ICT4ICTD: Computational Social Science for Digital Development

Hilbert, Martin

doi:10.1109/hicss.2015.258

Cited by 8 publications

(4 citation statements)

References 56 publications

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“…Subscriptions per capita vs. capacity per capita (in optimally compressed kbps of installed capacity) across 100 countries for 1986 and 2010 Note: Size of the bubbles represents Gross National Income (GNI) per capita. Source: Hilbert (). …”

Section: Digital Big Data Dividementioning

confidence: 99%

“…The most powerful candidates are so‐called agent‐based models (Epstein and Axtell, ; Bonabeau, ; Gilbert and Troitzsch, ; Farmer and Foley, ). The combination of theory‐driven simulation models and Big Data input to calibrate those models is becoming the new gold standard of so‐called computational social science (Hilbert, ). It reminds us that Big Data by itself is limited by the same constraints as all empirical work: it is exclusively post factum.…”

Section: Critical Reflection: All Power To the Algorithms?mentioning

confidence: 99%

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Big Data for Development: A Review of Promises and Challenges

Hilbert¹

2015

Development Policy Review

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494

255

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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.

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Section: Digital Big Data Dividementioning

confidence: 99%

Section: Critical Reflection: All Power To the Algorithms?mentioning

confidence: 99%

Big Data for Development: A Review of Promises and Challenges

Hilbert¹

2015

Development Policy Review

Self Cite

494

255

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“…Big data driven pattern recognition can only provide useful insights if the environmental patterns stay the same. If the patterns change, the model based on previous data (and therefore the resulting strategy recommendation) cannot explain the new pattern [73]. No stable patterns, no straightforward exploitation of the pattern.…”

Section: Discussionmentioning

confidence: 99%

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Hilbert

2017

Entropy

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In our information age, information alone has become a driver of social growth. Information is the fuel of "big data" companies, and the decision-making compass of policy makers. Can we quantify how much information leads to how much social growth potential? Information theory is used to show that information (in bits) is effectively a quantifiable ingredient of growth. The article presents a single equation that allows both to describe hands-off natural selection of evolving populations and to optimize population fitness in uncertain environments through intervention. The setup analyzes the communication channel between the growing population and its uncertain environment. The role of information in population growth can be thought of as the optimization of information flow over this (more or less) noisy channel. Optimized growth implies that the population absorbs all communicated environmental structure during evolutionary updating (measured by their mutual information). This is achieved by endogenously adjusting the population structure to the exogenous environmental pattern (through bet-hedging/portfolio management). The setup can be applied to decompose the growth of any discrete population in stationary, stochastic environments (economic, cultural, or biological). Two empirical examples from the information economy reveal inherent trade-offs among the involved information quantities during growth optimization.

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“…ABMs in policy contexts, for example, are especially useful to identify dynamics to be expected under certain policy interventions (Gilbert et al, 2018). This approach is particularly promising for exploring possible futures in the sense of what-if scenarios or scenarios for which observational data are not available (Hilbert, 2015;Hilbert et al, 2019).…”

Section: Predictionmentioning

confidence: 99%

Worlds of Agents: Prospects of Agent-Based Modeling for Communication Research

Waldherr¹,

Hilbert

González‐Bailón

2021

Communication Methods and Measures

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Agent-based computational models create virtual laboratories in which to formalize and simulate dynamic, multi-level theories of communication. They allow the systematic development of thought experiments, and they improve our understanding of the generative mechanisms that underlie patterns observed in empirical data. Simulation models help explore hypothetical and unprecedented scenarios, serving as powerful hypothesis generators for future theoretical and empirical research. This Special Issue showcases a collection of studies that demonstrate the analytical potential and methodological contribution of agent-based modeling (ABM) for media and communication research. In this introduction, we highlight five major benefits of this modeling approach to communication scholarship: (1) formalization, (2) understanding, (3) explanation, (4) prediction, and (5) exploration. We then present the four studies of this special issue, which contribute methodologically and theoretically to diverse key areas of communication: the emergence of meanings; political deliberation; information diffusion; and media use and social influence. We conclude with outlining future perspectives of ABM in communication research.

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ICT4ICTD: Computational Social Science for Digital Development

Abstract: While the ICT for Development (ICTD)

Cited by 8 publications

References 56 publications

Big Data for Development: A Review of Promises and Challenges

Big Data for Development: A Review of Promises and Challenges

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Worlds of Agents: Prospects of Agent-Based Modeling for Communication Research

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