Modelling Social Care Provision in An Agent-Based Framework with Kinship Networks

Current demographic trends in the UK include a fast-growing elderly population and dropping birth rates, and demand for social care amongst the aged is rising. The UK depends on informal social care -- family members or friends providing care -- for some 50\% of care provision. However, lower birth rates and a graying population mean that care availability is becoming a significant problem, causing concern amongst policy-makers that substantial public investment in formal care will be required in decades to come. In this paper we present an agent-based simulation of care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need, and geographical location. Results demonstrate that the model can produce similar patterns of care need and availability as is observed in the real world, despite the model containing minimal empirical data. We propose that our model better captures the complexities of social care provision than other methods, due to the socioeconomic details present and the use of kinship networks to distribute care amongst family members.Comment: 15 pages, 12 figure

Empiricism in artificial life

Strong artificial life research is often thought to rely on Alife systems as sources of novel empirical data. It is hoped that by augmenting our observations of natural life, this novel data can help settle empirical questions, and thereby separate fundamental properties of living systems from those aspects that are merely contingent on the idiosyncrasies of terrestrial evolution. Some authors have questioned whether this approach can be pursued soundly in the absence of a prior, agreed-upon definition of life. Here we compare Alife’s position to that of more orthodox empirical tools that nevertheless suffer from strong theory-dependence. Drawing on these examples, we consider what kind of justification might be needed to underwrite artificial life as empirical enquiry. In the title of the first international artificial life conference

Bringing ALife and complex systems science to population health research

Despite tremendous advancements in population health in recent history, human society currently faces significant challenges from wicked health problems. These are problems where the causal mechanisms at play are obscured and difficult to address, and consequently they have defied efforts to develop effective interventions and policy solutions using traditional population health methods. Systems-based perspectives are vital to the development of effective policy solutions to seemingly intractable health problems like obesity and population aging. ALife in particular is well placed to bring interdisciplinary modeling and simulation approaches to bear on these challenges. This article summarizes the current status of systems-based approaches in population health, and outlines the opportunities that are available for ALife to make a significant contribution to these critical issues

Methodological Investigations in Agent-Based Modelling: With Applications for the Social Sciences

This open access book examines the methodological complications of using complexity science concepts within the social science domain. The opening chapters take the reader on a tour through the development of simulation methodologies in the fields of artificial life and population biology, then demonstrates the growing popularity and relevance of these methods in the social sciences. Following an in-depth analysis of the potential impact of these methods on social science and social theory, the text provides substantive examples of the application of agent-based models in the field of demography. This work offers a unique combination of applied simulation work and substantive, in-depth philosophical analysis, and as such has potential appeal for specialist social scientists, complex systems scientists, and philosophers of science interested in the methodology of simulation and the practice of interdisciplinary computing research.

Convolutional Neural Networks for Cellular Automata Classification

Wolfram famously developed a four-way classification of CA behaviour, with Class IV containing CAs that generate complex, localised structures. However, finding Class IV rules is far from straightforward, and can require extensive, time-consuming searches. This work presents a Convolutional Neural Network (CNN) that was trained on visual examples of CA behaviour, and learned to classify CA images with a high degree of accuracy. I propose that a refinement of this system could serve as a useful aid to CA research, automatically identifying possible candidates for Class IV behaviour and universality, and significantly reducing the time required to find interesting CA rules

After Cannibal Tours: Cargoism and Marginality in a Post-touristic Sepik River Society

This article challenges the ethical allegory of the widely hailed film Cannibal Tours, drawing on two decades of ethnographic research in the Sepik region of Papua New Guinea, most recently in 2010. First, I sketch the contemporary plight of a middle Sepik, Iatmul-speaking community that yearns for a “road” to modernity and tourism but increasingly sees itself as “going backwards.” Second, I argue that tourism allows middle Sepik inhabitants to express artistically subtle messages about contemporary gender, identity, and sociality in the Melanesian postcolony. Third, I demonstrate what happens when the tourists go home. And almost all of them have done so, especially after the sale of the tourist ship, th

Underlying socio-political processes behind the 2016 US election

Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated ‘bot’ accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes