School of Historical and Philosophical Studies - Research Publications
Now showing items 1-12 of 1600
Bimal k. Matilal's philosophy: Language, realism, dharma, and ineffability
(Peoples' Friendship University of Russia, 2021-01-01)
The article considers the theoretical and practical consequences of the so-called "soft" version of epistemological realism in Bimal K. Matilal's philosophical project. The author offers an analytical view on Matilal's philosophy, which helps to understand it in a broader prospective, comparing his arguments on perception and objectivity with contemporary arguments in Western analytical philosophy; in fact, it is possible to view Matilal not only as the proponent of revised Nyāya-Vaiśeṣika approach, but also as the follower of realistic view on language, following L. Wittgenstein, W. Quine, H. Putnam and M. Dummett. Despite the fact that such interpretation may sound diverse or multivocal, it nevertheless helps to better understand both lineages of argumentation: the critical review of the impossibility of private language can be compared in both Western and Indian philosophical discourses, which leads into the domain of social epistemology. The second part of the article discusses the ethical arguments on the vulnerability of moral virtues, and the place of Dharma as a term in moral philosophy. Poetical and metaphorical language appears to be a fruitful strategy to discover the ineffable - and also via negativa and catuṣkoṭi - which is shown by Matilal on the example of the unacceptability of lying. The ethical ineffability and its interconnection with Matilal's commentaries on practical wisdom play the crucial part in the interpretations of Dharmaśāstra texts.
[REVIEW] Studying Japan: Handbook of Research Designs, Fieldwork and Methods
(The Japan Foundation, Sydney, 2021-09-01)
A review of "Studying Japan: Handbook of Research Designs, Fieldwork and Methods" edited by Nora Kottmann and Cornelia Reiher, Nomos Verlagsgesellschaft (Baden-Baden, Germany), 2020.
Introduction to the 'Beyond Japanese Studies' Special Issue
(The Japan Foundation, Sydney, 2021-09-01)
Introduction to New Voices in Japanese Studies, Volume 13 [Special Issue] Beyond Japanese Studies: Challenges, Opportunities and COVID-19
Mathematically aggregating experts' predictions of possible futures.
(Public Library of Science (PLoS), 2021)
Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.