This deliberately wide-ranging book addresses issues related to trust, compassion, well-being, grace, dignity and integrity. It explores these within the context of higher education, giving existential and empirical accounts of how these moral duties can be expressed within the academy and why they ought to be. The chapters range from values used in the marketing and management of institutions to their realisation in therapeutic and teacher training spaces. The book opens with a specific introduction which positions the work and outlines the context of duties and obligations at play. This is followed by two distinct but related sections including chapters on theoretical issues, organisational practices and personal praxis. The first part is more abstract and theoretical, the second locates the values discussed within the practices of the university. In doing so the book encompasses a wide range of issues from multi-disciplinary and geo-political regions. The authors are a mixture of world-leading authorities on values in higher education and earlier career researchers, who are nonetheless equally passionate contributors. This mix gives the book vibrancy and offers insight which appeals to both an academic and managerial readership.

The world is full of people who are very certain—in politics, in religion, in all manner of things. In addition, political, religious, and social organizations are marketing certainty as a cure all to all life’s problems. But is such certainty possible? Or even good? The Certainty of Uncertainty explores the question of certainty by looking at the reasons human beings crave certainty and the religious responses we frequently fashion to help meet that need. The book takes an in-depth view of religion, language, our senses, our science, and our world to explore the inescapable uncertainties they reveal. We find that the certainty we crave does not exist. As we reflect on the unavoidable uncertainties in our world, we come to understand that letting go of certainty is not only necessary, it’s beneficial. For, in embracing doubt and uncertainty, we find a more meaningful and courageous religious faith, a deeper encounter with mystery, and a way to build strong relationships across religious and philosophical lines. In The Certainty of Uncertainty, we see that embracing our belief systems with humility and uncertainty can be transformative for ourselves and for our world.

Subvolume b.

This volume focuses on motivation in education under changing and unsettling times and provides ideas on how global changes affect student and teacher motivation to learn and achieve.

Formal economic analysis using Shackle's ideas of historical time and nonprobabilistic uncertainty

A timeless classic of economic theory that remains fascinating and pertinent today, this is Frank Knight's famous explanation of why perfect competition cannot eliminate profits, the important differences between "risk" and "uncertainty," and the vital role of the entrepreneur in profitmaking. Based on Knight's PhD dissertation, this 1921 work, balancing theory with fact to come to stunning insights, is a distinct pleasure to read. FRANK H. KNIGHT (1885-1972) is considered by some the greatest American scholar of economics of the 20th century. An economics professor at the University of Chicago from 1927 until 1955, he was one of the founders of the Chicago school of economics, which influenced Milton Friedman and George Stigler.

Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.