Just ticking the box: A social informatics model of the consequences of consent

Given the societal diffusion, proliferation and ubiquity of computerised systems and platforms, it is generally perceived by consumers that systems and eBusiness platforms often pose a threat to the privacy of their supplied information (Srnicek, 2017; Andreotti et al., 2018). Furthermore, as we see the replacement of systems that were once manual and paper-based migrate to digital processes and information systems (Lunt et al., 2019), consent in the information era is reduced to ‘Yes’ or ‘No’ option, often in the form of a tick box. Additionally, despite the arrival of the General Data Protection Regulation in 2018 as means to provide protection in relation to data processing, we argue that there is a lack of transparency in relation to the intention of this data processing and secondary data use for the purposes of research and marketing, for example. In light of this, we argue that there exists an increasingly difficult challenge to establish a mutual understanding of what consent actually is and what the wider permutations of it represents and comprehends. The lack of mutual understanding, in a digital world that is becoming increasingly reliant on the perceived benefits of acquiring and processing large sets of data (Kitchin, 2014; Breidbach et al., 2019) is deeply problematic. It is not only problematic for the consumer, but also to system developers, platform owners, and data processors alike. To this end, this paper presents a model, derived from action research, which positions the concept of consent within a socio-technical framing. This model approaches consent, in the context of digital platforms and eBusiness and how it comes to be represented in information systems, as a socio-technical construct of moral orders that imbues the feelings, convictions and aspirations of the consumer as they are engaged in the use of digital systems. We offer that consent is merely approached as an attribute in a data model, rather than relaying the communicative understanding of the consumer. This model introduces the areas of information processing systems and information communication systems as two differing interpretations within which digital platforms can be perceived. We offer these two distinctions as a mechanism to explain and, more importantly, explore the notion of the governance of consent and how this comes to be manifested in information systems

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

This paper provides an analysis of how a firm's decision to serve a foreign market by exporting or by engaging in foreign direct investment (FDI) affects firm productivity, when productivity is endogeneous as a function of training. The main result of our paper is that, with endogeneous productivity, exporting results in lower productivity than does FDI, but exporting may result in higher or lower employment and output than does FDI. We also show that FDI has lower employment, higher training, higher wages and higher productivity than does production for the home market. A further interesting and unexpected result of our model is that exporting results in the same level of training and productivity as does production for the home market. However, under the same demand conditions, the exporting firm employs less labour for foreign production than for home production and, consequently, output for the foreign market is lower than output for the home market. In addition, we investigate the firm's decision to serve the foreign market by exporting or by engaging in FDI and determine parameter values for which either regime is chosen.International topics; Labour markets; Productivity

Combining Canadian Interest-Rate Forecasts

Model risk is a constant danger for financial economists using interest-rate forecasts for the purposes of monetary policy analysis, portfolio allocations, or risk-management decisions. Use of multiple models does not necessarily solve the problem as it greatly increases the work required and still leaves the question "which model forecast should one use?" Simply put, structural shifts or regime changes (not to mention possible model misspecifications) make it difficult for any single model to capture all trends in the data and to dominate all alternative approaches. To address this issue, we examine various techniques for combining or averaging alternative models in the context of forecasting the Canadian term structure of interest rates using both yield and macroeconomic data. Following Bolder and Liu (2007), we study alternative implementations of four empirical term structure models: this includes the Diebold and Li (2003) approach and three associated generalizations. The analysis is performed using more than 400 months of data ranging from January 1973 to July 2007. We examine a number of model-averaging schemes in both frequentist and Bayesian settings, both following the literature in this field (such as de Pooter, Ravazzolo and van Dijk (2007)) in addition to introducing some new combination approaches. The forecasts from individual models and combination schemes are evaluated in a number of ways; preliminary results show that model averaging generally assists in mitigating model risk, and that simple combination schemes tend to outperform their more complex counterparts. Such findings carry significant implications for central-banking analysis: a unified approach towards accounting for model uncertainty can lead to improved forecasts and, consequently, better decisions.Interest rates; Econometric and statistical methods

The Canadian Debt-Strategy Model: An Overview of the Principal Elements

As part of managing a debt portfolio, debt managers face the challenging task of choosing a strategy that minimizes the cost of debt, subject to limitations on risk. The Bank of Canada provides debt-management analysis and advice to the Government of Canada to assist in this task, with the Canadian debt-strategy model being developed to help in this regard. The authors outline the main elements of the model, which include: cost and risk measures, inflation-linked debt, optimization techniques, the framework used to model the government’s funding requirement, the sensitivity of results to the choice of joint stochastic macroeconomic term-structure model, the effects of shocks to macroeconomic and term-structure variables and changes to their long-term values, and the relationship between issuance yield and issuance amount. Emphasis is placed on the degree to which changes to the formulation of model elements impact key results. The model is an important part of the decision-making process for the determination of the government’s debt strategy. However, it remains one of many tools that are available to debt managers and is to be used in conjunction with the judgment of an experienced debt manager.Debt management; Econometric and statistical methods; Financial markets; Fiscal policy

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

The stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt-management strategy provides a wide variety of useful information. It does not, however, assist in determining an optimal debt-management strategy for the government in its current form. Including optimization in the debt-strategy model would be useful, since it could substantially broaden the range of policy questions that can be addressed. Finding such an optimal strategy is nonetheless complicated by two challenges. First, performing optimization with traditional techniques in a simulation setting is computationally intractable. Second, it is necessary to define precisely what one means by an "optimal" debt strategy. The authors detail a possible approach for addressing these two challenges. They address the first challenge by approximating the numerically computed objective function using a function-approximation technique. They consider the use of ordinary least squares, kernel regression, multivariate adaptive regression splines, and projection-pursuit regressions as approximation algorithms. The second challenge is addressed by proposing a wide range of possible government objective functions and examining them in the context of an illustrative example. The authors' view is that the approach permits debt and fiscal managers to address a number of policy questions that could not be fully addressed with the current stochastic simulation engine.Debt management; Econometric and statistical methods; Fiscal policy; Financial markets

Expression of natural killer receptor alleles at different Ly49 loci occurs independently and is regulated by major histocompatibility complex class I molecules.

Ly49 receptor genes are expressed by subsets of natural killer (NK) cells in an overlapping fashion, accounting for the capacity of NK subsets to attack host cells that have selectively downregulated self-major histocompatibility complex (MHC) class I molecules. It was shown previously that most NK cells express only one or the other allele of a given Ly49 gene, while a smaller population expresses both alleles. However, the methods used to detect monoallelic and biallelic cells were nonquantitative. Here, new allele-specific antibodies were used to provide the first quantitative examination of biallelic and monoallelic expression of Ly49A and Ly49G2. The results demonstrate conclusively that most Ly49A(+) and Ly49G2(+) NK cells express the corresponding gene in a monoallelic fashion, with a smaller subset expressing both alleles. Unexpectedly, biallelic Ly49A(+) NK cells were more numerous than predicted by completely independent allelic expression, suggesting some heterogeneity among NK progenitors in the potential to express a given Ly49 gene. The data also show that cells expressing one allele of Ly49G2 may express Ly49A from the same or opposite chromosome with equal likelihood, indicating that the expressed allele is chosen independently for different Ly49 genes. Finally, the data demonstrate that biallelic expression of Ly49A or Ly49G2 occurs least frequently in mice that express ligands for these receptors (H-2(d) mice), and most frequently in class I-deficient mice. Thus, biallelic expression of Ly49 genes is regulated by interactions of NK cell progenitors with MHC class I molecules

A Stochastic Simulation Framework for the Government of Canada's Debt Strategy

Debt strategy is defined as the manner in which a government finances an excess of government expenditures over revenues and any maturing debt issued in previous periods. The author gives a thorough qualitative description of the complexities of debt strategy analysis and then demonstrates that it is, in fact, a problem in stochastic optimal control. Although this formal definition is conceptually useful, the author recommends the use of simulation to help characterize the set of strategies that a government can use to fund its borrowing requirements. He then describes in detail a stochastic simulation framework, building from previous work in Bolder (2001, 2002); this framework forms one important element in the debt strategy decision-making process employed by the Government of Canada. The primary objective in constructing this stochastic simulation framework is to learn about the nature of the risk and cost trade-offs associated with different financing strategies. To this end, the paper includes a detailed description of the model; a set of possible debt cost and risk measures, including one potentially useful conditional risk measure; illustrative results under normal stochastic conditions; an analysis of the sensitivity of the results to various key model parameters; a novel approach to stress testing; and a possible framework for selecting a financing strategy, given assumptions about government risk preferences.Debt management; Econometric and statistical methods; Economic models