Viktor Mayer-Schönberger

Bridging the Gap: The Role of Spatial Information Technologies in the Integration of Traditional Environmental Knowledge and Western Science

Agenda 21 dedicates a whole chapter to the role and importance of information for sustainable development. Among the provisions on harnessing the potential of information and communication technologies (ICT) one paragraph addresses the need for a strengthening of the capacity for traditional information. Local communities and resource users should benefit from mechanisms that provide them with the know‐how they need to manage their environment and resources sustainably, applying traditional and indigenous knowledge and approaches.

Big Data – Eine Revolution, die unser Leben verändern wird

Big data denotes our capacity to gain insights from (in relative terms!) large amounts of data that we could not have had by just looking at samples. Our difficulty in working with data has shaped our methods in the small data age. As these limitations with respect to data diminish, we will have to rethink and adjust our scientific methods. In return, we will gain a wealth of new insights, perhaps leading towards a new golden era of scientific discovery. Big Data power demands, however, that we also are cognizant of its limitations and the significant dangers of abusing it.ZusammenfassungBig Data ermöglicht es, aus einer – relativ gesehen – großen Datenmenge Einsichten in die Wirklichkeit zu gewinnen, die bisher so für uns nicht zugänglich waren. Unsere bisherige Schwierigkeit im Umgang mit großen Datenmengen hat die Methoden wissenschaftlicher Erkenntnis geprägt. In dem Maß, in dem sich das Sammeln und Analysieren von Daten durch die digitalen Werkzeuge erleichtert und verbessert, werden wir auch unsere Erkenntnismethoden anpassen müssen. Im Gegenzug dazu, erhalten wir einen beschleunigten und verbesserten Zugang zu wissenschaftlicher Erkenntnis, insbesondere in den Bereichen der Lebens- und Sozialwissenschaften. Die Mächtigkeit von Big Data gebietet aber auch, dass wir uns seiner Grenzen ebenso gewahr sind wie der außergewöhnlichen Gefahren einer missbräuchlichen Verwendung.AbstractBig data denotes our capacity to gain insights from (in relative terms!) large amounts of data that we could not have had by just looking at samples. Our difficulty in working with data has shaped our methods in the small data age. As these limitations with respect to data diminish, we will have to rethink and adjust our scientific methods. In return, we will gain a wealth of new insights, perhaps leading towards a new golden era of scientific discovery. Big Data power demands, however, that we also are cognizant of its limitations and the significant dangers of abusing it.

Big Data for cardiology: novel discovery?

AIM Big Data promises to change cardiology through a massive increase in the data gathered and analysed; but its impact goes beyond improving incrementally existing methods. METHODS AND RESULTS The potential of comprehensive data sets for scientific discovery is examined, and its impact on the scientific method generally and cardiology in particular is posited, together with likely consequences for research and practice. CONCLUSION Big Data in cardiology changes how new insights are being discovered. For it to flourish, significant modifications in the methods, structures, and institutions of the profession are necessary.

The determinants of monetary value of virtual goods: An empirical study for a cross-section of MMORPGs

This study investigates the monetary value of virtual goods in the context of 24 most popular massively multiplayer online role-playing games (MMORPGs). Building on classic economic theory, we approach this issue through a combination of experimentation and cross-sectional time series data analysis. Our findings suggest that more intensive social networking and flatter social hierarchical structures are associated with lower monetary value of virtual goods across various MMORPGs. Instead, a larger base of active users increases the potential demand and thus the monetary value of virtual goods in the short run. A steeper social hierarchical structure further strengthens the effect. The implication is that social networking and hierarchical structure can be two effective angles for game developers or policy makers to address the issue of real-money trading of virtual goods.

Can We Reinvent the Internet

To build a better Internet may require us to rewire the social communities that created its code. Recently, researchers who support “network neutrality” have become worried that the Internet may lose its innovative edge. They are concerned that control could be shifting from the edges of the Internet toward the service providers at the center, which would allow the providers to have “gatekeeper” capacity and would contradict the Internets “end-to-end” principle (1–3). This core tenet states that control over information flows should take place, to the extent possible, at the end points of the network (4). President Obama supported net neutrality during his campaign (4) and in recent statements (5), and the European Parliament has added net neutrality to its recent telecom bill (6). Taking the end-to-end principle from protocols to users, Jonathan Zittrain has called for maintaining the Internets “generativity,” the ability of users at the networks end points to create, distribute, and run whatever software code they choose (7). There are good reasons to preserve network neutrality and generativity, but it is unclear whether these are sufficient to ensure continued innovation. The larger issue is what policies are optimal to foster innovation on the Internet?

The Monetary Value of Virtual Goods: An Exploratory Study in MMORPGs

This study investigates the monetary value of time spent in virtual worlds in the context of 24 most popular MMORPGs. Building on classic economic theory, we approach this issue through a combination of theoretical modeling, experiment, and cross-sectional time series data analysis. Our findings suggest that intensive social networking and flatter social hierarchy structures are associated with lower monetary value of time spent in-game. Further, two opposite network effects on the monetary value of ingame time spent were observed: One is the positive network effect from the active user base; the other is the negative network effect from the intensity of social networking. Both are strengthened by steeper social hierarchy structures. The implication is that social networking and hierarchy structure can be two effective angles for game developers or policy makers to address the issue of real-money trading of virtual goods.

Big Data and medicine: a big deal?

Big Data promises huge benefits for medical research. Looking beyond superficial increases in the amount of data collected, we identify three key areas where Big Data differs from conventional analyses of data samples: (i) data are captured more comprehensively relative to the phenomenon under study; this reduces some bias but surfaces important trade‐offs, such as between data quantity and data quality; (ii) data are often analysed using machine learning tools, such as neural networks rather than conventional statistical methods resulting in systems that over time capture insights implicit in data, but remain black boxes, rarely revealing causal connections; and (iii) the purpose of the analyses of data is no longer simply answering existing questions, but hinting at novel ones and generating promising new hypotheses. As a consequence, when performed right, Big Data analyses can accelerate research. Because Big Data approaches differ so fundamentally from small data ones, research structures, processes and mindsets need to adjust. The latent value of data is being reaped through repeated reuse of data, which runs counter to existing practices not only regarding data privacy, but data management more generally. Consequently, we suggest a number of adjustments such as boards reviewing responsible data use, and incentives to facilitate comprehensive data sharing. As datas role changes to a resource of insight, we also need to acknowledge the importance of collecting and making data available as a crucial part of our research endeavours, and reassess our formal processes from career advancement to treatment approval.

Learning from Our Mistakes: The Future of Validating Complex Diagnostics

In 2009, Google unveiled “Flu Trends,” a program designed to estimate rates of influenza infection based solely on the use of search terms submitted by users across the US. In their initial published report of the work in Nature (1), the authors demonstrated a striking match between their Google search–based estimates and the official flu statistics for 2007–08. In addition to the novelty of using search data for public health purposes, one of the most interesting aspects of this work was that the predictive algorithm was not developed using preselected, candidate search terms. Rather, using the “big data” available to Google from hundreds of millions of users, developers identified the most predictive search terms out of the 50 million most frequently used terms regardless of whether they “made sense.” This unguided approach not only tracked the spread of the flu with high accuracy, but also provided its results 1–2 weeks earlier than similar estimates from the CDC. However, Google Flu Trends was significantly less successful in predicting CDC winter flu data in 2012 (2). One important reason for the inaccuracy was a change in the behavior of users. Whereas search term statistics up until 2007 were able to adequately predict influenza in 2007–08, changes in term usage led to a subsequent degradation in performance. This raises a critical question: would Googles diagnostic algorithm be more effective if it were allowed to retrain itself and learn new terms over time? Indeed, retrospectively incorporating changes from 2010 and 2011 into the algorithm led to improved predictions for the 2012 flu season (3). An analogous question has been …

Big Data – Eine Revolution, die unser Leben verändern wird@@@Big data: a revolution that will transform our lives

Big data denotes our capacity to gain insights from (in relative terms!) large amounts of data that we could not have had by just looking at samples. Our difficulty in working with data has shaped our methods in the small data age. As these limitations with respect to data diminish, we will have to rethink and adjust our scientific methods. In return, we will gain a wealth of new insights, perhaps leading towards a new golden era of scientific discovery. Big Data power demands, however, that we also are cognizant of its limitations and the significant dangers of abusing it.ZusammenfassungBig Data ermöglicht es, aus einer – relativ gesehen – großen Datenmenge Einsichten in die Wirklichkeit zu gewinnen, die bisher so für uns nicht zugänglich waren. Unsere bisherige Schwierigkeit im Umgang mit großen Datenmengen hat die Methoden wissenschaftlicher Erkenntnis geprägt. In dem Maß, in dem sich das Sammeln und Analysieren von Daten durch die digitalen Werkzeuge erleichtert und verbessert, werden wir auch unsere Erkenntnismethoden anpassen müssen. Im Gegenzug dazu, erhalten wir einen beschleunigten und verbesserten Zugang zu wissenschaftlicher Erkenntnis, insbesondere in den Bereichen der Lebens- und Sozialwissenschaften. Die Mächtigkeit von Big Data gebietet aber auch, dass wir uns seiner Grenzen ebenso gewahr sind wie der außergewöhnlichen Gefahren einer missbräuchlichen Verwendung.AbstractBig data denotes our capacity to gain insights from (in relative terms!) large amounts of data that we could not have had by just looking at samples. Our difficulty in working with data has shaped our methods in the small data age. As these limitations with respect to data diminish, we will have to rethink and adjust our scientific methods. In return, we will gain a wealth of new insights, perhaps leading towards a new golden era of scientific discovery. Big Data power demands, however, that we also are cognizant of its limitations and the significant dangers of abusing it.

Paradoxe Intervention. Grundsätzliche Grenzen und Möglichkeiten der Regulierung von Online-Anbietern

Philip Rosedale, der Grunder und CEO von Linden Lab, konnte zufrieden sein. Sein Unternehmen betreibt Second Life, eine virtuelle Welt mit Millionen von Teilnehmern. Mehr als eine halbe Million davon verbringen 15 Stunden und mehr jede Woche in dieser Welt. Bis zu 80 000 davon sind gleichzeitig online.1 Linden Lab ist profitabel, ganz ohne Borsengang. In funf Jahren hat Rosedale erreicht, wofur Microsoft die dreifache Zeit benotigte: zum unangefochtenen Marktfuhrer zu werden. Wer sich heute nach einem virtuellen Zweitwohnsitz sehnt, der findet ihn in Second Life.