Scalable and Efficient Probabilistic Topic Model Inference for Textual Data
Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

Author: Måns Magnusson

Publisher: Linköping University Electronic Press

Published: 2018-04-27

Total Pages: 75

ISBN-13: 9176852881

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Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.

Emergency Vehicle Approaching
Emergency Vehicle Approaching

Author: Kajsa Weibull

Publisher: Linköping University Electronic Press

Published: 2024-10-17

Total Pages: 115

ISBN-13: 9180758053

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Driving an emergency vehicle can be difficult. The driver of the emergency vehicle must navigate, communicate with emergency services, often drive at high speeds, and take surrounding traffic into account. Civilian drivers are required by law to give way to emergency vehicles with lights and sirens activated. Despite this, they sometimes fail to move over. One reason is not noticing the emergency vehicle in time. This dissertation aims to understand how technology can support civilian drivers in their interactions with emergency vehicles. One form of technology used to make drivers move over is emergency vehicle lighting. The results of this dissertation show that alternative designs of emergency vehicle lighting can affect driver behavior and that the current designs are not always suited to promote the most desirable driver behavior. Another technological approach to supporting drivers in their interactions with emergency vehicles is the use of Cooperative Intelligent Transport Systems (C-ITS). One C-ITS service is the Emergency Vehicle Approaching (EVA) warning. An EVA warning is an early in-car warning sent out to the driver before being overtaken by an emergency vehicle, providing more time to move over. Three driving simulator studies with EVA warnings were conducted in this dissertation. The results indicate that EVA warnings make drivers move over more quickly and thereby decrease delay time for emergency vehicles. Furthermore, there is a learning effect when receiving multiple EVA warnings, implying that drivers move over more quickly once they are familiar with the system. One of the simulator studies used eye tracking and showed that EVA warnings make drivers scan mirrors earlier, compared to when not receiving an EVA warning. An EVA warning is distributed based on the most probable path of the emergency vehicle. If the driver of the emergency vehicle decides on another route, there is a risk of false EVA warnings. Therefore, this dissertation explored how false alarms, and false expectations of EVA warnings, affect drivers. Receiving false alarms makes drivers move over more slowly in future interactions and negatively affects attitudes toward the EVA system. Furthermore, wrongly expecting an EVA warning makes drivers less attentive to the road ahead. In conclusion, both emergency vehicle lighting and EVA warnings can support civilian drivers in their interactions with emergency vehicles. It can decrease the risks of both collisions and delays. However, to implement a large-scale deployment of C-ITS, Sweden needs digital infrastructure to support secure data exchange Att framföra ett utryckningsfordon är utmanande. Utryckningsföraren förväntas navigera, kommunicera med larmcentralen, framföra utryckningsfordonet i inte sällan höga hastigheter och samtidigt ta hänsyn till omgivande trafik. Bilister är enligt lag tvungna att lämna fri väg för utryckningsfordon med blåljus och sirener. Trots det misslyckas ibland förare med att lämna fri väg. En anledning är att de inte hinner uppfatta utryckningsfordonet i tid. Syftet med denna avhandling är att förstå hur teknik kan stödja förare vid interaktioner med utryckningsfordon. En form av teknik som används för att få förare att lämna fri väg är blåljus. Resultaten av denna avhandling visar att alternativa designlösningar för blåljus kan påverka förarnas beteende och att de nu-varande utformningarna inte alltid är optimala för att främja det mest önskvärda förarbeteendet. En annan metod för att stötta förare i deras interaktion med utryckningsfordon är uppkopplad fordonsteknik, så kallat Cooperative Intelligent Transport Systems (C-ITS). En typ av C-ITS-tjänst är Emergency Vehicle Approaching (EVA)-varningar. En EVA-varning är en tidig varning som skickas ut till bilisten innan utryckningsfordonet kör ikapp, vilket ger föraren mer tid att lämna fri väg. Tre förarsimulatorstudier med EVA-varningar genomfördes inom ramen för avhandlingen. Resultaten visar på att EVA-varningar kan få förare att lämna fri väg snabbare och därmed minska förseningar för utryckningsfordon. Dessutom finns det en inlärningseffekt med EVA varningar som innebär att förare lämnar fri väg snabbare när de är bekanta med EVA systemet. I en av simulatorstudierna användes ögonrörelsemätning som visade att EVA-varningar får förare att skanna av speglarna i bilen tidigare, jämfört med när de inte får någon EVA-varning. En EVA-varning distribueras baserat på den mest sannolika vägen för utryckningsfordonet. Om föraren av utryckningsfordonet väljer en annan väg finns det risk för falska EVA-varningar. I den här avhandlingen undersöktes därför hur falska larm och en falsk förväntan om EVA-varningar påverkar förare. Att ta emot falska larm påverkade förarnas framtida interaktioner och inställning till EVA-systemet. Dessutom gjorde en felaktig förväntan på en EVA-varning till att förarna var mindre uppmärksamma på vägen framför dem. Sammanfattningsvis kan både blåljus och EVA-varningar stödja civila förare i interaktionen med utryckningsfordon. Varningssystemen kan minska riskerna för både kollisioner och förseningar. För att genomföra en storskalig utbyggnad av C-ITS behöver Sverige dock en digital infrastruktur för att stödja säkert datautbyte.

Orchestrating a Resource-aware Edge
Orchestrating a Resource-aware Edge

Author: Klervie Toczé

Publisher: Linköping University Electronic Press

Published: 2024-09-02

Total Pages: 122

ISBN-13: 9180757480

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More and more services are moving to the cloud, attracted by the promise of unlimited resources that are accessible anytime, and are managed by someone else. However, hosting every type of service in large cloud datacenters is not possible or suitable, as some emerging applications have stringent latency or privacy requirements, while also handling huge amounts of data. Therefore, in recent years, a new paradigm has been proposed to address the needs of these applications: the edge computing paradigm. Resources provided at the edge (e.g., for computation and communication) are constrained, hence resource management is of crucial importance. The incoming load to the edge infrastructure varies both in time and space. Managing the edge infrastructure so that the appropriate resources are available at the required time and location is called orchestrating. This is especially challenging in case of sudden load spikes and when the orchestration impact itself has to be limited. This thesis enables edge computing orchestration with increased resource-awareness by contributing with methods, techniques, and concepts for edge resource management. First, it proposes methods to better understand the edge resource demand. Second, it provides solutions on the supply side for orchestrating edge resources with different characteristics in order to serve edge applications with satisfactory quality of service. Finally, the thesis includes a critical perspective on the paradigm, by considering sustainability challenges. To understand the demand patterns, the thesis presents a methodology for categorizing the large variety of use cases that are proposed in the literature as potential applications for edge computing. The thesis also proposes methods for characterizing and modeling applications, as well as for gathering traces from real applications and analyzing them. These different approaches are applied to a prototype from a typical edge application domain: Mixed Reality. The important insight here is that application descriptions or models that are not based on a real application may not be giving an accurate picture of the load. This can drive incorrect decisions about what should be done on the supply side and thus waste resources. Regarding resource supply, the thesis proposes two orchestration frameworks for managing edge resources and successfully dealing with load spikes while avoiding over-provisioning. The first one utilizes mobile edge devices while the second leverages the concept of spare devices. Then, focusing on the request placement part of orchestration, the thesis formalizes it in the case of applications structured as chains of functions (so-called microservices) as an instance of the Traveling Purchaser Problem and solves it using Integer Linear Programming. Two different energy metrics influencing request placement decisions are proposed and evaluated. Finally, the thesis explores further resource awareness. Sustainability challenges that should be highlighted more within edge computing are collected. Among those related to resource use, the strategy of sufficiency is promoted as a way forward. It involves aiming at only using the needed resources (no more, no less) with a goal of reducing resource usage. Different tools to adopt it are proposed and their use demonstrated through a case study.

Companion Robots for Older Adults
Companion Robots for Older Adults

Author: Sofia Thunberg

Publisher: Linköping University Electronic Press

Published: 2024-05-06

Total Pages: 175

ISBN-13: 9180755747

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This thesis explores, through a mixed-methods approach, what happens when companion robots are deployed in care homes for older adults by looking at different perspectives from key stakeholders. Nine studies are presented with decision makers in municipalities, care staff and older adults, as participants, and the studies have primarily been carried out in the field in care homes and activity centres, where both qualitative (e.g., observations and workshops) and quantitative data (surveys) have been collected. The thesis shows that companion robots seem to be here to stay and that they can contribute to a higher quality of life for some older adults. It further presents some challenges with a certain discrepancy between what decision makers want and what staff might be able to facilitate. For future research and use of companion robots, it is key to evaluate each robot model and potential use case separately and develop clear routines for how they should be used, and most importantly, let all stakeholders be part of the process. The knowledge contribution is the holistic view of how different actors affect each other when emerging robot technology is introduced in a care environment. Den här avhandlingen utforskar vad som händer när sällskapsrobotar införs på omsorgsboenden för äldre genom att titta på perspektiv från olika intressenter. Nio studier presenteras med kommunala beslutsfattare, vårdpersonal och äldre som deltagare. Studierna har i huvudsak genomförts i fält på särskilda boenden och aktivitetscenter där både kvalitativa- (exempelvis observationer och workshops) och kvantitativa data (enkäter) har samlats in. Avhandlingen visar att sällskapsrobotar verkar vara här för att stanna och att de kan bidra till en högre livskvalitet för vissa äldre. Den visar även på en del utmaningar med en viss diskrepans mellan vad beslutsfattare vill införa och vad personalen har möjlighet att utföra i sitt arbete. För framtida forskning och användning av sällskapsrobotar är det viktigt att utvärdera varje robotmodell och varje användningsområde var för sig och ta fram tydliga rutiner för hur de ska användas, och viktigast av allt, låta alla intressenter vara en del av processen. Kunskapsbidraget med avhandlingen är en helhetssyn på hur olika aktörer påverkar varandra när ny robotteknik introduceras i en vårdmiljö

Empirical Studies in Machine Psychology
Empirical Studies in Machine Psychology

Author: Robert Johansson

Publisher: Linköping University Electronic Press

Published: 2024-10-09

Total Pages: 201

ISBN-13: 9179295061

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This thesis presents Machine Psychology as an interdisciplinary paradigm that integrates learning psychology principles with an adaptive computer system for the development of Artificial General Intelligence (AGI). By synthesizing behavioral psychology with a formal intelligence model, the Non-Axiomatic Reasoning System (NARS), this work explores the potential of operant conditioning paradigms to advance AGI research. The thesis begins by introducing the conceptual foundations of Machine Psychology, detailing its alignment with the theoretical constructs of learning psychology and the formalism of NARS. It then progresses through a series of empirical studies designed to systematically investigate the emergence of increasingly complex cognitive behaviors as NARS interacts with its environment. Initially, operant conditioning is established as a foundational principle for developing adaptive behavior with NARS. Subsequent chapters explore increasingly sophisticated cognitive capabilities, all studied with NARS using experimental paradigms from operant learning psychology: Generalized identity matching, Functional equivalence, and Arbitrarily Applicable Relational Responding. Throughout this research, Machine Psychology is demonstrated to be a promising framework for guiding AGI research, allowing both the manipulation of environmental contingencies and the system’s intrinsic logical processes. The thesis contributes to AGI research by showing how using operant psychological paradigms with NARS can enable cognitive abilities similar to human cognition. These findings set the stage for AGI systems that learn and adapt more like humans, potentially advancing the creation of more general and flexible AI. Denna avhandling introducerar Maskinpsykologi som ett tvärvetenskapligt område där principer från inlärningspsykologi integreras med ett adaptivt datorsystem. Genom att kombinera forskning från beteendepsykologi med en formell modell för intelligens (Non-Axiomatic Reasoning System; NARS), undersöker avhandlingen hur operant betingning kan användas för att driva utvecklingen av Artificiell General Intelligens (AGI) framåt. Avhandlingen börjar med att förklara grunderna i Maskinpsykologi och hur dessa relaterar till både inlärningspsykologi och NARS. Därefter presenteras en serie experiment som systematiskt undersöker hur allt mer komplexa kognitiva beteenden kan uppstå när NARS interagerar med sin omgivning. Till att börja med etableras operant betingning som en central metod för att utveckla adaptiva beteenden med NARS. I de följande kapitlen utforskas hur NARS, genom experiment inspirerade av operant inlärningspsykologi, kan utveckla mer avancerade kognitiva förmågor som till exempel generaliserad identitetsmatchning, funktionell ekvivalens och så kallade arbiträrt applicerbara relationsresponser. Denna forskning visar att Maskinpsykologi är ett lovande verktyg för att vägleda AGI-forskning, eftersom det möjliggör att både påverka omgivningsfaktorer och styra systemets interna logiska processer. Avhandlingen bidrar till AGI-forskning genom att visa hur operanta psykologiska metoder, tillämpade på NARS, kan möjliggöra kognitiva förmågor som liknar mänskligt tänkande. Dessa insikter öppnar nya möjligheter för att utveckla AI-system som kan lära sig och anpassa sig på ett mer mänskligt sätt, vilket kan leda till skapandet av mer generell och flexibel AI.

Beyond Recognition
Beyond Recognition

Author: Le Minh-Ha

Publisher: Linköping University Electronic Press

Published: 2024-05-06

Total Pages: 103

ISBN-13: 918075676X

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This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.

Machine Learning-Based Bug Handling in Large-Scale Software Development
Machine Learning-Based Bug Handling in Large-Scale Software Development

Author: Leif Jonsson

Publisher: Linköping University Electronic Press

Published: 2018-05-17

Total Pages: 149

ISBN-13: 9176853063

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This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.

Distributed Moving Base Driving Simulators
Distributed Moving Base Driving Simulators

Author: Anders Andersson

Publisher: Linköping University Electronic Press

Published: 2019-04-30

Total Pages: 60

ISBN-13: 9176850900

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Development of new functionality and smart systems for different types of vehicles is accelerating with the advent of new emerging technologies such as connected and autonomous vehicles. To ensure that these new systems and functions work as intended, flexible and credible evaluation tools are necessary. One example of this type of tool is a driving simulator, which can be used for testing new and existing vehicle concepts and driver support systems. When a driver in a driving simulator operates it in the same way as they would in actual traffic, you get a realistic evaluation of what you want to investigate. Two advantages of a driving simulator are (1.) that you can repeat the same situation several times over a short period of time, and (2.) you can study driver reactions during dangerous situations that could result in serious injuries if they occurred in the real world. An important component of a driving simulator is the vehicle model, i.e., the model that describes how the vehicle reacts to its surroundings and driver inputs. To increase the simulator realism or the computational performance, it is possible to divide the vehicle model into subsystems that run on different computers that are connected in a network. A subsystem can also be replaced with hardware using so-called hardware-in-the-loop simulation, and can then be connected to the rest of the vehicle model using a specified interface. The technique of dividing a model into smaller subsystems running on separate nodes that communicate through a network is called distributed simulation. This thesis investigates if and how a distributed simulator design might facilitate the maintenance and new development required for a driving simulator to be able to keep up with the increasing pace of vehicle development. For this purpose, three different distributed simulator solutions have been designed, built, and analyzed with the aim of constructing distributed simulators, including external hardware, where the simulation achieves the same degree of realism as with a traditional driving simulator. One of these simulator solutions has been used to create a parameterized powertrain model that can be configured to represent any of a number of different vehicles. Furthermore, the driver's driving task is combined with the powertrain model to monitor deviations. After the powertrain model was created, subsystems from a simulator solution and the powertrain model have been transferred to a Modelica environment. The goal is to create a framework for requirement testing that guarantees sufficient realism, also for a distributed driving simulation. The results show that the distributed simulators we have developed work well overall with satisfactory performance. It is important to manage the vehicle model and how it is connected to a distributed system. In the distributed driveline simulator setup, the network delays were so small that they could be ignored, i.e., they did not affect the driving experience. However, if one gradually increases the delays, a driver in the distributed simulator will change his/her behavior. The impact of communication latency on a distributed simulator also depends on the simulator application, where different usages of the simulator, i.e., different simulator studies, will have different demands. We believe that many simulator studies could be performed using a distributed setup. One issue is how modifications to the system affect the vehicle model and the desired behavior. This leads to the need for methodology for managing model requirements. In order to detect model deviations in the simulator environment, a monitoring aid has been implemented to help notify test managers when a model behaves strangely or is driven outside of its validated region. Since the availability of distributed laboratory equipment can be limited, the possibility of using Modelica (which is an equation-based and object-oriented programming language) for simulating subsystems is also examined. Implementation of the model in Modelica has also been extended with requirements management, and in this work a framework is proposed for automatically evaluating the model in a tool.

Robust Stream Reasoning Under Uncertainty
Robust Stream Reasoning Under Uncertainty

Author: Daniel de Leng

Publisher: Linköping University Electronic Press

Published: 2019-11-08

Total Pages: 234

ISBN-13: 9176850137

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Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement. The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.

Designing for Resilience
Designing for Resilience

Author: Vanessa Rodrigues

Publisher: Linköping University Electronic Press

Published: 2020-05-05

Total Pages: 165

ISBN-13: 9179298672

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Services are prone to change in the form of expected and unexpected variations and disruptions, more so given the increasing interconnectedness and complexity of service systems today. These changes require service systems to be resilient and designed to adapt, to ensure that services continue to work smoothly. This thesis problematises the prevailing view and assumptions underpinning the current understanding of resilience in services. Drawing on literature from service management, service design, systems thinking and social-ecological resilience theory, this work investigates how service design can foster resilience in service systems. Supported by empirical input from three research projects in healthcare, the findings show service design can contribute to the adaptability and transformability of service systems through its holistic, human-centred, participatory and experimental approaches. Through the analysis, this research identifies key intervention points for cultivating service systems resilience through service design, including the design of service interactions, processes, enabling structures and multi-level governance. The study makes two important contributions. First, it extends the understanding of service systems resilience as the collective capacity for intentional action in responding to ongoing change, coordinated across scales in order to create value. This is supported by offering alternative assumptions about resilience in service. Second, it positions service design as an enabler of service resilience by explicitly linking design practice(s) to processes that contribute to resilience. By extending the understanding of service systems resilience, this thesis lays the groundwork for future research at the intersection of service design, systemic change and resilience.