The Overview, Design concepts and Details (ODD) protocol for describing Individual-and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of su icient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model's underlying narrative, and the means by which the model's fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table ); using ODD to JASSS, ( ) , http://jasss.soc.surrey.ac.uk/ / / .html Doi: . /jasss.point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.
The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people's lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.
Abstract. The observance of unpredictable episodes of clustered volatility in some data series has led to the development of models of social processes that will give rise to such clustered volatility. Such models are not, however, validated directly against qualitative evidence about the behaviour of individuals and how they interact. An agent based simulation model of the effect of drought on domestic water consumption is reported here that is the outcome of a process of development involving stakeholders to inform and validate the model qualitatively at micro level while ensuring that numerical outputs from the model cohere with observed time series data. We argue that this cross-validation of agent based social simulation models is a significant advancement in the analysis of social process. The issues.The relationship between social processes and institutions and social statistics has been an important and controversial issue in sociology at least since the publication in 1904-5 of Max Weber's The Protestant Ethic and the Spirit of Capitalism (Weber 1958). In this paper, we are concerned with that relationship. We argue that, on the basis of the evidence of social enquiry, analytic models do not obviously explain important properties of social statistics. However, a class of simulation models does generate numerical outputs that are consistent with important properties of real social statistics. These models have two further properties that should be of consuming interest to sociologists. One is that they appear to produce data with empirically relevant properties because they capture features of social order that are the subject of sociological enquiry -the social embeddedness of individuals together with the emergence of social norms. The other is that these models naturally draw upon and cohere with the sort of detailed, qualitative studies of social processes found in core strands of the sociological literature.Our central argument can be seen as an operationalisation of some elements of structuration theory (Giddens 1984). According to Blaikie (1993), Giddens proposed that social research can take place at four related levels: (1) hermeneutic elucidation of frames of meaning, (2) investigation of context and form of practical consciousness, (3) identification of bounds of knowledgeability and (4) specification of institutional orders. Of these four levels, says Blaikie, the first two are "micro" and best investigated qualitatively while the second two are "macro" and best investigated with quantitative methods. This view is very close to ours. The micro behaviour is the behaviour of observed actors and described by autonomous software modules called agents. The macro behaviour is the behaviour of a social institution (organisation, community, set of customers, or whatever) or a collection of such institutions and is described by the properties of the model containing the agents. The properties of the model as a whole are amenable to summary using descriptive statistics while the behaviour of the indivi...
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 di erent 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 rechecked, re-validated and maybe even rebuilt in a di erent way. Here we review some of the di erent 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.
This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%.
In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.
Socio-Ecological Systems (SESs) are the systems in which our everyday lives are embedded, so understanding them is important. The complex properties of such systems make modelling an indispensable tool for their description and analysis. Human actors play a pivotal role in SESs, but their interactions with each other and their environment are often underrepresented in SES modelling. We argue that more attention should be given to social aspects in models of SESs, but this entails additional kinds of complexity. Modelling choices need to be as transparent as possible, and to be based on analysis of the purposes and limitations of modelling. We recommend thinking in terms of modelling projects rather than single models. Such a project may involve multiple models adopting di↵erent modelling methods. We argue that agent-based models (ABMs) are an essential tool in an SES modelling project, but their expressivity, which is their major advantage, also produces problems with model transparency and validation. We propose the use of formal ontologies to make the structure and meaning of models as explicit as possible, facilitating model design, implementation, assessment, comparison and extension.
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