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This article comprises a set of commentaries for the paper written by Carroll and Esposito Amideo from different scholars and OR practitioners. The commentaries agree on the importance of documenting the gender situation across the OR community and the need to address it through multiple actions.

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Contains fulltext : 209202pre.pdf (preprint version ) (Open Access)

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Contains fulltext : 209202pre.pdf (Author’s version preprint ) (Open Access)

Contains fulltext : 209202pre.pdf (Author’s version preprint ) (Open Access)

Contains fulltext : 178390pos.pdf (postprint version ) (Open Access) Contains fulltext : 178390.pdf (publisher's version ) (Closed access), Group model building refers to a process of building system dynamics models with decision makers, experts, and other stakeholders. Involving stakeholders in building system dynamics models has a long history going back several decades (Andersen, Vennix, Richardson, & Rouwette, 2007). In early studies participants were mainly asked to participate in order to bring their knowledge into the modelling process, contributing insights and data that often cannot be found in other data sources. Later it was recognised that clients who have an active role in setting the focus of a modelling project and building the model are likely to understand and accept results. A series of studies has addressed the impact of group model building on changes in participants’ knowledge and behaviour. Single case studies, going back to the 1960s, describe how a particular group of participants work on a specific problem, the insights that emerge and whether or not results were implemented. Recently these cases studies were contrasted and analysed in review papers. The general conclusion from these reviews is that group model building does lead to changes in knowledge and behaviour, but much remains unclear on the underlying mechanism. Modelling engagements focus on messy unstructured situations, and at the start of a project it is not clear which information is relevant for understanding what is going on. By jointly developing a model, a consistent picture of the situation gradually emerges. How model construction changes participants’ insights, and when and how participants translate insights into actions, are questions that researchers have begun to address. Group model building research has gradually shifted from a focus on changes in participant behaviour after the modelling project, to behaviour in modelling sessions.

Contains fulltext : 178390pos.pdf (postprint version ) (Open Access) Contains fulltext : 178390.pdf (publisher's version ) (Closed access), Group model building refers to a process of building system dynamics models with decision makers, experts, and other stakeholders. Involving stakeholders in building system dynamics models has a long history going back several decades (Andersen, Vennix, Richardson, & Rouwette, 2007). In early studies participants were mainly asked to participate in order to bring their knowledge into the modelling process, contributing insights and data that often cannot be found in other data sources. Later it was recognised that clients who have an active role in setting the focus of a modelling project and building the model are likely to understand and accept results. A series of studies has addressed the impact of group model building on changes in participants’ knowledge and behaviour. Single case studies, going back to the 1960s, describe how a particular group of participants work on a specific problem, the insights that emerge and whether or not results were implemented. Recently these cases studies were contrasted and analysed in review papers. The general conclusion from these reviews is that group model building does lead to changes in knowledge and behaviour, but much remains unclear on the underlying mechanism. Modelling engagements focus on messy unstructured situations, and at the start of a project it is not clear which information is relevant for understanding what is going on. By jointly developing a model, a consistent picture of the situation gradually emerges. How model construction changes participants’ insights, and when and how participants translate insights into actions, are questions that researchers have begun to address. Group model building research has gradually shifted from a focus on changes in participant behaviour after the modelling project, to behaviour in modelling sessions.

Contains fulltext : 178390pos.pdf (Author’s version postprint ) (Open Access) Contains fulltext : 178390.pdf (Publisher’s version ) (Closed access), Group model building refers to a process of building system dynamics models with decision makers, experts, and other stakeholders. Involving stakeholders in building system dynamics models has a long history going back several decades (Andersen, Vennix, Richardson, & Rouwette, 2007). In early studies participants were mainly asked to participate in order to bring their knowledge into the modelling process, contributing insights and data that often cannot be found in other data sources. Later it was recognised that clients who have an active role in setting the focus of a modelling project and building the model are likely to understand and accept results. A series of studies has addressed the impact of group model building on changes in participants’ knowledge and behaviour. Single case studies, going back to the 1960s, describe how a particular group of participants work on a specific problem, the insights that emerge and whether or not results were implemented. Recently these cases studies were contrasted and analysed in review papers. The general conclusion from these reviews is that group model building does lead to changes in knowledge and behaviour, but much remains unclear on the underlying mechanism. Modelling engagements focus on messy unstructured situations, and at the start of a project it is not clear which information is relevant for understanding what is going on. By jointly developing a model, a consistent picture of the situation gradually emerges. How model construction changes participants’ insights, and when and how participants translate insights into actions, are questions that researchers have begun to address. Group model building research has gradually shifted from a focus on changes in participant behaviour after the modelling project, to behaviour in modelling sessions.

There are several models of financial markets which look at the herding effect. This is a situation where many market traders act as a herd in that they all behave in a similar way with their trading. This type of behaviour may explain certain observed characteristics (or ‘stylised facts’) in real markets. However, the various models have different herding mechanisms and market settings This paper sets out the rationale of our approach and our initial work in trying to get a better understanding of herding in financial markets. Our research, though, is at an early stage. The basic methodology is to reproduce and compare some of the existing models, hopefully leading to a more general understanding and measure of herding and the relationship with market behaviour. One model has been investigated so far and this is described. A more general issue is the research importance of reproducing previous studies.

There are several models of financial markets which look at the herding effect. This is a situation where many market traders act as a herd in that they all behave in a similar way with their trading. This type of behaviour may explain certain observed characteristics (or ‘stylised facts’) in real markets. However, the various models have different herding mechanisms and market settings This paper sets out the rationale of our approach and our initial work in trying to get a better understanding of herding in financial markets. Our research, though, is at an early stage. The basic methodology is to reproduce and compare some of the existing models, hopefully leading to a more general understanding and measure of herding and the relationship with market behaviour. One model has been investigated so far and this is described. A more general issue is the research importance of reproducing previous studies.

The problem considered in this paper is whether an inequality of omega-terms is valid in a given level of a concatenation hierarchy of star-free languages. The main result shows that this problem is decidable for all (integer and half) levels of the Straubing-Th\'erien hierarchy.

Research on facilitation in discrete event simulation (DES) is gathering pace but there is still a need to put forward real examples to explain the process to newcomers. Most of the research has focussed on facilitation in the initial stages of the simulation modelling process. In this paper we focus on one of the postmodel coding stages. More specifically we focus on the implementation stage, the final stage in the modelling process. The primary contributions of this paper are the description of the process followed and the introduction of tools that can be used during this stage to support workshop activities. A real case study is provided describing the sequence of the interactions undertaken in the workshop. Extracts from the transcripts are also included, with the view to bringing evidence of the stakeholders’ involvement and their mood during the workshop. The paper concludes with a discussion on the process followed and the importance of using tools in this stage.

Systems that benefit from the ongoing use of simulation, often require considerable input by the modeller(s) to update and maintain the models. This paper proposes automating the evolution of the modelling process for discrete event simulation (DES) and therefore limiting the majority of the human modeller’s input to the development of the model. This mode of practice could be named Self-Adaptive Discrete Event Simulation (SADES). The research is driven from ideas emerging from simulation model reuse, automations in the modelling process, real time simulation, dynamic data driven application systems, autonomic computing and self-adaptive software systems. This paper explores some of the areas that could inform the development of SADES and proposes a modified version of the MAPE-K feedback control loop as a potential process. The expected outcome from developing SADES would be a simulation environment that is self-managing and more responsive to the analytical needs of real systems.