Can Terrorism Abroad Influence Migration Attitudes at Home?

This article demonstrates that public opinion on migration “at home” is systematically driven by terrorism in other countries. Although there is little substantive evidence linking refugees or migrants to most recent terror attacks in Europe, news about terrorist attacks can trigger more negative views of immigrants. However, the spatial dynamics of this process are neglected in existing research. We argue that feelings of imminent danger and a more salient perception of migration threats do not stop at national borders. The empirical results based on spatial econometrics and data on all terrorist attacks in Europe for the post-9/11 period support these claims. The effect of terrorism on migration concern is strongly present within a country, but also diffuses across states in Europe. This finding improves our understanding of public opinion on migration, spill-over effects of terrorism, and it highlights crucial lessons for scholars interested in the security implications of population movements

Simplicity in Visual Representation: A Semiotic Approach

Simplicity, as an ideal in the design of visual representations, has not received systematic attention. High-level guidelines are too general, and low-level guidelines too ad hoc, too numerous, and too often incompatible, to serve in a particular design situation. This paper reviews notions of visual simplicity in the literature within the analytical framework provided by Charles Morris' communication model, specifically, his trichotomy of communication levels—the syntactic, the semantic, and the pragmatic. Simplicity is ultimate ly shown to entail the adjudication of incompatibilities both within, and between, levels.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68281/2/10.1177_105065198700100103.pd

'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing Systems (CHI'18), April 21--26, Montreal, Canad

Efficiently Learning Structured Distributions from Untrusted Batches

We study the problem, introduced by Qiao and Valiant, of learning from untrusted batches. Here, we assume $m$ users, all of whom have samples from some underlying distribution $p$ over $1, \ldots, n$. Each user sends a batch of $k$ i.i.d. samples from this distribution; however an $\epsilon$-fraction of users are untrustworthy and can send adversarially chosen responses. The goal is then to learn $p$ in total variation distance. When $k = 1$ this is the standard robust univariate density estimation setting and it is well-understood that $\Omega (\epsilon)$ error is unavoidable. Suprisingly, Qiao and Valiant gave an estimator which improves upon this rate when $k$ is large. Unfortunately, their algorithms run in time exponential in either $n$ or $k$. We first give a sequence of polynomial time algorithms whose estimation error approaches the information-theoretically optimal bound for this problem. Our approach is based on recent algorithms derived from the sum-of-squares hierarchy, in the context of high-dimensional robust estimation. We show that algorithms for learning from untrusted batches can also be cast in this framework, but by working with a more complicated set of test functions. It turns out this abstraction is quite powerful and can be generalized to incorporate additional problem specific constraints. Our second and main result is to show that this technology can be leveraged to build in prior knowledge about the shape of the distribution. Crucially, this allows us to reduce the sample complexity of learning from untrusted batches to polylogarithmic in $n$ for most natural classes of distributions, which is important in many applications. To do so, we demonstrate that these sum-of-squares algorithms for robust mean estimation can be made to handle complex combinatorial constraints (e.g. those arising from VC theory), which may be of independent technical interest.Comment: 46 page

Genetic influences on spatial ability: Transmission in an extended kindred

Transmission of six spatial tests, Card Rotations, Cube Comparisons, Group Embedded Figures, Hidden Patterns, Mental Rotations, and portable Rod and Frame, is examined among 73 members in four generations of an extended kindred. Nonadditive genetic variance is substantial for one of the six tests, Card Rotations. Whether this nonadditive genetic variance is due to a major autosomal gene is equivocal based on results from segregation and linkage analysis. There is no evidence for genetic variance for Mental Rotations or Hidden Patterns, in contrast to previous findings suggesting major gene involvement (Ashton et al. , 1979). If spatial ability is due, in part, to an autosomal major gene, the gene has variable expression (reflected in different tests) or genetic heterogeneity is pronounced.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44106/1/10519_2005_Article_BF01065907.pd