Bungee jumping for science
Someone in a different field or organization could have the key to unlocking the problem they are working on. When the problems get tough, scientists want to build the best team, even if the partner is a fierce competitor. How to apply it : Look at those problems and opportunities in your business or organization that cannot be solved in isolation. Areas such as cybersecurity, global political and economic forces, or significant technological requirements, all benefit from collaboration across the industry and across sectors. When corporations come together, as they do at Davos, they can make important things happen.
Bringing together industry, government and higher education can be even more powerful. Collaborate like a scientist. Where something is unknown, it is an opportunity to be pursued rather than avoided. This requires the ability to deal with ambiguity and uncertainty, which most people find difficult.
In experiments, a lack of correlation moves science forward as much as a positive correlation.
No information is ever complete. Scientists are comfortable with moving forward purposefully when faced with incomplete or problematic data sets. Even the biggest problems, such as the spread of malaria or the age of the universe, can be approached in a systematic and rational way. How to apply it : Break down problems into smaller hypotheses to be tested. Evaluate probabilities and the interrelation between factors affecting probability and move forward armed with that imperfect knowledge.
Build a team that can deal with uncertainty and ambiguity by pooling their understanding and gaining confidence. We at Imperial have learned much from our relationships with business leaders. Sound business practices are necessary to keeping a university strong.
At the same time, incorporating sound scientific thinking into business decisions can help keep businesses strong by thinking more boldly and creatively. I am inspired by the scientific mindset at institutions such as Imperial.
The views expressed in this article are those of the author alone and not the World Economic Forum. I accept. Information-theoretic accounts of measurement were originally developed by metrologists with little involvement from philosophers. Metrologists typically work at standardization bureaus or at specialized laboratories that are responsible for the calibration of measurement equipment, the comparison of standards and the evaluation of measurement uncertainties, among other tasks.
It is only recently that philosophers have begun to engage with the rich conceptual issues underlying metrological practice, and particularly with the inferences involved in evaluating and improving the accuracy of measurement standards Chang ; Boumans a: Chap. Further philosophical work is required to explore the assumptions and consequences of information-theoretic accounts of measurement, their implications for metrological practice, and their connections with other accounts of measurement.
Independently of developments in metrology, Bas van Fraassen — has recently proposed a conception of measurement in which information plays a key role. He views measurement as composed of two levels: on the physical level, the measuring apparatus interacts with an object and produces a reading, e. Measurement locates an object on a sub-region of this abstract parameter space, thereby reducing the range of possible states and This reduction of possibilities amounts to the collection of information about the measured object.
Since the early s a new wave of philosophical scholarship has emerged that emphasizes the relationships between measurement and theoretical and statistical modeling. The central goal of measurement according to this view is to assign values to one or more parameters of interest in the model in a manner that satisfies certain epistemic desiderata, in particular coherence and consistency. A central motivation for the development of model-based accounts is the attempt to clarify the epistemological principles underlying aspects of measurement practice.
For example, metrologists employ a variety of methods for the calibration of measuring instruments, the standardization and tracing of units and the evaluation of uncertainties for a discussion of metrology, see the previous section. Traditional philosophical accounts such as mathematical theories of measurement do not elaborate on the assumptions, inference patterns, evidential grounds or success criteria associated with such methods. As Frigerio et al.
By contrast, model-based accounts take scale construction to be merely one of several tasks involved in measurement, alongside the definition of measured parameters, instrument design and calibration, object sampling and preparation, error detection and uncertainty evaluation, among others —7. Other, secondary interactions may also be relevant for the determination of a measurement outcome, such as the interaction between the measuring instrument and the reference standards used for its calibration, and the chain of comparisons that trace the reference standard back to primary measurement standards Mari Although measurands need not be quantities, a quantitative measurement scenario will be supposed in what follows.
Two sorts of measurement outputs are distinguished by model-based accounts [JCGM 2. As proponents of model-based accounts stress, inferences from instrument indications to measurement outcomes are nontrivial and depend on a host of theoretical and statistical assumptions about the object being measured, the instrument, the environment and the calibration process. Measurement outcomes are often obtained through statistical analysis of multiple indications, thereby involving assumptions about the shape of the distribution of indications and the randomness of environmental effects Bogen and Woodward — Measurement outcomes also incorporate corrections for systematic effects, and such corrections are based on theoretical assumptions concerning the workings of the instrument and its interactions with the object and environment.
Systematic corrections involve uncertainties of their own, for example in the determination of the values of constants, and these uncertainties are assessed through secondary experiments involving further theoretical and statistical assumptions. Moreover, the uncertainty associated with a measurement outcome depends on the methods employed for the calibration of the instrument. Calibration involves additional assumptions about the instrument, the calibrating apparatus, the quantity being measured and the properties of measurement standards Rothbart and Slayden ; Franklin ; Baird Ch.
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Finally, measurement involves background assumptions about the scale type and unit system being used, and these assumptions are often tied to broader theoretical and technological considerations relating to the definition and realization of scales and units. These various theoretical and statistical assumptions form the basis for the construction of one or more models of the measurement process. Measurement is viewed as a set of procedures whose aim is to coherently assign values to model parameters based on instrument indications. Models are therefore seen as necessary preconditions for the possibility of inferring measurement outcomes from instrument indications, and as crucial for determining the content of measurement outcomes.
As proponents of model-based accounts emphasize, the same indications produced by the same measurement process may be used to establish different measurement outcomes depending on how the measurement process is modeled, e. As Luca Mari puts it,. Similarly, models are said to provide the necessary context for evaluating various aspects of the goodness of measurement outcomes, including accuracy, precision, error and uncertainty Boumans , a, , b; Mari b.
Model-based accounts diverge from empiricist interpretations of measurement theory in that they do not require relations among measurement outcomes to be isomorphic or homomorphic to observable relations among the items being measured Mari Indeed, according to model-based accounts relations among measured objects need not be observable at all prior to their measurement Frigerio et al.see url
Science outside the laboratory: measurement in field science and economics
Instead, the key normative requirement of model-based accounts is that values be assigned to model parameters in a coherent manner. The coherence criterion may be viewed as a conjunction of two sub-criteria: i coherence of model assumptions with relevant background theories or other substantive presuppositions about the quantity being measured; and ii objectivity, i. The first sub-criterion is meant to ensure that the intended quantity is being measured, while the second sub-criterion is meant to ensure that measurement outcomes can be reasonably attributed to the measured object rather than to some artifact of the measuring instrument, environment or model.
Taken together, these two requirements ensure that measurement outcomes remain valid independently of the specific assumptions involved in their production, and hence that the context-dependence of measurement outcomes does not threaten their general applicability. Besides their applicability to physical measurement, model-based analyses also shed light on measurement in economics. Like physical quantities, values of economic variables often cannot be observed directly and must be inferred from observations based on abstract and idealized models.
The nineteenth century economist William Jevons, for example, measured changes in the value of gold by postulating certain causal relationships between the value of gold, the supply of gold and the general level of prices Hoover and Dowell —; Morgan Taken together, these models allowed Jevons to infer the change in the value of gold from data concerning the historical prices of various goods.
The ways in which models function in economic measurement have led some philosophers to view certain economic models as measuring instruments in their own right, analogously to rulers and balances Boumans , c, , a, , a; Morgan Marcel Boumans explains how macroeconomists are able to isolate a variable of interest from external influences by tuning parameters in a model of the macroeconomic system.
This technique frees economists from the impossible task of controlling the actual system. As Boumans argues, macroeconomic models function as measuring instruments insofar as they produce invariant relations between inputs indications and outputs outcomes , and insofar as this invariance can be tested by calibration against known and stable facts.
Another area where models play a central role in measurement is psychology.
The measurement of most psychological attributes, such as intelligence, anxiety and depression, does not rely on homomorphic mappings of the sort espoused by the Representational Theory of Measurement Wilson These models are constructed from substantive and statistical assumptions about the psychological attribute being measured and its relation to each measurement task.
For example, Item Response Theory, a popular approach to psychological measurement, employs a variety of models to evaluate the validity of questionnaires. One of the simplest models used to validate such questionnaires is the Rasch model Rasch New questionnaires are calibrated by testing the fit between their indications and the predictions of the Rasch model and assigning difficulty levels to each item accordingly.
The model is then used in conjunction with the questionnaire to infer levels of English language comprehension outcomes from raw questionnaire scores indications Wilson ; Mari and Wilson Psychologists are typically interested in the results of a measure not for its own sake, but for the sake of assessing some underlying and latent psychological attribute.
It is therefore desirable to be able to test whether different measures, such as different questionnaires or multiple controlled experiments, all measure the same latent attribute. A construct is an abstract representation of the latent attribute intended to be measured, and. Constructs are denoted by variables in a model that predicts which correlations would be observed among the indications of different measures if they are indeed measures of the same attribute.
Several scholars have pointed out similarities between the ways models are used to standardize measurable quantities in the natural and social sciences. Others have raised doubts about the feasibility and desirability of adopting the example of the natural sciences when standardizing constructs in the social sciences. As Anna Alexandrova points out, ethical considerations bear on questions about construct validity no less than considerations of reproducibility. Such ethical considerations are context sensitive, and can only be applied piecemeal.
Examples of Ballung concepts are race, poverty, social exclusion, and the quality of PhD programs. Such concepts are too multifaceted to be measured on a single metric without loss of meaning, and must be represented either by a matrix of indices or by several different measures depending on which goals and values are at play see also Cartwright and Bradburn In a similar vein, Leah McClimans argues that uniformity is not always an appropriate goal for designing questionnaires, as the open-endedness of questions is often both unavoidable and desirable for obtaining relevant information from subjects.
Rather than emphasizing the mathematical foundations, metaphysics or semantics of measurement, philosophical work in recent years tends to focus on the presuppositions and inferential patterns involved in concrete practices of measurement, and on the historical, social and material dimensions of measuring. In the broadest sense, the epistemology of measurement is the study of the relationships between measurement and knowledge.