String Diagrams for Layered Explanations

We propose a categorical framework to reason about scientific explanations: descriptions of a phenomenon meant to translate it into simpler terms, or into a context that has been already understood. Our motivating examples come from systems biology, electrical circuit theory, and concurrency. We demonstrate how three explanatory models in these seemingly diverse areas can be all understood uniformly via a graphical calculus of layered props. Layered props allow for a compact visual presentation of the same phenomenon at different levels of precision, as well as the translation between these levels. Notably, our approach allows for partial explanations, that is, for translating just one part of a diagram while keeping the rest of the diagram untouched. Furthermore, our approach paves the way for formal reasoning about counterfactual models in systems biology

Distinguishing Context Dependent Events in Quotients of Causal Stories

National audienceCausality analysis of rule-based models allows the reconstruction of the causalpaths leading to chosen events of interest. This potentially reveals emerging paths that werecompletely unknown at the time of creation of a model. However, current implementationsprovide results in the form of a collection of stories. For large models, this can amount tohundreds of story graphs to read and interpret for a single event of interest. In this work,we hence develop a method to fold a collection of stories into a single quotient graph.The main challenge is to find a trade-off in the partitioning of story events which willmaximize compactness without loosing important details about information propagation inthe model. The partitioning criterion proposed is relevant context, the context from anevent’s past which remains useful in its future. Each step of the method is illustrated on atoy rule-based model. This work is part of a longer term objective to automatically extractbiological pathways from rule-based models

Causality Analysis and Fault Ascription in Component-Based Systems

This article introduces a general framework for fault ascription, which consists in identifying, within a multi-component system, the components whose faulty behavior has caused the failure of said system. Our framework uses configuration structures as a general semantical model to handle truly concurrent executions, partial and distributed observations in a uniform way. We define a set of expected properties for counterfactual analysis, and present a refined analysis that conforms to our requirements. This contrasts with current practice of evaluating definitions of counterfactual causality a posteriori on a set of toy examples. As an early study of the behavior of our analysis under abstraction we establish its monotony under refinement.Cet article introduit un cadre général pour l’attribution de fautes qui consiste à identifier, dans un système à composants, les composants dont le comportement incorrect a causé le dysfonctionnement du système. Nous définissons un ensemble de propriétés attendues de l’analyse contrefactuelle, et nous présentons une analyse raffinée qui satisfait ces besoins. Ceci contraste avec la pratique courante d’évaluer les définitions de causalité contrefactuelle a posteriori sur un ensemble d’exemples jouets. Nous établissons la monotonie de notre analyse sous différentes notions de raffinement

Fiscal discretion, growth and output volatility in new EU member countries

This paper analyses the link between discretionary fiscal policy and output growth in ten CEE countries. Three aspects are considered: cyclical pattern in the fiscal discretion, contributions to GDP growth, and the link between policy aggressiveness and output volatility. Fiscal discretion is estimated from quarterly data over 2000q1 to 2014q1 using a SVAR model in GDP, net taxes and spending. Decomposition of the GDP suggests that fiscal discretion induced rather small contributions to economic growth. Correlation between fiscal policy aggressiveness and output volatility is weak to moderate positive, notwithstanding whether spending or balance is used as the underlying indicator. The cyclical pattern has identified a mix of pro- and counter-cyclical episodes in the years before the crisis, implying that governments might not have consistently used the good times to create buffers. Overall, this evidence supports the view that policy makers in the CEE countries should mainly rely on rule-based fiscal policy rather than (aggressive) fiscal discretion

Systematic Biases in Weak Lensing Cosmology with the Dark Energy Survey

PhD thesis submitted to the University of Manchester, School of Physics and Astronomy, August 2017. Abstract: This thesis presents a practical guide to applying shear measurements as a cosmological tool. We first present one of two science-ready galaxy shape catalogues from Year 1 of the Dark Energy Survey (DES Y1), which covers 1500 square degrees in four bands griz, with a median redshift of 0.59. We describe the shape measurement process implemented by the DES Y1 im3shape catalogue, which contains 21.9M high-quality r-band bulge/disc fits. In Chapter 3 a new suite of image simulations, referred to as hoopoe, are presented. The hoopoe dataset is tailored to DES Y1 and includes realistic blending, spatial masks and variation in the point spread function. We derive shear corrections, which we show are robust to changes in calibration method, galaxy binning and variance within the simulated dataset. Sources of systematic uncertainty in the simulation-based shear calibration are discussed, leading to a final estimate of the 1 sigma uncertainties in the residual multiplicative bias after calibration of 0.025. Chapter 4 describes an extension of the analysis on the hoopoe simulations into a detailed investigation of the impact of galaxy neighbours on shape measurement and shear cosmology. Four mechanisms by which neighbours can have a non-negligible influence on shear measurement are identified. These effects, if ignored, would contribute a net multiplicative bias of m ~ 0.03 - 0.09 in DES Y1, though the precise impact will depend on both the measurement code and the selection cuts applied. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude S8 = sigma_8 (Omega_m /0.3)^0.5 by 1.5 sigma towards low values. Finally, we use the hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the cosmological impact to be subdominant to statistical error at the current level of precision. Another major uncertainty in shear cosmology is the accuracy of our ensemble redshift distributions. Chapter 5 presents a numerical investigation into the combined constraining power of cosmic shear, galaxy clustering and their cross-correlation in DES Y1, and the potential for internal calibration of redshift errors. Introducing a moderate uniform bias into the redshift distributions used to model the weak lensing (WL) galaxies is shown to produce a > 2 sigma bias in S8. We demonstrate that this cosmological bias can be eliminated by marginalising over redshift error nuisance parameters. Strikingly, the cosmological constraint of the combined dataset is largely undiminished by the loss of prior information on the WL distributions. We demonstrate that this implicit self-calibration is the result of complementary degeneracy directions in the combined data. In Chapter 6 we present the preliminary results of an investigation into galaxy intrinsic alignments. Using the DES Y1 data, we show a clear dependence in alignment amplitude on galaxy type, in agreement with previous results. We subject these findings to a series of initial robustness tests. We conclude with a short overview of the work presented, and discuss prospects for the future

Investing in Kids: Early Childhood Programs and Local Economic Development

Early childhood programs, if designed correctly, pay big economic dividends down the road because they increase the skills of their participants. And since many of those participants will remain in the same state or local area as adults, the local economy benefits: more persons with better skills attract business, which provides more and better jobs for the local economy. Bartik measures ratios of local economic development benefits to costs for both early childhood education and business incentives. He shows that early childhood programs and the best-designed business incentives can provide local benefits that significantly exceed costs. Given this, states and municipalities would do well to adopt economic development strategies that balance high-quality business incentives with early childhood programs.https://research.upjohn.org/up_press/1223/thumbnail.jp