Imagining others

While empirical research on democratic innovations is characterised by steady growth in output, a study of the top journals in political science shows that in the last ten years the vast majority of these empirical studies focused on best practices. Empirical evaluation of the lessons that we can draw from failures in participatory and deliberative processes are extremely rare. This pattern of success and failure is not representative of reality. Various specialized monographs and studies in less prominent journals have investigated a variety of problems and failures. Why do we see this pattern of ‘failure neglect’ in top journals? In this paper we explore some of the causes of this pattern elucidating the impact of sampling bias, innovation bias, publication bias, and the loss of independence due to the difficulty of implementing participatory action research in politically contested projects. We argue that this lack of representativeness in the real-world cases of deliberation that command the attention of general political science audiences is currently a major barrier to understanding democratic improvements. Without a comparison of success and failure, our models for successful outcomes will be chronically overdetermined, which ultimately reduces their chances of adoption in practice.

1. Nest predation negatively affects most avian populations. Studies of nest predation usually group all nest failures when attempting to determine temporal and parental activities, habitat or landscape predictors of success. Often these studies find few significant predictors and interpret patterns as essentially random. 2. Relatively little is known about the importance of individual predator species or groups on observed patterns of nest success, and how the ecology of these predators may influence patterns of success and failure. 3. In 2006 and 2007, time-lapse, infrared video systems were deployed at nests of Swainson's warblers (Limnothlypis swainsonii Audubon) in east-central Arkansas to identify dominant nest predators and determine whether factors predicting predation differed among these predators. 4. Analysis of pooled data yielded few predictors of predation risk, whereas separate analyses for the three major predator groups revealed clear, but often conflicting, patterns. 5. Predation by ratsnakes (Elaphe obsoleta) and raptors was more common during the nestling period, whereas predation by brown-headed cowbirds (Molothrus ater) occurred more during incubation. Additionally, the risk of predation by raptors and cowbirds decreased throughout the breeding season, whereas ratsnake predation risk increased. 6. Contrary to expectations, predation by ratsnakes and cowbirds was more common far from edges, whereas raptor predation was more common close to agricultural edges. 7. Collectively, our results suggest that associating specific predators with the nests they prey on is necessary to understand underlying mechanisms.

In this study, we compiled lists of successful and unsuccessful passeriform introductions to nine sites in New Zealand, Australia and the United States. We limited our analysis to introductions during the 19th century to minimize potential variation in transport modes and habitat quality changes, such as those due to increasing urbanization. We compared introduction success rates at three levels. First we included all passeriforms introduced to any of the sites in the three locations, then we compared the fates of just those species with a European origin and finally we compared success rates of just the 13 species released into all three locations. We found that the pattern of success or failure differed significantly across the three locations: Passeriforms introduced by acclimatization organizations to the United States were significantly more likely to fail than those introduced to New Zealand or Australia. Several species that succeeded in either New Zealand or Australia failed in the United States, even after the introduction of seemingly sufficient numbers.

We introduce quantum utility , a new approach to evaluating quantum performance that aims to capture the user experience by considering the overhead costs associated with a quantum computation. A demonstration of quantum utility by the quantum processing unit (QPU) shows that the QPU can outperform classical solvers at some tasks of interest to practitioners, when considering the costs of computational overheads. A milestone is a test of quantum utility that is restricted to a specific subset of overhead costs and input types. We illustrate this approach with a benchmark study of a D-Wave annealing-based QPU versus seven classical solvers for a variety of problems in heuristic optimization. We consider overhead costs that arise in standalone use of the D-Wave QPU (as opposed to a hybrid computation). We define three early milestones on the path to broad-scale quantum utility. Milestone 0 is the purely quantum computation with no overhead costs and is demonstrated implicitly by positive results on other milestones. We evaluate the performance of a D-Wave Advantage QPU with respect to milestones 1 and 2: For milestone 1, the QPU outperformed all classical solvers in 99% of our tests. For milestone 2, the QPU outperformed all classical solvers in 19% of our tests, and the scenarios in which the QPU found success correspond to cases where classical solvers most frequently failed. This approach of isolating subsets of overheads for separate analysis reveals distinct mechanisms in quantum versus classical performance, which explain the observed differences in patterns of success and failure. We present evidence-based arguments that these distinctions bode well for annealing quantum processors to support demonstrations of quantum utility on ever-expanding classes of inputs and with more challenging milestones in the very near future.

Classical science advances via the dance between quantitative observation and theoretical construction. It has been ten years since the first International Software Architecture Workshop, and since that time there has been a steady increase in the number of people who call themselves architect and a similar growth in the value that organizations place in software architecture. It is a sign of maturity for any given engineering discipline when we can name, study, and apply the patterns relevant to that domain but, unfortunately, no such reference yet exists for software-intensive systems. We've architected and deployed many systems, but have studied their patterns of success and failure only a little; we've explored theoretical frameworks for describing software architectures and processes to build them, but we've done only a modest job in validating them in the real world. In this presentation, I'll summarize the things we know and the things we don’t know (and speculate on the things we don't know we know) about software architecture. I'll then examine an effort to create a handbook of software architecture to help fill this gap between observation and construction.

Medical marijuana (MMJ) is currently legal in 35 US states, with an estimated 3.6 million state-legal medical cannabis patients. Although there are currently over 440,000 MMJ patients in the state of Florida, there are limited data on their demographics, reasons and patterns of use, and successes or failures of treatment. The goal of this study is to examine the demographics, perceptions, and use of medical cannabis among patients in Florida, as well as their perceptions of the quality of information received from physicians and dispensaries. One hundred and fifty-seven MMJ patients completed an online survey regarding their characteristics and use. Patients in Florida shared many characteristics with patients in other states, such as race, use frequency, and administration technique. Patients most commonly used MMJ to treat their symptoms of anxiety, pain, and stress and reported great therapeutic effectiveness. A majority (65%) of patients also reported either a reduction or total discontinuation of at least one prescription or over-the-counter drug. The findings highlight the need to further investigate the use of MMJ as a viable treatment for pain and anxiety and to improve access of quality cannabis information to patients, physicians, and dispensary employees.

Many animals are able to perform recognition feats that astound us-such as a rodent recognizing kin it has never met. Yet in other contexts, animals appear clueless as when reed warblers rear cuckoo chicks that bear no resemblance to their own species. Failures of recognition when it would seem adaptive have been especially puzzling. Here, we present a simple tug-of-war game theory model examining how individuals should optimally invest in affecting the accuracy of discrimination between desirable and undesirable recipients. In the game, discriminating individuals (operators) and desirable and undesirable recipients (targets and mimics, respectively) can all invest effort into their own preferred outcome. We demonstrate that stable inaccurate recognition will arise when undesirable recipients have large fitness gains from inaccurate recognition relative to the pay-offs that the other two parties receive from accurate recognition. The probability of accurate recognition is often determined by just the relative pay-offs to the desirable and undesirable recipients, rather than to the discriminator. Our results provide a new lens on long-standing puzzles including a lack of nepotism in social insect colonies, tolerance of brood parasites and male birds caring for extra-pair young in their nests, which our model suggests should often lack accurate discrimination. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.

Ecological filters and availability of propagules play key roles structuring natural communities. Propagule pressure has recently been suggested to be a fundamental factor explaining the success or failure of biological introductions. We tested this hypothesis with a remarkable data set on trees introduced to Isla Victoria, Nahuel Huapi National Park, Argentina. More than 130 species of woody plants, many known to be highly invasive elsewhere, were introduced to this island early in the 20th century, as part of an experiment to test their suitability as commercial forestry trees for this region. We obtained detailed data on three estimates of propagule pressure (number of introduced individuals, number of areas where introduced, and number of years during which the species was planted) for 18 exotic woody species. We matched these data with a survey of the species and number of individuals currently invading the island. None of the three estimates of propagule pressure predicted the current pattern of invasion. We suggest that other factors, such as biotic resistance, may be operating to determine the observed pattern of invasion, and that propagule pressure may play a relatively minor role in explaining at least some observed patterns of invasion success and failure.

Despite 50 years and more of ‘progressive education’ in the United Kingdom, classed patterns of educational success and failure stubbornly prevail. So how, where and when does it all go wrong for the many children who continue to fail or underachieve? Drawing on the work of Basil Bernstein, this article centres processes within early years education which are claimed to help launch children into careers as either educational successes or failures. Our data suggest that children more or less happily play their lives away in the progressive play pedagogies of early years education, in the process learning their position in social and ability hierarchies that help define their future careers inside and outside schools. That such hierarchies prevail is the fault of neither teachers nor parents. Indeed, it is what early years education settings are legitimised to do: sieve and sort, and make children ‘school ready’, pliant and prepared for a lifetime of learning to succeed or fail.

Student retention is an important topic in Computer Science departments across the country. Keeping strong students and helping struggling students perform better are two fundamental components of improving retention. Isolating the cause(s) of students leaving the major is an important area of research. We endeavor to explore this problem using a visualization tool to probe student data within the beginning course sequence in Computer Science. We would like to see what patterns exist amongst students, focusing on success, failure, and repetition patterns throughout the first three courses. Identifying these patterns can help isolate some of the causes of decreased retention within the department, allowing us to address individual projects, courses, or exams that may be causing students exceptional difficulty or loss of interest. Due to the large amount of data and the variety of students' paths through their courses, it is essential that a visualization be developed to represent the data. Using graph layouts, parallel coordinates, color-mapping, and interactive selection, users can explore and query the data. Users can discover patterns within the data by selecting subgroups of students and examining the event sequences to find patterns of success, failure, and repetition amongst those students. Departments can use this information to isolate profiles of students for retention, remediation, and recruitment efforts as well as identify areas of the curriculum or instruction that can be improved.

Humans are remarkably efficient at decision making, even in "open-ended" problems where the set of possible actions is too large for exhaustive evaluation. Our success relies, in part, on processes for calling to mind the right candidate actions. When these processes fail, the result is a kind of puzzle in which the value of a solution would be obvious once it is considered, but never gets considered in the first place. Recently, machine learning (ML) architectures have attained or even exceeded human performance on open-ended decision making tasks such as playing chess and Go. We ask whether the broad architectural principles that underlie ML success in these domains generate similar consideration failures to those observed in humans. We demonstrate a case in which they do, illuminating how humans make open-ended decisions, how this relates to ML approaches to similar problems, and how both architectures lead to characteristic patterns of success and failure.

For those who seek to conduct qualitative research that makes a positive difference in the lives of women in poverty, feminist research methodologies offer the most productive guide. Researchers must partner with those we study and foreground participants’ own perceptions of their challenges, while analyzing structural discrimination and identifying ‘sites of possibilities’ to leverage social and policy change. Attempting to follow all the recommendations of feminist methodologists can, however, overwhelm researchers with desirable but difficult ethical demands. Bloom and Sawin examine their ethnographic projects relative to the recommendations of key feminist methodologists, identifying patterns of success and failure. We then draw on Walker’s feminist model of ‘nongeneric accountability’ to argue that treating feminist methodologists’ models as inflexible rules reinscribes patriarchal ethics. It is both more effective for social change and more feminist to negotiate research commitments with our research participants and colleagues.

Chapter 3 - Recurrent Event Theory

How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distributions in the newborn’s environment. To test whether space-time fitting is a viable theory for learning how to see, we performed parallel controlled-rearing experiments on newborn chicks and deep neural networks (DNNs), including CNNs and transformers. First, we raised newborn chicks in impoverished environments containing a single object, then simulated those environments in a video game engine. Second, we recorded first-person images from agents moving through the virtual animal chambers and used those images to train DNNs. Third, we compared the viewpoint-invariant object recognition performance of the chicks and DNNs. When DNNs received the same visual diet (training data) as chicks, the models developed common object recognition skills as chicks. DNNs that used time as a teaching signal—space-time fitters—also showed common patterns of successes and failures across the test viewpoints as chicks. Thus, DNNs can learn object recognition in the same impoverished environments as newborn animals. We argue that space-time fitters can serve as formal scientific models of newborn visual systems, providing image-computable models for studying how newborns learn to see from raw visual experiences.

This chapter explores patterns of success and failure of anti-deportation protests. Based on 15 qualitative case studies from Germany, Austria, and Switzerland, the authors identify four relevant mechanisms that may explain successful protest outcomes: public preferences with regard to the issue of asylum and deportation, political access to decision-making authorities, judicial means, and disruption by protest actors and/or people to be deported. These mechanisms do not exist exogenously, but need to be activated. All of them involve different degrees of risk and unintended consequences. To what degree they can be successfully activated also depends on the context of the case and reflects political opportunity structures. Furthermore, the reconstruction of the cases initially understood as unsuccessful has led the authors to problematize the criterion of success. They therefore propose a chronological perspective for the evaluation of success that goes beyond the specific deportation event and highlights the role of support that is continued in the post-deportation phase and often leads to the person’s return to the country from which he or she has been deported.