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Register a new userQualified Trust, not Surveillance, is the Basis of a Stable Society
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Authors
Dirk Helbing
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Peaceful citizens and hard-working taxpayers are under government surveillance. Confidential communication of journalists is intercepted. Civilians are killed by drones, without a chance to prove their innocence. How could it come that far? And what are the alternatives?
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Our society is changing. Almost nothing these days works without a computer chip. Computing power doubles every 18 months, and in ten years it will probably exceed the capabilities of a human brain. Computers perform approximately 70 percent of all financial transactions today and IBMs Watson now seems to give better customer advise than some human telephone hotlines. What does this imply for our future society?
The world is changing at an ever-increasing pace. And it has changed in a much more fundamental way than one would think, primarily because it has become more connected and interdependent than in our entire history. Every new product, every new invention can be combined with those that existed before, thereby creating an explosion of complexity: structural complexity, dynamic complexity, functional complexity, and algorithmic complexity. How to respond to this challenge? And what are the costs?
The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other cases that directly affect society. We will deal with different aspects of the issue, from a bibliometric analysis of the publications, to a detailed discussion of the literature, to an overview on the productive and working context inside and outside academia. The analysis will be conducted on the basis of three key elements: the non-neutrality of science, understood as its intrinsic relationship with history and society; the non-neutrality of the algorithms, in the sense of the presence of elements that depend on the choices of the programmer, which cannot be eliminated whatever the technological progress is; the problematic nature of a paradigm shift in favour of a data-driven science (and society). The deconstruction of the presumed universality of scientific thought from the inside becomes in this perspective a necessary first step also for any social and political discussion. This is the subject of this work in the case study of ML.
Models that explain the economical and political realities of nowadays societies should help all the worlds citizens. Yet, the last four years showed that the current models are missing. Here we develop a dynamical society-deciders model showing that the long lasting economical stress can be solved when increasing fairness in nations. fairness is computed for each nation using indicators from economy and politics. Rather than austerity versus spending, the dynamical model suggests that solving crises in western societies is possible with regulations that reduce the stability of the deciders, while shifting wealth in the direction of the people. This shall increase the dynamics among socio-economic classes, further increasing fairness.
Peer punishment of free-riders (defectors) is a key mechanism for promoting cooperation in society. However, it is highly unstable since some cooperators may contribute to a common project but refuse to punish defectors. Centralized sanctioning institutions (for example, tax-funded police and criminal courts) can solve this problem by punishing both defectors and cooperators who refuse to punish. These institutions have been shown to emerge naturally through social learning and then displace all other forms of punishment, including peer punishment. However, this result provokes a number of questions. If centralized sanctioning is so successful, then why do many highly authoritarian states suffer from low levels of cooperation? Why do states with high levels of public good provision tend to rely more on citizen-driven peer punishment? And what happens if centralized institutions can be circumvented by individual acts of bribery? Here, we consider how corruption influences the evolution of cooperation and punishment. Our model shows that the effectiveness of centralized punishment in promoting cooperation breaks down when some actors in the model are allowed to bribe centralized authorities. Counterintuitively, increasing the sanctioning power of the central institution makes things even worse, since this prevents peer punishers from playing a role in maintaining cooperation. As a result, a weaker centralized authority is actually more effective because it allows peer punishment to restore cooperation in the presence of corruption. Our results provide an evolutionary rationale for why public goods provision rarely flourishes in polities that rely only on strong centralized institutions. Instead, cooperation requires both decentralized and centralized enforcement. These results help to explain why citizen participation is a fundamental necessity for policing the commons.
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