Queer(y)ing agent-based modeling for use in LGBTQ Studies: an example from workplace inequalities

This article explores the contribution agent-based modeling (ABM) can make to the study of LGBTQ workplace inequalities and, conversely, how ABM can adapt to theoretical traditions integral to LGBTQ studies. It introduces an example LGBTQ workplace model, developed as part of the CILIA-LGBTQI+ project, to illustrate how ABM complements existing methods, can address methodological binarism and bridge macro and micro accounts within LGBTQ studies of the workplace. The model is intended as an important starting point in developing the role of ABM in LGBTQ research and for bridging qualitative- and quantitative-derived insights. Likewise, the article discusses some approaches for negotiating theoretical and methodological tensions identified when integrating queer and intersectional insight with ABM

Sensitive intervention points: a strategic approach to climate action

While some countries are making progress reducing greenhouse gas emissions, few are progressing rapidly enough to be on track to reach net zero emissions by mid-century. The transition to net zero involves deep structural transformation of the global economy and its associated complex socio-technical systems. Here, we set out a conceptual framework to identify ‘sensitive intervention points’ (SIPs) in systems where a small or moderately-sized intervention could drive outsized impacts and transformational change. These points take three forms: (i) critical tipping points, such as a critical price threshold, (ii) critical nodes in networks, such as an influential actor in a social network, and (iii) critical points in time, where windows of opportunity for change open up. We also propose an assessment methodology for prioritizing interventions in terms of their potential impacts, risks, and ease of implementation. We apply our framework and assessment methodology to evaluate a list of proposed interventions for accelerating global decarbonization. Promising interventions include investing in key clean energy technologies with consistent cost declines, introducing central bank policies to reduce the value of polluting collateral, and enhancing climate-related financial risk disclosure

Sociology and Non-Equilibrium Social Science

This chapter addresses the relationship between sociology and Non-Equilibrium Social Science (NESS). Sociology is a multiparadigmatic discipline with significant disagreement regarding its goals and status as a scientific discipline. Different theories and methods coexist temporally and geographically. However, it has always aimed at identifying the main factors that explain the temporal stability of norms, institutions and individuals' practices; and the dynamics of institutional change and the conflicts brought about by power relations, economic and cultural inequality and class struggle. Sociologists considered equilibrium could not sufficiently explain the constitutive, maintaining and dissolving dynamics of society as a whole. As a move from the formal apparatus for the study of equilibrium, NESS does not imply a major shift from traditional sociological theory. Complex features have long been articulated in sociological theorization, and sociology embraces the complexity principles of NESS through its growing attention to complex adaptive systems and non-equilibrium sciences, with human societies seen as highly complex, path-dependent, far-from equilibrium, and self-organising systems. In particular, Agent-Based Modelling provides a more coherent inclusion of NESS and complexity principles into sociology. Agent-based sociology uses data and statistics to gauge the 'generative sufficiency' of a given microspecification by testing the agreement between 'real-world' and computer generated macrostructures. When the model cannot generate the outcome to be explained, the microspecification is not a viable candidate explanation. The separation between the explanatory and pragmatic aspects of social science has led sociologists to be highly critical about the implementation of social science in policy. However, ABM allows systematic exploration of the consequences of modelling assumptions and makes it possible to model much more complex phenomena than previously. ABM has proved particularly useful in representing socio-technical and socio-ecological systems, with the potential to be of use in policy. ABM offers formalized knowledge that can appear familiar to policymakers versed in the methods and language of economics, with the prospect of sociology becoming more influential in policy

Mitigating the impact of air pollution on brain health and dementia: Policy and practice brief

Emerging research suggests exposure to high levels of air pollution at critical points in the life course is detrimental to brain health, including cognitive decline and dementia. Social determinants such as socio-economic deprivation, environmental factors, and heightened health and social inequalities also play a significant role and make the problem more complicated. While policy and practice strategies have been proposed to address air pollution’s impact on public health more generally, their benefits for brain health, including dementia, remain undeveloped [1, 2]. This policy brief suggests necessary advances across policy and practice to mitigate air pollution and its impact on brain health and dementia

Using agent-based modelling to simulate social-ecological systems across scales

Agent-based modelling (ABM) simulates Social-Ecological-Systems (SESs) based on the decision-making and actions of individual actors or actor groups, their interactions with each other, and with ecosystems. Many ABM studies have focused at the scale of villages, rural landscapes, towns or cities. When considering a geographical, spatially-explicit domain, current ABM architecture is generally not easily translatable to a regional or global context, nor does it acknowledge SESs interactions across scales sufficiently; the model extent is usually determined by pragmatic considerations, which may well cut across dynamical boundaries. With a few exceptions, the internal structure of governments is not included when representing them as agents. This is partly due to the lack of theory about how to represent such as actors, and because they are not static over the time-scales typical for social changes to have significant effects. Moreover, the relevant scale of analysis is often not known a priori, being dynamically determined, and may itself vary with time and circumstances. There is a need for ABM to cross the gap between micro-scale actors and larger-scale environmental, infrastructural and political systems in a way that allows realistic spatial and temporal phenomena to emerge; this is vital for models to be useful for policy analysis in an era when global crises can be triggered by small numbers of micro-level actors. We aim with this thought-piece to suggest conceptual avenues for implementing ABM to simulate SESs across scales, and for using big data from social surveys, remote sensing or other sources for this purpose

Participatory systems mapping for population health research, policy and practice: guidance on method choice and design

Executive Summary: What is participatory systems mapping? Participatory systems mapping engages stakeholders with varied knowledge and perspectives in creating a visual representation of a complex system. Its purpose is to explore, and document perceived causal relations between elements in the system. This guidance focuses on six causal systems mapping methods: systems-based theory of change maps; causal loop diagrams; CECAN participatory systems mapping; fuzzy cognitive maps; systems dynamics models; and Bayesian belief networks. What is the purpose of this guidance? This guidance includes a Framework that aids the choice and design of participatory systems mapping approaches for population health research, policy and practice. It offers insights on different systems mapping approaches, by comparing them and highlighting their applications in the population health domain. This guidance also includes case studies, signposting to further reading and resources, and recommendations on enhancing stakeholder involvement in systems mapping. Who is this guidance for? This guidance is designed for anyone interested in using participatory systems mapping, regardless of prior knowledge or experience. It primarily responds to calls to support the growing demand for systems mapping (and systems-informed approaches more broadly) in population health research, policy and practice. This guidance can however also be applied to other disciplines. How was it developed? The guidance was created by an interdisciplinary research team through an iterative, rigorous fivestage process that included a scoping review, key informant interviews, and a consultation exercise with subject experts. What is the ‘Participatory Systems Design Framework’ included in this guidance? The Design Framework supports users to choose between different methods and enhance the design of participatory systems mapping projects. Specifically, it encourages users to consider: 1) the added value of adopting a participatory approach to systems mapping; 2) the differences between methods, including their relative advantages and disadvantages; and 3) the feasibility of using particular methods for a given purpose. An editable version of the Framework is available to download as a supplementary file. How will this guidance support future use of these methods? Participatory systems mapping is an exciting and evolving field. This guidance clarifies and defines the use of these methods in population health research, policy and practice, to encourage more thoughtful and purposeful project design, implementation, and reporting. The guidance also identifies several aspects for future research and development: methodological advancements; advocating for and strengthening participatory approaches; strengthening reporting; understanding and demonstrating the use of maps; and developing skills for the design and use of these methods

CECAN Evaluation and Policy Practice Note (EPPN) for policy analysts and evaluators - Participatory Systems Mapping in action - Supporting the evaluation of the Renewable Heat Incentive

In this case study, CECAN, BEIS and CAG Consultants applied CECAN’s approach to Participatory Systems Mapping to support the evaluation of the Renewable Heat Incentive

Uses of agent-based modeling for health communication: The TELL ME case study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals’ protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics

Case-based methods and agent-based modelling: bridging the divide to leverage their combined strengths

Two leading camps for studying social complexity are case-based methods (CBM) and agent-based modelling (ABM). Despite the potential epistemological links between ‘cases’ and ‘agents,’ neither camp has leveraged their combined strengths. A bridge can be built, however, by drawing on Abbott’s insight that ‘agents are cases doing things’, Byrne’s suggestion that ‘cases are complex systems with agency’, and by viewing CBM and ABM within the broader trend towards computational modelling of cases. To demonstrate the utility of this bridge, we describe how CBM can utilise ABM to identify case-based trends; explore the interactions and collective behaviour of cases; and study different scenarios. We also describe how ABM can utilise CBM to identify agent types; construct agent behaviour rules; and link these to outcomes to calibrate and validate model results. To further demonstrate the bridge, we review a public health study that made initial steps in combining CBM and ABM