Significant Digits: Responsible Use of Quantitative Information

We live in an age when good policies are assumed to be evidence-based. And that evidential base is assumed to be at its best when expressed in numbers. The digital information may be derived from quantitative data organised in statistics, or from qualitative data organised in indicators. Either way, evidence in digital form provides the accepted foundation of policy arguments over a very broad range of issues. In the policy realm there are frequent debates over particular policy issues and their associated evidence. But only rarely is the nature of the evidence called into question. Such a faith in numbers can be dangerous. Policies in economic and financial policy, based on numbers whose significance was less than assumed, recently turned out to be quite disastrously wrong. Other examples can easily be cited. The decades-long period of blaming dietary fats for heart disease, rather than sugar, is a notable recent case. We are concerned here with the systemic problem: whether we are regularly placing too much of an evidentiary burden on quantitative sciences whose strength and maturity are inherently inadequate. The harm that has been done to those sciences, as well as to the policy process, should be recognised. Only in that way can future errors be avoided. The present report gives an account of the conversations held at a workshop organised by the JRC: Significant Digits, Responsible Use of Information held in June 2015 in Brussels, offering recommendations for the operation at the JRC.JRC.DDG.01-Econometrics and applied statistic

Significant Digits: Responsible use of quantitative information

We live in an age when good policies are assumed to be evidence-based. And that evidential base is assumed to be at its best when expressed in numbers. The digital information may be derived from quantitative data organised in statistics, or from qualitative data organised in indicators. Either way, evidence in digital form provides the accepted foundation of policy arguments over a very broad range of issues. In the policy realm there are frequent debates over particular policy issues and their associated evidence. But only rarely is the nature of the evidence called into question. Such a faith in numbers can be dangerous. Policies in economic and financial policy, based on numbers whose significance was less than assumed, recently turned out to be quite disastrously wrong. Other examples can easily be cited. The decades-long period of blaming dietary fats for heart disease, rather than sugar, is a notable recent case. We are concerned here with the systemic problem: whether we are regularly placing too much of an evidentiary burden on quantitative sciences whose strength and maturity are inherently inadequate. The harm that has been done to those sciences, as well as to the policy process, should be recognised. Only in that way can future errors be avoided.JRC.DDG.01-Econometrics and applied statistic

Black gold: trustworthiness in artistic research (seen from the sidelines of arts and health)

Rigour plays a central role in contemporary research culture. But how appropriate a concept is it to think, perform, and make judgements with on what is trustworthy and excellent in artistic research and its neighbouring field of arts and health? The historical meanings of rigour suggest severity and rigidity: straight lines, austere habits, privations. As a word, rigour has a mixed ancestry – French, Latin, Middle English. Some of its earliest uses coincide with a feudal system of government in Europe, with rigge [verb] meaning to plough a straight line in a narrow strip, and rig [verb] to provide a straight ridge to a house. Rig [noun] a derivation of ridge, was used in England five hundred years ago of human and animal backbones, perhaps reflecting everyday physical burdens. Rigours [noun] conveyed the meting out of un-cautioned punishments and cruelty. While the temperament of rigour might be appropriate for research that follows pre-set norms and standards of repeatability, its use to judge what is trustworthy in artistic research is questionable. Though artistic researchers need to understand the rigour concept, by contrast, artistic research as a kind of ‘thinking through making’ (Ravetz, 2011, 159; Ingold, 2013, 6), places value on improvisation, chance encounter, unforeseen admixture and the in- and outward- folding of process, affect and material. Once it is accepted that poiesis is part of the research process (Ingold, 2013; Haraway, 2016), it becomes apparent that artistic research cannot easily accommodate straight backed rigour

Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

Scientific knowledge and its social problems

https://works.swarthmore.edu/alum-books/4092/thumbnail.jp

Reflections on the Indicator for Vulnerability and their Application to Chemical Hazards

Abstract not availableJRC.(ISEI)-Institute For Systems Engineering And Informatic