Sondoss Elsawah

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. We review five common integrated modelling approaches.Model choice considers purpose, data type, scale and uncertainty treatment.We present a guiding framework for selecting the most appropriate approach.

Integrated assessment and modelling

Integrated assessment and its inherent platform, integrated modelling, present an opportunity to synthesize diverse knowledge, data, methods and perspectives into an overarching framework to address complex environmental problems. However to be successful for assessment or decision making purposes, all salient dimensions of integrated modelling must be addressed with respect to its purpose and context. The key dimensions include: issues of concern; management options and governance arrangements; stakeholders; natural systems; human systems; spatial scales; temporal scales; disciplines; methods, models, tools and data; and sources and types of uncertainty. This paper aims to shed light on these ten dimensions, and how integration of the dimensions fits in the four main phases in the integrated assessment process: scoping, problem framing and formulation, assessing options, and communicating findings. We provide examples of participatory processes and modelling tools that can be used to achieve integration. This is an overview on integrated assessment and modelling (IAM) for environmental problems.We examine the ten key dimensions of integration in IAM including what is being integrated, why and how.We discuss how the integration dimensions fit into the IAM process.

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model.

Using a Cognitive Mapping Approach to Frame the Perceptions of Water Users About Managing Water Resources: A Case Study in the Australian Capital Territory

In complex socio-ecological systems, such as managing natural resources, human frames and mental models play a central role in deriving the system’s behaviour. Differences in stakeholder views and perceptions may impede the design and implementation of collective policies. Understanding stakeholder views and mental models is a pre-requisite for understanding decision making, improving communication, and eventually developing management policies that cater to the diversity of values and interests. Motivated by this premise, this research uses a cognitive mapping approach to examine the frames used by a group of water users with regard to managing available water resources. We focus on the Australian Capital Territory as a case study. Two different frames have emerged from the results: hard and soft. Differences in frames embody various perceptions about the problem definition, its causes, effective management strategies, and hence, responsibility attribution. The paper describes both frames and highlights those perceptions that may stand as barriers against sustainable management. These findings can be transferred to other arid and semi-arid areas.

Discretization of continuous predictor variables in Bayesian networks

Bayesian networks (BNs) are a popular tool in natural resource management but are limited when dealing with ecological assemblage data and when discretizing continuous variables. We present a method that addresses these challenges using a BN model developed for the Upper Murrumbidgee River Catchment (south-eastern Australia). A selection process was conducted to choose the taxa from the whole macroinvertebrate assemblage that were incorporated in the BN as endpoints. Furthermore, two different approaches to the discretization of continuous predictor variables for the BN were compared. One approach used Threshold Indicator Taxa Analysis (TITAN) which estimates the thresholds based on the biological community. The other approach used was the expert opinion. The TITAN-based discretizations provided comparable predictions to expert opinion-based discretizations but in combining statistical rigor and ecological relevance, offer a novel and objective approach to the discretization. The TITAN-based method may be used together with expert opinion. We propose the empirical estimation of thresholds to discretize continuous predictor variables within Bayesian networks.We used a case study to illustrate it.Predefined criteria were used to select five macroinvertebrate taxa that were incorporated in the BN as endpoints.Continuous predictor variables were discretized using Threshold Indicator Taxa Analysis (TITAN).TITAN-based discretizations provided comparable predictions to expert opinion-based discretizations.

Communicating About Water Issues in Australia

Background. Unlike traditional communication approaches, simulation/gaming has the capacity to communicate about the complexity and uncertainty aspects of managing natural resources in engaging and informative ways. Purpose. This article aims to present a system dynamics-based framework for using simulation/gaming to communicate about complex water issues Method. We used communication about water resources issues in the Australian Capital Territory as a case study to support the development and implementation of the modelling framework. Results. Three scenarios are designed and analysed to challenge the mental models that stakeholders may have about the effects of various water policies. Contribution. The system dynamics-based simulation/gaming framework contributes to communicating about complex water resources issues.

Toward best practice framing of uncertainty in scientific publications: A review of Water Resources Research abstracts

Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation. But uncertainty is also addressed outside the analysis, in writing scientific publications. The language that authors use conveys their perspective of the role of uncertainty when interpreting a claim —what we call here “framing” the uncertainty. This article promotes awareness of uncertainty framing in four ways. 1) It proposes a typology of eighteen uncertainty frames, addressing five questions about uncertainty. 2) It describes the context in which uncertainty framing occurs. This is an interdisciplinary topic, involving philosophy of science, science studies, linguistics, rhetoric, and argumentation. 3) We analyze the use of uncertainty frames in a sample of 177 abstracts from the Water Resources Research journal in 2015. This helped develop and tentatively verify the typology, and provides a snapshot of current practice. 4) Provocative recommendations promote adjustments for a more influential, dynamic science. Current practice in uncertainty framing might be described as carefully-considered incremental science. In addition to uncertainty quantification and degree of belief (present in ∼5% of abstracts), uncertainty is addressed by a combination of limiting scope, deferring to further work (∼25%) and indicating evidence is sufficient (∼40%) – or uncertainty is completely ignored (∼8%). There is a need for public debate within our discipline to decide in what context different uncertainty frames are appropriate. Uncertainty framing cannot remain a hidden practice evaluated only by lone reviewers.

An empirical investigation into the learning effects of management flight simulators: A mental models approach

There are many claims about the learning effects of Management Flight Simulators (MFS) as a public education and communication tool in water resource management. However, there is still lack of empirical evidence to support this claim. To address this issue, an exploratory experimental study was conducted to examine the learning effects a series of MFS had on the mental model of water users in the Australian Capital Territory (ACT). Participants’ mental models of the causal relationships that influence water availability were elicited before and after interacting with a series of increasingly complex MFS, and compared to the reference model structure underpinning the MFS. Results showed that the MFS experience improved participants’ causal knowledge of the model structure and generated Critical Learning Incidents. The findings are interpreted in light of the study limitations along with future research directions.

Model-based multi-objective decision making under deep uncertainty from a multi-method design lens

Abstract Several approaches within the exploratory modelling literature—each with strengths and limitations—have been introduced to address the complexity and uncertainty of decision problems. Recent model-based approaches for decision making emphasise the advantage of mixing approaches from different areas in leveraging the strengths of each. This article shows how a multi-method lens to the design of decision-making approaches can better address different characteristics of multi-objective decision problems under deep uncertainty. The article focuses on interactions between two broad areas in model-based decision making: exploratory modelling and multi-objective optimisation. The article reviews this literature using a specific multi-method lens to analyse previous researches and to identify the knowledge gap. The article then addresses this gap by demonstrating a multi-method approach for designing adaptive robust solutions. The suggested approach uses a Pareto optimal search from multi-objective optimisation for enumerating alternative solutions. It also uses Robust Decision Making and Dynamic Adaptive Policy Pathways approaches from exploratory modelling for analysing the robustness of enumerated solutions in the face of many future scenarios. A hypothetical case study is used to illustrate how the approach can be applied. The article concludes that a new lens from a multi-method design perspective is needed on exploratory modelling to provide practical guidance into how to combine exploratory modelling techniques, to shed light on exiting knowledge gaps and to open up a range of potential combinations of exiting approaches for leveraging the strengths of each.

An agent-monitored framework for the output-oriented design of experiments in exploratory modelling

Abstract Exploratory modelling is an approach for modelling under uncertainty based on the generation and analysis of computational experiments. The results of exploratory modelling are sensitive to the way that experiments are designed, such as the way that the uncertainty space is delineated. This article introduces an agent-monitored framework—i.e. a design metaphor of the interactions among modellers and stakeholders and the simulation process—for controlling the design of experiments based on monitoring model behaviour in the output space. To demonstrate the benefits of the suggested framework in the exploratory modelling process, the article shows how the use of the framework with an output-oriented approach informs the delineation of an appropriate uncertainty space with an illustrative example in the decision-making context. The article concludes that the design of experiments based on feedback from the output space can be a useful approach: to control simulations in exploratory modelling; to build more confidence in final results; and to inform the design of other aspects of experiments, such as selecting policy levers and sampling method.