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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.

Providing for the needs of the vulnerable is a critical component of social and health policy-making. In particular, caring for children and for vulnerable older people is vital to the wellbeing of millions of families throughout the world. In most developed countries, this care is provided through both formal and informal means, and is therefore governed by complex policies that interact in non-obvious ways with other areas of policy-making. In this paper we present an agent-based model of social and child care provision in the UK, in which agents can provide informal care or pay for private care for their relatives. Agents make care decisions based on numerous factors including their health status, employment, financial situation, and social and physical distance to those in need. Simulation results show that the model can produce plausible patterns of care need and availability, and therefore can provide an important aid to this complex area of policy-making. We conclude that the model's use of kinship networks for distributing care and the explicit modelling of interactions between social care and child care will enable policy-makers to develop more informed policy interventions in these critical areas. The moral test of government is how it treats those who are in the dawn of life, the children; those who are in the twilight of life, the aged; and those in the shadows of life, the sick, the needy and the handicapped." — Hubert Humphrey Jr.

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.

Intro -- Foreword -- References -- Acknowledgements -- Contents -- Acronyms -- Part I Agent-Based Models -- 1 Introduction -- 1.1 Overview -- 1.2 Artificial Life as Digital Biology -- 1.2.1 Artificial Life as Empirical Data-Point -- 1.3 Social Simulation and Sociological Relevance -- 1.3.1 Methodological Concerns in Social Simulation -- 1.4 Case Study: Schelling's Residential Segregation Model -- 1.4.1 Implications of Schelling's Model -- 1.5 Social Simulation in Application: The Case of Demography -- 1.5.1 Building Model-Based Demography -- 1.6 General Summary -- 1.6.1 Alife Modelling -- 1.6.2 Simulation for the Social Sciences -- 1.6.3 Schelling's Model as a Case Study in Modelling -- 1.6.4 Developing a Model-Based Demography -- 1.6.5 General Conclusions of the Text: Messages for the Modeller -- 1.6.6 Chapter Summaries -- 1.6.7 Contributions -- References -- 2 Simulation and Artificial Life -- 2.1 Overview -- 2.2 Introduction to Simulation Methodology -- 2.2.1 The Goals of Scientific Modelling -- 2.2.2 Mathematical Models -- 2.2.3 Computational Models -- 2.2.4 The Science Versus Engineering Distinction -- 2.2.5 Connectionism: Scientific Modelling in Psychology -- 2.2.6 Bottom-Up Modelling and Emergence -- 2.3 Evolutionary Simulation Models and Artificial Life -- 2.3.1 Genetic Algorithms and Genetic Programming -- 2.3.2 Evolutionary Simulations and Artificial Life -- 2.3.3 Bedau and the Challenges Facing ALife -- 2.4 Truth in Simulation: The Validation Problem -- 2.4.1 Validation and Verification in Simulation -- 2.4.2 The Validation Process in Engineering Simulations -- 2.4.3 Validation in Scientific Simulations: Concepts of Truth -- 2.4.4 Validation in Scientific Models: Kuppers and Lenhard Case Study -- 2.5 The Connection Between Theory and Simulation -- 2.5.1 Simulation as `Miniature Theories'.