Bargaining in the shadow of precedent: the surprising irrelevance of asymmetric stakes

We develop a model of bargaining and litigation in the context of patent licensing (or any contractual setting). Following Priest and Klein (1984) we developed a model that explicitly allows for (1) multiple parties (leading to asymmetry of stakes), (2) binding precedent, and (3) pre-dispute bargaining done in the “shadow” of precedent-setting courts. The pre-dispute bargaining creates an endogenous opportunity cost of litigation for both plaintiff and defendant; i.e., the harm is endogenous. We show that the effects of asymmetric stakes on the litigation rate and plaintiff win rate are offset by opportunity costs (forgone licensing). That is, the degree of asymmetry does not appear to substantially impact the rate of litigation or the observed win rate of plaintiffs at trial. This result is in stark contrast to the previous theoretical literature, and has implications for interpreting the empirical literature.

A control analysis perspective on Katz centrality

Methods for efficiently controlling dynamics propagated on networks are usually based on identifying the most influential nodes. Knowledge of these nodes can be used for the targeted control of dynamics such as epidemics, or for modifying biochemical pathways relating to diseases. Similarly they are valuable for identifying points of failure to increase network resilience in, for example, social support networks and logistics networks. Many measures, often termed `centrality', have been constructed to achieve these aims. Here we consider Katz centrality and provide a new interpretation as a steady-state solution to continuous-time dynamics. This enables us to implement a sensitivity analysis which is similar to metabolic control analysis used in the analysis of biochemical pathways. The results yield a centrality which quantifies, for each node, the net impact of its absence from the network. It also has the desirable property of requiring a node with a high centrality to play a central role in propagating the dynamics of the system by having the capacity to both receive flux from others and then to pass it on. This new perspective on Katz centrality is important for a more comprehensive analysis of directed networks

An investigation of machine-learning algorithms for the estimation of galaxy redshift

The next wave of large radio telescopes is being commissioned, with plans to observe deeper, in wider areas than ever before. The Evolutionary Map of the Universe (EMU) project is expected to increase the number of known radio galaxies from ∼2.5 million to ∼70 million, allowing for statistical studies of unprecedented size in the radio regime. However, most of the studies planned by the EMU project require redshift estimates. While the redshift measurements required don’t need to be measured to excellent resolution and can be roughly binned, they do require a low level of outliers. Even with recent advancements in multi-object spectroscopy, spectroscopic redshifts will only be possible for a small fraction of sources. The majority of the newly discovered radio sources will have limited multi-wavelength photometry, whereas traditional photometric template fitting methods requires high-quality, complete multiwavelength photometry. Previous research has used machine learning (ML) to estimate redshift, but has primarily focused on trying to match the best results provided by photometric template fitting, using the best, and most complete data available. For the most part, the datasets used are not radio-selected – which typically fail using photometric template fitting methods – and are limited in redshift. While Machine Learning (ML) techniques have proved to be effective, most have not been conclusively tested on radio-selected datasets, at the higher redshift ranges expected from the EMU project. In this thesis, I examine the utility of the k-NearestNeighbours (kNN) and Random Forest (RF) regression and classification algorithms for estimating the redshift of a source from its features. The kNN tests include using five different distance metrics. I use a radio-selected dataset, built from the Australia Telescope Large Area Survey (ATLAS) 1.4 GHz radio survey which was completed in anticipation of the EMU project, and has been observed to around the depth of the EMU project. The 1.4 GHz flux –measured by ATLAS – was combined with Infrared (IR) fluxes from the Spitzer Wide-area Infrared Extragalactic Survey (SWIRE), optical magnitudes from the DES, and spectroscopic redshi. measurements from the OzDES. Based on the combined multi-wavelength catalogue, I create three datasets. Dataset A consists of all sources with a spectroscopic redshift, with the sources with missing observations included, and those missing values filled with the mean of that feature across the entire dataset. Dataset B is a subset of Dataset A, with those sources without complete multi-wavelength photometry removed. Dataset C is a subset of Dataset B, with the sources removed that have optical or IR photometry below the detection limits of all-sky surveys. To test the generalisation of the algorithms across the sky, I use three different training and test sets. Set 1 uses a training set randomly selected from the dataset. Set 2 uses a training set made up entirely of sources from the European Large Area ISO Survey-South 1 (ELAIS-S1) field, with the test set made up from the Extended Chandra Deep Field South (eCDFS) field. Set 3 uses a training sample made up entirely of sources from the eCDFS field, with the test set made up from the ELAIS-S1 field. This thesis shows that traditionally simple ML algorithms like kNN and RFs can provide acceptable redshift estimations on radio selected data, with the best results coming from redshift binned to a lower resolution. By extending the algorithms to suit the data, the kNN classification algorithm using the Largest Margin Nearest Neighbour (LMNN) learned distance metric provided a decrease in the number of outliers, reaching, an ƞ0:15 outlier rate of ∼5%, with accuracies of σ∆z/(1+zspec) ≈ 0.09. Once completed, the EMU project is expected to have optical and IR counterparts for ≈ 40% of the 70 million detected radio galaxies. By 2020, this is expected to increase to ≈ 70% of the galaxies detected by the EMU project. This thesis shows that the EMU project can be provided with reliable redshift for ≈ 95% of sources with optical and IR photometry – ∼ 27 million sources when the EMU project is completed, increasing to ∼ 47 million sources by 2020. This will enable many of the key science goals of the EMU project to be completed

2-(1,4-Dioxo-1,4-dihydro-2-naphthyl)-2-methylpropanoic acid

The sterically crowded title compound, C₁₄H₁₂O₄, crystallizes as centrosymmetric hydrogen-bonded dimers involving the carboxyl groups. The naphthoquinone ring system is folded by 11.5 (1)° about a vector joining the 1,4-C atoms, and the quinone O atoms are displaced from the ring plane, presumably because of steric interactions with the bulky substituent

A Geospatial Analysis of CDC-funded HIV Prevention Programs for African Americans in the United States

Given the increase in HIV/AIDS infection rates among racial and ethnic minorities, particularly African Americans, this study was undertaken as part of a larger research effort to examine the distribution of HIV prevention services focusing on African American populations within the United States. Data were gathered via a national survey of community-based organizations (CBOs) funded by the Centers for Disease Control and Prevention (CDC). A geocoded national database was constructed to identify, locate, and map these HIV prevention programs. A total of 1,020 CBOs responded to the survey, yielding a response rate of 70.3%. These CBOs administered a total of 3,028 HIV prevention programs. Data describing intervention types and persons served, combined with the address and service area of responding CBOs, were integrated with census data (2000) and analyzed by using a geographic information system (GIS). The results of our national level analysis show that HIV prevention services for African Americans have fair coverage where African Americans comprise a substantial proportion of the population in urban areas in northeastern states, but that HIV prevention services for African Americans are inadequately distributed in the southeastern states. A local-level analysis was conducted for Alabama, where 68% of HIV/AIDS cases are among African Americans. Specific interventions such as street and community outreach, health communications, and public information are fairly well provided to African Americans in more urban cities in Alabama, however, individual- and group-level interventions have poor coverage in rural areas where a large percentage of African-Americans live. Overall, our study illustrates that the use of GIS adds value when used with other data sources to provide prevention services that are accessible to the populations most in need

How will integrator perceptions affect the adoption of radio frequency identification technology

Purpose - This study aims to assess the perceptions of leading Australian integrators within the RFID (radio frequency identification) Industry about the future of the industry and barriers to more widespread adoption of the technology.Methodology/approach - Five leading Australian integrators presently working within the RFID Industry were interviewed.Findings - We find that the interviewed managers were realistic and circumspect about the industry’s future and potential supply chain savings, which can be contrasted with the “hype” evident in the commercial literature (for example, AIMRFID Connections, 2003).Research implications - Understanding integrators’ current perceptions about the industry will help vendors and integrators to develop applications that will be more likely to gain widespread acceptance in the future.Originality/value - This paper provides a unique insight into the perceptions of leading Australian RFID suppliers and integrators regarding the future of the industry and barriers to more widespread adoption of the technology

Discrete-time moment closure models for epidemic spreading in populations of interacting individuals

AbstractUnderstanding the dynamics of spread of infectious diseases between individuals is essential for forecasting the evolution of an epidemic outbreak or for defining intervention policies. The problem is addressed by many approaches including stochastic and deterministic models formulated at diverse scales (individuals, populations) and different levels of detail. Here we consider discrete-time SIR (susceptible–infectious–removed) dynamics propagated on contact networks. We derive a novel set of ‘discrete-time moment equations’ for the probability of the system states at the level of individual nodes and pairs of nodes. These equations form a set which we close by introducing appropriate approximations of the joint probabilities appearing in them. For the example case of SIR processes, we formulate two types of model, one assuming statistical independence at the level of individuals and one at the level of pairs. From the pair-based model we then derive a model at the level of the population which captures the behavior of epidemics on homogeneous random networks. With respect to their continuous-time counterparts, the models include a larger number of possible transitions from one state to another and joint probabilities with a larger number of individuals. The approach is validated through numerical simulation over different network topologies