University of California, Santa Barbara

About: University of California, Santa Barbara is a education organization based out in Santa Barbara, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 30281 authors who have published 80852 publications receiving 4626827 citations. The organization is also known as: UC Santa Barbara & UCSB.

The Economic Impacts of Climate Change: Evidence from Agricultural Profits and Random Fluctuations in Weather

Abstract: This paper measures the economic impact of climate change on US agricultural land by estimating the effect of the presumably random year-to-year variation in temperature and precipitation on agricultural profits. Using long-run climate change predictions from the Hadley 2 Model, the preferred estimates indicate that climate change will lead to a $1.3 billion (2002$) or 4.0% increase in annual profits. The 95% confidence interval ranges from -$0.5 billion to $3.1 billion and the impact is robust to a wide variety of specification checks, so large negative or positive effects are unlikely. There is considerable heterogeneity in the effect across the country with California's predicted impact equal to -$0.75 billion (or nearly 15% of state agricultural profits). Further, the analysis indicates that the predicted increases in temperature and precipitation will have virtually no effect on yields among the most important crops, which suggest that the small effect on profits are not due to short-run price increases. The paper also implements the hedonic approach that is predominant in the previous literature and finds that it may be unreliable, because it produces estimates of the effect of climate change that are extremely sensitive to seemingly minor decisions about the appropriate control variables, sample and weighting. Overall, the findings contradict the popular view that climate change will have substantial negative welfare consequences for the US agricultural sector.

Quasinormal modes and holography

Abstract: Quasinormal frequencies of electromagnetic and gravitational perturbations in asymptotically anti-de Sitter spacetime can be identified with poles of the corresponding real-time Green's functions in a holographically dual finite temperature field theory. The quasinormal modes are defined for gauge-invariant quantities which obey an incoming-wave boundary condition at the horizon and a Dirichlet condition at the boundary. As an application, we explicitly find poles of retarded correlation functions of R-symmetry currents and the energy-momentum tensor in strongly coupled finite temperature N=4 supersymmetric SU(Nc) Yang-Mills theory in the limit of large Nc.

Limiting the spread of misinformation in social networks

Abstract: In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.

Isolated and dynamical horizons and their applications

Abstract: Over the past three decades, black holes have played an important role in quantum gravity, mathematical physics, numerical relativity and gravitational wave phenomenology. However, conceptual settings and mathematical models used to discuss them have varied considerably from one area to another. Over the last five years a new, quasi-local framework was introduced to analyze diverse facets of black holes in a unified manner. In this framework, evolving black holes are modelled by dynamical horizons and black holes in equilibrium by isolated horizons. We review basic properties of these horizons and summarize applications to mathematical physics, numerical relativity, and quantum gravity. This paradigm has led to significant generalizations of several results in black hole physics. Specifically, it has introduced a more physical setting for black hole thermodynamics and for black hole entropy calculations in quantum gravity, suggested a phenomenological model for hairy black holes, provided novel techniques to extract physics from numerical simulations, and led to new laws governing the dynamics of black holes in exact general relativity.