scholarly journals Big Data, Cognitive Biases, Horror Tropes, and Think Tanks: The Future of Historiography between Bold Cross-disciplinary Experiments and Scientific Reductionism

2019 ◽  
Vol 0 (0) ◽  
pp. 7-17
Author(s):  
Leonardo Ambasciano ◽  
Nickolas P. Roubekas
Keyword(s):  
Big Data ◽  
Cognitive Biases ◽  
Think Tanks ◽  
The Future

Artistic Challenges within Control Societies: Big Data and Democratic Resistance

MedienJournal ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 50-61 ◽  
Author(s):  
Jan Jagodzinski
Keyword(s):  
Big Data ◽  
Gilles Deleuze ◽  
Data Archive ◽  
Current State ◽  
The Future ◽  
The Way

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.

Procurement Analytics: Enabling Data Architectures in the Age of Big Data

2020 ◽  
Author(s):  
Michael Goul ◽  
T. S. Raghu ◽  
Ziru Li
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Research Literature ◽  
Structured Interviews ◽  
Data Space ◽  
Prescriptive Analytics ◽  
Data Architecture ◽  
The Future ◽  
High Level ◽  
Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

scholarly journals Nudge Versus Boost: Agency Dynamics Under Libertarian Paternalism

2019 ◽  
Vol 130 (629) ◽  
pp. 1384-1415 ◽  
Author(s):  
Ralph Hertwig ◽  
Michael D Ryall
Keyword(s):  
Cognitive Biases ◽  
Policy Implications ◽  
Best Interests ◽  
Libertarian Paternalism ◽  
The Future ◽  
Set Up ◽  
The Individual

ABSTRACT Thaler and Sunstein (2008) advance the concept of ‘nudge’ policies—non-regulatory and non-fiscal mechanisms designed to enlist people's cognitive biases or motivational deficits so as to guide their behaviour in a desired direction. A core assumption of this approach is that policymakers make artful use of people's cognitive biases and motivational deficits in ways that serve the ultimate interests of the nudged individual. We analyse a model of dynamic policymaking in which the policymaker's preferences are not always aligned with those of the individual. One novelty of our set-up is that the policymaker has the option to implement a ‘boost’ policy, equipping the individual with the competence to overcome the nudge-enabling bias once and for all. Our main result identifies conditions under which the policymaker chooses not to boost in order to preserve the option of using the nudge (and its associated bias) in the future—even though boosting is in the immediate best interests of both the policymaker and the individual. We extend our analysis to situations in which the policymaker can be removed (e.g., through an election) and in which the policymaker is similarly prone to bias. We conclude with a discussion of some policy implications of these findings.

FEATURES

2015 ◽  
Vol 19 (10) ◽  
pp. 17-35 ◽  
Keyword(s):  
Big Data ◽  
Disease Management ◽  
Clinical Research ◽  
Health Informatics ◽  
Healthcare Management ◽  
Spatial Awareness ◽  
Digital Tool ◽  
Future Health ◽  
Research Sector ◽  
The Future

Amplifying Spatial Awareness via GIS — Tech which brings Healthcare Management, Preventative & Predictive Measures under the same Cloud When it is not just about size, you gotta' be Smart, too! Chew on It! How Singapore-based health informatics company MHC Asia Group crunches big-data to uncover your company's health Digital tool when well-used, it is Passion Carving the Digital Route to Wellness Big Data, Bigger Disease Management and Current preparations to manage the Future Health of Singaporeans A Conversation with Mr Arun Puri Extreme Networks: Health Solutions Big Data in Clinical Research Sector

Policy analysis in think tanks

2017 ◽  
Author(s):  
Mauricio I. Dussauge-Laguna ◽  
Marcela I. Vazquez
Keyword(s):  
Policy Analysis ◽  
Communication Channels ◽  
Relevant Information ◽  
Think Tanks ◽  
The Third ◽  
The Future ◽  
Future Challenges ◽  
The Impact

The chapter provides an overview of how policy analysis takes place in Mexican Think Tanks. It focuses on two of the few organisations of this kind that currently exist in the country: the Centro de Investigación para el Desarrollo (CIDAC, or Centre for Research for Development) and the Centro de Estudios Espinosa Yglesias (CEEY, or Centre of Studies Espinosa Yglesias). The chapter is divided into four sections. The first discusses the main features of think tanks, with a particular focus on the Mexican ones. The second presents the origins and general objectives of CIDAC and CEEY, and describes how these two organizations conduct policy analysis. The third compares both cases, paying particular attention to how they define their topics of interest, how they gather relevant information, what kind of policy products they generate, what kind of communication channels they use, and how they assess the impact that their analyses may have had. The chapter closes with some conclusions and general remarks about the future challenges of policy analysis in Mexican think tanks.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

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