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Workarounds are goal-driven deviations from standard operatingprocedures, performed to overcome obstacles constraining day-to-day work.Related research has elicited antecedents and types of workarounds but not investigatedhow and why workarounds diffuse in an organization, leaving theirmark on co-workers, IT artifacts, and organizational structures. Drawing onorganizational routines and business processes as complementary theoreticallenses, we perform a multiple case study to elicit how and why workaroundsoccur and impact organizations. We start by pinpointing workarounds as individualtemporary fixes for technology, or for organizational, and strategicmisfits in organizations. Once established, workarounds can diffuse in an organizationif observed by or communicated to co-workers, initiating processesof restructuration and re-design on an organizational level. If appropriategovernance structures are in place, workarounds can become official workpractices or trigger the innovation of processes and IT artifacts that resolvetheir underlying misfits. Our results provide unique empirical insights intothe mechanisms of bottom-up initiated change in organizations, substantiatedby integrating previously unconnected theory on organizational routines andBusiness Process Management (BPM)., Verena Wolf, Christian Bartelheimer and Daniel Beverungen

Business Process Management is a boundary spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, infor mation systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business pro cesses. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field

Business Process Management is a boundary spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, infor mation systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business pro cesses. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field

Discrete-state stochastic models are a popular approach to describe the inherent stochasticity of gene expression in single cells. The analysis of such models is hindered by the fact that the underlying discrete state space is extremely large. Therefore hybrid models, in which protein counts are replaced by average protein concentrations, have become a popular alternative. The evolution of the corresponding probability density functions is given by a coupled system of hyperbolic PDEs. This system has Markovian nature but its hyperbolic structure makes it difficult to apply standard functional analytical methods. We are able to prove convergence towards the stationary solution and determine such equilibrium explicitly by combining abstract methods from the theory of positive operators and elementary ideas from potential analysis.

Business Process Management is a boundary spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, infor mation systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business pro cesses. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field

Business Process Management is a boundary spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, infor mation systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business pro cesses. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field

Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However, reducing the statistical uncertainty of the corresponding estimators requires sampling a large number of trajectories. We propose control variates based on the statistical moments of the process to reduce the estimators' variances. We develop an algorithm that selects an efficient subset of infinitely many control variates. To this end, the algorithm uses resampling and a redundancy-aware greedy selection. We demonstrate the efficiency of our approach in several case studies., Comment: arXiv admin note: substantial text overlap with arXiv:1905.00854

Dynamical systems in which local interactions among agents give rise to complex emerging phenomena are ubiquitous in nature and society. This work explores the problem of inferring the unknown interaction structure (represented as a graph) of such a system from measurements of its constituent agents or individual components (represented as nodes). We consider a setting where the underlying dynamical model is unknown and where different measurements (i.e., snapshots) may be independent (e.g., may stem from different experiments). We propose GINA (Graph Inference Network Architecture), a graph neural network (GNN) to simultaneously learn the latent interaction graph and, conditioned on the interaction graph, the prediction of a node's observable state based on adjacent vertices. GINA is based on the hypothesis that the ground truth interaction graph -- among all other potential graphs -- allows to predict the state of a node, given the states of its neighbors, with the highest accuracy. We test this hypothesis and demonstrate GINA's effectiveness on a wide range of interaction graphs and dynamical processes.

To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation that employs a state-space lumping scheme, aggregating states in a grid structure. The resulting approximate stationary distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection tailored to the stationary behavior. We demonstrate the method's applicability to a wide range of non-linear problems with complex stationary behaviors., Comment: arXiv admin note: text overlap with arXiv:2010.10096

Learning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characteristics depend on those of the underlying learning approach. Here, we consider a benchmark planning problem from the reinforcement learning domain, the Racetrack, to investigate the properties of agents derived from different deep (reinforcement) learning approaches. We compare the performance of deep supervised learning, in particular imitation learning, to reinforcement learning for the Racetrack model. We find that imitation learning yields agents that follow more risky paths. In contrast, the decisions of deep reinforcement learning are more foresighted, i.e., avoid states in which fatal decisions are more likely. Our evaluations show that for this sequential decision making problem, deep reinforcement learning performs best in many aspects even though for imitation learning optimal decisions are considered., Comment: Extended Version of the Conference Paper published in the Proceedings of the 17th International Conference on Quantitative Evaluation of SysTems (QEST)

Die digitale Transformation prägt die Entwicklung von innovativen Dienstleistungssystemen und deren Management. Einerseits entstehen Potentiale durch die Verfügbarkeit von neuen Ressourcen, wie z.B. Daten und digitalen Technologien, sodass neue Wertversprechen ermöglicht werden. Andererseits entstehen durch die hohe Dynamik am Markt auch Herausforderungen, da Innovationszyklen verkürzt werden müssen. Auf der Unternehmensseite besteht dadurch der Bedarf, dass Entwicklung und Anwendung von Dienstleistungsinnovationen verschmelzen. Mithilfe der Theorie der Ambidextrie werden in der Dissertation neue deskriptive und präskriptive Forschungsbeiträge geschaffen, welche eine integrierte Sichtweise auf Innovation ermöglichen. Dabei wird Innovation in Dienstleistungssystemen als sich gegenseitig konstituierende Mechanismen von Exploration und Exploitation konzeptualisiert. Exploration kann durch agile und flexible Vorgehensmodelle und Methoden des Service System Engineering erfolgen. Eine wichtige Aktivität stellt dabei die Transformation von bestehenden Strukturen, Geschäftsprozessen und Organisationsroutinen dar. Die eigenständige Abweichung von Mitarbeitern in Organisationsroutinen stellt eine Form von Exploitation dar. Insbesondere Workarounds bilden eine wichtige Quelle für Innovation, welche das Potential haben eine Organisation bottom-up zu transformieren., Digital transformation affects the development and management of innovative service systems. On the one hand, potentials are created through the availability of new types of resources, e.g., data and digital technologies, enabling new value propositions. On the other hand, challenges arise through highly dynamic markets, requiring faster innovation cycles. Therefore, organizations need to span the boundaries between the development and management of service system innovation.By taking ambidexterity as a theoretical lens, the dissertation provides descriptive and prescriptive knowledge to achieve an integrated view on service system innovation. In this regard, innovation can be conceptualized as mutual constitutive mechanisms of exploration and exploitation. Exploration can be achieved through agile and flexible methods of service system engineering. Essential activities of exploration are the transformation of existing structures, business processes, and organizational routines. The unacknowledged deviation of employees in organizational routines constitutes a type of exploitation. Especially workarounds can be an important source of innovation, having the potential to transform an organization bottom-up., von Verena Wolf ; Gutachter: Prof. Dr. Daniel Beverungen, Prof. Dr. Lauri Wessel, Tag der Verteidigung: 30.11.2020, Universität Paderborn, Dissertation, 2020

Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method's applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.

We consider the problem of bounding mean first passage times for a class of continuous-time Markov chains that captures stochastic interactions between groups of identical agents. The quantitative analysis of such probabilistic population models is notoriously difficult since typically neither state-based numerical approaches nor methods based on stochastic sampling give efficient and accurate results. Here, we propose a technique that extends recently developed methods using semi-definite programming to determine bounds on mean first passage times. We further apply the technique to hybrid models and demonstrate its accuracy and efficiency for some examples from biology.

Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the preferred - sometimes the only feasible - way to investigate such systems. Previous research focused primarily on Markovian models where the random time until an interaction happens follows an exponential distribution. In this work, we study a general framework to model systems where each agent is in one of several states. Agents can change their state at random, influenced by their complete neighborhood, while the time to the next event can follow an arbitrary probability distribution. Classically, these simulations are hindered by high computational costs of updating the rates of interconnected agents and sampling the random residence times from arbitrary distributions. We propose a rejection-based, event-driven simulation algorithm to overcome these limitations. Our method over-approximates the instantaneous rates corresponding to inter-event times while rejection events counterbalance these over-approximations. We demonstrate the effectiveness of our approach on models of epidemic and information spreading., Comment: 14 pages including 2 pages Appendix and 2 pages references

A widely used approach to describe the dynamics of gene regulatory networks is based on the chemical master equation, which considers probability distributions over all possible combinations of molecular counts. The analysis of such models is extremely challenging due to their large discrete state space. We therefore propose a hybrid approximation approach based on a system of partial differential equations, where we assume a continuous-deterministic evolution for the protein counts. We discuss efficient analysis methods for both modeling approaches and compare their performance. We show that the hybrid approach yields accurate results for sufficiently large molecule counts, while reducing the computational effort from one ordinary differential equation for each state to one partial differential equation for each mode of the system. Furthermore, we give an analytical steady-state solution of the hybrid model for the case of a self-regulatory gene.

Stochastic processes can model many emerging phenomena on networks, like the spread of computer viruses, rumors, or infectious diseases. Understanding the dynamics of such stochastic spreading processes is therefore of fundamental interest. In this work we consider the wide-spread compartment model where each node is in one of several states (or compartments). Nodes change their state randomly after an exponentially distributed waiting time and according to a given set of rules. For networks of realistic size, even the generation of only a single stochastic trajectory of a spreading process is computationally very expensive. Here, we propose a novel simulation approach, which combines the advantages of event-based simulation and rejection sampling. Our method outperforms state-of-the-art methods in terms of absolute run-time and scales significantly better, while being statistically equivalent.

Complex networks play an important role in human society and in nature. Stochastic multistate processes provide a powerful framework to model a variety of emerging phenomena such as the dynamics of an epidemic or the spreading of information on complex networks. In recent years, mean-field type approximations gained widespread attention as a tool to analyze and understand complex network dynamics. They reduce the model's complexity by assuming that all nodes with a similar local structure behave identically. Among these methods the approximate master equation (AME) provides the most accurate description of complex networks' dynamics by considering the whole neighborhood of a node. The size of a typical network though renders the numerical solution of multistate AME infeasible. Here, we propose an efficient approach for the numerical solution of the AME that exploits similarities between the differential equations of structurally similar groups of nodes. We cluster a large number of similar equations together and solve only a single lumped equation per cluster. Our method allows the application of the AME to real-world networks, while preserving its accuracy in computing estimates of global network properties, such as the fraction of nodes in a state at a given time.

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information spreading. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), like degree-based mean field (DBMF), approximate master equation (AME), or pair approximation (PA). The number of differential equations so obtained is typically proportional to the maximum degree kmax of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large kmax. In this paper, we extend AME and PA, which has been proposed only for the binary state case, to a multi-state setting and provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy., Comment: 16 pages with the Appendix

This publication is the English version of the summary of the German report „Meilensteine 2030“ (THRÄN et al. 2015) which is published in the series of the funding programme “Biomass energy use”. The report describes elements and milestones for the development of a stable and sustainable bioenergy strategy.

This publication is the English version of the summary of the German report „Meilensteine 2030“ (THRÄN et al. 2015) which is published in the series of the funding programme “Biomass energy use”. The report describes elements and milestones for the development of a stable and sustainable bioenergy strategy.

In einer weitgehend auf erneuerbaren Energien fußenden Energieversorgung in Deutschland muss Bioenergie künftig die Lücken füllen, die nicht aus anderen Quellen gespeist werden können – diese These hat die Diskussion um Bioenergie im beginnenden 21. Jahrhundert stark bestimmt (BARZANTNY et al., 2009; KIRCHNER & MATTHES, 2009; SaCHVERSTÄNDIGENRAT FÜR UMWELTFRAGEN, 2011; SCHLESINGER et al., 2010, 2011). Dabei gibt es sowohl starke Argumente für den flexiblen Einsatz im Strombereich als auch für ausgewählte Kraftstoffpfade (z. B. Schwerlastverkehr, Schifffahrt, Flugverkehr), während im Wärmebereich Bioenergie als gut durch alternative erneuerbare Versorgungskonzepte ersetzbar gilt. Jedoch hat sich auch gezeigt, dass Biomasse zwar regenerativ, jedoch für den konkreten Zeitraum und unter Nachhaltigkeitsaspekten nur begrenzt verfügbar ist. Künftig wird erwartet, dass der Bedarf an Nahrungs- und Futtermitteln wie auch für die stoffliche Nutzung steigt. Damit wird eine Priorisierung der Einsatzbereiche für den weiteren Ausbau zunehmend notwendig (BMVBS, 2010; THRÄN et al., 2011; KOALITIONSVERTRAG, 2013; MAJER et al., 2013). Es herrscht Einigkeit, dass Bioenergienutzung im Einklang mit den Zielen der nachhaltigen Entwicklung stehen muss und insbesondere gegenüber der Ernährungssicherung nachrangig ist, dass die Nutzung zunehmend an den Erfordernissen des Energiesystems ausgerichtet sein müssen und dass nur bei stetiger Weiterentwicklung der Technologien ein angemessener Beitrag der Bioenergie erreicht werden kann. Auch scheint es sinnvoll, dass man – vor dem Hintergrund der vielfältigen aktuellen Entwicklungen im Bereich der regenerativen, nicht-biogenen Energietechnologien und Energieträger – Bioenergiestrategien favorisiert, die geringe Pfadabhängigkeiten aufweisen und z. B. Technologiekonzepte berücksichtigen, die sowohl im Strom- / Wärme-Bereich als auch im Kraftstoffsektor genutzt werden können.

We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe networks of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient probability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We implemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology., Comment: 10 pages

This paper presents an on-the-fly uniformization technique for the analysis of time-inhomogeneous Markov population models. This technique is applicable to models with infinite state spaces and unbounded rates, which are, for instance, encountered in the realm of biochemical reaction networks. To deal with the infinite state space, we dynamically maintain a finite subset of the states where most of the probability mass is located. This approach yields an underapproximation of the original, infinite system. We present experimental results to show the applicability of our technique.