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Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore’s Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.

A description of a NetLogo extension and the reasoning behind its des ign and implementation. The extension makes use of Gnu’s Pretty Good Privacy sof tware suite to encrypt arbitrary data sources in Netlogo. This both secures the data to a reasonable degree and protects any sensitive data that might be in use for a publicly available model.

Agent-based models typically have stochastic elements and many potential parameter combinations. This requires that we conduct multiple model runs to sweep the parameter space, creating large quantities of computationally generated, hyper-dimensional, “big data”. Understanding the models’ implications requires structured exploration of these complex output data. In response to this need, the MIRACLE team has developed a prototype web application that enables researchers who archive their model output data and analysis methods to perform online output data exploration and reproducible, re-parameterizable data analysis. We plan to build on this prototype, integrating with broader reproducibility initiatives in scientific computation and big data, to facilitate improved communication within research groups, and increase access and transparency for external research community and the general public. This paper provides contextual background and a case study of the prototype MIRACLE data storage and analysis web tool.

This paper uses large-scale micro data to identify key factors affecting the decision to adopt renewable energy generation (wind, solar and biomass) on farms in Scotland. We construct an integrated dataset that includes the compulsory agricultural census and farm structural survey that cover almost all farms in Scotland. In addition to farm owner demographics and farm business structures, we also assess the effect of diversification activities such as tourism and forestry, as well as the spatial, biophysical and geophysical attributes of the farms on the adoption decision. We find that diversified farms are more likely to adopt renewable energy, especially solar and biomass energy. Farms are also more likely to adopt renewable energy if they have high local demand for energy, or suitable conditions for renewable energy production. We find that biophysical factors such as the agricultural potential of farm land are important in adoption decisions. We identify adopter profiles for each type of renewable energy, and map the geographic location of potential adopters. We argue that renewable energy policy should be more integrated with farm diversification policy and farm support schemes. It should also be tailored for each type of renewable energy, for the potential adopter profiles of wind, solar and biomass energy all differ in farm characteristics and geographic distribution.

International audience

Tropical countries lie at the nexus of three pressing issues for global sustainability: agricultural production, climate change mitigation and biodiversity conservation. The forces that drive forest protection do not necessarily oppose those that drive forest clearance for development. This decoupling, enhanced by the stronger economic forces compared to conservation, is detrimental for the social-ecological sustainability of forested tropical landscapes. This paper presents an integrated, and spatially-explicit, Agent-Based Model that examines the future impacts of land-use change scenarios on the sustainability of the Wet Tropics region of tropical Queensland, Australia. In particular, the model integrates Bayesian Belief Networks, Geographical Information Systems, empirical data and expert knowledge, under a land-sharing/land-sparing analysis, to study the impact of different landscape configurations on trade-offs and synergies among biodiversity and two ecosystem services (sugarcane production and carbon sequestration). Contrary to most tropical regions, model simulations show that Business As Usual is helping to reconcile these contrasting goals in the forested landscape of the Wet Tropics. The paper analyses which combination of governance and socio-economic factors is causing these positive results. This is an outstanding achievement for a tropical region, considering that most tropical areas are characterized for having stronger economic-land clearing forces compared to conservation forces, which reduce important ecosystem services for human wellbeing and the health of ecosystems.

Organizational Identification, social norms and norm transmission are investigated. Organizational identification was found to be a predictor of norm transmission. Results varied depending on whether or not transmission was to subordinates. Regression trees were used to explore the existence of subgroups/interactions.

Pro-environmental behaviors have been analyzed in the home, with little attention to other important contexts of everyday life, such as the workplace. The research reported here explored three categories of pro-environmental behavior (consumption of materials and energy, waste generation, and work-related commuting) in a public large-scale organization in Spain, with the aim of identifying the most effective policy options for a sustainable organization. Agent-based modeling was used to design a virtual simulation of the organization. Psychologically informed profiles of employees were defined using data gathered through a questionnaire, measuring knowledge, motivations, and ability. Future scenarios were developed using a participatory backcasting scenario development methodology, and policy tracks were derived. Dynamic simulations indicated that, to be effective, organizational policy should strengthen worker participation and autonomy, be sustained over time, and should combine different measures of medium intensity for behavior change, instead of isolated policies of high intensity.

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. We demonstrate and argue that systemic change pushes the model boundary by changing the language used to describe the system.We briefly review the history of modelling systemic change in coupled socio-environmental systems.A framework highlighting challenges in modelling systemic change is used to introduce papers in this thematic issue.

Increasing attention to regime shifts, critical transitions, non-marginal changes, and systemic shocks calls for the development of models that are able to reproduce or grow structural changes that occur over time periods perceived as abrupt. This paper highlights specific modelling challenges to consider when exploring coupled socio-environmental systems experiencing regime shifts. We explore these challenges in the context of four modelling approaches that have been applied to the study of regime shifts in coupled socio-environmental systems: statistical, system dynamics, equilibrium and agent-based modelling. When reviewing these modelling approaches we reflect on a set of criteria including the ability of an approach (1) to capture feedbacks between social and environmental system, (2) to represent the sources of regime shifts, (3) to incorporate complexity aspects, and (4) to deal with regime shift identification. Many of the modelling examples considered do not provide information on all these criteria, which receive a lot of attention in empirical studies of registered regime shifts. This suggests a need to develop a common modelling terminology in the domain of modelling for resilience and regime shifts. When discussing strengths and weaknesses of various modelling paradigms we conclude that a hybrid approach is likely to provide most insights into the processes and consequences of regime shifts. Challenges and frontier directions of research for designing models to study regime shifts in coupled socio-environmental systems are outlined. Models may help exploring, understanding and possibly anticipating future regime shifts.There is no one single best modelling technique to study regime shifts.Model ontology is especially important when regime shifts are involved.Integrating across modelling techniques and methods helps understand regime shifts and explore scenarios when they occur.

Context Habitat networks are often advocated as an effective measure for adaptation to climate change, while intensification of land use is a possible response to threats to food security.

This chapter compares four implementations of (Lindenberg and Steg, J Soc Issues 63(1):117–137, 2007) Goal-Framing Theory of everyday pro-environmental behaviour. Two are from different versions of CEDSS (Community Energy Demand Social Simulator, versions 3.3 and 3.4); the other two are different versions of a completely different model that also draws on Goal-Framing Theory (Rangoni and Jager, Modeling social phenomena in spatial context. Lit Verlag, Zurich, Switzerland, 2013). We find that despite some similarities in the models, the implementations are different in a number of important ways, driven in part by the case studies to which they are applied, but also by areas where Goal-Framing Theory doesn’t specify any mechanism. We anticipate that as more and more agent-based models draw on social theories, comparisons such as that herein will enable advances in both modelling and the social sciences.

Rapid economic and population growth have posed challenges to Aberdeen City and Shire in UK. Some social policies can potentially be helpful to alleviate traffic congestion and help people maintain a healthy work–life balance. In this initial model, we study the impact of flexi-time work arrangement and the construction of a new bypass on average daily commute time and CO2 emissions. We find that both flexi-time scheme and the new bypass will effectively reduce average daily commute time. Introducing a 30-min flexi-time range will reduce daily commute time by 6.5 min on average. However, further increasing flexi-time range will produce smaller saving in commute time. The new bypass will also reduce daily commute time, but only by one minute on average. As for environmental impact, introducing a 30-min flexi-time range will decrease CO2 emissions by 7 %. Not only that, it also flattens the peak emission at rush hour. The bypass, on the other hand, will increase CO2 emissions by roughly 2 %.

The CEDSS-3.4 agent-based model of domestic energy demand at community level is described. CEDSS (Community Energy Demand Social Simulator) is focused on household decisions (the model’s agents are households) to buy energy-using appliances, heating systems, and insulation, over the period from 2000 to 2049. Its empirical basis is a survey of households in Aberdeen and Aberdeenshire, Scotland, carried out in 2010, combined with publicly available data on household finances and equipment, and energy prices. CEDSS-3.4 emphasises mechanisms concerning value-strength dynamics and goal selection which influence such decisions, drawing on goal-framing theory. Results of experiments with the model are presented; the most important parameters for determining energy demand turn out to be economic (rates of change of incomes and of fuel prices), and the presence or absence of external (extra-community) influences on value-strengths. However, the value-strength dynamics used led in most runs to a single set of values dominating the population by 2049 – but even with identical parameters, different sets of values could become dominant, and which did so made a very considerable difference to demand. This resulted in bimodal distributions of outcome measures across the runs using a given parameter-setting in many cases; initial experiments indicated that changing parameters determining how far households influence each others’ values could at least reduce this tendency. Issues in the analysis of complex models with aspects unconstrained by either data or theory are discussed in the final section.

This chapter will briefly describe some common methods by which people make quantitative estimates of how well they expect empirical models to make predictions. However, the chapter’s main argument is that fit-to-data, the traditional yardstick for establishing confidence in models, is not quite the solid ground on which to build such belief some people think it is, especially for the kind of system agent-based modelling is usually applied to. Further, the chapter will show that the amount of data required to establish confidence in an arbitrary model by fit-to-data is often infeasible, unless there is some appropriate ‘big data’ available. This arbitrariness can be reduced by constraining the choice of model. In agent-based models, these constraints are introduced by their descriptiveness rather than by removing variables from consideration or making assumptions for the sake of simplicity. By comparing with neural networks, we show that agent-based models have a richer ontological structure. For agent-based models, in particular, this richness means that the ontological structure has a greater significance and yet is all too commonly taken for granted or assumed to be ‘common sense’. The chapter therefore also discusses some approaches to validating ontologies.