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This article describes and justifies a reporting standard to improve data use documentation in Agent-Based Modelling. Following the development of reporting standards for models themselves, empirical modelling has now developed to the point where these standards need to take equally effective account of data use (which previously has tended to be an afterthought to model description). It is particularly important that a standard should allow the reporting of the different uses to which data may be put (specification, calibration and validation), but also that it should be compatible with the integration of different kinds of data (for example, survey, ethnographic and experimental) sometimes known as mixed methods research. The article motivates the need for standards generally, and positions the distinctive contribution of the RAT-RS reporting standard. It describes how the standard was developed to ensure its usability, presents and explains it, and describes possibilities for future development.

GAM, combining games and agent-based models, shows potential for investigating complex social phenomena. Games offer engaging environments generating insights into social dynamics, perceptions, and behaviours, while agent-based models support the analysis of complexity. Games and agent-based models share the important ability both to input and output qualitative and quantitative data. Currently, there is no overview of GAM approaches. In a systematic literature review, we identified six research design types in empirical studies to date. The functional range of these design types is wide, with diverse application domains involving analogue, digital, and hybrid games. This makes GAM a highly versatile approach, appealing to researchers in both natural and social sciences, along with the gaming community itself. To consolidate the GAM field, we propose recording the design and implementation of studies that combine games and agent-based models by using a dedicated documentation scheme.

This article demonstrates how a technique called Agent-Based Modelling can address a significant challenge for effective interdisciplinarity. Different disciplines and research methods make diverge...

This article considers the implications of an approach to computer simulation called agent-based modeling for process-oriented analysis. It argues that many theoretical and methodological debates found in the latter field can be effectively advanced by the former. The argument is presented and then extended using a ubiquitous agent-based model proposed to improve understanding of ethnic residential segregation. The argument has three strands. The first is that theoretical and methodological debates are unlikely to progress unless they can be "cashed out" empirically. The second is that agent-based modeling (and its distinctive methodology) has capabilities to do this that existing research methods lack and, in fact, that agent-based models are a natural way to represent "social process" as apparently conceived by process-oriented analysis. The third is that possibilities exist for productive synthesis between agent-based modeling and process-oriented analysis with the former clarifying, instantiating, and perhaps even testing notions of process developed by the latter.Cet article se penche sur les répercussions d'une approche à la simulation par ordinateur appelée modélisation à base d'agents (ci-après MBA) pour l'analyse axée sur les processus (ci-après AAP). Il fait valoir que de nombreux débats théoriques et méthodologiques de ce dernier domaine peuvent faire progresser efficacement le premier. L'argument est présenté, puis élargi à l'aide d'une MBA généralisée proposée dans le but d'améliorer la compréhension de la ségrégation résidentielle ethnique. L'argumentation comporte trois volets. Le premier, c'est qu'il est peu probable que les débats théoriques et méthodologiques ne progressent, à moins qu'ils ne puissent être extirpés de manière empirique. Le deuxième, c'est que la MBA (et sa méthodologie distinctive) peuvent accomplir ce qui manque aux méthodes de recherche existantes, en fait, que la MBA est une façon naturelle de représenter le « processus social » tel que le conçoit apparemment l'AAP. Le troisième, c'est que des possibilités existent pour une synthèse productive entre la MBA et l'AAP, et cette dernière clarifiant, instanciant et peut-être même mettant à l’épreuve les notions des processus élaborés par la première.

This article proposes (and demonstrates the effectiveness of) a new strategy for assessing the results of epidemic models which we designate reproduction. The strategy is to build an independent model that uses (as far as possible) only the published information about the model to be assessed. In the example presented here, the independent model also follows a different modelling approach (agent-based modelling) to the model being assessed (the London School of Hygiene and Tropical Medicine compartmental model which has been influential in COVID lockdown policy). The argument runs that if the policy prescriptions of the two models match then this independently supports them (and reduces the chance that they are artefacts of assumptions, modelling approach or programming bugs). If, on the other hand, they do not match then either the model being assessed is not provided with sufficient information to be relied on or (perhaps) there is something wrong with it. In addition to justifying the approach, describing the two models and demonstrating the success of the approach, the article also discusses additional benefits of the reproduction strategy independent of whether match between policy prescriptions is actually achieved.

How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

Background Antimicrobial resistance (AMR) is one of the greatest threats in 21st century medicine. AMR has been characterized as a social dilemma. A familiar version describes the situation in which a collective resource (in this case, antibiotic efficacy) is exhausted due to over-exploitation. The dilemma arises because individuals are motivated to maximize individual payoffs, although the collective outcome is worse if all act in this way. Objectives We aim to outline the implications for antimicrobial stewardship of characterizing antibiotic overuse as a social dilemma. Sources We conducted a narrative review of the literature on interventions to promote the conservation of resources in social dilemmas. Content The social dilemma of antibiotic over-use is complicated by the lack of visibility and imminence of AMR, a loose coupling between individual actions and the outcome of AMR, and the agency relationships inherent in the prescriber role. We identify seven strategies for shifting prescriber behaviour and promoting a focus on the collectively desirable outcome of conservation of antibiotic efficacy: (1) establish clearly defined boundaries and access rights; (2) raise the visibility and imminence of the problem; (3) enable collective choice arrangements; (4) conduct behaviour-based monitoring; (5) use social and reputational incentives and sanctions; (6) address misalignment of goals and incentives; and (7) provide conflict resolution mechanisms. Implications We conclude that this theoretic analysis of antibiotic stewardship could make the problem of optimizing antibiotic prescribing more tractable, providing a theory base for intervention development.

Historically, over the long run, evolutionary approaches have struggled in sociology with great effort being expended (sometimes purely rhetorically rather than scientifically) to criticise them or, even more radically, to rule them out of court altogether as “not sociological”. This approach implies that such approaches are optional to the sociological project. By contrast, this article takes an opposing position and argues that sociology has no real alternative to evolutionary approaches in at least two key areas. First and foremost, we need an approach that can explain social organisation without relying on implausible levels of deliberation (while still compatible with the, sometimes successful, exercise of reason). Secondly, we need an approach that is “properly” historical in being able to engage with both macro (structural) change and genuine novelty. This article not only discusses what is needed and why but also illustrates how such an approach could work using an Agent-Based Model (hereafter ABM).

Antimicrobial resistance is a global health threat, partly driven by inappropriate antibiotic prescriptions for acute medical patients in hospitals.To provide a systematic review of qualitative research on antibiotic prescribing decisions in hospitals worldwide, including broad-spectrum antibiotic use.A systematic search of qualitative research on antibiotic prescribing for adult hospital patients published between 2007 and 2017 was conducted. Drawing on the Health Belief Model, a framework synthesis was conducted to assess threat perceptions associated with antimicrobial resistance, and perceived benefits and barriers associated with antibiotic stewardship.The risk of antimicrobial resistance was generally perceived to be serious, but the abstract and long-term nature of its consequences led physicians to doubt personal susceptibility. While prescribers believed in the benefits of optimizing prescribing, the direct link between over-prescribing and antimicrobial resistance was questioned, and prescribers' behaviour change was frequently considered futile when fighting the complex problem of antimicrobial resistance. The salience of individual patient risks was a key barrier to more conservative prescribing. Physicians perceived broad-spectrum antibiotics to be effective and low risk; prescribing broad-spectrum antibiotics involved low cognitive demand and enabled physicians to manage patient expectations. Antibiotic prescribing decisions in low-income countries were shaped by a context of heightened uncertainty and risk due to poor microbiology and infection control services.When tackling antimicrobial resistance, the tensions between immediate individual risks and long-term collective risks need to be taken into account. Efforts to reduce diagnostic uncertainty and to change risk perceptions will be critical in shifting practice.

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This article has two goals. Firstly, it shows how a relatively novel technique (Agent Based Modelling, hereafter ABM) can integrate different data types that are often used only in separate strands of research (interviews, experiments and surveys). It does this by comparing a well-known ABM of attitude dynamics with an alternative model using data from surveys and experiments. Secondly, the article explains ABM methodology and why it is important to the distinctiveness of ABM as a research method. In particular, the ramifications of differing approaches to ABM calibration and validation are discussed using the two different ABM as examples. The article concludes by showing how ABM might provide a progressive research strategy for integrating different data types and thus different disciplines in attitude research.

The design of agent-based models (ABMs) is often ad-hoc when it comes to defining their scope. In order for the inclusion of features such as network structure, location, or dynamic change to be justified, their role in a model should be systematically analysed. We propose a mechanism to compare and assess the impact of such features. In particular we are using techniques from software engineering and semantics to support the development and assessment of ABMs, such as graph transformations as semantic representations for agent-based models, feature diagrams to identify ingredients under consideration, and extension relations between graph transformation systems to represent model fragments expressing features., Comment: In Proceedings GaM 2017, arXiv:1712.08345

In a 2007 article in Sociology, Gezelius offers an account of information exchange between Norwegian fishermen using game theoretic analysis of ethnographic data. In this article, I consider what his analysis reveals about using game theory in sociology. To be effective, this must combine systematic use of ethnography with effective application of game theory. In addition, the methods of simulation and evolutionary game theory are important tools to explain rich sets of norms and practices observable ethnographically.

From a sociological perspective, Rational Choice Theory neglects an important question: How do agents come to conceptualise choices as they do? In particular, agents not only communicate about their choices and resulting outcomes but also draw attention to options unconsidered by others. This paper presents an agent-based simulation in which different kinds of information about choices are transmitted. This approach also provides a concrete model for certain aspects of “hegemonic discourse”. In standard Rational Choice where options are common knowledge, all actors with the same preferences should make the same decisions. By contrast if information is transmitted socially, the concerns of a majority may reduce the ability of a minority to choose options appropriate to them even without any exercise of coercion or discrimination.

I'm very pleased to see this paper offered commentary in a “subject” journal rather than, as the authors worry, somewhere only seen by agent-based modeling (hereafter ABM) specialists. In my contri...

Review of:Youngman, Paul A. (2014) Complexity and the Human Experience: Modeling Complexity in the Humanities and Social Sciences. Pan Stanford Publishing: Singapore

This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.

Although Agent Based Models (hereafter ABM) are now regularly reported in sociology journals, explaining the approach, describing models and reporting results leaves little opportunity to examine wider implications of ABM for sociological practice. This article uses an established ABM (the Schelling model) for this. The first part argues that ABM integrates qualitative and quantitative data distinctively, provides novel tools for understanding social causes and offers a significantly different perspective on theory building. The second part shows how the emerging ABM methodology is compatible with existing sociological practice while undermining several criticisms of ABM perceived to limit its sociological relevance.

This note discusses two challenges to simulating the social process of science. The first is developing an adequately rich representation of the underlying Data Generation Process which scientific progress can "learn". The second is how to get effective data on what, in broad terms, the properties of the "future" are. Paradoxically, with due care, we may learn a lot about the future by studying the past.