Shocks in coupled socio-ecological systems: what are they and how can we model them?

Coupled socio-ecological systems (SES) are complex systems characterized by self-organization, non-linearities, interactions among heterogeneous elements within each subsystem, and feedbacks across scales and among subsystems. When such a system experiences a shock or a crisis, the consequences are difficult to predict. In this paper we first define what a shock or a crisis means for SES. Depending on where the system boundary is drawn, shocks can be seen as exogenous or endogenous. For example, human intervention in environmental systems could be seen as exogenous, but endogenous in a socio-environmental system. This difference in the origin and nature of shocks has certain consequences for coupled SES and for policies to ameliorate negative consequences of shocks. Having defined shocks, the paper then focuses on modelling challenges when studying shocks in coupled SES. If we are to explore, study and predict the responses of coupled SES to shocks, the models used need to be able to accommodate (exogenous) or produce (endogenous) a shock event. Various modelling choices need to be made. Specifically, the ‘sudden’ aspect of a shock suggests the time period over which an event claimed to be a shock occurred might be ‘quick’. What does that mean for a discrete event model? Turning to magnitude, what degree of change (in a variable or set of variables) is required for the event to be considered a shock? The ‘surprising’ nature of a shock means that none of the agents in the model should expect the shock to happen, but may need rules enabling them to generate behaviour in exceptional circumstances. This requires a certain design of the agents’ decision-making algorithms, their perception of a shock, memory of past events and formation of expectations, and the information available to them during the time the shock occurred

Semantic Support for Computational Land-Use Modelling

Postprin

A Semantic Workflow Mechanism to Realize Experimental Goals and Constraints

Postprin

An Ontology for Experimentation with a Social Simulation of Land Use Change

Publisher PD

Taxonomy of grain legumes

The taxonomy; of grain legumes is relatively uncomplicated compared to that of cereals, brassicas and some other groups of plants because, in general, only limited gene pools have been available for selection and subsequent plant breeding. Then again, intergeneric legume hybrids are not known in nature and artificial crosses attempting to create them are seldom, if ever, successful [64]. Indeed, the genetic barriers between species and species groups are often substantial [86,87]. The classification' of interfertile species and infraspecific variants is inherently more difficult and the taxonomic situation in grain legumes is not exceptional. In some instances the available information would now seem to justify updating of the taxonomic framework

FEARLUS-G : A Semantic Grid Service for Land-Use Modelling

The project is supported by the UK Economic & Social Research Council (ESRC) under the “Pilot Projects in E-Social Science” programme (Award Reference: RES-149-25-0011).Postprin

Computational models that matter during a global pandemic outbreak: A call to action

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research

Illustrating a new 'conceptual design pattern' for agent-based models of land use via five case studies—the MR POTATOHEAD framework

This chapter presents a "conceptual design pattern" (CDP) that represents key elements of standard ABM/LUCC models in a comprehensive logical framework and includes basic functionality and data often present in ABM/LUCC models. The CDP illustrates the key building blocks for ABM/LUCC models, creating a template to assist scholars new to the field to understand existing models and design their own models. Second, the framework facilitates direct comparison of the structure and function of existing models. We present five separately developed models within this framework (SLUDGE, SOME, FEARLUS, LUCITA, and SYPRIA), demonstrating how multiple models can be represented and compared within the same meta-structure. The exercise highlights elements common to all models, demonstrates the unique contributions of each model, reveals commonalities between models, and highlights processes associated with land-use change that are not covered by our models. The CDP as presented here is very much a work in progress, and we welcome feedback from other ABM/LUCC developers, in the hopes of ultimately developing a shared model representation that will accelerate the development of not only ABM/LUCC, but also the theory of land-use change

Empirical agent-based modelling of everyday pro-environmental behaviours at work

We report on agent-based modelling work in the LOCAW project (Low Carbon at Work: Modelling Agents and Organisations to Achieve Transition to a Low Carbon Europe). The project explored the effectiveness of various backcasting scenarios conducted with case study organisations in bringing about pro-environmental change in the workforce in the domains of transport, energy use and waste. The model used qualitative representations of workspaces in formalising each scenario, and decision trees learned from questionnaire responses to represent decision-making. We describe the process by which the decision trees were constructed, noting that the use of decision trees in agent-based models requires particular considerations owing to the potential use of explanatory variables in model dynamics. The results of the modelling in various scenarios emphasise the importance of structural environmental changes in facilitating everyday pro-environmental behaviour, but also show there is a role for psychological variables such as norms, values and efficacy. As such, the topology of social interactions is a potentially important driver, raising the interesting prospect that both workplace geography and organisational hierarchy have a role to play in influencing workplace pro-environmental behaviours

Representation of decision-making in European agricultural agent-based models

The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers' decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers' decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers' behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers' decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers' emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.Swiss National Science Foundatio