Socio-Environmental Systems Modelling https://sesmo.org/ <p><em>SESMO seeks to transform society and socio-environmental decision making through model-based research that integrates multiple issues, domain expertise and interest groups&nbsp;</em></p> International Environmental Modelling and Software Society en-US Socio-Environmental Systems Modelling 2663-3027 Eight grand challenges in socio-environmental systems modeling https://sesmo.org/article/view/16226 <p style="margin: 0px 0px 10.66px; border: medium;"><span style="margin: 0px; color: black; font-size: 12pt;" lang="EN-US">Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</span></p> Sondoss Elsawah Tatiana Filatova Anthony J. Jakeman Albert J. Kettner Moira L. Zellner Ioannis N. Athanasiadis Serena H. Hamilton Robert L. Axtell Daniel G. Brown Jonathan M. Gilligan Marco A. Janssen Derek T. Robinson Julie Rozenberg Isaac I. T. Ullah Steve J. Lade Copyright (c) 2020 Sondoss Elsawah, Tatiana Filatova, Anthony J. Jakeman, Albert J. Kettner, Moira L. Zellner, Ioannis N. Athanasiadis, Serena H. Hamilton, Robert L. Axtell, Daniel G. Brown, Jonathan M. Gilligan, Marco A. Janssen, Derek T. Robinson, Julie Rozenberg, Isaac I. T. Ullah, Steve J. Lade http://creativecommons.org/licenses/by-nc/4.0 2020-01-01 2020-01-01 2 16226 16226 10.18174/sesmo.2020a16226 Contrasting stakeholder and scientist conceptual models of food-energy-water systems: a case study in Magic Valley, Southern Idaho https://sesmo.org/article/view/16312 <p>One of the factors for the success of simulation studies is close collaboration with stakeholders in developing a conceptual model. Conceptual models are a useful tool for communicating and understanding how real systems work. However, models or frameworks that are not aligned with the perceptions and understanding of local stakeholders can induce uncertainties in the model outcomes. We focus on two sources of epistemic uncertainty in building conceptual models of food-energy-water systems (FEWS): (1) context and framing; and (2) model structure uncertainty. To address these uncertainties, we co-produced a FEWS conceptual model with key stakeholders using the Actor-Resources-Dynamics-Interaction (ARDI) method. The method was adopted to specifically integrate public (and local) knowledge of stakeholders in the Magic Valley region of Southern Idaho into a FEWS model. We first used the ARDI method with scientists and modellers (from various disciplines) conducting research in the system, and then repeated the process with local stakeholders. We compared results from the two cohorts and refined the conceptual model to align with local stakeholders’ understanding of the FEWS. This co-development of a conceptual model with local stakeholders ensured the incorporation of different perspectives and types of knowledge of key actors within the socio-ecological systems models.</p> Grace B. Villamor David L. Griffith Andrew Kliskey Lilian Alessa Copyright (c) 2019 Grace Villamor, David L Griffith, Andrew Kliskey, Lilian Alessa http://creativecommons.org/licenses/by-nc/4.0 2019-10-22 2019-10-22 2 16312 16312 10.18174/sesmo.2020a16312 Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review https://sesmo.org/article/view/16325 <p>Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.</p> Meike Will Jürgen Groeneveld Karin Frank Birgit Müller Copyright (c) 2020 Meike Will, Jürgen Groeneveld, Karin Frank, Birgit Müller http://creativecommons.org/licenses/by-nc/4.0 2020-02-28 2020-02-28 2 16325 16325 10.18174/sesmo.2020a16325 A bricolage-style exploratory scenario analysis to manage uncertainty in socio-environmental systems modeling: investigating integrated water management options https://sesmo.org/article/view/16227 <p>Exploratory analysis, while useful in assessing the implications of model assumptions under large uncertainty, is considered at best a semi-structured activity. There is no algorithmic way for performing exploratory analysis and the existing canonical techniques have their own limitations. To overcome this, we advocate a bricolage-style exploratory scenario analysis, which can be crafted by pragmatically and strategically combining different methods and practices. Our argument is illustrated using a case study in integrated water management in the Murray-Darling Basin, Australia. Scenario ensembles are generated to investigate potential policy innovations, climate and crop market conditions, as well as the effects of uncertainties in model components and parameters. Visualizations, regression trees and marginal effect analyses are exploited to make sense of the ensemble of scenarios. The analysis includes identifying patterns within a scenario ensemble, by visualizing initial hypotheses that are informed by prior knowledge, as well as by visualizing new hypotheses based on identified influential variables. Context-specific relationships are explored by analyzing which values of drivers and management options influence outcomes. Synthesis is achieved by identifying context-specific solutions to consider as part of policy design. The process of analysis is cast as a process of finding patterns and formulating questions within the ensemble of scenarios that merit further examination, allowing end-users to make the decision as to what underlying assumptions should be accepted, and whether uncertainties have been sufficiently explored. This approach is especially advantageous when the precise intentions of management are still subject to deliberations. By describing the reasoning and steps behind a bricolage-style exploratory analysis, we hope to raise awareness of the value of sharing this kind of (common but not often documented) analysis process, and motivate further work to improve sharing of know-how about bricolage in practice.</p> Baihua Fu Joseph H.A. Guillaume Anthony J. Jakeman Michael J. Asher Copyright (c) 2020 Baihua Fu, Joseph H.A. Guillaume, Anthony J Jakeman, Michael J. Asher http://creativecommons.org/licenses/by-nc/4.0 2020-03-17 2020-03-17 2 16227 16227 10.18174/sesmo.2020a16227