Katharina Brunner

Wirtschaft

Pros and Cons of a Social Index

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Heather Krause writes one of my favorite newsletter. She works at Datassist, a company working with NGOs and data journalists.

Recently, she wrote about social indices:

A social index is a summary of a complex issue (or issues). Generally, social indexes take a large number of variables related to a specific topic or situation and combine them to get one number. It’s often a single number, but can also be a rank (#1 country out of 180) or a category (“high performing”).

Heather Krause

Pros of social indices:

  • attract public interest
  • allow comparisons over time
  • provide a big picture
  • „reduce vast amounts of information to a manageable size“

Cons:

  • „disguise a massive amount of inequality in the data“
  • simplistic interpretations
  • hide emerging problems of some variables

So, should we use them?

Krause says, „yes“, but …

If we’re using an index to understand a trend or situation, we also need to look at the individual elements that make up that index.

Datassist published a list with various indicators here.

Zuboff und das Zeitalter des Überwachungskapitalismus

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Shoshanna Zuboff beobachtet und interpretiert die Digitalisierung seit 40 Jahren. Die Professorin an der Harvard Business School hat ein Buch geschrieben, das der Guardian in eine Reihe mit Adam Smith, Karl Marx, Max Weber, Karl Polanyi und Thomas Piketty stellt. Es heißt The Age of Surveillance Capital und es sollte bald in meinem Buchregal stehen, wie ich finde.

Ein Auszug aus einem Interview mit dem Guardian:

In my early fieldwork in the computerising offices and factories of the late 1970s and 80s, I discovered the duality of information technology: its capacity to automate but also to “informate”, which I use to mean to translate things, processes, behaviours, and so forth into information. This duality set information technology apart from earlier generations of technology: information technology produces new knowledge territories by virtue of its informating capability, always turning the world into information. The result is that these new knowledge territories become the subject of political conflict. The first conflict is over the distribution of knowledge: “Who knows?” The second is about authority: “Who decides who knows?” The third is about power: “Who decides who decides who knows?”

(…)

Surveillance capitalists were the first movers in this new world. They declared their right to know, to decide who knows, and to decide who decides. In this way they have come to dominate what I call “the division of learning in society”, which is now the central organising principle of the 21st-century social order, just as the division of labour was the key organising principle of society in the industrial age.

Shoshanna Zuboff

An economist’s view on AI

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Michaela Schmöller, writes in „Secular stagnation: A false alarm in the euro area?“ for the Bank of Finland

It is important to note that productivity growth evolves in a two-stage process: the initial invention of new technologies through research and development, subsequently followed by technological diffusion, i.e. the incorporation of these new technologies in the production processes of firms. As a result, even though many important technology advances may have been invented in recent times, they will only exert an effect on output and productivity once firms utilise these technologies in production. Potential productivity gains from technologies that have yet to be widely adopted may be sizable. A central example is the field of artificial intelligence in which future productivity gains may be considerable once AI-related technologies diffuse to the wider economy. (…)

AI may represent — as did the steam engine, the internal combustion engine and personal computers — a general purpose technology, meaning that it is far-reaching, holds the potential for further future improvements and has the capability of spurring other major, complementary innovations over time with the power of drastically boosting productivity. Incorporating AI in production requires substantial changes on the firm-level, including capital stock adjustments, the revision of internal processes and infrastructures, as well as adapting supply and value chains to enable the absorption of these new technologies. Consequently, this initial adjustment related to the incorporation of general purpose technologies in firms‘ production may take time and may initially even be accompanied by a drop in labour productivity before delivering positive productivity gains.


Die Wirtschaft und das Mitgefühl

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WIR statt Gier

Die Zeit schreibt darüber, dass langsam auch in der Ökonomie die Einsicht einkehrt, dass – ja, echt jetzt – nicht der Egoismus alleine die Welt, und damit auch die Wirtschaft, antreibt. Na gut, dass weiß man seitdem Experimente in der Spieltheorie nicht so ausgehen, wie sie mathematisch zu erwarten sind.

Es sind also auch die Beziehungen zu anderen Menschen, die unser Verhalten und unsere Motivation beeinflussen. Im positivsten Sinne durch Mitgefühl. Was das für die Wirtschaft bedeuten kann, darum geht’s in dem Zeit-Text. Eine Studie, auf die sich darin bezogen wird, erklärt die Neurowissenschaftlerin Tania Singer hier:

Mit Mitgefühl hat sich vor wenigen Wochen auch die Sendung Radiowissen auf Bayern 2 befasst: Mitgefühl – Was Hirnforscher darüber wissen hieß die Sendung. Darin wird deutlich: Empathie und Mitgefühl ist nicht das gleiche:

  • Empathie ist es, wenn wir ein Gefühl des anderen nachvollziehen können. „Eine Kopie des Gefuühlszustands der anderen Person“, wie der Wiener Neurowissenschaftler Claus Lamm sagt.
  • Mitgefühl ist mehr: Empathie plus der Wunsch, die Freude der anderen Person zu vergrößeren oder Leid zu verringern.

Eine gute Beschreibung von Artificial Intelligence beim Economist

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„One way of understanding this [Artificial Intelligence] is that for humans to do things they find difficult, such as solving differential equations, they have to write a set of formal rules. Turning those rules into a program is then pretty simple. For stuff human beings find easy, though, there is no similar need for explicit rules—and trying to create them can be hard. To take one famous example, adults can distinguish pornography from non-pornography. But describing how they do so is almost impossible, as Potter Stewart, an American Supreme Court judge, discovered in 1964. Frustrated by the difficulty of coming up with a legally watertight definition, he threw up his hands and wrote that, although he could not define porn in the abstract, “I know it when I see it.”

Machine learning is a way of getting computers to know things when they see them by producing for themselves the rules their programmers cannot specify. The machines do this with heavy-duty statistical analysis of lots and lots of data.“

Das Ende des Textes:

„But for now, the best advice is to ignore the threat of computers taking over the world—and check that they are not going to take over your job first.“

aus: Rise of the machines

Besitz ist sowas von Industriezeitalter

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„Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.“

Der Einstieg zu einem TechCrunch-Text mit folgender Grundaussage: Nicht das Produkt zählt, sondern die Software, die Zugang dazu ermöglicht. Kein ganz neuer Gedanke, aber ein wichtiger.

via Christian Fuchs

Update: Zuerst lautete der Titel „Produzieren ist sowas von Industriezeitalter“. Nach einer Diskussion auf Twitter mit Robert Weißgraeber habe ich das Wort „Produzieren“ zu „Besitz“ geändert.