Modeling Through

Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can. Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article that stand to enhance policymaking. Computer-aided modeling holds promise in delivering on the broader goals of forecasting and systems analysis developed in the 1970s, arming policymakers with the means to anticipate the impacts of state intervention along several lines—to model, instead of muddle. A few policymakers have already dipped a toe into these waters, others are being told that the water is warm. The prospect that economic, physical, and even social forces could be modeled by machines confronts policymakers with a paradox. Society may expect policymakers to avail themselves of techniques already usefully deployed in other sectors, especially where statutes or executive orders require the agency to anticipate the impact of new rules on particular values. At the same time, “modeling through” holds novel perils that policymakers may be ill equipped to address. Concerns include privacy, brittleness, and automation bias, all of which law and technology scholars are keenly aware. They also include the extension and deepening of the quantifying turn in governance, a process that obscures normative judgments and recognizes only that which the machines can see. The water may be warm, but there are sharks in it. These tensions are not new. And there is danger in hewing to the status quo. As modeling through gains traction, however, policymakers, constituents, and academic critics must remain vigilant. This being early days, American society is uniquely positioned to shape the transition from muddling to modeling

The Internet of Things Connectivity Binge: What are the Implications?

Despite wide concern about cyberattacks, outages and privacy violations, most experts believe the Internet of Things will continue to expand successfully the next few years, tying machines to machines and linking people to valuable resources, services and opportunities

Modeling Through

Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers cannot see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can. Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article that stand to enhance policymaking. Computer-aided modeling holds promise in delivering on the broader goals of forecasting and system analysis developed in the 1970s, arming policymakers with the means to anticipate the impacts of state intervention along several lines—to model, instead of muddle. A few policymakers have already dipped a toe into these waters, others are being told that the water is warm. The prospect that economic, physical, and even social forces could be modeled by machines confronts policymakers with a paradox. Society may expect policymakers to avail themselves of techniques already usefully deployed in other sectors, especially where statutes or executive orders require the agency to anticipate the impact of new rules on particular values. At the same time, “modeling through” holds novel perils that policymakers may be ill-equipped to address. Concerns include privacy, brittleness, and automation bias of which law and technology scholars are keenly aware. They also include the extension and deepening of the quantifying turn in governance, a process that obscures normative judgments and recognizes only that which the machines can see. The water may be warm but there are sharks in it. These tensions are not new. And there is danger in hewing to the status quo. (We should still pursue renewable energy even though wind turbines as presently configured waste energy and kill wildlife.) As modeling through gains traction, however, policymakers, constituents, and academic critics must remain vigilant. This being early days, American society is uniquely positioned to shape the transition from muddling to modeling

Connectivism, Chaos and Chaoids: How Practitioners Might Find Inspiration from Chaos to Find New Spaces for Teaching and Learning

The rapid development of Web 2.0 technologies has created excitement and opportunity alongside fear and confusion. It seems no part of society, culture, economy and human life generally has been untouched as a new sense of chaos emerges. Across all sectors change has been experienced with a mixture of terror and exhilaration as disruption offers opportunity while often creating more oppressive structures than before. Alongside technological development has been the proliferation of a neoliberal takeover of the ways we live, work and educate; A social condition that Mark Fisher (2010) calls capitalist realism.  The impact of this growing sense of chaos on education seems significant if uncertain, generating transformative rhetoric if often ambiguous around what has been transformed. This paper looks at adult education as a space being fought over by increasingly corporate institutions and sees one thread of resistance, connectivism – a ‘new learning theory for the digital age’ - introducing chaos theory as a means of resistance. The paper goes on to argue that connectivism offers practical reflections without clear purpose. We need the philosophical purpose of Deleuze and Guattari’s approach to chaoids and chaos to go from identifying patterns to creating new forms of creating order. The paper includes a discussion on where we are now; what the significance of these two approaches to chaos are; provides exemplars of chaoids that respond to the challenge and provide alternative models of education

After the Gold Rush: The Boom of the Internet of Things, and the Busts of Data-Security and Privacy

This Article addresses the impact that the lack of oversight of the Internet of Things has on digital privacy. While the Internet of Things is but one vehicle for technological innovation, it has created a broad glimpse into domestic life, thus triggering several privacy issues that the law is attempting to keep pace with. What the Internet of Things can reveal is beyond the control of the individual, as it collects information about every practical aspect of an individual’s life, and provides essentially unfettered access into the mind of its users. This Article proposes that the federal government and the state governments bend toward consumer protection while creating a cogent and predictable body of law surrounding the Internet of Things. Through privacy-by-design or self-help, it is imperative that the Internet of Things—and any of its unforeseen progeny—develop with an eye toward safeguarding individual privacy while allowing technological development

Conversations on a probable future: interview with Beatrice Fazi

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Data literacy on the road: Setting up a large-scale data literacy initiative in the DataBuzz project

This paper presents the DataBuzz Project. DataBuzz is a high-tech, mobile educational lab, which is housed in a 13-meter electric bus. Its specific goal is to increase the data literacy of different segments of society in the Brussels region through inclusive and participatory games and workshops. In this paper, we will explore how to carry out practical data literacy initiatives geared to the general public. We discuss the different interactive workshops, which have been specifically developed for DataBuzz. We highlight the background, design choices, and execution of this large-scale data literacy initiative. We describe the factors that need to be taken into account to reach successful execution for such an ambitious project and the actions undertaken to become a long-term, sustainable solution. Throughout the article, we use the Data Literacy Competence Model as an analytical lens to analyse individual projects on data literacy and DataBuzz as an integrated project