Economic Information, Decision, and Prediction: Selected Essays: Volume III

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Economic Information, Decision, and Prediction: Selected Essays: Volume II

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Jacob Marschak - Wikipedia's Jacob Marschak as translated by GramTrans

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Undergraduate Admissions. Admissions Related Policies. October 9, Decision Making: Economic Aspects The Economic Man's Logic Economics of Acting, Thinking, and Surviving Springer Book Archives Fakta. Avbryt Send e-post. Les mer. Om boka Economic Information, Decision, and Prediction The papers of Jacob Marschak which follow in these volumes are an extraordinary combination of original and fruitful departures in economic and social thought, superb clarity of exposition, and sensitivity to the values of earlier work and even competing traditions.

They make us marvel alike at their variety, their quantity, and their quality. This is not a criticism of bodies such as the Office for National Statistics ONS , which does now produce data on underemployment. But so long as politicians continue to deflect criticism by pointing to the unemployment rate, the experiences of those struggling to get enough work or to live on their wages go unrepresented in public debate. The rise of identity politics since the s has put additional strain on such systems of classification.

Statistical data is only credible if people will accept the limited range of demographic categories that are on offer, which are selected by the expert not the respondent. But where identity becomes a political issue, people demand to define themselves on their own terms, where gender, sexuality, race or class is concerned. Opinion polling may be suffering for similar reasons. One also needs to know whether they feel strongly enough about doing so to bother.

And when it comes to capturing such fluctuations in emotional intensity, polling is a clumsy tool. Statistics have faced criticism regularly over their long history. The challenges that identity politics and globalisation present to them are not new either. Why then do the events of the past year feel quite so damaging to the ideal of quantitative expertise and its role in political debate? I n recent years, a new way of quantifying and visualising populations has emerged that potentially pushes statistics to the margins, ushering in a different era altogether.

Statistics, collected and compiled by technical experts, are giving way to data that accumulates by default, as a consequence of sweeping digitisation. Traditionally, statisticians have known which questions they wanted to ask regarding which population, then set out to answer them.

By contrast, data is automatically produced whenever we swipe a loyalty card, comment on Facebook or search for something on Google. As our cities, cars, homes and household objects become digitally connected, the amount of data we leave in our trail will grow even greater. In this new world, data is captured first and research questions come later. In the long term, the implications of this will probably be as profound as the invention of statistics was in the late 17th century. But it is not just the quantity of data that is different. It represents an entirely different type of knowledge, accompanied by a new mode of expertise.

These vast new data sets can be mined in search of patterns, trends, correlations and emergent moods.


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This is a form of aggregation suitable to a more fluid political age, in which not everything can be reliably referred back to some Enlightenment ideal of the nation state as guardian of the public interest. Second, the majority of us are entirely oblivious to what all this data says about us, either individually or collectively.

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There is no equivalent of an Office for National Statistics for commercially collected big data. We live in an age in which our feelings, identities and affiliations can be tracked and analysed with unprecedented speed and sensitivity — but there is nothing that anchors this new capacity in the public interest or public debate. The anonymity and secrecy of the new analysts potentially makes them far more politically powerful than any social scientist.

A company such as Facebook has the capacity to carry quantitative social science on hundreds of millions of people, at very low cost. But it has very little incentive to reveal the results.

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Why not just do the study and keep quiet? W hat is most politically significant about this shift from a logic of statistics to one of data is how comfortably it sits with the rise of populism.

Populist leaders can heap scorn upon traditional experts, such as economists and pollsters, while trusting in a different form of numerical analysis altogether. Such politicians rely on a new, less visible elite, who seek out patterns from vast data banks, but rarely make any public pronouncements, let alone publish any evidence. These data analysts are often physicists or mathematicians, whose skills are not developed for the study of society at all. This, for example, is the worldview propagated by Dominic Cummings , former adviser to Michael Gove and campaign director of Vote Leave.

Figures close to Donald Trump, such as his chief strategist Steve Bannon and the Silicon Valley billionaire Peter Thiel, are closely acquainted with cutting-edge data analytics techniques, via companies such as Cambridge Analytica, on whose board Bannon sits. During the presidential election campaign, Cambridge Analytica drew on various data sources to develop psychological profiles of millions of Americans, which it then used to help Trump target voters with tailored messaging. This ability to develop and refine psychological insights across large populations is one of the most innovative and controversial features of the new data analysis.

In a world where the political feelings of the general public are becoming this traceable, who needs pollsters? Few social findings arising from this kind of data analytics ever end up in the public domain. This means that it does very little to help anchor political narrative in any shared reality. With the authority of statistics waning, and nothing stepping into the public sphere to replace it, people can live in whatever imagined community they feel most aligned to and willing to believe in.

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Where statistics can be used to correct faulty claims about the economy or society or population, in an age of data analytics there are few mechanisms to prevent people from giving way to their instinctive reactions or emotional prejudices. On the contrary, companies such as Cambridge Analytica treat those feelings as things to be tracked.

But even if there were an Office for Data Analytics, acting on behalf of the public and government as the ONS does, it is not clear that it would offer the kind of neutral perspective that liberals today are struggling to defend. The new apparatus of number-crunching is well suited to detecting trends, sensing the mood and spotting things as they bubble up. It serves campaign managers and marketers very well. It is less well suited to making the kinds of unambiguous, objective, potentially consensus-forming claims about society that statisticians and economists are paid for.

In this new technical and political climate, it will fall to the new digital elite to identify the facts, projections and truth amid the rushing stream of data that results. The question to be taken more seriously, now that numbers are being constantly generated behind our backs and beyond our knowledge, is where the crisis of statistics leaves representative democracy.

On the one hand, it is worth recognising the capacity of long-standing political institutions to fight back. What is less clear is how the benefits of digital analytics might ever be offered to the public, in the way that many statistical data sets are. Bodies such as the Open Data Institute, co-founded by Tim Berners-Lee, campaign to make data publicly available, but have little leverage over the corporations where so much of our data now accumulates. Statistics began life as a tool through which the state could view society, but gradually developed into something that academics, civic reformers and businesses had a stake in.

But for many data analytics firms, secrecy surrounding methods and sources of data is a competitive advantage that they will not give up voluntarily. A post-statistical society is a potentially frightening proposition, not because it would lack any forms of truth or expertise altogether, but because it would drastically privatise them.

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Statistics are one of many pillars of liberalism, indeed of Enlightenment. The experts who produce and use them have become painted as arrogant and oblivious to the emotional and local dimensions of politics. No doubt there are ways in which data collection could be adapted to reflect lived experiences better. But the battle that will need to be waged in the long term is not between an elite-led politics of facts versus a populist politics of feeling. It is between those still committed to public knowledge and public argument and those who profit from the ongoing disintegration of those things.