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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.

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.

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).

Under intensifying climate change, demand management policies are likely to become increasingly important means for increasing the resilience of surface water systems during scarcity periods. Yet, there is little knowledge focused on how these policies might function in the face of climate change. In this sense, understanding the likely effectiveness of demand management policies under climate change is critical for ensuring sustainable and equitable long‐term planning of water systems. Here, we build an agent‐based model to evaluate the reservoir storage in the cities of Denver (Colorado), Las Vegas (Nevada), and Phoenix (Arizona) under climate change and demand management scenarios. The results indicate that in some scenarios, demand management policies can counteract the negative impacts of climate change. In others, however, the demand management policies have minimal impact, suggesting that demand management alone is not always enough to counteract climate change. Overall, the model can be used to test other demand management policies, while also serving as a basis for improved understanding of the interactions between climate change, water conservation attitudes, and demand management policies within the Colorado River Basin.

The first book to address the underlying premises of systems integration and how to exposit them into a practical and productive manner, this book prepares systems managers and systems engineers to consider their decisions in light of systems integration metrics. The book addresses two questions: Is there a way to express the interplay of human actions and the result of system interactions of a product with its environment, and are there methods that combine to improve the integration of systems? The systems integration theory and integration frameworks proposed in the book tie General Systems Theory with practice.

While system engineering concepts have been broadly applied in healthcare systems, most improvements have focused on a specific segment or unit of the delivery system. Using a system engineering perspective, this handbook offers theoretical foundations, methodologies, and case studies in each main sector of the system. It explores how system engineering methodologies and their applications could improve patient outcomes and cost effectiveness and offers a comprehensive description of the healthcare delivery system from the macro level (hospitals) to the micro level (operating room).

Purpose Commercial aviation is feasible thanks to the complex socio-technical air transportation system, which involves interactions between human operators, technical systems, and procedures. In view of the expected growth in commercial aviation, significant changes in this socio-technical system are in development both in the USA and Europe. Such a complex socio-technical system may generate various types of emergent behavior, which may range from simple emergence, through weak emergence, up to strong emergence. The purpose of this paper is to demonstrate that agent-based modeling and simulation allows identifying changed and novel rare emergent behavior in this complex socio-technical system. Methods An agent based model of a specific operation at an airport has been developed. The specific operation considered is the controlled crossing by a taxiing aircraft of a runway that is in use for controlled departures. The agent-based model includes all relevant human and technical agents, such as the aircraft, the pilots, the controllers and the decision support systems involved. This agent-based model is used to conduct rare event Monte Carlo (MC) simulations. Results The MC simulation results obtained confirm that agent based modeling and simulation of a socio-technical air transportation system allows to identify rare emergent behavior that was not identified through earlier, non-agent-based simulations, including human-in-the-loop simulations of the same operation. A typical example of such emergent behavior is the finding that alerting systems do not really reduce the safety risk. Conclusions Agent based MC simulations of commercial aviation operations has been demonstrated as a viable way to be evaluated regarding rare emergent behaviour. This rare emergent behaviour could not have been found through the more traditional simulation approaches.

Coastal cities face increasing flood hazards due to climate change. Physical infrastructures, such as levees, are commonly used to reduce flood hazards. To effectively manage flood risks, it is important to understand the degree to which physical infrastructures change both hazard and exposure. For example, many studies suggest that levee construction causes an overall increase in risk because levees promote exposure growth to a greater degree than they reduce flood hazards. Although this so‐called “levee effect” is widely studied, there are knowledge gaps surrounding how uncertainties related to levee construction and flood risk translate into the occurrence and strength of the levee effect in coastal communities. Here, we use agent‐based modeling to simulate the influence of flood risk information pathways on the dynamics around the levee effect, first under idealized conditions and then within a real‐world coastal case study. We finally conduct a global sensitivity analysis to identify which model factors contribute to the strength of the levee effect. We find that, under idealized conditions, the strength of the levee effect is highly sensitive to economic (e.g., population growth) and engineering (e.g., levee failure) factors. However, under more complex coastal conditions, factors related to household behavior (e.g., risk aversion) are more influential on the strength of the levee effect. Overall, our findings emphasize the importance of capturing the interactions and uncertainties among multiple behavioral, economic, and engineering factors when measuring flood risk in coastal communities.

Agent‐based models (ABMs) are increasingly recognized as valuable tools in applied ecology for simulating species behavior, ecological interactions, and responses to management. However, their adoption in conservation and policy contexts has been limited by a reliance on simplified representations and a lack of integration with empirical data. This paper presents a structured, data‐informed framework for developing applied ABMs using high‐resolution spatial, behavioral, and environmental datasets. By incorporating telemetry data, remote sensing products, and site‐level ecological monitoring, the framework enables realistic simulations of ecological systems that can be used to virtually test management strategies and policy interventions. These models support real‐time scenario testing, guide field data collection by identifying knowledge gaps, and facilitate transparent communication with stakeholders. We demonstrate the utility of this framework using a published case study on badger movement and bovine tuberculosis risk in a disturbance‐driven landscape, showing how it reveals emergent behavioral patterns with implications for disease management. By formalizing a repeatable protocol for model development, validation, and stakeholder engagement, this research enhances the accessibility and applicability of ABMs in conservation planning, biodiversity monitoring, and human–wildlife conflict mitigation. The framework supports evidence‐based decision‐making while promoting transparency, adaptability, and cross‐sector collaboration.

An agent‐based model of farmers' crop and best management practice (BMP) decisions is developed and linked to a hydrologic‐agronomic model of a watershed, to examine farmer behavior, and the attendant effects on stream nitrate load, under the influence of markets for conventional crops, carbon allowances, and a second‐generation biofuel crop. The agent‐based approach introduces interactions among farmers about new technologies and market opportunities, and includes the updating of forecast expectations and uncertainties using Bayesian inference. The model is applied to a semi‐hypothetical example case of farmers in the Salt Creek Watershed in Central Illinois, and a sensitivity analysis is performed to effect a first‐order assessment of the plausibility of the results. The results show that the most influential factors affecting farmers' decisions are crop prices, production costs, and yields. The results also show that different farmer behavioral profiles can lead to different predictions of farmer decisions. The farmers who are predicted to be more likely to adopt new practices are those who interact more with other farmers, are less risk averse, quick to adjust their expectations, and slow to reduce their forecast confidence. The decisions of farmers have direct water quality consequences, especially those pertaining to the adoption of the second‐generation biofuel crop, which are estimated to lead to reductions in stream nitrate load. The results, though empirically untested, appear plausible and consistent with general farmer behavior. The results demonstrate the usefulness of the coupled agent‐based and hydrologic‐agronomic models for normative research on watershed management on the water‐energy nexus. Key Points An agent‐based model of farmer decision‐making has been developed The agent‐based model incorporates agent learning and interactions Different assumptions of agent behaviors lead to different outcomes