This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of the (...) problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. (shrink)

For decades the study of social perception was largely compartmentalized by type of social cue: race, gender, emotion, eye gaze, body language, facial expression etc. This was partly due to good scientific practice, and partly due to assumptions that each type of social cue was functionally distinct from others. Herein, we present a functional forecast approach to understanding compound social cue processing that emphasizes the importance of shared social affordances across various cues. We review the traditional theories of emotion and (...) face processing that argued for dissociable and noninteracting pathways, as well as more recent evidence for combinatorial processing of social cues. We argue here that early, and presumably reflexive, visual integration of such cues is necessary for adaptive behavioral responding to others. In support of this claim, we review contemporary work that reveals a flexible visual system, one that readily incorporates meaningful contextual influences in even nonsocial visual processing, thereby establishing the functional and neuroanatomical bases necessary for compound social cue integration. Finally, we explicate three likely mechanisms driving such integration. Together, this work implicates a role for cognitive penetrability in visual perceptual abilities that have often been ascribed to direct encapsulated perceptual processes. (shrink)

The Air Quality Index (AQI) shows the state of air pollution in a unique and more understandable way. This work aims to forecast the AQI in Algeciras (Spain) 8 hours in advance. The AQI is calculated indirectly through the predicted concentrations of five pollutants (O3, NO2, CO, SO2 and PM10) to achieve this goal. Artificial neural networks (ANNs), sequence-to-sequence long short-term memory networks (LSTMs) and a newly proposed method combing a rolling window with the latter (LSTMNA) are employed as (...) the forecasting techniques. Besides, two input approaches are evaluated: using only the data from the own time series of the pollutant in the first case or adding exogenous variables in the second one. Several window sizes are employed (24, 28 and 72 hours) with ANNs and LSTMNAs. Additionally, several feature ranking methods are applied in the exogenous approach to select the most relevant lagged variables to feed the models. Results show how the proposed exogenous approach increases the performance of the prediction models. Besides, the newly proposed method LSTMNA provides the best performances in most of the cases evaluated. Hence, it constitutes an exciting alternative to standard LSTMs and ANNs to predict pollutants concentrations and, consequently, the AQI. (shrink)

Nowadays decision making is strongly supported by the high-confident point estimations produced by deep learning algorithms. In many activities, they are sufficient for the decision-making process. However, in some other cases, confidence intervals are required too for an appropriate decision-making process. In this work, a first attempt to generate point estimations with confidence intervals for the $^{222}$Rn radiation level time series at Canfranc Underground Laboratory is presented. To predict the low-radiation periods allows correctly scheduling the unshielded periods for maintenance (...) operations in the experiments hosted in this facility. This should minimize the deposition of radioactive dust on the exposed surfaces during these unshielded periods. An approach based on deep learning with stochastic regulation is evaluated in the forecasting of point estimations and confidence intervals of the $^{222}$Rn time series and compared with a second approach based on Gaussian processes. As a consequence of this work, an evaluation of the capacity of Gaussian process and deep learning with stochastic regularization for generating point estimations and their confidence intervals for this time series is stated. (shrink)

This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and (...) a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of |$10$| minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. (shrink)

In this work, we study and analyze the aggregate death counts of COVID-19 reported by the United States Centers for Disease Control and Prevention (CDC) for the fifty states in the United States. To do this, we derive a stochastic model describing the cumulative number of deaths reported daily by CDC from the first time Covid-19 death is recorded to June 20, 2021 in the United States, and provide a forecast for the death cases. The stochastic model derived in (...) this work performs better than existing deterministic logistic models because it is able to capture irregularities in the sample path of the aggregate death counts. The probability distribution of the aggregate death counts is derived, analyzed, and used to estimate the count’s per capita initial growth rate, carrying capacity, and the expected value for each given day as at the time this research is conducted. Using this distribution, we estimate the expected first passage time when the aggregate death count is slowing down. Our result shows that the expected aggregate death count is slowing down in all states as at the time this analysis is conducted (June 2021). A formula for predicting the end of Covid-19 deaths is derived. The daily expected death count for each states is plotted as a function of time. The probability density function for the current day, together with the forecast and its confidence interval for the next four days, and the root mean square error for our simulation results are estimated. (shrink)

ArgumentWe continue our analysis of modeling practices that focus more on qualitative understanding of system behavior than the attempt to provide sharp forecasts. The argument here is built around three episodes: the ambitious work of the Princeton Meteorological Project; the seemingly simple models of convection in weather systems by Edward Lorenz at MIT; and then finally analysis of the dripping faucet by Robert Shaw and the Dynamical Systems Collective at UC Santa Cruz. Using the Princeton Meteorological Project as an (...) argumentative foil for the later chaos work of Lorenz and Shaw, we first show how the epistemological interest of modeling came to shift fromissuingpredictions toprobingthe very meaning and limits of prediction. The second step of our argument shows that what may be seen in one context of use as a modeling technology that is error ridden, imprecise, or inadequate, may be parsed completely differently in another context. This argument about technology and practice, we argue, feeds through to epistemological conceptions of error. Far from being something that can be defined in the absolute, the notion of error is shown to be contextually plastic. (shrink)

In the midst of the ultra modernity of stock exchanges and financial markets, certain practitioners are increasingly using forecasting models of changes in rates whose scientific rationality is particularly contested. We refer to ‘technical analysis’, or, in the jargon of finance, of ‘chartism’. The adherents of this practice affirm that their models offer the possibility of detecting and of reading, by means of stereotyped graphical configurations, rising or falling market trends. Now, for a large number of people who hold scientific (...) economic and financial theories to describe the ‘nature’ of changes in rates as so many random and unpredictable phenomena, these models arise above all from the sphere of ‘superstition’ and ‘beliefs’, in the worst sense of the term. To listen to them, one would think there was hardly any difference between sorcery, prophecy, and chartism, since in short: “it works because people believe in it”. These are remarks that can only arouse the curiosity of the anthropologist, because they actually serve merely to recall epistemologica! controversies that depend on the rationality and the irrationality of observed practices and debates in various cultures. At the heart of these contradictory debates, it is precisely prophecy as a cultural mode of managing unforeseen events that has been the subject of multiple anthropological, historical, and epistemological studies which can, in a comparative perspective, clarify the structure and functioning of forecasting tools employed by the chartists in the context of financial speculation. (shrink)

In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural (...) network models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting. (shrink)

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice (...) is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years. (shrink)

ArgumentThis paper is the first part of a two-part examination of computer modeling practice and philosophy. It discusses electrical engineer Jay Forrester's work on Industrial Dynamics, later called System Dynamics. Forrester developed Industrial Dynamics after being recruited to the newly-established School of Industrial Management at the Massachusetts Institute of Technology (MIT), which had been seeking a novel pedagogical program for management for five years before Forrester's arrival. We argue that Industrial Dynamics should be regarded in light of this institutional (...) context. Unlike economics, as well as operations research and management science (twin fields which were also gaining traction at that time), Industrial Dynamics was not meant to be applied by mathematicians and technical specialists in consultation with managers, but by managers themselves. This concern shifted the emphasis in modeling from the specialist act of analysis of a crucial problem to the managerial act of choosing which problems were crucial and developing effective policies around them. (shrink)

There are distinctive methodological and conceptual challenges in rare and severe event (RSE) forecast verification, that is, in the assessment of the quality of forecasts of rare but severe natural hazards such as avalanches, landslides or tornadoes. While some of these challenges have been discussed since the inception of the discipline in the 1880s, there is no consensus about how to assess RSE forecasts. This article offers a comprehensive and critical overview of the many different measures used to capture the (...) quality of categorical, binary RSE forecasts – forecasts of occurrence and non-occurrence – and argues that of skill scores in the literature there is only one adequate for RSE forecasting. We do so by first focusing on the relationship between accuracy and skill and showing why skill is more important than accuracy in the case of RSE forecast verification. We then motivate three adequacy constraints for a measure of skill in RSE forecasting. We argue that of skill scores in the literature only the Peirce skill score meets all three constraints. We then outline how our theoretical investigation has important practical implications for avalanche forecasting, basing our discussion on a study in avalanche forecast verification using the nearest-neighbour method (Heierli et al., 2004). Lastly, we raise what we call the “scope challenge”; this affects all forms of RSE forecasting and highlights how and why working with the right measure of skill is important not only for local binary RSE forecasts but also for the assessment of different diagnostic tests widely used in avalanche risk management and related operations, including the design of methods to assess the quality of regional multi-categorical avalanche forecasts. (shrink)

Climate change is confronting African farmers with growing uncertainties. Advances in seasonal climate predictions offer potential for assisting farmers in dealing with climate risk. Experimental cases of forecast dissemination to African rural communities suggest that participatory approaches can facilitate understanding and use of uncertain climate information. But few of these studies integrate critical reflections on participation that have emerged in the last decade which reveal how participatory approaches can miss social dynamics of power at the community level and in the (...) broader context. Furthermore, neither climate application research nor theoretical critiques of participation fully examine the culturally constructed nature of participation. Drawing on sociolinguistic analysis, in-depth interviews, and ethnographic observation, this paper examines how Ugandan farmers engage in participation in the context of discussions of seasonal climate forecasts. Forecasts were presented to farmers groups whose members were then asked to discuss the forecast among themselves. In doing so, groups sought to develop a common understanding of the forecast and consensual plans for response strategies. Focusing on one particular group meeting as an example, we show how different cultural styles of participation affect the interpretation of the forecast and the formulation of response strategies. Group interaction is shown to be mostly structured around two styles of participation. On the one hand, there is the “Western” style advocated by NGOs and the government, which centers on ensuring that all individuals who are present have opportunities to speak during discussion and to vote on group decisions. On the other hand, a “Kiganda” style of participation emphasizes the importance of affirming ties to a collectivity, respect for social hierarchy, deployment of good manners, and consensus building. The case study illuminates how the performance of different styles of participation is grounded in localized frameworks of language and culture but also draw on political and policy discourses at the national level. Although a cultural high value on consensus may work in favor of prominent members, the availability of multiple styles of participation also enables group members to exercise their agency in positive ways. Attention to the interplay of different styles of participation throws light on the subtle social processes that shape how knowledge is assessed, which sources are trusted, which and whose interpretations prevail, what options are deemed viable, how costs and benefits are calculated, and whose resources are mobilized in the effort to reduce vulnerability to climate risk. These are key questions for an assessment of the role of boundary organizations, such as farmer associations, in the communication and application of climate forecasts in agriculture. (shrink)

The outputs of economic forecasting—predictions for national economic indicators such as GDP, unemployment rates and inflation—are all highly visible. The production of these forecasts is a much more private affair, however, typically being thought of as the work of individual forecasters or forecast teams using their economic model to produce a forecast that is then made public. This conception over-emphasises the individual and the technical whilst silencing the broader social context through which economic forecasters develop the expertise that is (...) essential for the credibility of their predictions. In particular, economic forecasts are given meaning and fine-tuned through the social and institutional networks that give forecasters access to the expertise of a heterogeneous mix of academics, policy-makers and business people. Within these broader groups, individual forecasters often create private forecast ‘clubs’, where subscribers have privileged access to the expertise of the economist, but where the forecasters also have privileged access to their clients’ own expert knowledge. In examining these aspects of the forecasters’ work I show that the visible and audible activities of modelling and forecasting are made possible and plausible by virtue of the modeller’s invisible interaction with a wider network.Keywords: Expertise; Economic forecasting; Periodic table of expertise; Judgement. (shrink)

In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of (...) predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of (...) knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)

Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over (...) four periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used. (shrink)

The price of copper is unstable but it is considered an important indicator of the global economy. Changes in the price of copper point to higher global growth or an impending recession. In this work, the forecasting of the spot prices of copper from the New York Commodity Exchange is studied using a machine learning method, support vector regression coupled with different model schemas (recursive, direct and hybrid multi-step). Using these techniques, three different time series analyses are built and (...) its performance are compared. The numerical results show that the hybrid direct-recursive obtains the best results. (shrink)

For efficient energy distribution, microgrids provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics, are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at (...) MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. (shrink)

From John Maynard Keynes’s prediction of a fifteen-hour workweek to present-day speculation about automation, we have not stopped forecasting the end of work. Critical theory and political philosophy have turned their attention away from the workplace to focus on other realms of domination and emancipation. But far from coming to an end, work continues to occupy a central place in our lives. This is not only because of the amount of time people spend on the job. Many of (...) our deepest hopes and fears are bound up in our labor—what jobs we perform, how we relate to others, how we might flourish. The Return of Work in Critical Theory presents a bold new account of the human significance of work and the human costs of contemporary forms of work organization. A collaboration among experts in philosophy, social theory, and clinical psychology, it brings together empirical research with incisive analysis of the political stakes of contemporary work. The Return of Work in Critical Theory begins by looking in detail at the ways in which work today fails to meet our expectations. It then sketches a phenomenological description of work and examines the normative premises that underlie the experience of work. Finally, it puts forward a novel conception of work that can renew critical theory’s engagement with work and point toward possibilities for transformation. Inspired by Max Horkheimer’s vision of critical theory as empirically informed reflection on the sources of social suffering with emancipatory intent, The Return of Work in Critical Theory is a lucid diagnosis of the malaise and pathologies of contemporary work that proposes powerful remedies. (shrink)

'Practices without foundations' is, in genesis and in effect, a discussion of the following quotation , which serves therefore as an epigraph to it: ;Nelson Goodman's discussion of the 'new riddle of induction' ... deserves comparison with Wittgenstein's work. Indeed ... the basic strategy of Goodman's treatment of the 'new riddle' is strikingly close to Wittgenstein's sceptical arguments .... Although our paradigm of Wittgenstein's problem was formulated for a mathematical problem it ... is completely general and can be applied (...) to any rule or word. In particular, if it were formulated for the language of color impressions, as Wittgenstein himself suggests, Goodman's 'grue', or something similar, would play the role of 'quus'. But the problem would not be Goodman's about induction--"Why not predict that grass, which has been grue in the past will be grue in the future?"--but Wittgenstein's about meaning: "Who is to say that in the past I didn't mean grue by 'green', so that now I should call the sky, not the grass, 'green'?" vskip9pt ;This fascinating remark of Kripke's suggests a project, that of charting out both the interpretive and philosophic points of connection and disconnection between Wittgenstein, Goodman and Kripke. That project is this dissertation. ;My work severely tests Kripke's remark. For I argue that Kripke not only misreads Wittgenstein, but also mistakes the extremity of the 'scepticism' of his version of Wittgenstein for the priority of the philosophical puzzle that Goodman has bequeathed to us. ;To reach this conclusion, I show that Kripke's Wittgenstein's 'meaning-scepticism' is unanswerable if statable, but actually unstatable; and that, under certain interpretations, the same is true of putative scepticisms derivable from Goodman's text. The upshot is that the sceptical reading of most twentieth century philosophy must be displaced in favour of 'post-scepticism' and 'philosophical ethnography'. The latter concepts, sketched toward the end of the dissertation, are perhaps its telos. (shrink)

Catastrophic climate change overshadows the present and the future. Wrenching economic transformations have devastated workers and hollowed out communities. However, those fighting for jobs and those fighting for the planet have often been at odds. Does the world face two separate crises, environmental and economic? The promise of the Green New Deal is to tackle the threat of climate change through the empowerment of working people and the strengthening of democracy. In this view, the crisis of nature and the crisis (...) of work must be addressed together-or they will not be addressed at all. This book brings together leading experts to explore the possibilities of the Green New Deal, emphasizing the future of work. Together, they examine transformations that are already underway and put forth bold new proposals that can provide jobs while reducing carbon consumption-building a world that is sustainable both economically and ecologically. Contributors also debate urgent questions: What is the value of a federal jobs program, or even a jobs guarantee? How do we alleviate the miseries and precarity of work? In key economic sectors, including energy, transportation, housing, agriculture, and care work, what kind of work is needed today? How does the New Deal provide guidance in addressing these questions, and how can a Green New Deal revive democracy? Above all, this book shows, the Green New Deal offers hope for a better tomorrow-but only if it accounts for work's past transformations and shapes its future. (shrink)

Summary This study is a further attempt to apply correspondence training in the classroom in order to improve learning outcomes. Using this strategy a class of middle school pupils was encouraged to show more initiative, independence and self?regulation in planning and carrying out topic work. The pupils were required to forecast what they proposed to achieve in a certain period and then to check up at the end to see how far they had been successful. In addition, upon hearing (...) an audible signal, they were required to decide whether they were ?on?task? and whether they were working well. The outcome was that the pupils achieved a better work output in terms of both quantity and quality than before. In addition, they gained valuable experience in forward planning and in working independently. (shrink)

Ambient assisted living technologies can provide assistance and support to persons with dementia. They might allow them the possibility of living at home for longer whilst maintaining their comfort and security as well as offering a way towards reducing the huge economic and personal costs forecast as the incidence of dementia increases worldwide over coming decades. However, the development, introduction and use of AAL technologies also trigger serious ethical issues. This paper is a systematic literature review of the on-going scholarly (...) debate about these issues. More specifically, we look at the ethical issues involved in research and development, clinical experimentation, and clinical application of AAL technologies for people with dementia and related stakeholders. In the discussion we focus on: the value of the goals of AAL technologies, the special vulnerability of persons with dementia in their private homes, the complex question of informed consent for the usage of AAL technologies. (shrink)

Justice in production is concerned with ensuring the benefits and burdens of work are distributed in a way that is reflective of persons' status as moral equals. While a variety of accounts of productive justice have been offered, insufficient attention has been paid to the distribution of work's benefits and burdens in the future. In this article, after granting for the sake of argument forecasts of widespread future technological unemployment, we consider the implications this has for egalitarian requirements (...) of productive justice. We argue that in relation to all the benefits affiliated with work, other than undertaking social contribution, the technological replacement of work is unproblematic as these benefits could in principle be attained elsewhere. But because social contribution uniquely corresponds to work (when work is understood as more than a paid job), the normative assessment of technological unemployment will turn on the value that theories of justice give to contributive activity. We then argue that despite technological replacement being plainly beneficial insofar as it relieves persons from the burdens of work, such as dangerous work or drudgery, because the nature of care work makes it less susceptible to technological replacement, egalitarian concern will require the burdens of care work to be shared equally between individuals. (shrink)

In a career extending over almost six decades, Nicholas Rescher has conducted researches in almost every principal area of philosophy, historical and systematic alike. In this extraordinary volume, two dozen scholars join in offering penetrating discussions of various facets of Rescher s investigations. The result is an instructively critical panorama of the many-faceted contributions of this important American philosopher. Born in Germany in 1928, Nicholas Rescher came to the U.S. at the age of nine. He is University Professor of Philosophy (...) at the University of Pittsburgh where he has also served as Chairman of the Philosophy Department and as director (and currently chairman) of the Center for Philosophy of Science. In a productive research career extending over six decades, he has established himself as a systematic philosopher of the old style. His work represents a many-sided approach to fundamental philosophical issues that weaves together threads of thought from continental idealism and American pragmatism. And apart from this larger program Rescher has made various specific contributions to logic (the conception autodescriptive systems of many-sided logic), the history of logic (the medieval Arabic theory of modal syllogistic), to the theory of knowledge (epistemetrics as a quantitative approach in theoretical epistemology), and to the philosophy of science (the theory of a logarithmic retardation of scientific progress). Rescher has also worked in the area of futuristics, and along with Olaf Helmer and Norman Dalkey is co-inaugurator of the so-called Delphi method of forecasting. Ten books about Rescher s philosophy have been published in four languages. Rescher earned his doctorate at Princeton in 1951 while still at the age of twenty-two a record for Princeton s Department of Philosophy. He has served as a President of the American Philosophical Association, of the American Catholic Philosophy Association, of the American G. W. Leibniz Society, of the C. S. Peirce Society, and of the American Metaphysical Society. He was the founder of the American Philosophical Quarterly. An honorary member of Corpus Christi College, Oxford, he has been elected to membership in the European Academy of Arts and Sciences (Academia Europaea), the Royal Society of Canada, the Institut International de Philosophie, and several other learned academies. Having held visiting lectureships at Oxford, Constance, Salamanca, Munich, and Marburg, he has been awarded fellowships by the Ford, Guggenheim, and National Science Foundations. Author of some hundred books ranging over many areas of philosophy, over a dozen of them translated from English into other languages, he is the recipient of eight honorary degrees from universities on three continents. He was awarded the Alexander von Humboldt Prize for Humanistic Scholarship in 1984, the Belgian Prix Mercier in 2005, and the Aquinas Medal of the American Catholic Philosophical Association in 2007.". (shrink)

Predictions concerning the end of the world have proven less reliable than your broker’s recommendations or your fondest hopes. Whether you await the end fearfully or eagerly, you may rest assured that it will never come—not because the world is everlasting but because it has already ended, if indeed it ever began. But we need not mourn, for the world is indeed well lost, and with it the stultifying stereotypes of absolutism: the absurd notions of science as the effort to (...) discover a unique, prepackaged, but unfortunately undiscoverable reality, and of truth as agreement with that inaccessible reality. All notions of pure givenness and unconditional necessity and of a single correct perspective and system of categories are lost as well.If there is no such thing as the world, what are we living in? The answer might be “A world” or, better, “Several worlds.” For to deny that there is any such thing as the world is no more to deny that there are worlds than to deny that there is any such thing as the number between two and seven is to deny that there are numbers between two and seven. The task of describing the world is as futile as the task of describing the number between two and seven.The world is lost once we appreciate a curious feature of certain pairs of seemingly contradictory statements: if either is true, both are. Although “The earth is in motion” and “The earth is at rest” apparently contradict each other, both are true. But from a contradiction, every statement follows. So unless we are prepared to acknowledge the truth of every statement, the appearance of contradiction in cases like these must somehow be dispelled. Nelson Goodman is professor emeritus of philosophy at Harvard University. He has written Of Mind and Other Matters, Ways of Worldmaking, Problems and Projects, Languages of Art, The Structure of Appearance, and Fact, Fiction, and Forecast. His most recent contribution to Critical Inquiry is “How Buildings Mean” . Catherine Z. Elgin is associate professor of philosophy at the University of North Carolina, Chapel Hill. She is the author of With Reference to Reference and is currently writing a book entitled Philosophy without Foundations. (shrink)

Historical experience shows that politics, despite all its shortcomings, is the best tool created by people to organize the common life of large groups of the population and manage them. Therefore, the desire of thinkers of all times and peoples to obtain knowledge about politics is understandable, which, in its rigor, clarity, and accuracy of forecast, would be comparable to scientific knowledge. The present review analyzes the works of the Russian social scientist Viktor P. Makarenko, who researched the triad “politics–authority–bureaucracy” (...) for fifty years. The result was the creation of a political conceptology, which the author positions as a transdisciplinary approach to the study of politics. Analytical philosophy became the theoretical and methodological platform of political conceptology. The monographs, articles, and speeches of Makarenko reflect the process of establishing the author’s position, expanding the scope of problems, identifying additional points of contact between bureaucracy and politics, as well as improving the methodology of political analysis. (shrink)