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Institute
Portal Wissen = Glauben
(2014)
Menschen wollen wissen, was wirklich ist. Kinder lassen sich gern eine Geschichte erzählen, aber spätestens mit vier Jahren fragten meine, ob diese Geschichte so passiert sei oder nur erfunden. Das setzt sich fort: Auch unsere wissenschaftliche Neugier wird vom Interesse befeuert herauszufinden, was wirklich ist. Selbst dort, wo wir poetische Texte oder Träume erforschen, tun wir es in der Absicht, die realen sprachlichen Strukturen bzw. die neurologischen Faktoren von bloß vermuteten zu unterscheiden. Im Idealfall können wir Ergebnisse präsentieren, die von anderen logisch nachvollzogen und empirisch wiederholbar sind. Meistens geht das aber nicht. Wir können nicht jedes Buch lesen und nicht in jedes Mikroskop schauen, nicht einmal innerhalb der eigenen Disziplin. Wie viel mehr sind wir in der Lebenswelt darauf angewiesen, den Ausführungen anderer zu vertrauen, wenn wir wissen wollen, wo es zum Bahnhof geht oder ob es in Ulan Bator schön ist. Deshalb haben wir uns daran gewöhnt, anderen Glauben zu schenken, vom Freund bis zum Tagesschausprecher. Das ist kein kindliches Verhalten, sondern eine Notwendigkeit. Freilich ist das riskant, denn alle anderen könnten uns – wie in der „Truman- Show“ – anlügen. In der Wirklichkeit wissen wir uns erst dann, wenn wir unser Selbstbewusstsein verlassen und akzeptieren, dass wir erstens nicht nur Objekte, sondern Subjekte im Bewusstsein von anderen sind, und zweitens, dass alle unsere dialogischen Beziehungen noch einmal von einem Dritten betrachtet werden, der nicht Teil dieser Welt ist.
Für Religiöse ist das der Glaube. Glaube als Unterstellung, dass alle menschlichen Beziehungen erst dann wirklich, ernst und über Zweifel erhaben sind, wenn sie sich vor den Augen Gottes wissen. Erst vor ihm ist etwas als es selbst und nicht nur „für mich“ oder „unter uns“. Daher unterscheidet die biblische Sprache drei Formen des Glaubens: die Beziehung zur Ding-Welt („glauben, dass“), die Beziehung zur Subjekt-Welt („jemandem glauben“) und die Annahme einer subjekthaften überirdischen Wirklichkeit („glauben an“). Wissenschaftstheoretisch gesehen ist Glaube also eine Totalhypothese. Glaube ist nicht das Gegenteil von Wissen, sondern der Versuch, Wirklichkeit vor dem Zweifel zu retten, indem man die fragile empirische Welt als Ausdruck einer stabilen transzendenten Welt begreift.
Oft wollen Studierende in Gesprächen nicht nur wissen, was ich weiß, sondern, was ich glaube. Als Religionswissenschaftler und gleichzeitig gläubiger Katholik sitze ich zwischen den Stühlen: Einerseits ist es als Professor meine Aufgabe, alles zu bezweifeln, d.h. jeden religiösen Text auf seine historischen Kontexte und soziologischen Funktionen zurückzuführen. Andererseits hält der Christ in mir bestimmte religiöse Dokumente – in meinem Fall die Bibel – zwar für einen interpretierbaren, aber doch irreversiblen, offenbarten Text, der vom Ursprung der Wirklichkeit handelt. Werktags ist das Neue Testament eine antike Schriftensammlung neben vielen anderen, am Sonntag ist es die Offenbarung. Beides kann klar unterschieden werden, aber es ist schwer zu entscheiden, ob das Zweifeln oder das Glauben wirklicher ist.
Das vorliegende Heft geht diesem doppelten Verhältnis zum Glauben nach: Wie steht Wissenschaft zum Glauben – ob religiös oder nicht? Wo bringt Wissenschaft Dinge ans Licht, die wir kaum glauben mögen oder uns (wieder) glauben lassen? Was passiert, wenn Forschung irrige Annahmen oder Mythen aufklärt? Ist Wissenschaft in der Lage, Dingen auf den Grund zu gehen, die zwar überzeugend, aber unerklärbar sind? Wie kann sie selbst glaubwürdig bleiben und sich dennoch weiterentwickeln?
In den Beiträgen dieser „Portal Wissen“ scheinen diese Fragen immer wieder auf. Sie bilden ein vielfältiges, spannendes und auch überraschendes Bild der Forschungsprojekte und der Wissenschaftler an der Universität Potsdam. Glauben Sie mir, es erwartet Sie eine anregende Lektüre!
Prof. Dr. Johann Hafner
Professor für Religionswissenschaft mit dem Schwerpunkt Christentum
Dekan der Philosophischen Fakultät
The interdisciplinary workshop STOCHASTIC PROCESSES WITH APPLICATIONS IN THE NATURAL SCIENCES was held in Bogotá, at Universidad de los Andes from December 5 to December 9, 2016. It brought together researchers from Colombia, Germany, France, Italy, Ukraine, who communicated recent progress in the mathematical research related to stochastic processes with application in biophysics.
The present volume collects three of the four courses held at this meeting by Angelo Valleriani, Sylvie Rœlly and Alexei Kulik.
A particular aim of this collection is to inspire young scientists in setting up research goals within the wide scope of fields represented in this volume.
Angelo Valleriani, PhD in high energy physics, is group leader of the team "Stochastic processes in complex and biological systems" from the Max-Planck-Institute of Colloids and Interfaces, Potsdam.
Sylvie Rœlly, Docteur en Mathématiques, is the head of the chair of Probability at the University of Potsdam.
Alexei Kulik, Doctor of Sciences, is a Leading researcher at the Institute of Mathematics of Ukrainian National Academy of Sciences.
We construct marked Gibbs point processes in R-d under quite general assumptions. Firstly, we allow for interaction functionals that may be unbounded and whose range is not assumed to be uniformly bounded. Indeed, our typical interaction admits an a.s. finite but random range. Secondly, the random marks-attached to the locations in R-d-belong to a general normed space G. They are not bounded, but their law should admit a super-exponential moment. The approach used here relies on the so-called entropy method and large-deviation tools in order to prove tightness of a family of finite-volume Gibbs point processes. An application to infinite-dimensional interacting diffusions is also presented.
Convoluted Brownian motion
(2016)
In this paper we analyse semimartingale properties of a class of Gaussian periodic processes, called convoluted Brownian motions, obtained by convolution between a deterministic function and a Brownian motion. A classical
example in this class is the periodic Ornstein-Uhlenbeck process. We compute their characteristics and show that in general, they are neither
Markovian nor satisfy a time-Markov field property. Nevertheless, by enlargement
of filtration and/or addition of a one-dimensional component, one can in some case recover the Markovianity. We treat exhaustively the case of the bidimensional trigonometric convoluted Brownian motion and the higher-dimensional monomial convoluted Brownian motion.
In this paper, we consider families of time Markov fields (or reciprocal classes) which have the same bridges as a Brownian diffusion. We characterize each class as the set of solutions of an integration by parts formula on the space of continuous paths C[0; 1]; R-d) Our techniques provide a characterization of gradient diffusions by a duality formula and, in case of reversibility, a generalization of a result of Kolmogorov.
We develop a cluster expansion in space-time for an infinite-dimensional system of interacting diffusions where the drift term of each diffusion depends on the whole past of the trajectory; these interacting diffusions arise when considering the Langevin dynamics of a ferromagnetic system submitted to a disordered external magnetic field.
We consider infinite-dimensional diffusions where the interaction between the coordinates has a finite extent both in space and time. In particular, it is not supposed to be smooth or Markov. The initial state of the system is Gibbs, given by a strong summable interaction. If the strongness of this initial interaction is lower than a suitable level, and if the dynamical interaction is bounded from above in a right way, we prove that the law of the diffusion at any time t is a Gibbs measure with absolutely summable interaction. The main tool is a cluster expansion in space uniformly in time of the Girsanov factor coming from the dynamics and exponential ergodicity of the free dynamics to an equilibrium product measure.
We consider infinite-dimensional diffusions where the interaction between the coordinates has a finite extent both in space and time. In particular, it is not supposed to be smooth or Markov. The initial state of the system is Gibbs, given by a strong summable interaction. If the strongness of this initial interaction is lower than a suitable level, and if the dynamical interaction is bounded from above in a right way, we prove that the law of the diffusion at any time t is a Gibbs measure with absolutely summable interaction. The main tool is a cluster expansion in space uniformly in time of the Girsanov factor coming from the dynamics and exponential ergodicity of the free dynamics to an equilibrium product measure.
We consider a system of infinitely many hard balls in R<sup>d undergoing Brownian motions and submitted to a smooth pair potential. It is modelized by an infinite-dimensional stochastic differential equation with a local time term. We prove that the set of all equilibrium measures, solution of a detailed balance equation, coincides with the set of canonical Gibbs measures associated to the hard core potential added to the smooth interaction potential.
The authors analyse different Gibbsian properties of interactive Brownian diffusions X indexed by the d-dimensional lattice. In the first part of the paper, these processes are characterized as Gibbs states on path spaces. In the second part of the paper, they study the Gibbsian character on R^{Z^d} of the law at time t of the infinite-dimensional diffusion X(t), when the initial law is Gibbsian. AMS Classifications: 60G15 , 60G60 , 60H10 , 60J60
We prove in this paper an existence result for infinite-dimensional stationary interactive Brownian diffusions. The interaction is supposed to be small in the norm ||.||∞ but otherwise is very general, being possibly non-regular and non-Markovian. Our method consists in using the characterization of such diffusions as space-time Gibbs fields so that we construct them by space-time cluster expansions in the small coupling parameter.
Reciprocal processes, whose concept can be traced back to E. Schrödinger, form a class of stochastic processes constructed as mixture of bridges, that satisfy a time Markov field property. We discuss here a new unifying approach to characterize several types of reciprocal processes via duality formulae on path spaces: The case of reciprocal processes with continuous paths associated to Brownian diffusions and the case of pure jump reciprocal processes associated to counting processes are treated. This presentation is based on joint works with M. Thieullen, R. Murr and C. Léonard.
We consider a class of infinite-dimensional diffusions where the interaction between the components is both spatial and temporal. We start the system from a Gibbs measure with finiterange uniformly bounded interaction. Under suitable conditions on the drift, we prove that there exists t0 > 0 such that the distribution at time t = t0 is a Gibbs measure with absolutely summable interaction. The main tool is a cluster expansion of both the initial interaction and certain time-reversed Girsanov factors coming from the dynamics.
We are interested in modeling the Darwinian evolution of a population described by two levels of biological parameters: individuals characterized by an heritable phenotypic trait submitted to mutation and natural selection and cells in these individuals influencing their ability to consume resources and to reproduce. Our models are rooted in the microscopic description of a random (discrete) population of individuals characterized by one or several adaptive traits and cells characterized by their type. The population is modeled as a stochastic point process whose generator captures the probabilistic dynamics over continuous time of birth, mutation and death for individuals and birth and death for cells. The interaction between individuals (resp. between cells) is described by a competition between individual traits (resp. between cell types). We are looking for tractable large population approximations. By combining various scalings on population size, birth and death rates and mutation step, the single microscopic model is shown to lead to contrasting nonlinear macroscopic limits of different nature: deterministic approximations, in the form of ordinary, integro- or partial differential equations, or probabilistic ones, like stochastic partial differential equations or superprocesses.
We are interested in modeling some two-level population dynamics, resulting from the interplay of ecological interactions and phenotypic variation of individuals (or hosts) and the evolution of cells (or parasites) of two types living in these individuals. The ecological parameters of the individual dynamics depend on the number of cells of each type contained by the individual and the cell dynamics depends on the trait of the invaded individual. Our models are rooted in the microscopic description of a random (discrete) population of individuals characterized by one or several adaptive traits and cells characterized by their type. The population is modeled as a stochastic point process whose generator captures the probabilistic dynamics over continuous time of birth, mutation and death for individuals and birth and death for cells. The interaction between individuals (resp. between cells) is described by a competition between individual traits (resp. between cell types). We look for tractable large population approximations. By combining various scalings on population size, birth and death rates and mutation step, the single microscopic model is shown to lead to contrasting nonlinear macroscopic limits of different nature: deterministic approximations, in the form of ordinary, integro- or partial differential equations, or probabilistic ones, like stochastic partial differential equations or superprocesses. The study of the long time behavior of these processes seems very hard and we only develop some simple cases enlightening the difficulties involved.
In this article we analyse the structure of Markov processes and reciprocal processes to underline their time symmetrical properties, and to compare them. Our originality consists in adopting a unifying approach of reciprocal processes, independently of special frameworks in which the theory was developped till now (diffusions, or pure jump processes). This leads to some new results, too.
For an irreducible continuous time Markov chain, we derive the distribution of the first passage time from a given state i to another given state j and the reversed passage time from j to i, each under the condition of no return to the starting point. When these two distributions are identical, we say that i and j are in time duality. We introduce a new condition called permuted balance that generalizes the concept of reversibility and provides sufficient criteria, based on the structure of the transition graph of the Markov chain. Illustrative examples are provided.
Transport molecules play a crucial role for cell viability. Amongst others, linear motors transport cargos along rope-like structures from one location of the cell to another in a stochastic fashion. Thereby each step of the motor, either forwards or backwards, bridges a fixed distance and requires several biochemical transformations, which are modeled as internal states of the motor. While moving along the rope, the motor can also detach and the walk is interrupted. We give here a mathematical formalization of such dynamics as a random process which is an extension of Random Walks, to which we add an absorbing state to model the detachment of the motor from the rope. We derive particular properties of such processes that have not been available before. Our results include description of the maximal distance reached from the starting point and the position from which detachment takes place. Finally, we apply our theoretical results to a concrete established model of the transport molecule Kinesin V.