New Economic Analysis of Law: Beyond Technocracy and Market Design

This special issue on New Economic Analysis of Law features illuminating syntheses of social science and law. What would law and economics look like if macroeconomics were a concern of scholars now focused entirely on microeconomics? Do emerging online phenomena, such as algorithmic pricing and platform capitalism, promise to perfect economic theories of market equilibrium, or challenge their foundations? How did simplified economic models gain ideological power in policy circles, and how can they be improved or replaced? This issue highlights scholars whose work has made the legal academy more than an “importer” of ideas from other disciplines—and who have, instead, shown that rigorous legal analysis is fundamental to understanding economic affairs.The essays in this issue should help ensure that policymakers’ turn to new economic thinking promotes inclusive prosperity. Listokin, Bayern, and Kwak have identified major aporias in popular applications of law and economics methods. Ranchordás, Stucke, and Ezrachi have demonstrated that technological fixes, ranging from digital ranking and rating systems to artificial intelligence-driven personal assistants, are unlikely to improve matters unless they are wisely regulated. McCluskey and Rahman offer a blueprint for democratic regulation, which shapes the economy in productive ways and alleviates structural inequalities. Taken as a whole, this issue of Critical Analysis of Law shows that legal thinkers are not merely importers of ideas and models from economics, but also active participants, with a great deal to contribute to social science research

The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

The principle goal of computational mechanics is to define pattern and structure so that the organization of complex systems can be detected and quantified. Computational mechanics developed from efforts in the 1970s and early 1980s to identify strange attractors as the mechanism driving weak fluid turbulence via the method of reconstructing attractor geometry from measurement time series and in the mid-1980s to estimate equations of motion directly from complex time series. In providing a mathematical and operational definition of structure it addressed weaknesses of these early approaches to discovering patterns in natural systems. Since then, computational mechanics has led to a range of results from theoretical physics and nonlinear mathematics to diverse applications---from closed-form analysis of Markov and non-Markov stochastic processes that are ergodic or nonergodic and their measures of information and intrinsic computation to complex materials and deterministic chaos and intelligence in Maxwellian demons to quantum compression of classical processes and the evolution of computation and language. This brief review clarifies several misunderstandings and addresses concerns recently raised regarding early works in the field (1980s). We show that misguided evaluations of the contributions of computational mechanics are groundless and stem from a lack of familiarity with its basic goals and from a failure to consider its historical context. For all practical purposes, its modern methods and results largely supersede the early works. This not only renders recent criticism moot and shows the solid ground on which computational mechanics stands but, most importantly, shows the significant progress achieved over three decades and points to the many intriguing and outstanding challenges in understanding the computational nature of complex dynamic systems.Comment: 11 pages, 123 citations; http://csc.ucdavis.edu/~cmg/compmech/pubs/cmr.ht

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

A Future of Failure? The Flow of Technology Talent into Government and Civil Society

This report is an evaluation of the technology talent landscape shows a severe paucity of individuals with technical skills in computer science, data science, and the Internet or other information technology expertise in civil society and government. It investigates broadly the health of the talent pipeline that connects individuals studying or working in information technology-related disciplines to careers in public sector and civil society institutions. Barriers to recruitment and retention of individuals with the requisite skills include compensation, a perceived inability to pursue groundbreaking work, and cultural aversion to innovation

Psychopower and Ordinary Madness: Reticulated Dividuals in Cognitive Capitalism

Despite the seemingly neutral vantage of using nature for widely-distributed computational purposes, neither post-biological nor post-humanist teleology simply concludes with the real "end of nature" as entailed in the loss of the specific ontological status embedded in the identifier "natural." As evinced by the ecological crises of the Anthropocene—of which the 2019 Brazil Amazon rainforest fires are only the most recent—our epoch has transfixed the “natural order" and imposed entropic artificial integration, producing living species that become “anoetic,” made to serve as automated exosomatic residues, or digital flecks. I further develop Gilles Deleuze’s description of control societies to upturn Foucauldian biopower, replacing its spacio-temporal bounds with the exographic excesses in psycho-power; culling and further detailing Bernard Stiegler’s framework of transindividuation and hyper-control, I examine how becoming-subject is predictively facilitated within cognitive capitalism and what Alexander Galloway terms “deep digitality.” Despite the loss of material vestiges qua virtualization—which I seek to trace in an historical review of industrialization to postindustrialization—the drive-based and reticulated "internet of things" facilitates a closed loop from within the brain to the outside environment, such that the aperture of thought is mediated and compressed. The human brain, understood through its material constitution, is susceptible to total datafication’s laminated process of “becoming-mnemotechnical,” and, as neuroplasticity is now a valid description for deep-learning and neural nets, we are privy to the rebirth of the once-discounted metaphor of the “cybernetic brain.” Probing algorithmic governmentality while posing noetic dreaming as both technical and pharmacological, I seek to analyze how spirit is blithely confounded with machine-thinking’s gelatinous cognition, as prosthetic organ-adaptation becomes probabilistically molded, networked, and agentially inflected (rather than simply externalized)

Enabling Entrepreneurial Ecosystems

Inspired by research on the importance of entrepreneurship for sustained economic growth and improved wellbeing, many governments and non-governmental grantmaking organizations have sought over the past decade to implement policies and programs intended to support entrepreneurs. Over this interval, growing appreciation of the limits of strategies focused narrowly on financing or training entrepreneurs has prompted a number of such entities to shift their efforts toward more broadbased strategies aimed at enabling "entrepreneurial ecosystems" at the city or sub-national regional scale.This paper takes the metaphor of the "ecosystem" seriously, seeking to draw lessons from evolutionary biology and ecology to inform policy for entrepreneurship. In so doing, the paper provides a framework for data gathering and analysis of practical value in assessing the vibrancy of entrepreneurial ecosystems