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Many developing countries are looking to scale-up what works through major systems strengthening investments. With leadership, conviction and commitment, systems thinking can facilitate and accelerate the strengthening of systems to more effectively deliver interventions to those in need and be better able to improve health in an equitable way. Systems thinking is not a panacea. Its application does not mean that resolving problems and weaknesses will come easily or naturally or without overcoming the inertia of the established way of doing things. But it will identify, with more precision, where some of the true blockages and challenges lie. It will help to: 1) explore these problems from a systems perspective; 2) show potentials of solutions that work across sub-systems; 3) promote dynamic networks of diverse stakeholders; 4) inspire learning; and 5) foster more system-wide planning, evaluation and research. And it will increase the likelihood that health system strengthening investments and interventions will be effective. The more often and more comprehensively the actors and components of the system can talk to each other from within a common framework --communicating, sharing, problem-solving - the better chance any initiative to strengthen health systems has. Real progress will undoubtedly require time, significant change, and momentum to build capacity across the system. However, the change is necessary - and needed now. This report therefore speaks to health system stewards, researchers and funders and maps out a set of strategies and activities to harness these approaches, to link them to these emerging opportunities and to assist systems thinking to become the norm in design and evaluation of interventions in health systems. But, the final message is to the funders of health system strengthening and health systems research who will need to recognize the potential in these opportunities, be prepared to take risks in investing in such innovations, and play an active role in both driving and following this agenda towards more systemic and evidence-informed health development.

1. World-wide declines in pollinators, including bumblebees, are attributed to a multitude of Stressors such as habitat loss, resource availability, emerging viruses and parasites, exposure to pesticides, and climate change, operating at various spatial and temporal scales. Disentangling individual and interacting effects of these Stressors, and understanding their impact at the individual, colony and population level are a challenge for systems ecology. Empirical testing of all combinations and contexts is not feasible. A mechanistic multilevel systems model (individual-colony-population-community) is required to explore resilience mechanisms of populations and communities under stress. 2. We present a model which can simulate the growth, behaviour and survival of six UK bumblebee species living in any mapped landscape. Bumble-BEEHAVE simulates, in an agent-based approach, the colony development of bumblebees in a realistic landscape to study how multiple Stressors affect bee numbers and population dynamics. We provide extensive documentation, including sensitivity analysis and validation, based on data from literature. The model is freely available, has flexible settings and includes a user manual to ensure it can be used by researchers, farmers, policy-makers, NGOs or other interested parties. 3. Model outcomes compare well with empirical data for individual foraging behaviour, colony growth and reproduction, and estimated nest densities. 4. Simulating the impact of reproductive depression caused by pesticide exposure shows that the complex feedback mechanisms captured in this model predict higher colony resilience to stress than suggested by a previous, simpler model. 5. Synthesis and applications. The Bumble-BEEHAVE model represents a significant step towards predicting bumblebee population dynamics in a spatially explicit way. It enables researchers to understand the individual and interacting effects of the multiple Stressors affecting bumblebee survival and the feedback mechanisms that may buffer a colony against environmental stress, or indeed lead to spiralling colony collapse. The model can be used to aid the design of field experiments, for risk assessments, to inform conservation and farming decisions and for assigning bespoke management recommendations at a landscape scale.

Purpose A crowd of pedestrians is a complex system in which individuals exhibit conflicting behavioural mechanisms leading to self-organisation phenomena. Computer models for the simulation of crowds represent a consolidated type of application, employed on a day-to-day basis to support designers and decision makers. Most state of the art models, however, generally do not consider the explicit representation of pedestrians aggregations (groups) and their implications on the overall system dynamics. This work is aimed at discussing a research effort systematically exploring the potential implication of the presence of groups of pedestrians in different situations (e.g. changing density, spatial configurations of the environment). Methods The paper describes an agent-based model encompassing both traditional individual motivations (i.e. tendency to stay away from other pedestrians while moving towards the goal) and an adaptive mechanism representing the influence of group presence in the simulated population. The mechanism is designed to preserve the cohesion of specific types of groups (e.g. families and friends) even in high density and turbulent situations. The model is tested in simplified scenarios to evaluate the implications of modelling choices and the presence of groups. Results The model produces results in tune with available evidences from the literature, both from the perspective of pedestrian flows and space utilisation, in scenarios not comprising groups; when groups are present, the model is able to preserve their cohesion even in challenging situations (i.e. high density, presence of a counterflow), and it produces interesting results in high density situations that call for further observations and experiments to gather empirical data. Conclusions The introduced adaptive model for group cohesion is effective in qualitatively reproducing group related phenomena and it stimulates further research efforts aimed at gathering empirical evidences, on one hand, and modelling efforts aimed at reproducing additional related phenomena (e.g. leader-follower movement patterns).

Invasive mammals are a major threat to biodiversity. Understanding how their distributions and abundance could be affected by different temporal and spatial control strategies is fundamental for planning effective management programs. We developed a spatially explicit, agent‐based model to test the impacts of different spatiotemporal management strategies on a pest population. As a case study, we used the common brushtail possum (Trichosurus vulpecula) population in the Cape‐to‐City treatment area in Hawke's Bay, New Zealand. We found striking differences in the effectiveness of different spatial control strategies – a well designed spatial control strategy could be up to twice as cost effective as poorly designed ones. The optimal spatial control strategy may depend on the total control effort. At low control effort, habitat‐targeted control was more effective; at high control effort, homogeneously distributed control was more effective. Immigration rather than in situ breeding is likely to initiate the population recovery in treated areas after an initial knockdown. Therefore, increasing the size of treatment areas and maximizing the use of natural barriers to immigration could prolong treatment persistence. Synthesis and applications. We have demonstrated how a spatially explicit, agent‐based model can be used to identify key criteria for a control strategy of open pest populations. The integration of available information on pest habitat use, population dynamics and actual levels of control allowed us to assess the deployment of control sites and assisted the choice of spatial control strategies. Important roles for this type of model are to help predict hotspots of mammalian pest activities, to suggest the most effective control strategy and to identify important parameters and data for improving predictions. Ultimately, further integration of spatial and temporal analyses is critical for updating and optimizing management strategies. We have demonstrated how a spatially explicit, agent‐based model can be used to identify key criteria for a control strategy of open pest populations. The integration of available information on pest habitat use, population dynamics and actual levels of control allowed us to assess the deployment of control sites and assisted the choice of spatial control strategies. Important roles for this type of model are to help predict hotspots of mammalian pest activities, to suggest the most effective control strategy and to identify important parameters and data for improving predictions. Ultimately, further integration of spatial and temporal analyses is critical for updating and optimizing management strategies.

Despite the growing interest in basic income (BI) in recent years, the existing research has mainly focused on its impact on household finances. However, changes in household behavior may affect the actions of other decision makers, such as businesses and governments, leading to unanticipated outcomes. Therefore, any analysis of BI must use a model with multilayered feedback from the actions of individual decision makers. To actualize such a model, household budgets, firms, and other entities must autonomously determine production levels, prices, and other factors, thereby encompassing a complete circulation of funds. This study constructs a macroeconomic model using agent-based modeling as a basic framework to achieve these goals, and it analyzes the emergent behaviors generated by BI and the labor supply in the economic system. The results show that although BI brings about more equitable consumption by households, it also creates a unique phenomenon wherein Gross Domestic Product increases but economic activity in terms of capital investment stagnates. Upon examining the impact of BI, the results of this study present the need to examine the multilayered feedback influencing mutual decision makers, which arises from the behavioral changes of individual decision makers caused by BI.

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect.

1. Increasing land abandonment in many areas of the world presents an opportunity for ecosystem recovery, which is often driven by seed dispersal by vertebrate frugivores. However, we are far from understanding the most effective way of using common management actions (i.e. planting fruiting trees) to stimulate animal seed dispersal and thus the restoration of human-altered abandoned habitats. 2. To investigate how to stimulate animal seed dispersal, we combined long-term field data with individual-based, spatially explicit simulation models. We used our approach to assess the effectiveness of contrasting Iberian pear Pyrus bourgaeana planting strategies in enhancing restoration of abandoned lands through seed dispersal by red foxes Vulpes vulpes and Eurasian badgers Meles meles in the Doñana World Biosphere Reserve (South West Spain). 3. Our simulation results indicate that planting trees in an aggregated fashion is less efficient in terms of seed arrival than planting them regularly or randomly. For aggregated planted trees, the increase in the area of the oldfield that received seeds was only 7%-9% compared to the baseline scenario of no intervention, whereas for regularly distributed planted trees the increment was up to 40%. 4. Doubling the number of planted P. bourgaeana trees appeared cost-effective for regular and random tree distributions, but not for the aggregated one. For example, while doubling the number of trees planted regularly leads up to 12% increase in the number of seeds arriving into the oldfield, no increment on the number of arrived seeds was detected when trees were planted aggregately. 5. Synthesis and applications. Choosing the spatial distribution and density of planted trees in abandoned lands depends on a number of ecological and socio-economical factors. Given our results, the strong seed dispersal limitation of the target tree population and that our study site was fully protected for conservation, planting Pyrus bourgaeana trees regularly appeared to be the most efficient strategy to enhance seed arrival into the target oldfield. Combining long-term field data with individual-based, spatially explicit simulation models have the potential to guide local restoration efforts in diverse human-altered habitats and thus bridge the existing gap between basic and applied research on animal seed dispersal.

The planning of transport infrastructure must consider both the short-term capacity demand and the equipment’s maintenance needs, as well as the long-term evolution of the transportation system. In the freight transportation system, long-term evolutions stem from the individual behaviours of several stakeholders and their dynamic relationships. Their actions may change how the infrastructure is used, giving rise to bottlenecks, or novel development opportunities. However, existing project planning techniques tend to make static assumptions on these agents’ behaviours when predicting future developments. Furthermore, there is a need for models able to simulate short-term developments in transport operations alongside long-term system evolutions. This work aims to contribute towards solving these gaps, particularly in what concerns the consideration of stakeholder adaptation strategies when confronted with infrastructure alterations. We propose a novel a dual-approach Agent-based Modelling concept, based on Hybrid Modelling methodologies. This framework is comprised of two distinct modules: the micro module reproduces short-term freight transport operations in the physical network, while the macro module captures the stakeholders’ decision-making in the long-term. These modules continuously communicate with one another, updating critical information regarding system conditions throughout the simulation period. The developed modelling concept was assessed through a set of simulation trials, which revealed its sensitivity to different scenarios of railway project implementation and demonstrated its potential for capturing the possible outcomes of distinct infrastructure projects, stimulating the responses of stakeholders to the new market conditions, and identifying network bottlenecks.

Alpine treeline ecotones, when viewed up close, display considerable variation in spatial patterns, which have been associated with different responses to climate change. Two important dimensions of treeline-ecotone spatial patterns are the abruptness of the change in tree height (“abrupt” vs. “gradual”) and the change in canopy cover (“discrete” vs. “diffuse”) when moving from closed forest to treeless alpine vegetation. These dimensions are suited to classify treeline ecotones into different types of patterns, but this is typically done intuitively without explicitly stated criteria, and patterns are not quantified. Consistent, robust metrics allowing comparisons between sites are lacking. We suggest several metrics to quantify abruptness and discreteness of treeline ecotones and describe how to derive these metrics from point-pattern data of tree positions and sizes, and from high-resolution treecover data. We developed these based on field data from the Spanish Pyrenees and an extensive dataset of treeline patterns created by the individual-based Spatial Treeline-Ecotone Model (STEM). We quantified the abruptness of a treeline by the largest change in canopy height, determined in 5-m bands, between the top of the ecotone (i.e., alpine vegetation) and the first band where canopy height exceeds 3 m. We quantified the discreteness by the steepness of a logistic function fitted to tree cover. Band widths and cut-off values were optimised for our data. Although they can be flexibly adjusted to specific case studies, standard settings are recommended to assure comparability. Our results indicate that the “discreteness” metric provides a satisfactory quantification of this pattern dimension within the dataset used here, whereas the “abruptness” pattern dimension turned out to be more difficult to capture. The metrics developed here may provide field researchers with a tool to compare their field sites in a standardised way, and potentially promote synthesis on treeline data and dynamics on a global scale.

Versatile approaches for urban modelling need to simultaneously consider the physical characteristics of a city (urban form) and urban function as a manifestation of economically, socially, and culturally motivated human activities. Exposure and risk assessment studies concerning urban heat or air pollution can greatly benefit from modelling that dynamically connects physical and socio‐economic urban spaces and represents humans as active components of the urban system (e.g., agent‐based modelling). The spatio‐temporal complexity and variability of urban form, function, human behaviour, and micro‐climate put high demands on input data of such models. We present a general methodology for creating a suite of data connecting and harmonising available information for high‐resolution modelling. This is demonstrated for London, UK. The multi‐scale database covers urban neighbourhoods (at 500 m grid‐cell resolution), localised microenvironments of activity, buildings, and extends down to the scale of individuals. Data include neighbourhood land‐cover fractions that provide boundary conditions for urban land‐surface models and building typologies generated by assessing building function, form, and materials (via building age) that are suitable for building energy modelling. Urban populations (residential, workplace) and demographic composition of households in building typologies are derived. Temporal profiles (10 min resolution) of human activities by age cohort, household size, day type, work patterns, and season derived from time‐use survey data are mapped to various socio‐economic microenvironments, alongside assessments of activity‐dependent electrical energy consumption and human metabolic output. A transport database provides available travel options (1 min resolution) between London neighbourhoods by mode, making use of public transport schedules, road networks, and traffic speeds. A new methodology and comprehensive database is developed (Multi‐scale harmonisation Across Physical and Socio‐Economic Characteristics of a City region, MAPSECC) that connects physical characteristics of a city (building morphology and materials, land‐surface cover) with socio‐economic aspects (building function, microenvironments of activity, urban transport infrastructure, residential and workplace populations, human activities) and is demonstrated for London, UK.