Infallible Divine Foreknowledge cannot Uniquely Threaten Human Freedom, but its Mechanics Might

It is not uncommon to think that the existence of exhaustive and infallible divine foreknowledge uniquely threatens the existence of human freedom. This paper shows that this cannot be so. For, to uniquely threaten human freedom, infallible divine foreknowledge would have to make an essential contribution to an explanation for why our actions are not up to us. And infallible divine foreknowledge cannot do this. There remains, however, an important question about the compatibility of freedom and foreknowledge. It is a question not about the existence of foreknowledge, but about its mechanics

Faith as an Epistemic Disposition

This paper presents and defends a model of religious faith as an epistemic disposition. According to the model, religious faith is a disposition to take certain doxastic attitudes toward propositions of religious significance upon entertaining certain mental states. Three distinct advantages of the model are advanced. First, the model allows for religious faith to explain the presence and epistemic appropriateness of religious belief. Second, the model accommodates a variety of historically significant perspectives concerning the relationships between faith and evidence, faith and volition, and faith and doubt. And, finally, the model offers an appealing account of what unifies religious faith with other kinds of faith

Explanationism, Super-Explanationism, Ecclectic Explanationism: Persistent Problems on Both Sides

We argue that explanationist views in epistemology continue to face persistent challenges to both their necessity and their sufficiency. This is so despite arguments offered by Kevin McCain in a paper recently published in this journal which attempt to show otherwise. We highlight ways in which McCain’s attempted solutions to problems we had previously raised go awry, while also presenting a novel challenge for all contemporary explanationist views

Sitting in the Hoop of the People: Linking Lakota Values and Business Ethics

As self described, the Lakota (or Teton Sioux) are a group of Native Americans characterized by their emphasis on ideals such as community, affinity, generosity, cooperation, and strength. The term Lakota roughly translates to an alliance of people. Traditionally, they are a people strongly motivated by personal responsibility to the whole of society and philosophically wedded to the notion of “affinity,” which involves living in harmony with others, having a sense of belonging to one’s community, valuing interpersonal relationships, and trusting one another (Marshall, 2005). This manner of living has allowed the Lakota to synergize efforts through teamwork and cooperation and to achieve great benefits for both the community and its individuals. In our modern times, as we struggle with human, social, and environmental challenges and a call for greater individual and collective responsibility, there is much we might learn and emulate from the Lakota. Such possibilities are explored in this paper by examining theoretical approaches to the institutionalization of personal and collective responsibility, particularly Schwartz’s (1977) norm activation model (NAM). In this regard, theory provides a useful connection to the past by considering the situational and personal values characteristics that might serve to link modern notions of personal and collective responsibility to those of our early Native Americans

CREATING SUSTAINABLE BUSINESS: HOW DOES IT HAPPEN?: An Exploration of Motivators & Facilitators in Three Organizational Settings in the U.S.

Despite the fact that serious concerns regarding the deterioration of the environment and natural resources have been voiced for decades, current business and organizational approaches toward sustainability remain inadequate and substantially unsustainable. An important research question, therefore, has to do with understanding how to make positive behavior more prevalent in the face of many urgent global challenges. Newer business and organizational models that are significantly moving toward sustainability, for instance, serve as remarkable examples of such. What can we learn from them? This study seeks more specific answers to this broad question. How are modern organizations motivated to embrace sustainability initiatives in a genuine manner? How have they created their sustainable business models? How do they continue to sustain the initial momentum? What are the key factors that assist in the implementation of sustainability strategies? Lastly, how are they defining and achieving sustainability success? Answers to these questions were sought through an inductive and qualitative case research design that explored three quite different organizational settings, each pursuing sustainability objectives with advancing success and yet finding its own way in very different environments based on industrial, regulatory, and cultural influences. Nevertheless, several general characteristics seemed to accrue across organizational and industrial divides. A model for sustainability management, derived from the lessons learned in this study, is thus proposed.

Creating Sustainable Business: How Does It Happen? An Exploration of Motivators & Facilitators in Three Organizational Settings in the U.S.

Despite the fact that serious concerns regarding the deterioration of the environment and natural resources have been voiced for decades, current business and organizational approaches toward sustainability remain inadequate and substantially unsustainable. An important research question, therefore, has to do with understanding how to make positive behavior more prevalent in the face of many urgent global challenges. Newer business and organizational models that are significantly moving toward sustainability, for instance, serve as remarkable examples of such. What can we learn from them? This study seeks more specific answers to this broad question. How are modern organizations motivated to embrace sustainability initiatives in a genuine manner? How have they created their sustainable business models? How do they continue to sustain the initial momentum? What are the key factors that assist in the implementation of sustainability strategies? Lastly, how are they defining and achieving sustainability success? Answers to these questions were sought through an inductive and qualitative case research design that explored three quite different organizational settings, each pursuing sustainability objectives with advancing success and yet finding its own way in very different environments based on industrial, regulatory, and cultural influences. Nevertheless, several general characteristics seemed to accrue across organizational and industrial divides. A model for sustainability management, derived from the lessons learned in this study, is thus proposed

Collective Virtue

In this paper, we aim to advance the nascent discussion of collective virtue in three ways. First, in section 1, we offer two new arguments for the existence of collective virtues. Second, in section 2, we offer a new account of the nature of collective virtues in general which has significant advantages over the leading rival account of the nature of collective virtues. Third, in section 3, we contribute to the project of classifying collective virtues by distinguishing between collective virtues which have individual virtue analogues from those that do not, and by offering examples of some of the distinctively collective virtues which have no individual analogues. We argue that distinctively collective virtues provide a profitable place of focus for future work on collective virtue, since analyses of these virtues cannot be derived in a straightforward manner from analyses of their individual virtue analogues

Spring 1973

Planting Trees and Shrubs That Will Attract Birds and Add Beauty to Your Garden (3) Mode of Action of the Disease Organism (5) 1973 Turf Conference (12) University of Massachusetts Alumni Turf Research Committee (14) The Environment and Agriculture (17

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Capsules are the name given by Geoffrey Hinton to vector-valued neurons. Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which Hinton argues correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model's achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. First, a very simple monolithic CNN that when using HVCs achieved a 63% improvement in top-1 classification accuracy and a 35% improvement in top-5 classification accuracy over the baseline architecture. Second, with the CNN architecture referred to as Inception v3 that achieved similar accuracies both with and without HVCs. Additionally, the simple monolithic CNN when using HVCs showed no overfitting after more than 300 epochs whereas the baseline showed overfitting after 30 epochs. We use the ImageNet ILSVRC 2012 classification challenge dataset with both networks.https://arxiv.org/abs/1906.08676v

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Copyright © 2021 The Author(s). Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which has been shown to correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model’s achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. For the CNN architecture referred to as Inception v3, replacing the fully connected layers with HVCs increased the test accuracy by an average of 1.32% across all experiments conducted. For a simple monolithic CNN, we show HVCs improve test accuracy by an average of 19.16%