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Dynamic models have long been a common tool to support management of ecological and economic systems and played a prominent role in the early days of resilience research. Model applications have largely focused on policy assessment, the development of optimal management strategies, or analysis of system stability. However, modeling can serve many other purposes such as understanding system responses that emerge from complex interactions of system components, supporting participatory processes, and analyzing consequences of human behavioral complexity. The diversity of purposes, types, and applications of models offers great potential for social-ecological systems (SESs) research, but has created much confusion because modeling approaches originate from different disciplines, are based on different assumptions, focus on different levels of analysis, and use different analytical methods. This diversity makes it difficult to identify which approach is most suitable for addressing a specific question. Here, our aims are: (1) to introduce the most common types of dynamic models used in SESs research and related fields, and (2) to align these models with SESs research aims to support the selection and communication of the most suitable approach for a given study. To this end, we organize modeling approaches into a reference scheme called “modelling for social-ecological systems research” (ModSES) along two dimensions: the degree of realism and the degree of knowledge integration. These two dimensions capture key challenges of SESs research related to the need to account for context dependence and the intertwined nature of SESs as systems of humans embedded in nature across multiple scales, as well as to acknowledge different problem framings, understandings, interests, and values. We highlight the need to be aware of the potentials, limitations, and conceptual backgrounds underlying the different approaches. Critical engagement with modeling for different aims of SESs research can contribute to developing integrative understanding and action toward enhanced resilience and sustainability.

The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated and real data. Several approaches for improving the identifiability of parameters in the presence of positivity violations are reviewed. Potential responses to data sparsity include restriction of the covariate adjustment set, use of an alternative projection function to define the target parameter within a marginal structural working model, restriction of the sample, and modification of the target intervention. All of these approaches can be understood as trading off proximity to the initial target of inference for identifiability; we advocate approaching this tradeoff systematically.

Effective seismic damage simulation is an important task in improving earthquake resistance and safety of dense urban areas. There exist two significant technical challenges for realizing such a simulation: accurate prediction and realistic display. A high-fidelity structural model is proposed herein to accurately predict the seismic damage that was inflicted on a large number of buildings in an urban area via time-history analysis, with which the local damage to different building stories is also explicitly obtained. The accuracy and efficiency of the proposed model are validated by a refined finite element analysis of a typical building. A physics engine-based algorithm is also proposed that realistically displays building collapse, thus overcoming the limitations of the high-fidelity structural model. Furthermore, a visualization system integrating the proposed model and collapse simulation is developed so as to completely display the seismic damage in detail. Finally, the simulated seismic damage of a real medium-sized Chinese city is evaluated to demonstrate the advantages of the proposed techniques, which can provide critically important reference information for urban disaster prevention and mitigation.

The exponential increase in available neural data has combined with the exponential growth in computing (\"Moore's law\") to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.

This chapter discusses the uses and benefits of full (integrated) risk models. It starts with a discussion of their main characteristics, in particular highlighting their differences and benefits compared to risk register approaches. Then, the chapter provides a detailed discussion of their benefits when compared to traditional static (non‐risk) modelling approaches. Full risk models are complete models that incorporate the effects of risks and uncertainties on all variables; they are simply the counterpart (or extension) of traditional static models, but in which risk and uncertainty are incorporated. Almost any future situation contains risks or uncertainties, and to conduct decision‐support analysis that does not reflect this would not seem sensible. The chapter also discusses how the use of risk modelling allows the creation of more accurate and realistic models. Structural issues (or biases) are often overlooked, and provide a compelling argument to use risk modelling techniques and conduction simulation.

U radu se analizira poveznica između Kantova “vječnog mira” i paradigme demokratskog mira kojom se interpretiraju suvremeni međunarodni odnosi. Pri tome je naglasak stavljen na monadičku i dijadičku verziju liberalne teorije demokratskog mira objašnjene na temelju institucionalno-strukturalnog i kulturno- normativnog modela. Kritički se preispituje teorija demokratskog mira kroz uzročnu vezu između nezavisne varijable, demokratskog režima, i zavisne varijable, mira, te se analiziraju empirijska istraživanja slučajeva u kojima krize među demokratskim državama nisu rezultirale ratom, čime se dovodi u pitanje kauzalna logika same teorije. U kritičkom promišljanju teorije demokratskog mira poseban je naglasak stavljen na realističke interpretacije uzroka za koje se smatra da pridonose demokratskom miru, kao i na postojanje tzv. demokratskog rata. Niz je faktora kojima se objašnjava takvo vanjskopolitičko djelovanje demokracija i skrivanje iza teza teorije demokratskog mira, poput pozicije moći koju demokracije zauzimaju u međunarodnim odnosima, zbog čega se u obzir, uz liberalne zavisne varijable, moraju uzeti i one realističke, poput koncentracije moći, ekonomske međuzavisnosti i nacionalnih interesa.