The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour.
Thanks to a grant of the Nordrhein-Westfälische Akademie der Wissenschaften und der Künste, Bielefeld University has started a fifteen-year project (2015–2030) that includes the production of a critical edition of Niklas Luhmann’s extant works and manuscripts, as well as the digitalization of his famous card index. This valuable enterprise has rekindled interest in what many scholars hold to be a ‘holy grail’: a marvelous instrument that aided great creativity and scientific production by the German sociologist. Indeed, people feel that looking inside the filing cabinet is like looking inside the mind of a genius at work. This article suggests a different point of view, rooted in the Enlightenment project of the sociologist of Bielefeld. The main hypothesis is that in the use of a card index as a surprise generator, there is nothing particularly surprising if one considers the evolution of knowledge management in early modern Europe. Rather, the question should be: how it is possible to explain the evolutionary improbability of the social use of ‘machines’ as secondary memories for knowledge management and reproduction? This article provides some suggestions for research and tries to determine where Luhmann’s card index comes from.
Social sciences are experiencing an anticipatory turn. A core issue of this turn are the so-called 'weak signals'. In order to speak of this type of signals, we must use the distinction between weak and strong. The question may be raised, who handles this distinction? That is, who is the observer? It seems that only two answers are possible: the observer is either outside or inside, i.e., either he is a worldobserver, or he is a extra-world-observer. In the latter case, the problem of weak signals disappears; after the fact, everybody is able to say BI told you!^. In the former case, the system has to face the dilemma of warning signals. As social systems cannot observe themselves from the outside, the issue of weak signals should be explained as the outcome of a selfreferential dynamics that finally leads to the paradox of knowing the unknown. In fact, the difference between weak and strong refers not to the future as such (to what is signalized), but to the observing system itself. The main hypothesis of this contribution is that a signal is weak for a lack of redundancy that hinders the system to combine a reference to an environmental event with a concomitant reference to a systemic cognitive map. By means of a system theory of sign, it should be possible to see the difference between weak and strong as an unfolding device for temporal paradoxes arising in social systems, and to support the hypothesis that, since in social systems cognitive maps are contingent on time, signals can be only weak, never strong.
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