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In this paper, some conceptual issues are addressed in order to make sense of what string theory is supposed to tell us about spacetime. The dualities in string theory are used as a starting point for our argumentation. We explore the consequences of a standard view towards these dualities, namely that the dual descriptions represent the same physical situation. Given this view, one has to understand string theory in a manner such that what counts as physical spacetime is based only on the shared physical content-or common core-of the dual descriptions. In general such a spatiotemporal picture does not have to agree with, or be similar to, any of the ones suggested by naïve readings of the dual descriptions. However, in certain regimes or limits, one or the other of the initial dual descriptions may give a good effective description of physical spacetime., Author(s): Keizo Matsubara [sup.1] , Lars-Göran Johansson [sup.1] Author Affiliations: (Aff1) 0000 0004 1936 9457, grid.8993.b, Department of Philosophy, Uppsala University, , Box 627, 751 26, Uppsala, Sweden Introduction Philosophers [...]

• Complex temporal dynamics of human-nature interactions is one of the greatest challenges for understanding and managing social-ecological systems (SES). • Dynamical systems modeling (DSM) could provide the necessary theoretical framework for future research and help shape our understanding and management of SES. • Shifting research focus from equilibrium thinking and asymptotic dynamics to out-of-equilibrium states and transient dynamics could offer alternative explanations for observed phenomena in SES. • Combining DSM with empirical research methods and agent based modeling can help overcome some limitations of DSM, such as relying on simplified assumptions. Dynamical systems modeling (DSM) explores how a system evolves in time when its elements and the relationships between them are known. The basic idea is that the structure of a dynamical system, expressed by coupled differential or difference equations, determines attractors of the system and, in turn, its behavior. This leads to structural understanding that can provide insights into qualitative properties of real systems, including ecological and social-ecological systems (SES). DSM generally does not aim to make specific quantitative predictions or explain singular events, but to investigate consequences of different assumptions about a system's structure. SES dynamics and possible causal relationships in SES get revealed through manipulation of individual interactions and observation of their consequences. Structural understanding is therefore particularly valuable for assessing and anticipating the consequences of interventions or shocks and managing transformation toward sustainability. Taking into account social and ecological dynamics, recognizing that SES may operate on different time scales simultaneously and that achieving an attractor might not be possible or relevant, opens up possibilities for DSM setup and analysis. This also highlights the importance of assumptions and research questions for model results and calls for closer connection between modeling and empirics. Understanding the potential and limitations of DSM in SES research is important because the well-developed and established framework of DSM provides a common language and helps break down barriers to shared understanding and dialog within multidisciplinary teams. In this primer we introduce the basic concepts, methods, and possible insights from DSM. Our target audience are both beginners in DSM and modelers who use other model types, both in ecology and SES research. [ABSTRACT FROM AUTHOR]

In this article I argue for an empiricist view on laws. Some laws are fundamental in the sense that they are the result of inductive generalisations of observed regularities and at the same time in their formulation contain a new theoretical predicate. The inductive generalisations simultaneously function as implicit definitions of these new predicates. Other laws are either explicit definitions or consequences of other previously established laws. I discuss the laws of classical mechanics, relativity theory and electromagnetism in detail. Laws are necessary, whereas accidental generalisations are not. But necessity here is not a modal concept, but rather interpreted as short for the semantic predicate "... is necessarily true". Thus no modal logic is needed. The necessity attributed to law sentences is in turn interpreted as "necessary condition for the rest of the theory", which is true since fundamental laws are implicit definitions of theoretical predicates use in the theory. [ABSTRACT FROM AUTHOR]

Electromagnetism is usually understood as a theory describing how charged particles and eletromagnetic fields interact. In this paper I argue that a double ontology comprising both particles and fields is problematic. Either we should think of electromagnetism as a theory about charged particles directly interacting with each other, or as theory of fields whose local interactions are manifested as field quanta, called "particles." Froma purely theoretical point of view the choice between a particle and a field interpretation does notmattermuch when it concerns classical electromagnetism; both interpretations are possible and, as shown by Quine, there is a general method for translating a theory about one kind of objects into a theory assuming another kind of objects, provided these theories are empirically equivalent. From an empiricist point of view, however, the particle interpretation is the choice, since some particles are directly observable. Testable predictions of electromagnetism are predictions of the motion of charged bodies, in theory represented as particles, so thismust be the empiricists' choice of ontology. In quantum electrodynamics one is however forced to chose a field ontology, since a particle ontology for this theory is impossible. So called "quantum particles" are field quanta, which cannot be treated as individuals making up a domain of quantification. There is thus a tension regarding ontology between classical and quantum electrodynamics. But this tension is nothing else than the much debated measurement problem of quantum mechanics. [ABSTRACT FROM AUTHOR]

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation. [ABSTRACT FROM AUTHOR]

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different specific questions, but also vary widely in system conceptualization and complexity. With each model, certain ecological factors and mechanisms were analyzed in detail, while others were included but remained unchanged, or were excluded. Model-based causal analyses of the same system are thus inherently constrained by diverse implicit assumptions about possible determinants of causation. In developing recommendations for human action, awareness is needed of this strong context dependence of causal claims, which is often not entirely clear. Model comparisons can be supplemented by integrating findings from multiple models and confronting models with multiple observed patterns. • We reviewed how ecological models are used to study causes of complex phenomena. • Models for the same system were developed and analyzed in quite different ways. • Model-based causal claims strongly depend on context, which is often not entirely clear. • Awareness of hidden assumptions improves clarity and robustness of causal findings. [ABSTRACT FROM AUTHOR]