Month: January 2024

Computational philosophy: reflections on the PolyGraphs project

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Abstract

In this paper, we situate our computational approach to philosophy relative to other digital humanities and computational social science practices, based on reflections stemming from our research on the PolyGraphs project in social epistemology. We begin by describing PolyGraphs. An interdisciplinary project funded by the Academies (BA, RS, and RAEng) and the Leverhulme Trust, it uses philosophical simulations (Mayo-Wilson and Zollman, 2021) to study how ignorance prevails in networks of inquiring rational agents. We deploy models developed in economics (Bala and Goyal, 1998), and refined in philosophy (O’Connor and Weatherall, 2018; Zollman, 2007), to simulate communities of agents engaged in inquiry, who generate evidence relevant to the topic of their investigation and share it with their neighbors, updating their beliefs on the evidence available to them. We report some novel results surrounding the prevalence of ignorance in such networks. In the second part of the paper, we compare our own to other related academic practices. We begin by noting that, in digital humanities projects of certain types, the computational component does not appear to directly support the humanities research itself; rather, the digital and the humanities are simply grafted together, not fully intertwined and integrated. PolyGraphs is notably different: the computational work directly supports the investigation of the primary research questions, which themselves belong decidedly within the humanities in general, and philosophy in particular. This suggests an affinity with certain projects in the computational social sciences. But despite these real similarities, there are differences once again: the computational philosophy we practice aims not so much at description and prediction as at answering the normative and interpretive questions that are distinctive of humanities research.

“Things fall apart, the center cannot hold”: fractionalized and polarized party systems in Western democracies

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Abstract

Jean Blondel made many lasting contributions toward comparative politics, not least in his classification of party systems in Western democracies. Yet during the 5 decades since Blondel’s original contribution, party competition has been transformed by multiple developments, including changes in the grassroots electorate, as intermediary organizations connecting citizens and the state, and at the apex in legislatures and government. Does Blondel’s typology of party systems remain relevant today—or does it require substantial revision? And, does party system fragmentation predict ideological polarization? Part I sets out the theoretical framework. Part II compares trends from 1960 to 2020 in party system fragmentation in a wide range of democracies, measured by the effective number of parties in the electorate and in parliament. Not surprisingly, the effective number of electoral parties (ENEP) has generally grown in each country across Western democracies. This does not imply, however, that party systems are necessarily more polarized ideologically. Part III examines polarization in party systems across Western democracies, measured by standard deviations around the mean of several ideological values and issue positions in each country. The findings suggest that party system fractionalization and polarization should be treated as two distinct and unrelated dimensions of party competition. The conclusion reflects on the broader implications of the findings for understanding party polarization and threats of backsliding in democratic states.

“Things fall apart, the center cannot hold”: fractionalized and polarized party systems in Western democracies

Latest Results

Abstract

Jean Blondel made many lasting contributions toward comparative politics, not least in his classification of party systems in Western democracies. Yet during the 5 decades since Blondel’s original contribution, party competition has been transformed by multiple developments, including changes in the grassroots electorate, as intermediary organizations connecting citizens and the state, and at the apex in legislatures and government. Does Blondel’s typology of party systems remain relevant today—or does it require substantial revision? And, does party system fragmentation predict ideological polarization? Part I sets out the theoretical framework. Part II compares trends from 1960 to 2020 in party system fragmentation in a wide range of democracies, measured by the effective number of parties in the electorate and in parliament. Not surprisingly, the effective number of electoral parties (ENEP) has generally grown in each country across Western democracies. This does not imply, however, that party systems are necessarily more polarized ideologically. Part III examines polarization in party systems across Western democracies, measured by standard deviations around the mean of several ideological values and issue positions in each country. The findings suggest that party system fractionalization and polarization should be treated as two distinct and unrelated dimensions of party competition. The conclusion reflects on the broader implications of the findings for understanding party polarization and threats of backsliding in democratic states.

Probabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts

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Abstract

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.

A model output statistic-based probabilistic approach for statistical downscaling of temperature

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Abstract

Large-scale temperature projections need to be downscaled to river basin scale to facilitate a regional scale climate change impact assessment. A multi-stage statistical downscaling procedure is proposed in the current study, the first stage captures the climate change signals from the simulations of general circulation models (GCMs) by spatially downscaling the monthly GCM simulations. The second stage disaggregates the spatially downscaled monthly series to a daily scale by a weather generator which adds the regional climatic information into the spatially downscaled time series. A distribution-free post-processing shuffling is finally performed to rebuild the intervariable correlation of downscaled temperatures with regional rainfall which is important in reliable projection of streamflow. The procedure is validated by downscaling the maximum and minimum temperatures over the Bharathapuzha catchment in India for the period 1951–2005. The downscaled series of temperature shows Normalised Root Mean Square Error (NRMSE) less than 0.09 and correlation coefficients greater than 0.4. The ability of the procedure in capturing non-stationarity in the climate is also analysed by its performance in different phases of ENSO.

Landscape controls on fuel moisture variability in fire-prone heathland and peatland landscapes

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Abstract

Background

Cross-landscape fuel moisture content is highly variable but not considered in existing fire danger assessments. Capturing fuel moisture complexity and its associated controls is critical for understanding wildfire behavior and danger in emerging fire-prone environments that are influenced by local heterogeneity. This is particularly true for temperate heathland and peatland landscapes that exhibit spatial differences in the vulnerability of their globally important carbon stores to wildfire. Here we quantified the range of variability in the live and dead fuel moisture of Calluna vulgaris across a temperate fire-prone landscape through an intensive fuel moisture sampling campaign conducted in the North Yorkshire Moors, UK. We also evaluated the landscape (soil texture, canopy age, aspect, and slope) and micrometeorological (temperature, relative humidity, vapor pressure deficit, and windspeed) drivers of landscape fuel moisture variability for temperate heathlands and peatlands for the first time.

Results

We observed high cross-landscape fuel moisture variation, which created a spatial discontinuity in the availability of live fuels for wildfire spread (fuel moisture < 65%) and vulnerability of the organic layer to smoldering combustion (fuel moisture < 250%). This heterogeneity was most important in spring, which is also the peak wildfire season in these temperate ecosystems. Landscape and micrometeorological factors explained up to 72% of spatial fuel moisture variation and were season- and fuel-layer-dependent. Landscape factors predominantly controlled spatial fuel moisture content beyond modifying local micrometeorology. Accounting for direct landscape–fuel moisture relationships could improve fuel moisture estimates, as existing estimates derived solely from micrometeorological observations will exclude the underlying influence of landscape characteristics. We hypothesize that differences in soil texture, canopy age, and aspect play important roles across the fuel layers examined, with the main differences in processes arising between live, dead, and surface/ground fuels. We also highlight the critical role of fuel phenology in assessing landscape fuel moisture variations in temperate environments.

Conclusions

Understanding the mechanisms driving fuel moisture variability opens opportunities to develop locally robust fuel models for input into wildfire danger rating systems, adding versatility to wildfire danger assessments as a management tool.