CLIMATE POLICY WHEN THE DISTANT FUTURE MATTERS: CATASTROPHIC EVENTS WITH HYPERBOLIC DISCOUNTING

Low probability catastrophic climate change can have a signifcant influence on policy under hyperbolic discounting. We compare the set of Markov Perfect Equilibria (MPE) to the optimal policy under time-consistent commitment. For some initial levels of risk there are multiple MPE; these may involve either excessive or insufficient stabilization effort. These results imply that even if the free-rider problem amongst contemporaneous decision-makers were solved, there may remain a coordination problem amongst successive generations of decision-makers. A numerical example shows that under plausible conditions society should respond vigorously to the threat of climate change.abrupt climate change, event uncertainty, catastrophic risk, hyperbolic discounting, Markov Perfect Equilibria, Environmental Economics and Policy, C61, C73, D63, D99, Q54,

DISCOUNTING AND CLIMATE CHANGE POLICY

A constant social discount rate cannot reflect both a reasonable opportunity cost of public funds and an ethically defensible concern for generations in the distant future. We use a model of hyperbolic discounting that achieves both goals. We imbed this discounting model in a simple climate change model to calculate constant equivalent discount rates" and plausible levels of expenditure to control climate change. We compare these results to discounting assumptions and policy recommendations in the Stern Review on Climate Change.discounting, climate change modeling, Stern Review, Markov Perfect Equilibria, Environmental Economics and Policy, C61, C73, D63, D99, Q54,

Macro-micro feedback links of water management in South Africa : CGE analyses of selected policy regimes

The pressure on an already stressed water situation in South Africa is predicted to increase significantly under climate change, plans for large industrial expansion, observed rapid urbanization, and government programs to provide access to water to millions of previously excluded people. The present study employed a general equilibrium approach to examine the economy-wide impacts of selected macro and water related policy reforms on water use and allocation, rural livelihoods, and the economy at large. The analyses reveal that implicit crop-level water quotas reduce the amount of irrigated land allocated to higher-value horticultural crops and create higher shadow rents for production of lower-value, water-intensive field crops, such as sugarcane and fodder. Accordingly, liberalizing local water allocation in irrigation agriculture is found to work in favor of higher-value crops, and expand agricultural production and exports and farm employment. Allowing for water trade between irrigation and non-agricultural uses fueled by higher competition for water from industrial expansion and urbanization leads to greater water shadow prices for irrigation water with reduced income and employment benefits to rural households and higher gains for non-agricultural households. The analyses show difficult tradeoffs between general economic gains and higher water prices, making irrigation subsidies difficult to justify.Water Supply and Sanitation Governance and Institutions,Town Water Supply and Sanitation,Water Supply and Systems,Water and Industry,Water Conservation

Predicting Rising Follower Counts on Twitter Using Profile Information

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy, NY, US

Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions. Our analysis is based on the spread of low-level authoring conventions in the eprint arXiv over 24 years: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of them; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time--and at the individual level about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at https://github.com/CornellNLP/Macro

Comparison of Spectra in Unsequenced Species

International audienceWe introduce a new algorithm for the mass spectromet- ric identication of proteins. Experimental spectra obtained by tandem MS/MS are directly compared to theoretical spectra generated from pro- teins of evolutionarily closely related organisms. This work is motivated by the need of a method that allows the identication of proteins of unsequenced species against a database containing proteins of related organisms. The idea is that matching spectra of unknown peptides to very similar MS/MS spectra generated from this database of annotated proteins can lead to annotate unknown proteins. This process is similar to ortholog annotation in protein sequence databases. The difficulty with such an approach is that two similar peptides, even with just one mod- ication (i.e. insertion, deletion or substitution of one or several amino acid(s)) between them, usually generate very dissimilar spectra. In this paper, we present a new dynamic programming based algorithm: Packet- SpectralAlignment. Our algorithm is tolerant to modications and fully exploits two important properties that are usually not considered: the notion of inner symmetry, a relation linking pairs of spectrum peaks, and the notion of packet inside each spectrum to keep related peaks together. Our algorithm, PacketSpectralAlignment is then compared to SpectralAlignment [1] on a dataset of simulated spectra. Our tests show that PacketSpectralAlignment behaves better, in terms of results and execution tim

On the Origins of Memes by Means of Fringe Web Communities

Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, over the course of 13 months. We group the images posted on fringe Web communities (/pol/, Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that /pol/ substantially influences the meme ecosystem with the number of memes it produces, while \td has a higher success rate in pushing them to other communities.Comment: A shorter version of this paper appears in the Proceedings of 18th ACM Internet Measurement Conference (IMC 2018). This is the full versio

Intracoronary Injection of In Situ Forming Alginate Hydrogel Reverses Left Ventricular Remodeling After Myocardial Infarction in Swine

ObjectivesThis study sought to determine whether alginate biomaterial can be delivered effectively into the infarcted myocardium by intracoronary injection to prevent left ventricular (LV) remodeling early after myocardial infarction (MI).BackgroundAlthough injectable biomaterials can improve infarct healing and repair, the feasibility and effectiveness of intracoronary injection have not been studied.MethodsWe prepared a calcium cross-linked alginate solution that undergoes liquid to gel phase transition after deposition in infarcted myocardium. Anterior MI was induced in swine by transient balloon occlusion of left anterior descending coronary artery. At 4 days after MI, either alginate solution (2 or 4 ml) or saline was injected selectively into the infarct-related coronary artery. An additional group (n = 19) was treated with incremental volumes of biomaterial (1, 2, and 4 ml) or 2 ml saline and underwent serial echocardiography studies.ResultsExamination of hearts harvested after injection showed that the alginate crossed the infarcted leaky vessels and was deposited as hydrogel in the infarcted tissue. At 60 days, control swine experienced an increase in left ventricular (LV) diastolic area by 44%, LV systolic area by 45%, and LV mass by 35%. In contrast, intracoronary injection of alginate (2 and 4 ml) prevented and even reversed LV enlargement (p < 0.01). Post-mortem analysis showed that the biomaterial (2 ml) increased scar thickness by 53% compared with control (2.9 ± 0.1 mm vs. 1.9 ± 0.3 mm; p < 0.01) and was replaced by myofibroblasts and collagen.ConclusionsIntracoronary injection of alginate biomaterial is feasible, safe, and effective. Our findings suggest a new percutaneous intervention to improve infarct repair and prevent adverse remodeling after reperfused MI

Modeling the efficient access of full-text information

The title of this paper describes a research goal set by many offices within US DOE. This paper reviews efficient full-text searching techniques being developed to better understand and meet this goal. Classical computer human interaction (CHI) approaches provided by commercial information retrieval (IR) engines fail to contextualize information in ways that facilitate timely decision making. Use of advanced CHI techniques (eg, visualization) in combination with deductive database technology augment the weaknesses found in presentation capabilities of IR engines and are discussed. Various techniques employed in a Web-based prototype system currently under development are presented

An implementation model for reasoning with complex objects

Voir fichie