Modelling systemic change in coupled socio-environmental systems

Abrupt systemic changes in ecological and socio-economic systems are a regular occurrence. While there has been much attention to studying systemic changes primarily in ecology as well as in economics, the attempts to do so for coupled socio-environmental systems are rarer. This paper bridges the gap by reviewing how models can be instrumental in exploring significant, fundamental changes in such systems. The history of modelling systemic change in various disciplines contains a range of definitions and approaches. Even so, most of these efforts share some common challenges within the modelling context. We propose a framework drawing these challenges together, and use it to discuss the articles in this thematic issue on modelling systemic change in coupled social and environmental systems. The differing approaches used highlight that modelling systemic change is an area of endeavour that would benefit from greater synergies between the various disciplines concerned with systemic change.     

Communicative action-based business process and information systems modelling with DEMO

Abstract. Abstract. The field of communicative action-based modelling of business processes and information systems has attracted more and more attention in recent years. Inspired by the seminal work of Winograd and Flores, researchers have proposed several modelling approaches. In this article we discuss communicative action-based modelling approaches in general and the DEMO (dynamic essential modelling of organizations) approach in particular. Besides establishing the theoretical foundations of this modelling approach, we also apply DEMO to a case study, and we discuss how the resulting models can be used for information systems design and business process optimization.     

Efficiency of local models ensembles for time series prediction

Many current technological challenges require the capacity of forecasting future measurements of a phenomenon. This, in most cases, leads directly to solve a time series prediction problem. Statistical models are the classical approaches for tackling this problem. More recently, neural approaches such as Backpropagation, Radial Basis Functions and recurrent networks have been proposed as an alternative. Most neural-based predictors have chosen a global modelling approach, which tries to approximate a goal function adjusting a unique model. This philosophy of design could present problems when data is extracted from a phenomenon that continuously changes its operational regime or represents distinct operational regimes in a unbalanced manner. In this paper, two alternative neural-based local modelling approaches are proposed. Both follow the divide and conquer principle, splitting the original prediction problem into several subproblems, adjusting a local model for each one. In order to check their adequacy, these methods are compared with other global and local modelling classical approaches using three benchmark time series and different sizes (medium and high) of training data sets. As it is shown, both models demonstrate to be useful pragmatic paradigms to improve forecasting accuracy, with the advantages of a relatively low computational time and scalability to data set size.     

Values in socio-environmental modelling: Persuasion for action or excuse for inaction

Science in general and modelling in particular provide in-depth understanding of environmental processes and clearly demonstrate the present unsustainable use of resources on a global scale. The latest report by the Intergovernmental Panel on Climate Change (IPCC), for instance, shows that climate is changing and with a 95% certainty it is the humans have caused the change. The future climatic conditions are shown to be largely adversely affecting human wellbeing on this planet. Yet we see in numerous examples that societies are very slow in reacting to this rapid depletion of natural resources. What still seems lacking is the translation of scientific reports and the results of analysis and modelling into corrective actions. We argue that one of the reasons for this is the traditional workflow of environmental modelling, which starts with the purpose, the goal formulation, and ends with problem solutions or decision support tools. Instead, modelling, and applied science in general, has to enhance its scope beyond the problem solving stage, to do more on the problem definition and solution implementation phases. Modelling can be also used for identification of societal values and for setting purposes by appropriate communication of the modelling process and results. We believe this new approach for modelling can impact and bring the social values to the forefront of socio-environmental debate and hence turn scientific results into actions sooner rather than later. Instead of being separated from the modelling process, the translation of results should be an intrinsic part of it. We discuss several challenges for recent socio-environmental modelling and conclude with ten propositions that modellers and scientists in general can follow to improve their communication with the society and produce results that can be understood and used to improve awareness and education and spur action.     

A framework for coupling explanation and prediction in hydroecological modelling

Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r² for two macroinvertebrate indices = 0.64–0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r² for the same indices = 0.50–0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.     

Fuzzy bilevel programming with multiple non-cooperative followers: model,algorithm and application

In centralized decision problems, it is not complicated for decision-makers to make modelling technique selections under uncertainty. When a decentralized decision problem is considered, however, choosing appropriate models is no longer easy due to the difficulty in estimating the other decision-makers’ inconclusive decision criteria. These decision criteria may vary with different decision-makers because of their special risk tolerances and management requirements. Considering the general differences among the decision-makers in decentralized systems, we propose a general framework of fuzzy bilevel programming including hybrid models (integrated with different modelling methods in different levels). Specially, we discuss two of these models which may have wide applications in many fields. Furthermore, we apply the proposed two models to formulate a pricing decision problem in a decentralized supply chain with fuzzy coefficients. In order to solve these models, a hybrid intelligent algorithm integrating fuzzy simulation, neural network and particle swarm optimization based on penalty function approach is designed. Some suggestions on the applications of these models are also presented.     

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings.     

Seeing the forest through the trees: A review of integrated environmental modelling tools

Today’s interconnected socio-economic and environmental challenges require the combination and reuse of existing integrated modelling solutions. This paper contributes to this overall research area, by reviewing a wide range of currently available frameworks, systems and emerging technologies for integrated modelling in the environmental sciences. Based on a systematic review of the literature, we group related studies and papers into viewpoints and elaborate on shared and diverging characteristics. Our analysis shows that component-based modelling frameworks and scientific workflow systems have been traditionally used for solving technical integration challenges, but ultimately, the appropriate framework or system strongly depends on the particular environmental phenomenon under investigation. The study also shows that – in general – individual integrated modelling solutions do not benefit from components and models that are provided by others. It is this island (or silo) situation, which results in low levels of model reuse for multi-disciplinary settings. This seems mainly due to the fact that the field as such is highly complex and diverse. A unique integrated modelling solution, which is capable of dealing with any environmental scenario, seems to be unaffordable because of the great variety of data formats, models, environmental phenomena, stakeholder networks, user perspectives and social aspects. Nevertheless, we conclude that the combination of modelling tools, which address complementary viewpoints – such as service-based combined with scientific workflow systems, or resource-modelling on top of virtual research environments – could lead to sustainable information systems, which would advance model sharing, reuse and integration. Next steps for improving this form of multi-disciplinary interoperability are sketched.     

Multi-objective energy-efficient workflow scheduling using list-based heuristics

Workflow applications are a popular paradigm used by scientists for modelling applications to be run on heterogeneous high-performance parallel and distributed computing systems. Today, the increase in the number and heterogeneity of multi-core parallel systems facilitates the access to high-performance computing to almost every scientist, yet entailing additional challenges to be addressed. One of the critical problems today is the power required for operating these systems for both environmental and financial reasons. To decrease the energy consumption in heterogeneous systems, different methods such as energy-efficient scheduling are receiving increasing attention. Current schedulers are, however, based on simplistic energy models not matching the reality, use techniques like DVFS not available on all types of systems, or do not approach the problem as a multi-objective optimisation considering both performance and energy as simultaneous objectives. In this paper, we present a new Pareto-based multi-objective workflow scheduling algorithm as an extension to an existing state-of-the-art heuristic capable of computing a set of tradeoff optimal solutions in terms of makespan and energy efficiency. Our approach is based on empirical models which capture the real behaviour of energy consumption in heterogeneous parallel systems. We compare our new approach with a classical mono-objective scheduling heuristic and state-of-the-art multi-objective optimisation algorithm and demonstrate that it computes better or similar results in different scenarios. We analyse the different tradeoff solutions computed by our algorithm under different experimental configurations and we observe that in some cases it finds solutions which reduce the energy consumption by up to 34.5% with a slight increase of 2% in the makespan.     

Design for service compatibility

Service composition is a recent field that has seen a flurry of different approaches proposed towards the goal of flexible distributed heterogeneous interoperation of software systems, usually based on the expectation that such systems must be derived from higher-level models rather than be coded at low level. In practice, achieving service interoperability nonetheless continues to require significant modelling approach at multiple abstraction levels, and existing formal approaches typically require the analysis of the global space of joint executions of interacting services. Based on our earlier work on providing locally checkable consistency rules for guaranteeing the behavioural consistency of inheritance hierarchies, a model-driven approach for creating consistent service orchestrations is proposed. Service execution and interaction is represented with a high-level model in terms of extended Petri net notation; formal criteria are provided for service consistency that can be checked in terms of local model properties, and give a multi-step design approach for developing services that are guaranteed to be interoperable. Finally, it is outlined how the presented results can be carried over and applied to modelling processes using the Business Process Modelling Notation (BPMN).     

Translating community narratives into semi-quantitative models to understand the dynamics of socio-environmental crises

Acute socio-environmental crises often expose systemic problems that are linked by failures in management, environmental, or social systems. If recovery efforts are to address these systemic problems, these issues and the concerns of those impacted by the crisis need to be clearly articulated, rationally represented, and communicated to those responsible for the recovery. Although participatory approaches to crisis recovery often use environmental modeling, explicit ways in which stakeholders’ narratives and experiences can be translated into computer-based models for scenario analysis are not readily available to modelers or decision-makers. We present an approach to translating community narratives about crisis events using a free Fuzzy Cognitive Mapping software called Mental Modeler (www.mentalmodeler.org). We applied this process to the recent water crisis in Flint, Michigan, and demonstrate how participatory modeling can give communities a way to structure their thoughts, develop recovery actions, and communicate with those in charge of crisis recovery efforts.     

An automated selection algorithm for nonlinear solvers in MDO

There are two major types of approaches that are used for the multidisciplinary analysis (MDA) of coupled systems: fixed-point-iteration-based approaches and coupled Newton-based approaches. Fixed-point-iteration approaches are easier to implement, but can require a large number of iterations or diverge for strongly coupled problems. On the other hand, coupled-Newton approaches have superior convergence orders, but generally require more effort to implement and have more expensive iterations. Additionally, these two major approaches have many variations, including hybrid approaches where the MDA begins with a fixed-point iteration and then switches to a coupled-Newton approach after a certain number of iterations. However, there is a lack of criteria to govern how to select between these approaches, and when to switch between them in a hybrid approach. This paper compares these approaches and provides an algorithm that can be used to automatically select and switch between them. The proposed algorithm is implemented using OpenMDAO, NASA’s open-source framework for multidisciplinary analysis and optimization, and is tested using OpenAeroStruct, an open-source low-fidelity tool for aerostructural optimization. The results show that the proposed algorithm provides a balance of improved robustness and speed.     

A participatory approach for system conceptualization and analysis applied to coastal management in Egypt

This paper presents a participatory approach to conceptualizing system's models and to identifying critical issues in complex socio-environmental systems, combining information collected from individual experts and stakeholders. A method was developed to: (i) capture individuals mental models in the form of causal loop diagrams, using interaction matrices; (ii) build a conceptual model of the system combining the contribution of all stakeholders; (iii) identify critical issues for the system and (iv) prepare a combined causal loop diagram for further discussion and system dynamics simulations. This method was used to engage a group of stakeholders involved in the preparation of a plan for integrated coastal zone management in Egypt. The experience helped highlight the critical issues of the system in terms of importance given by the actors involved in the exercise and their impact on the coastal system. This approach also demonstrated the utility of conceptualizing complex socio-environmental systems for identifying critical issues in data-poor environments.     

TOTEM: Reconciling multi-level modelling with standard two-level modelling

Model driven Engineering (MDE) advocates the active use of models throughout the different software development phases. In MDE, models are described using meta-models, one meta-level above. This approach effectively leaves developers with one single meta-level to create their models. However, there are scenarios where the use of multiple meta-levels results in simpler models with less accidental complexity. Hence, to simplify modelling in these cases, several multi-level modelling approaches and tools have recently emerged to increase the flexibility in modelling. While they provide advanced primitives to simplify modelling, there are possibilities to improve interoperability with mainstream two-level modelling approaches based on the Meta-Object Facility (MOF) standard of the Object Management Group (OMG), and achieve wider adoption.For this purpose, we first characterise the design space of multi-level modelling approaches using a feature model. On such a basis, we provide a detailed comparison of existing multi-level modelling tools, identifying gaps and research opportunities. As a result of this gap analysis, we propose a new approach to multi-level modelling that embeds multiple meta-levels within one meta-model (i.e., encoding objects as classes, and instantiation as inheritance), and a tool – called TOTEM – which implements these concepts. The tool capabilities and its benefits in terms of interoperability with mainstream, standard modelling frameworks are illustrated through an example, as well as with empirical and analytical evaluations.     

Three computer-based models of storytelling: BRUTUS,MINSTREL and MEXICA

This paper attempts to establish criteria to analyse and evaluate computer models of creativity in writing. The paper provides a brief review of the antecedents of automatic story-generation and offers a proposal for the analysis and evaluation of computer models of creativity in writing. It reviews three major projects to develop computer-based storywriters published between 1993 and 2000 and analyses their approach, similarities, differences and contributions. It compares the three approaches and discusses implications for the modelling of creativity in writing and the design of future story generation systems.     

A framework for evolution of modelling languages

In model-driven engineering, evolution is inevitable over the course of the complete life cycle of complex software-intensive systems and more importantly of entire product families. Not only instance models, but also entire modelling languages are subject to change. This is in particular true for domain-specific languages, whose language constructs are tightly coupled to an application domain.The most popular approach to evolution in the modelling domain is a manual process, with tedious and error-prone migration of artefacts such as instance models as a result. This paper provides a taxonomy for evolution of modelling languages and discusses the different evolution scenarios for various kinds of modelling artefacts, such as instance models, meta-models, and transformation models. Subsequently, the consequences of evolution and the required remedial actions are decomposed into primitive scenarios such that all possible evolutions can be covered exhaustively. These primitives are then used in a high-level framework for the evolution of modelling languages.We suggest that our structured approach enables the design of (semi-)automatic modelling language evolution solutions.     

A framework for evolution of modelling languages

In model-driven engineering, evolution is inevitable over the course of the complete life cycle of complex software-intensive systems and more importantly of entire product families. Not only instance models, but also entire modelling languages are subject to change. This is in particular true for domain-specific languages, whose language constructs are tightly coupled to an application domain.The most popular approach to evolution in the modelling domain is a manual process, with tedious and error-prone migration of artefacts such as instance models as a result. This paper provides a taxonomy for evolution of modelling languages and discusses the different evolution scenarios for various kinds of modelling artefacts, such as instance models, meta-models, and transformation models. Subsequently, the consequences of evolution and the required remedial actions are decomposed into primitive scenarios such that all possible evolutions can be covered exhaustively. These primitives are then used in a high-level framework for the evolution of modelling languages.We suggest that our structured approach enables the design of (semi-)automatic modelling language evolution solutions.     

A modified Sobol′ sensitivity analysis method for decision-making in environmental problems

Sensitivity analysis is an important component of environmental modelling and in recent years, variance-based, global sensitivity analysis techniques, such as Sobol′, have been a preferred approach for achieving this. However, these techniques are generally only applicable to simulation models and not to models used to rank alternative options, such as multi-criteria decision analysis (MCDA) methods. In order to overcome this limitation, a modified Sobol′ method for MCDA (Sobol′-MCDA) is introduced in this paper. The method has the following features: (i) it enables the stability or robustness of the relative ranking of two alternatives to be assessed in the light of changes in assessment criteria and stakeholder preferences; and (ii) it enables the sensitivity of the ranking of two alternatives to changes in assessment criteria and stakeholder preferences to be assessed. The approach is demonstrated for a water resources case study from the literature consisting of seven alternatives and ten assessment criteria.     

Modular artifact synthesis from domain-specific models

Domain-specific modelling reduces the gap between problem domain and solution domain. It supports modelling using constructs familiar to experts of a specific domain. Domain-specific models (DSms) are (semi)automatically transformed to various lower-level artifacts, including configuration files, documentation and executable programs. Although various aspects of model-driven development have been investigated, such as model versioning, debugging and transformation, relatively not much attention has been paid to formalise how artifacts are synthesised from DSms. State-of-the-art approaches rely on ad hoc coded generators that essentially use modelling tool APIs to programmatically iterate through internal representations of DSm entities to produce target-platform artifacts. In this work, we propose a more structured approach to artifact generation, where layered model transformations are used to modularly isolate, compile and re-combine various concerns within DSms, while maintaining traceability links between corresponding constructs at different levels of abstraction. We study and demonstrate how our approach simplifies addressing non-functional requirements (e.g., timing and resource utilisation constraints) of modern embedded systems. This is achieved through the modular synthesis of performance models from DSms. We illustrate our work by means of the synthesis of fully functional Google Android applications, performance predictions, simulations and performance measurement facilities from DSms of mobile phone applications.