How integrative modelling can break down disciplinary silos

This paper has been published in a peer-reviewed journal as: Kragt, M.E., Robson, B.J. & Macleod, C.J.A. (2013) Modellers’ roles in structuring integrative research projects. Environmental Modelling & Software, 39(1): 322-330. DOI: 10.1016/j.envsoft.2012.06.015Environmental modelling, Interdisciplinary research, Transdisciplinarity, Integration, Research Methods/ Statistical Methods, Q57, Y80, Z19,

Learning from mixed OR method practice: The NINES case study

Despite continued interest in the use of mixed OR/MS methods, limited attention has been paid in the literature to generic lessons that could be gained from mixing methods . in practice. Many organisational problems demand the use of a mixed method approach and thus recognising and sharing lessons could prove beneficial to both practitioners and researchers. This paper reports on an in-depth evaluation of a case study involving risk identification and quantification of the Northern Isles New Energy Solutions (NINES) project which sought to trial and plan a new energy system. The intervention involved a mixed method approach and client feedback on the efficacy of the approach was sought. The evaluation reported in this paper is carried out using a set of themes taken from the literature and seeks to highlight transferable lessons. The set of lessons that emerge are presented along with their implications for both general OR modelling practice and the specific situation of mixing OR/MS methods. The paper concludes by discussing the implications of the work and directions for future work which will be of interest to both practitioners and researchers interested in mixed method OR/MS work

Model storage, exchange and integration

The field of Computational Systems Neurobiology is maturing quickly. If one wants it to fulfil its central role in the new Integrative Neurobiology, the reuse of quantitative models needs to be facilitated. The community has to develop standards and guidelines in order to maximise the diffusion of its scientific production, but also to render it more trustworthy. In the recent years, various projects tackled the problems of the syntax and semantics of quantitative models. More recently the international initiative BioModels.net launched three projects: (1) MIRIAM is a standard to curate and annotate models, in order to facilitate their reuse. (2) The Systems Biology Ontology is a set of controlled vocabularies aimed to be used in conjunction with models, in order to characterise their components. (3) BioModels Database is a resource that allows biologists to store, search and retrieve published mathematical models of biological interests. We expect that those resources, together with the use of formal languages such as SBML, will support the fruitful exchange and reuse of quantitative models

Experiences of mixed method OR Practitioners : moving beyond a technical focus to insights relating to modelling teams

Complex, real-world problems often benefit from being tackled using multiple OR methods. The ability to combine methods successfully therefore plays a key role in successful OR practice. The research described in this paper aims to augment current understanding of mixed methods modelling, moving beyond the predominant focus on technical aspects of which methods to use and how they can be combined. As such the research sought to explore the practice of mixed methods from the perspective of those with mixed methods experience to reflect on all aspects of a modelling intervention and identify generic lessons. The research involved a series of in-depth interviews with experienced OR practitioners (both academic and non-academic) to understand how they undertake mixed methods work. The paper describes the research methodology employed, the emergent data and the results of the analysis. The analysis reveals that an area of significance hitherto only peripherally addressed was consideration of the modelling team particularly a) additional skills, b) organisational culture and modeller personality and c) the role of the team leader. The paper concludes with some avenues for further exploration regarding teaching, research, and the practice of OR mixed methods work

Computer models as social learning tools in participatory integrated assessment

De Kraker, J., Kroeze, C., & Kirschner, P. A. (2011). Computer models as social learning tools in participatory integrated assessment. International Journal of Agricultural Sustainability, 9, 297-309. doi:10.1080/14735903.2011.582356Participatory integrated assessment (PIA) is a structured process conducted with stakeholders to assess the environmental, economic and social dimensions of a complex issue and the impacts of policy choices. PIA may result in social learning – a convergence in the stakeholders’ perspectives on the problem and its solutions – which creates a basis for more sustainable, collective action. This paper addresses the role of computer models used in integrated assessment in supporting social learning and discusses a selection of model-based PIA projects. We argue that models may play two important roles. First, with models the consequences of options can be explored turning the PIA process into an experiential learning cycle for the stakeholders. Second, models provide a platform and structure for stakeholders to communicate, negotiate and integrate their perspectives. However, in many PIA projects, computer models fail to play a significant supporting role in social learning. Experiences with other participatory modelling approaches indicate that a higher degree of stakeholder involvement in model development can increase the effectiveness of models as social tools. This, however, is time- and resource-intensive and difficult to scale up but insights from collaborative learning science and technology may help to enhance the effectiveness and efficiency of PIA model in supporting social learning

Systems Biology in ELIXIR: modelling in the spotlight

info:eu-repo/semantics/publishedVersio

Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR\u27s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives

Rethinking soft OR interventions: models as boundary objects

In this paper I draw on research on the role of objects in problem solving collaboration to make a case for the conceptualisation of models as potential boundary objects. Such conceptualisation highlights the possibility that the models used in Soft OR interventions perform three roles with specific effects: transfer to develop a shared language, translation to develop shared meanings, and transformation to develop common interests. If these roles are carried out effectively, models enable those involved to traverse the syntactic, semantic and pragmatic boundaries encountered when tackling a problem situation of mutual concern, and help create new knowledge that has consequences for action. I illustrate these roles and associated effects via two empirical case vignettes drawn from an ongoing action research programme studying the impact of Soft OR interventions. Building on the insights generated by the case vignettes, I develop an analytical framework that articulates the dynamics of knowledge creation within Soft OR interventions. The framework can shed new light on a core aspect of Soft OR practice, especially with regards to the impact of models on the possibilities for action they can afford to those involved. I conclude with a discussion of the prescriptive value of the framework for research into the evaluation of Soft OR interventions, and its implications for the conduct of Soft OR practice