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Role-taking, perspective taking, and empathy have developed through parallel literatures in sociology and psychology. All three concepts address the ways that people attune the self to others’ thoughts and feelings. Despite conceptual and operational overlap, researchers have yet to synthesize existing research across the three concepts. We undertake the task of theoretical synthesis, constructing a model in which role-taking emerges as a multidimensional process that includes perspective taking and empathy as component parts.

We review the literatures on role-taking, perspective taking, and empathy across disciplines. Focusing on definitions, measures, and interventions, we discern how the concepts overlap, how they are distinct, and how they work together in theoretically meaningful ways.

The review identifies two key axes on which each concept varies: the relative roles of affect and cognition, and the relative emphasis on self and structure. The review highlights the cognitive nature of perspective taking, the affective nature of empathy, and the structural nature of role-taking. In a move toward theoretical synthesis, we propose a definition that centers role-taking as a sociological construct, with perspective taking and empathy representing cognition and affect, respectively.

Role-taking is an important part of selfhood and community social life. It is a skill that varies in patterned ways, including along lines of status and power. Theoretical synthesis clarifies the process of role-taking and fosters the construction of effective interventions aimed at equalizing role-taking in interpersonal interaction.

Role-taking is a basic social process underpinning much of the structural social psychology paradigm – a paradigm built on empirical studies of human interaction. Yet today, our social worlds are occupied by bots, voice assistants, decision aids, and other machinic entities collectively referred to as artificial intelligence (AI). The integration of AI into daily life presents both challenges and opportunities for social psychologists. Through a vignette study, the authors investigate role-taking and gender in human-AI relations.

Participants read a first-person narrative attributed to either a human or AI, with varied gender presentation based on a feminine or masculine first name. Participants then infer the narrator's thoughts and feelings and report on their own emotions, producing indicators of cognitive and affective role-taking. The authors supplement results with qualitative analysis from two open-ended survey questions.

Participants score higher on role-taking measures when the narrator is human versus AI. However, gender dynamics differ between human and AI conditions. When the text is attributed to a human, masculinized narrators elicit stronger role-taking responses than their feminized counterparts, and women participants score higher on role-taking measures than men. This aligns with prior research on gender, status, and role-taking variation. When the text is attributed to an AI, results deviate from established findings and in some cases, reverse.

This first study of human-AI role-taking tests the scope of key theoretical tenets and sets a foundation for addressing group processes in a newly emergent form.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

Drawing on the results of the previous chapters, this chapter looks at current progress in terms of climate disaster risk incorporation into development planning and practice at three levels (national government, municipalities, and communities) and analyzes gaps, challenges, and opportunities. The chapter also discusses potential factors for enhancing local disaster risk management (DRM) capacity by collaborating with three levels of stakeholders.

Medicaid home and community-based services (HCBS) support community living for three million disabled people in the United States. As a state-federal partnership, these programs are highly variable across states. Because eligibility determination and services differ from state to state, this Medicaid structure becomes a barrier for those HCBS users whose desired futures include cross-state moves.

I examine narratives of citizenship and personhood for Medicaid HCBS users circulating within policy arenas and explore tensions between these and the stories Medicaid HCBS users tell of their own lives. Specifically, I explore the degree to which narratives about Medicaid HCBS users include an affirmation of the right to cross-state movement. My analysis includes data from public statements from policy makers, legislative texts, organizational framings of Medicaid policy, and 18 semi-structured interviews with Medicaid PCA users who desired or pursued cross-state moves.

I conclude that institutional narratives of Medicaid HCBS users are an inadequate representation of the stories told by those who rely on this program and, in consequence, programs stemming from policy fail to offer services that would allow service recipients to pursue their objectives.

Medicaid HCBS policy is part of a broader story of disability rights progress over the last four decades, making its role as an obstacle to cross-state movement a bit of a paradox. This paradox points to the value of narrative analysis in calling attention to invisible contradictions and the need for institutional and organizational change.

This chapter introduces the tourism–disaster–conflict nexus through a comprehensive review of the contemporary social science literature. After reviewing conceptual definitions of tourism, disaster and conflict, the chapter explores various axes that link through this nexus. The linkages between tourism and disaster include tourism as a trigger or amplifier of disasters, the impacts of disasters on the tourism industry, tourism as a driver of disaster recovery and disaster risk reduction strategies in the tourism sector. Linkages between tourism and conflict include the idea that tourism can be a force for peace and stability, the niche status of danger zone or dark heritage tourism, the concept of phoenix tourism in post-conflict destination rebranding, tourism and cultural conflicts, and tourism’s conflicts over land and resources. Linkages between disaster and conflict include disasters as triggers or intensifiers of civil conflict, disaster diplomacy and conflict resolution, disaster capitalism, and gender-based violence and intra-household conflict in the wake of disasters. These are some of the conversations that organise this volume, and this introductory chapter ends with a summary of the chapters that follow.

The impact of climate disasters (e.g., floods, storms, or landslides), which are generally of low intensity and high frequency, should not be overlooked in developing countries. Global experiences related to the damage due to these disasters indicate that such events can be devastating in communities that are vulnerable to hazardous impacts. Cumulative effects of climate disasters are a sign of a potential catastrophe. Moreover, the recent increase in these events poses additional issues that increase the cost of local public administration, including emergency operation and infrastructure recovery. This chapter explains key problems related to climate disasters that are increasing, particularly in the local area of developing countries, and clarifies the need to incorporate climate disaster risk reduction into public development planning and practice. The chapter also provides descriptions of the research location, approaches of the study, and the structure of this book.

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keane) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been viewed as extremely difficult (Wang, Iglesias, & Wooldridge, 2013). Nonetheless, for sparse covariance and precision matrices often encountered in spatial settings, the GHK can be applied to very large sample sizes as its operation counts and memory requirements increase almost linearly with n when using sparse matrix techniques.