Suchergebnisse

Cover -- Contents -- Preface -- Introduction: Cordelia on Trial -- Part I: Charismatic Love and Friendship -- 1 Problems of Reading the Language of Passionate Friendship -- 2 Virtue and Ennobling Love ( i ) : Antiquity and Early Christianity -- 3 Love of King and Court -- 4 Love, Friendship, and Virtue in Pre-Courtly Literature -- 5 Love in Education, Education in Love -- 6 Women -- Part II: Sublime Love -- 7: Sublime Love -- 8: Love Beyond the Body -- 9: Sleeping and Eating Together -- 10: Eros Denied, Eros Defied -- 11: Virtue and Ennobling Love (2): Value, Worth, Reputation -- Part III: Unsolvable Problems - Romantic Solutions: The Romantic Dilemma -- 12: The Epistolae duorum amantium, Heloise, and Her Orbit -- 13: The Loves of Christina of Markyate -- 14: Virtuous Chastity, Virtuous Passion - Romantic Solutions in Two Courtly Epics -- 15: The Grand Amatory Mode of die Noble Life -- Appendix: English Translations of Selected Texts -- Alcuin, one letter and three poems -- Hildesheim Letter, Epist. 36, a master to his student -- Letter of R. of Mainz to the students of the Worms cathedral school -- Baudri of Bourgueil, poem to a haughty boy -- Marbod of Rennes, "On the Good Woman," from the Book of Ten Chapters -- From the Regensburg Love Songs (No. 28) -- From the "Letters of Two Lovers" (Epistolae duorum amantium) -- Metamorphosis Goliae -- Notes -- Abbreviations -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.

When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAM n approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

International audience ; When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAM n approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine

When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

En 1187 la campagne militaire de Henri II en France s'enlisa lorsque son fils Richard Coeur de Lion, à qui il avait confié le commandement du quart de l'armée anglaise, s'éprit du roi ennemi, le jeune Philippe Auguste. Le chroniqueur Roger de Howden décrit cette relation en ces termes: (le roi de France)… l'honorait tant depuis si longtemps qu'ils mangeaient chaque jour à la même table et dans le même plat, et le soir le lit ne les séparait pas. Et le roi de France l'aimait comme son âme; et ils s'aimaient tant l'un l'autre que le roi d'Angleterre était profondément étonné par l'amour véhément qui existait entre eux.

The 2015 agreement setting forth the UN Agenda 2030 for Sustainable Development Goals (SDGs) is an important achievement that poses complex and demanding challenges. To adequately address them, judgments must determine contextually and culturally appropriate balances between independently valuable, but often conflicting targets (Singh et al. 2018). Simultaneously, a global coherence across local and regional actions must be ensured, so that local efforts do not destructively interfere with each other, nor overstep limitations in the resources of the planet (Randers et al. 2018). The Global Sustainability Strategy Forum (GSSF) responds to the fact that, after some 40 years sustainability science has produced many insights, but has not really affected our collective behavior with respect to its impact on the environment. Generally, that is deemed to be the result of lack of communication between scientists and the outside world. But might it be that the present practice of science is in itself deficient in producing results that are useful to implement the changes called for? The Forum was established in 2018 with funding from the VW Foundation to identify and address sustainability challenges at the global to regional scales by bringing together, in week-long work-shops, renowned experts in sustainable development and thought leaders in business, government, and civil society from around the world. Under the patronage of Prof. Dr Rita Süssmuth, former President of the German Bundestag, the first Forum was coordinated by Solène Droy with assistance from Paul Skaloud. Moderated by Ilan Chabay (IASS), Sander van der Leeuw (Arizona State University), Ortwin Renn (IASS), 14 panelists convened in Potsdam (Germany) 4-8 March 2019. Eight main lessons emerged from in-depth discussions and reflections towards the end of the forum. They capture some of the main approaches deemed as necessary for science and society to counter patterns and trends of unsustainability and are outlined in this paper. The results were ...