arXiv subjects

Simon D. Angus

Publications and source records attributed to Simon D. Angus.

6 recordsLinked to original sources

arXiv2026

Digital State Capacity

Digital State Capacity is the ability of governments to deploy ICT infrastructure and information systems to implement policy. This paper introduces a new measure of government ICT capacity based on an observable stock of deployable public-sector network infrastructure: public IPv4 address space held by government organisations. These address holdings are key inputs into digital administration because they support internet-facing systems, networked information exchange, and coordination across agencies and functions. The core panel covers approximately 150,000 country-entity records classified as government across more than 150 countries from 2019 to 2024 and can be disaggregated by administrative level and government function. In the 2019 to 2024 Admin-1 panel, government IP holdings are observed in 1,681 subnational regions across all years. We validate the measure at the crosscountry and subnational levels and apply it to government tasks related to corruption control and vaccination rollout. In illustrative country-year analysis, higher Digital State Capacity is associated with higher-quality governance and publicservice outcomes in the expected directions, including lower measured corruption and higher vaccination coverage. These associations are descriptive; they demonstrate the empirical relevance of the measure and are not causal estimates.

econ.GN↗

arXiv2024

Repeated and incontrovertible collective action failure leads to protester disengagement and radicalisation

Protest is ubiquitous in the 21st Century and the people who participate in such movements do so because they seek to bring about social change. However, social change takes time and involves repeated interactions between individual protesters, social movements and the authorities to whom they appeal for change. These complexities of time and scale have frustrated efforts to isolate the conditions that foster an enduring movement, on the one hand, and the adoption of more radical (unconventional, unacceptable) tactics on the other. Here, we present a novel, theoretically informed and empirically evidenced, agent-based model of collective action that provides a unified framework to address these dual challenges. We model ~10,000 iterations within a simulated society and show that where an authority is responsive, and protesters can (cognitively and/or socially) contest the failure of their movement, a moderate conventional movement prevails. Conversely, where an authority repeatedly and incontrovertibly fails the movement, the population disengages but becomes radicalised (latent radicalism). This latter finding, whereby the whole population is disengaged but prepared to use radical methods to bring about social change, likely reflects the febrile pre-cursor state to sudden, revolutionary change. Results highlight the potential for simulations to reveal emergent, as-yet under-theorized, phenomena.

cs.SI↗

arXiv2023

Identifying Promising Candidate Radiotherapy Protocols via GPU-GA in-silico

Around half of all cancer patients, world-wide, will receive some form of radiotherapy (RT) as part of their treatment. And yet, despite the rapid advance of high-throughput screening to identify successful chemotherapy drug candidates, there is no current analogue for RT protocol screening or discovery at any scale. Here we introduce and demonstrate the application of a high-throughput/high-fidelity coupled tumour-irradiation simulation approach, we call "GPU-GA", and apply it to human breast cancer analogue - EMT6/Ro spheroids. By analysing over 9.5 million candidate protocols, GPU-GA yields significant gains in tumour suppression versus prior state-of-the-art high-fidelity/-low-throughput computational search under two clinically relevant benchmarks. By extending the search space to hypofractionated areas (> 2 Gy/day) yet within total dose limits, further tumour suppression of up to 33.7% compared to state-of-the-art is obtained. GPU-GA could be applied to any cell line with sufficient empirical data, and to many clinically relevant RT considerations.

physics.med-ph↗

arXiv2022

Predicting Political Ideology from Digital Footprints

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.

econ.GN↗

arXiv2020

Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)

Alternative data is increasingly adapted to predict human and economic behaviour. This paper introduces a new type of alternative data by re-conceptualising the internet as a data-driven insights platform at global scale. Using data from a unique internet activity and location dataset drawn from over 1.5 trillion observations of end-user internet connections, we construct a functional dataset covering over 1,600 cities during a 7 year period with temporal resolution of just 15min. To predict accurate temporal patterns of sleep and work activity from this data-set, we develop a new technique, Segmented Functional Classification Analysis (SFCA), and compare its performance to a wide array of linear, functional, and classification methods. To confirm the wider applicability of SFCA, in a second application we predict sleep and work activity using SFCA from US city-wide electricity demand functional data. Across both problems, SFCA is shown to out-perform current methods.

stat.AP↗

arXiv2015

Shared intentions and the advance of cumulative culture in hunter-gatherers

It has been hypothesized that the evolution of modern human cognition was catalyzed by the development of jointly intentional modes of behaviour. From an early age (1-2 years), human infants outperform apes at tasks that involve collaborative activity. Specifically, human infants excel at joint action motivated by reasoning of the form "we will do X" (shared intentions), as opposed to reasoning of the form "I will do X [because he is doing X]" (individual intentions). The mechanism behind the evolution of shared intentionality is unknown. Here we formally model the evolution of jointly intentional action and show under what conditions it is likely to have emerged in humans. Modelling the interaction of hunter-gatherers as a coordination game, we find that when the benefits from adopting new technologies or norms are low but positive, the sharing of intentions does not evolve, despite being a mutualistic behaviour that directly benefits all participants. When the benefits from adopting new technologies or norms are high, such as may be the case during a period of rapid environmental change, shared intentionality evolves and rapidly becomes dominant in the population. Our results shed new light on the evolution of collaborative behaviours.

q-bio.PE↗