multiagent reinforcement learning approach to dynamically assign incentives to drivers to reduce both TTT and emissions, with travel times estimated using traffic simulation software. We demonstrate that, with an unlimited budget and an objective of minimising travel time, the incentive scheme reduces TTT by 16%, compared to the dynamic User-Equilibrium (UE) with a budget equivalent to about 11% of the UE total time. When the goal is to minimise emissions, a 9% reduction in CO2 emissions is observed under an unlimited budget. We demonstrate a critical trade-off: minimising TTT leads to an increase in emissions, while prioritising emission reductions raises TTT. However, with the right combination of weights in the multi-objective function, both TTT and total emissions are improved beyond the baseline
Politics, policy and private law Volume II: Contract, commercial and company law
No full textThis collection is the second volume of a two-part study exploring the role of policy and politics in shaping private law. Whilst the first volume examined equity, tort law and property law, the second volume focuses on contract, commercial and corporate law. Its chapters explore the challenging interface of policy and politics in areas including: contract interpretation; contractual discretions; consumer contracts; wrongful payments by banks; transnational commercial private law instruments, mistakes made by corporations; and the right to repair. This is a landmark and ambitious project which provides a rich exploration of policy-infused areas of private law, undertaken by a team of experts in their fields
Machine-learning regression methods for American-style path-dependent contracts
No full textEvaluating financial products with early-termination clauses, particularly those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back options, and callable certificates. We will compare regression methods for pricing and computing sensitivities, highlighting modern machine learning techniques against traditional polynomial basis functions. Specifically, we will analyze randomized recurrent and feed-forward neural networks, along with a novel approach using signatures of the underlying price process. For option sensitivities like Delta and Gamma, we will incorporate Chebyshev interpolation. Our findings show that machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates. Furthermore, we apply Chebyshev interpolation for Delta and Gamma calculations for the first time in Asian options and callable certificates
More than a ban on smoking? Behavioural spillovers of smoking bans in the workplace
No full textWe study the potential behavioural spillover effects of a workplace smoking ban (WSB) on a variety of health-related behaviours as well as on people who are not directly impacted by the bans. Drawing on quasi-experimental evidence comparing employed and unemployed individuals in Russia, we document that individuals who give up smoking are less likely to drink or cut back on alcohol consumption. Furthermore, we show that as expected the WSB exerts an impact on the health behaviours of those who are not directly exposed to the reform, such as never smokers. Finally, the effects of the WSB are driven by changes among men, 60 percent of whom were smoking before the ban