Efficient Information Theoretic Clustering on Discrete Lattices

We consider the problem of clustering data that reside on discrete, low dimensional lattices. Canonical examples for this setting are found in image segmentation and key point extraction. Our solution is based on a recent approach to information theoretic clustering where clusters result from an iterative procedure that minimizes a divergence measure. We replace costly processing steps in the original algorithm by means of convolutions. These allow for highly efficient implementations and thus significantly reduce runtime. This paper therefore bridges a gap between machine learning and signal processing.Comment: This paper has been presented at the workshop LWA 201

Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks

Properties of networks are often characterized in terms of features such as node degree distributions, average path lengths, diameters, or clustering coefficients. Here, we study shortest path length distributions. On the one hand, average as well as maximum distances can be determined therefrom; on the other hand, they are closely related to the dynamics of network spreading processes. Because of the combinatorial nature of networks, we apply maximum entropy arguments to derive a general, physically plausible model. In particular, we establish the generalized Gamma distribution as a continuous characterization of shortest path length histograms of networks or arbitrary topology. Experimental evaluations corroborate our theoretical results

Propagation Kernels

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains

Financial professionals and climate experts have diverging perspectives on climate action

To address the climate crisis, it is necessary to transform the economy, with the finance industry taking a central role by implementing sustainable investment policies. This study aims to understand the motivations and preferences of its key players—financial professionals and climate experts. Here we use an incentivized experiment to measure the willingness to forgo payout to curb carbon emissions and a survey to elicit attitudes and beliefs toward the climate crisis. We provide suggestive evidence that financial professionals have a lower willingness to curb carbon emissions, are less concerned about climate change, and are less supportive of carbon taxes compared to climate experts. We report differences in motivations and priorities, with financial professionals emphasizing economic and reputational considerations and climate experts prioritizing ecological and social consequences of the crisis. Our findings highlight the importance of financial incentives and reputational concerns in motivating financial professionals to address the climate crisis

Social risk effects: the 'experience of social risk' factor

Anticipating "social risk", or risk caused by humans, affects decision-making differently from anticipating natural risk. Drawing upon a large sample of the US population (n=3,982), we show that the phenomenon generalizes to risk experience. Experiencing adverse outcomes caused by another human reduces future risk-taking, but experiencing the same outcome caused by nature does not. While puzzling from a consequentialist perspective, the Experience of Social Risk Factor that we identify deepens our understanding of decision-making in settings in which outcomes are co-determined by different sources of uncertainty. Our findings imply that a unifying theory of social risk effects requires new explanations

The Behavioral Economics of Extreme Event Attribution

Can Attribution Science, a method for quantifying – ex post – humanity’s contribution to adverse climatic events, induce pro-environmental behavioral change? We conduct a conceptual test of this question by studying, in an online experiment with 3,031 participants, whether backwards-looking attribution affects future decisions, even when seemingly uninformative to a consequentialist decision-maker. By design, adverse events can arise as a result of participants’ pursuit of higher payoffs (anthropogenic cause) or as a result of chance (natural cause). Treatments vary whether adverse events are causally attributable and whether attribution can be acquired at cost. We find that ex-post attributability is behaviorally relevant: Attribution to an anthropogenic cause reduces future anthropogenic stress and leads to fewer adverse events compared to no attributability and compared to attribution to a natural cause. Average willingness-to-pay for ex-post attribution is positive. The conjecture that Attribution Science can be behaviorally impactful and socially valuable has empirical merit