We estimated the world's technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 10(20) optimally compressed bytes, communicate almost 2 × 10(21) bytes, and carry out 6.4 × 10(18) instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world's capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind's capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007).

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The article uses a conceptual framework to review empirical evidence and some 180 articles related to the opportunities and threats of Big Data Analytics for international development. The advent of Big Data delivers a cost‐effective prospect for improved decision‐making in critical development areas such as healthcare, economic productivity and security. At the same time, the well‐known caveats of the Big Data debate, such as privacy concerns and human resource scarcity, are aggravated in developing countries by long‐standing structural shortages in the areas of infrastructure, economic resources and institutions. The result is a new kind of digital divide: a divide in the use of data‐based knowledge to inform intelligent decision‐making. The article systematically reviews several available policy options in terms of fostering opportunities and minimising risks.

A single coherent framework is proposed to synthesize long-standing research on 8 seemingly unrelated cognitive decision-making biases. During the past 6 decades, hundreds of empirical studies have resulted in a variety of rules of thumb that specify how humans systematically deviate from what is normatively expected from their decisions. Several complementary generative mechanisms have been proposed to explain those cognitive biases. Here it is suggested that (at least) 8 of these empirically detected decision-making biases can be produced by simply assuming noisy deviations in the memory-based information processes that convert objective evidence (observations) into subjective estimates (decisions). An integrative framework is presented to show how similar noise-based mechanisms can lead to conservatism, the Bayesian likelihood bias, illusory correlations, biased self–other placement, subadditivity, exaggerated expectation, the confidence bias, and the hard–easy effect. Analytical tools from information theory are used to explore the nature and limitations that characterize such information processes for binary and multiary decision-making exercises. The ensuing synthesis offers formal mathematical definitions of the biases and their underlying generative mechanism, which permits a consolidated analysis of how they are related. This synthesis contributes to the larger goal of creating a coherent picture that explains the relations among the myriad of seemingly unrelated biases and their potential psychological generative mechanisms. Limitations and research questions are discussed.

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Highlights:  The digital divide measured in terms of bandwidth it not closing  Inequality fluctuates up-and down with technological progress and diffusion  Asia increased its global share in installed bandwidth from 23 to 51 % in 30 years  Bandwidth inequality is closely linked to income, which is notoriously unequal  It is urgent to start measuring bandwidth, not merely counting subscriptions

The article analyzes the nature of communication flows during social conflicts via the digital platform Twitter. We gathered over 150,000 Tweets from citizen protests for nine environmental social movements in Chile, and use a mixed-methods approach to show that longstanding paradigms for social mobilization and participation are neither replicated nor replaced, but reshaped. In digital platforms, long standing communication theories, like the 1955 two-step flow model, are still valid, while direct one-step flows and more complex network flows are also present. For example, we show that it is no contradiction that participants mainly refer to intermediating amplifiers (39 % of the mentions from participants go through this two-step flow), while at the same time traditional media outlets and official protest voices receive 80-90 % of their mentions directly through a direct one-step flow from the same participants. While non-intuitive at first sight, Bayes' theorem allows to detangle the different perspectives in the arising communication channel. We identify the strategic importance of a group of amplifying intermediaries in local positions of the networks, who coexist with specialized voices and professional media outlets at the center of the global network. We also show that direct personalized messages represent merely 20 % of the total communication. This shows that the fine-grained digital footprint from social media enable us to go beyond simplistic views of a single all-encompassing step-flow model for social communication. The resulting research agenda builds on longstanding theories with a new set of tools.

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