Die vorliegende Arbeit widmet sich der Darstellung des Kunstmarktgeschehens und der Entscheidungsfindung von Galeristen. Ziel ist es aufzuzeigen, welche Auswirkungen Erfahrung und Expertise sowie ein begrenzter Informationszugang auf die angewandte Entscheidungslogik und das Entscheidungsverhalten von Galeristen haben. Sie behandelt die Thematik der kausalen und effektualen Entscheidungslogik und der unterschiedlichen Entscheidungsansätze von Novizen und Experten. Gemäß Sarasvathy (2001) tendieren Novizen zu kausaler und Experten zur effektualer Logik. Sie unterscheiden sich durch ihren Grad an Expertise, welche auf Deliberate Practice, Erfahrung und kontinuierlich erbrachter überragender Leistungserbringung basiert (Ericsson 2006; Mitchell et al. 2005:3, Dew et al. 2009: 289). Gegenstand der Untersuchung war die Beantwortung der Fragen, ob sich die Berufserfahrung, das Geschlecht und der akademische Werdegang des Galeristen auf die angewandte Entscheidungslogik auswirken. Die Ergebnisse belegen, dass die Berufserfahrung einen signifikanten Einfluss auf die angewandte Entscheidungslogik der Galeristen hat: Novizen-Galeristen präferieren die kausale Entscheidungslogik, Experten-Galeristen die effektuale. In Bezug auf das Geschlecht ist nachweisbar, dass Galeristinnen am häufigsten die kausale Entscheidungslogik anwenden. Dasselbe Bild stellt sich bei Galeristen – ungeachtet ihres Geschlechts – ohne akademische Ausbildung ein. Die Arbeit setzt zudem ihren Fokus auf die experimentelle Untersuchung des Preisbildungsverfahrens durch Galeristen, wobei insbesondere der etwaige Einfluss der Overconfidence im Mittelpunkt steht. Die Studienergebnisse lassen darauf schließen, dass ein signifikantes Maß an Overconfidence dazu führt, dass die Preise für Kunstwerke niedriger gesetzt werden. Zudem konnte ein Wissenseffekt festgestellt werden: Je versierter ein Galerist im Kunstmarkt ist, desto höher setzt er den Preis für ein als „ausstellungswürdig" deklariertes Kunstwerk. / The objective of this dissertation is to shed more light on the primary art market and the decision-making processes of its protagonists, the gallery owners. The doctoral thesis focuses on the potential impact of experience and expertise on the gallerists‘ applied decision-making logic and the consequences of limited access to information for the gallery owners‘ decision-making behaviour. In particular, the distinction between novices and experts and their decision-making is addressed. According to Sarasvathy (2001), novices tend to use a predictive decision-making logic (causation), whereas experts apply a non-predictive logic (effectuation). They differ in their level of expertise, which is based on deliberate practice, experience and continuous outstanding and superior performance in a particular domain (Ericsson 2006; Mitchell et al. 2005:3, Dew et al. 2009: 289). The studies conducted examined whether the professional experience, gender and academic career of the gallery owner affects the applied decision-making logic. The results show that professional experience has a significant influence on the applied decision-making logic of the gallery owner: novice-gallerists prefer the causal approach, expert gallery owners favour the effectual decision-making logic. With regard to gender and the academic career, it can be proven that female gallery owners and gallerists without academic training most often apply the causal decision-making logic. This dissertation also focuses on the experimental analysis of the influence and impact of overconfidence on the price setting processes of gallery owners. The results indicate that a significant level of overconfidence leads to lower prices for works of art. In addition, a knowledge effect could be observed: the more sophisticated the gallery owners are, the higher will be the price they set for an art work they consider to be suitable for an exhibition.

A question concerning not only modellers but also practitioners is: Under what circumstances can mutual cooperation be established and maintained by a group of people facing a common pool dilemma" A step before this question of institutional influences there is need for a different way of modelling human behaviour that does not draw on the rational actor paradigm, because this kind of modelling needs to be able to integrate various deviations from this theory shown in economic experiments. We have chosen a new approach based on observations in form of laboratory and field observations of actual human behaviour. We model human decision making as using an adaptive toolbox following the notion of Gigerenzer. Humans draw on a number of simple heuristics that are meaningful in a certain situation but may be useless in another. This is incorporated into our agent-based model by having agents perceive their environment, draw on a pool of heuristics to choose an appropriate one and use that heuristic.Behavioural differences can be incorporated in two ways. First, each agent has a number of attributes that differ in values, for example there are more and less cooperative agents. The second behavioural difference lies in the way, in which heuristics are chosen. With this modelling approach we contribute to a new way of modelling human behaviour, which is simple enough to be included into morecomplex models while at the same time realistic enough to cover actual decision making processes of humans. Modellers should be able to use this approach without a need to get deep into psychological, sociological or economic theory. Stakeholders in social dilemmas, who may be confronted with such a model should understand, why an agent decides in the way it does.

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M. Tech. (Department of Marketing, Faculty of Management Sciences), Vaal University of Technology. / While more than half of the South African adult population are active microbloggers, the most common topics that are discussed include current affairs, sports and music entertainment. Nonetheless, there exists intrinsic fear about the credibility of digital content, which stems from the fact that there are limited standards for quality control in the microglogging sphere. Consequently, this apprehension exacerbates potential problems pertaining to ascertaining the reliability of electronic word of mouth (eWOM) communication. In this vein, this study seeks to investigate the salience of selected determinants on consumers’ perceptions about the credibility of microblog music reviews and future eWOM adoption. The sample comprised 485 self-reporting microbloggers residing in the southern Gauteng region of South Africa. The study applied a quantitative approach, whereas cross-sectional data were collected only once from the sample using a paper and pencil based self-administered questionnaire in 2018. Moreover, a positivist philosophy was followed, whereas hypotheses were framed from the credibility heuristics theory, posited as the underlying cues that aid consumers in the online credibility verification process. The formal protocol for research ethics were followed upon collecting empirical data, whereas the multi-item survey questionnaire was tested for reliability and validity. The hypotheses testing comprised an estimation of two regression models. From the findings obtained, it is evident that source credibility, information quality, homophily and prior beliefs confirmation are the four heuristics that pose a significant effect on the evaluation of microblog music reviews by consumers, yielding approximately 43.3 percent explanatory power on the overall credibility of microblog music reviews. Moreover, the second regression model proved the positive effect of microblog music review credibility on eWOM adoption, whereas the independent variable explains 30.7 percent of the variance in eWOM adoption. These findings point to the significance in applying the heuristics in evaluating microblog music reviews created by South African consumers. As such, the findings could assist both microblog administrators and marketing communication practitioners to better design the platforms to facilitate reader credibility evaluations about various products.

Darwinian evolution is a robustly supported scientific theory. Yet creationists continue to challenge its teaching in American public schools. Biology teachers in all 50 states are responsible for teaching science content standards that include evolution. As products of their backgrounds and affiliations teachers bring personal attitudes and beliefs to their teaching. The purpose of this study was to explore how biology teachers perceive, describe, and value their teaching of evolution. This research question was explored through a heuristic qualitative methodology. Eight veteran California high school biology teachers were queried as to their beliefs, perceptions, experiences and practices of teaching evolution. Both personal and professional documents were collected. Data was presented in the form of biographical essays that highlight teachers' backgrounds, experiences, perspectives and practices of teaching evolution. Of special interest was how they describe pressure over teaching evolution during a decade of standards and No Child Left Behind high-stakes testing mandates. Five common themes emerged. Standards have increased the overall amount of evolution that is taught. High-stakes testing has decreased the depth at which evolution is taught. Teacher belief systems strongly influence how evolution is taught. Fear of creationist challenges effect evolution teaching strategies. And lastly, concern over the potential effects of teaching evolution on student worldviews was mixed. Three categories of teacher concern over the potential impact of evolution on student worldviews were identified: Concerned, Strategist, and Carefree. In the final analysis teacher beliefs and attitudes still appeared to he the most important factor influencing how evolution is taught.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19114号 / 情博第560号 / 新制||情||99(附属図書館) / 32065 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 林 春男, 教授 田中 克己, 教授 喜多 一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the re- search problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

ABSTRACT           Date:   2023-05-31      Level:   Bachelor thesis in Business Administration, 15cr      Institution:   School of Business, Society and Engineering, Mälardalen University Authors:     Malin Godenius (95/10/06)   Victoria Karlson (94/04/01)   Sophia Röse (99/12/06)      Title:   Unravelling the Dynamics of Managerial Attention Allocation: Exploring the Impact of Digital Technologies and Heuristics      Supervisor:   Edward Gillmore      Keywords:   Attention-Based View, Heuristics, Digital Technologies, Digital Transformation, Attention Allocation, Managers      Research questions:  RQ1: What effect do digital technologies have on managers' attention allocation? RQ2: How do digital technologies substitute or complement managers' heuristics?      Purpose:   This study aims to explore and understand how increased access to digital technologies influences managers' attention allocation. The primary purpose is to investigate how digital technologies may substitute or complement managers' heuristics. Consequently, this can aid organisations' awareness of the potential impact to effectively adopt specific approaches to improve managers' decision-making and avoid information overload.      Method:   An exploratory, qualitative multiple case study consisting of six interviews was conducted to align with the purpose. The data was gathered through semi-structured interviews with managers at several companies within the energy and power industry. The data collection focuses on the three elements digital technologies, heuristics, and the attention-based view, which are then analysed through thematic analysis to identify their influence on one another.      Conclusion:   This study identifies the effect of digital technologies on attention allocation and heuristics. Attention allocation is negatively and positively impacted through digital technologies, allowing managers to, e.g., access an abundance of information which in turn can lead to information overload. Digital technologies additionally have a complementary effect on heuristics, the degree of which differs depending on several factors and situations. A substitutional effect is not as common and appears in combination with process automation.

In this thesis we consider three challenging vehicle routing problems representing specific aspects of complex real-world problems: (i) the vehicle routing problem with unit demands, (ii) the preemptive stacker crane problem and (iii) a multi-period vehicle and technician routing problem. For the vehicle routing problem with units demands we continue research on the exponential multi-insertion neighborhood, investigate its properties and propose heuristic solution methods utilizing the neighborhood. For the preemptive stacker crane problem we study structural properties and provide bounds on the benefits of preemption and the benefits of so-called explicit drop nodes that are used exclusively to facilitate preemption. We propose construction heuristics that improve on the state-of-the-art in computational time and solution quality. The multi-period vehicle and technician routing problem is the subject of the VeRoLog Solver Challenge 2019. We develop a solution method that adapts to the limited computational budget and the given instance parameters. In summary, this thesis contributes to the structural analysis of the considered problems and proposes efficient heuristic solution methods that are effective even on large-scale instances and under tight restrictions of the computational budget. The methods combine global and local search approaches and take the available computational budget into account to realize an adaptive best-effort allocation of the resources.

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers.