Information feudalism: Who owns the knowledge economy? A book review

Back in 1995, Peter Drahos wrote a futuristic article called ‘Information feudalism in the information society’. It took the form of an imagined history of the information society in the year 2015. Drahos provided a pessimistic vision of the future, in which the information age was ruled by the private owners of intellectual property. He ended with the bleak, Hobbesian image: "It is unimaginable that the information society of the 21st century could be like this. And yet if abstract objects fall out of the intellectual commons and are enclosed by private owners, private, arbitrary, unchecked global power will become a part of life in the information society. A world in which seed rights, algorithms, DNA, and chemical formulas are owned by a few, a world in which information flows can be coordinated by information-media barons, might indeed be information feudalism (p. 222)." This science fiction assumed that a small number of states would dominate the emerging international regulatory order set up under the World Trade Organization. In Information Feudalism: Who Owns the Knowledge Economy?, Peter Drahos and his collaborator John Braithwaite reprise and expand upon the themes first developed in that article. The authors contend: "Information feudalism is a regime of property rights that is not economicallyefficient, and does not get the balance right between rewarding innovation and diffusing it. Like feudalism, it rewards guilds instead of inventive individual citizens. It makes democratic citizens trespassers on knowledge that should be the common heritage of humankind, their educational birthright. Ironically, information feudalism, by dismantling the publicness of knowledge, will eventually rob the knowledge economy of much of its productivity (p. 219)." Drahos and Braithwaite emphasise that the title Information Feudalism is not intended to be taken at face value by literal-minded readers, and crudely equated with medieval feudalism. Rather, the title serves as a suggestive metaphor. It designates the transfer of knowledge from the intellectual commons to private corporation under the regime of intellectual property.

Unitary Taxation of the Finance Sector

Multinational financial institutions (MNFIs) play a significant role in financing the activities of their clients in developing nations. Consistent with the ‘follow-the-customer’ phenomenon which explains financial institution expansion, these entities are increasingly profiting from activities associated with this growing market. However, not only are MNFIs persistent users of tax havens, but also, more than other industries, have the opportunity to reduce tax through transfer pricing measures. This paper establishes a case for an industry-specific adoption of unitary taxation with formulary apportionment as a viable alternative to the current regime. In doing so, it considers the practicalities of implementing this by examining both definitional issues and possible formulas for MNFIs. This paper argues that, while there would be implementation difficulties to overcome, the current domestic models of formulary apportionment provide important guidance as to how the unitary business and business activities of MNFIs should be defined, as well as the factors that should be included in an allocation formula, and the appropriate weighting. This paper concludes that unitary taxation with formulary apportionment is a viable industry-specific alternative for MNFIs.

A preconditioned numerical solver for stiff nonlinear reaction-diffusion equations with fractional Laplacians that avoids dense matrices

The numerical solution of fractional partial differential equations poses significant computational challenges in regard to efficiency as a result of the spatial nonlocality of the fractional differential operators. The dense coefficient matrices that arise from spatial discretisation of these operators mean that even one-dimensional problems can be difficult to solve using standard methods on grids comprising thousands of nodes or more. In this work we address this issue of efficiency for one-dimensional, nonlinear space-fractional reaction–diffusion equations with fractional Laplacian operators. We apply variable-order, variable-stepsize backward differentiation formulas in a Jacobian-free Newton–Krylov framework to advance the solution in time. A key advantage of this approach is the elimination of any requirement to form the dense matrix representation of the fractional Laplacian operator. We show how a banded approximation to this matrix, which can be formed and factorised efficiently, can be used as part of an effective preconditioner that accelerates convergence of the Krylov subspace iterative solver. Our approach also captures the full contribution from the nonlinear reaction term in the preconditioner, which is crucial for problems that exhibit stiff reactions. Numerical examples are presented to illustrate the overall effectiveness of the solver.

Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data

A key component of robotic path planning is ensuring that one can reliably navigate a vehicle to a desired location. In addition, when the features of interest are dynamic and move with oceanic currents, vehicle speed plays an important role in the planning exercise to ensure that vehicles are in the right place at the right time. Aquatic robot design is moving towards utilizing the environment for propulsion rather than traditional motors and propellers. These new vehicles are able to realize significantly increased endurance, however the mission planning problem, in turn, becomes more difficult as the vehicle velocity is not directly controllable. In this paper, we examine Gaussian process models applied to existing wave model data to predict the behavior, i.e., velocity, of a Wave Glider Autonomous Surface Vehicle. Using training data from an on-board sensor and forecasting with the WAVEWATCH III model, our probabilistic regression models created an effective method for forecasting WG velocity.