The 1940s saw the reconciliation of mathematical wartime techniques with social scientific theorizing. This chapter examines how the economy was depicted as a huge optimization problem that would soon be solvable by electronic computers. Investigating input–output analysis as it was done at the Harvard Economic Research Project (HERP) under the directorship of Wassily Leontief illustrates the difficulties of making an economic abstraction work in measurement practice. Chapter 3 draws a trajectory to the Conference of Activity Analysis of 1949, where mathematical economists combined techniques of linear programming with what they saw as conventional economics. The move from planning tools to devices for theoretical speculation came along with a shift in modeling philosophies and notions of realism. Focusing entirely on mathematical formalisms and abandoning the concern with measurement brought about the main research object of the economics profession in the subsequent years: The economy as a flexible and efficient system of production in the form of a system of simultaneous equations. This was the economy that provided the primary point of reference for Solow’s model.

1 Introduction

2 Defining an Evolving Divide

3 The Meaning of “Access” Evolves

4 Theorizing the Digital Divide

5 Historical Perspective

6 Government Initiatives

  6.1 Public and Private Initiatives

  6.2 K-12 Research in the United States

  6.3 Lack of Permission to Access Technology

  6.4 Differences between Home and School

  6.5 School Policies and Procedures Restrict Use

  6.6 The Power of Teachers on Student Outcomes

7 The Global K-12 Digital Divide

  7.1 The Divide between Developed and Developing Countries

  7.2 Within-Country Digital Divides

8 The Divide for South Africa’s Students

9 The Future of the Divide

Computer modeling of specific psychological processes began over fifty years ago. Cognitive scientists do not use computers merely as tools, but also as inspiration about the nature of mental processes. Computational cognitive science has a long way to go. There are many unanswered questions.However, cognitive scientists believe that the mind/brain is in principle intelligible in terms of whatever turns out to be the best theory of what computers can do. The overview of cognitive science given in this chapter should suffice to show that significant progress has been made.

After a brief orientation to logic-based (computational) cognitive modeling, the necessary preliminaries are discussed in this chapter (e.g., what a logic is, and what it is for one to “capture" human cognition are explained). Three “microworlds" or domains that all readers should be comfortably familiar with (natural numbers and arithmetic; everyday vehicles; and residential schools, e.g., colleges and universities) are introduced, in order to facilitate exposition in the chapter. Then the ever-expanding universe U of formal logics, with an emphasis on three categories therein, is given: deductive logics having no provision for directly modeling cognitive states; nondeductive logics suitable for modeling rational belief through time without machinery to directly model cognitive states such as believes and knows; and finally, nondeductive logics that enable the kind of direct modeling of cognitive states absent from the first two types of logic. Then, there follows a focus spcifically on two important aspects of human-level cognition to be modeled in logic-based fashion: the processing of quantification, and defeasible (or nonmonotonic) reasoning. Finally, a brief evaluation of logic-based cognitive modeling is provided, together with some comparison to other approaches to cognitive modeling, and the future of the discipline is considered. The chapter presupposes nothing more than high-school mathematics of the standard sort on the part of the reader.

The burst of information generated by new statistical projects made the question of calculation paramount. The masses of data that the new National Sample Surveys yielded, and the increasing complexity of planning models, had made the state’s data processing needs evident. Chapter 3 reveals the campaign led by Mahalanobis and the Indian Statistical Institute to bring India its first computers. Unlike in other parts of the world, computers were not sought for military purposes in India. Instead, India pursued them because they were seen as a solution to central planning’s most knotty puzzle, that of big data. The chapter follows the decade-long quest to import computers from the United States, Europe, and the U.S.S.R, unearthing the Cold War politics in which it inevitably became embroiled. Overall, Part I of this book demonstrates the building of a technocratic, data-hungry, high-modernist state and its attempts to make the economic realm more legible.

In an epilogue that examines the current shift from human to animatronic performers in film and video, this book concludes with a meditation on the contemporary ambition to “go viral.” The history of computer-generated imaging (CGI) technology suggests that, in its pixelated form, a new - if also contested - concept of self has emerged for the twenty-first century. While fantasies of disembodiment and photo-realistic avatars of domination and destruction persist in contemporary media, pixilation offers us an alternative model of self that presages a post-Anthropogenic era. By fracturing the contours of a reified identity, the pixelated image invites us to re-envision ourselves as multi-celled organisms capable of co-existing with other such life forms within a shared biosphere. Inscribed in this new performance form, in other words, is yet more evidence for the ways we seek to comprehend and adapt to changes in our ever-modernizing world.

Determining whom to trust and whom not to trust has been critical since the early days of ancient civilizations. However, with the increasing use of digital technologies, trust situations have changed. We communicate less face-to-face. Rather, we communicate with other people over the Internet (e.g., Facebook) or we interact with technological artifacts (e.g., chatbots on the Internet or autonomous vehicles). This trend towards digitalization has major implications. It affects both the role of trust and how we should conceptualize trust and trustworthiness. In this chapter, insights on phenomena related to trust in a digital world are reviewed. This review integrates findings from various levels of analysis, including behavioral and neurophysiological. The structure of this chapter is based on four different scenarios of trust in a digital world that were developed by the author. Scenario A describes a technology-free situation of human-human interaction. Scenario B outlines a situation of computer-mediated human-human interaction. Scenario C denotes a situation of direct human-technology interaction. Scenario D refers to a situation of computer-mediated human-technology interaction. The common denominator of all situations is that a human acts in the role of trustor, while the role of trustee can be either another human or a technological artifact.