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The self-organized nest construction behaviors of European paper wasps (Polistes dominula) show potential for adoption in artificial intelligence and robotic systems where centralized control proves challenging. However, P. dominula nest construction mechanisms are not fully understood. This research investigated how nest structures stimulate P. dominula worker action at different stages of nest construction. A novel stochastic site selection model, weighted by simple rules for cell age, height, and wall count, was implemented in a three-dimensional, step-by-step nest construction simulation. The simulation was built on top of a hexagonal coordinate system to improve precision and performance. Real and idealized nest data were used to evaluate simulated nests via two parameters: outer wall counts and compactness numbers. Structures generated with age-based rules were not significantly different from real nest structures along both parameters.

Den agila arbetsmetoden har vuxit fram i takt med en volatil marknad som ställer krav på flexibilitet och innovation. Arbetsmetoden utmärker sig som en teamledningsmetod med ett dynamiskt och autonomt arbetssätt. Det råder ofullständig förståelse för vilka utmaningar arbetssättet medför. Syftet med denna studie är att bidra till en djupare förståelse för fenomenet och kunna bidra med insikter för hur organisationens val av styrmekanismer influerar medarbetare i det agila arbetssättet. Totalt genomfördes sju semistrukturerade intervjuer som analyserades med en tematisk analys vilket genererade tre teman: agilt mindset, teamwork och kultur som meningsskapande. Studien visar att de agila arbetsmetoderna och modellerna omfattar en bredare komplexitet än många tidigare forskare framställt. Företagskulturen visar kunna gagna arbetsmetoden när den upplevs meningsfull, transparent, inkluderande och stimulerar engagemang. En internaliserad företagskultur kan frigöra potential att stimulera arbetsmetodens fördelar. Vid implementering av en agil arbetsmetod bör organisationer beakta att det kan vara en tidskrävande process och följer av andra utmaningar än vid traditionella arbetsmetoder.

In this dissertation, we focus on multiple levels of optimized resource management techniques. We first consider a classic resource management problem, namely the scheduling of data-intensive applications. We define the Divisible Load Scheduling (DLS) problem, outline the system model based on the assumption that data staging and all communication with the sites can be done in parallel, and introduce a set of optimal divisible load scheduling algorithms and the related fault-tolerant coordination algorithm. The DLS algorithms introduced in this dissertation exploit parallel communication, consider realistic scenarios regarding the time when heterogeneous computing systems are available, and generate optimal schedules. Performance studies show that these algorithms perform better than divisible load scheduling algorithms based upon sequential communication. We have developed a self-organization model for resource management in distributed systems consisting of a very large number of sites with excess computing capacity. This self-organization model is inspired by biological metaphors and uses the concept of varying energy levels to express activity and goal satisfaction. The model is applied to Pleiades, a service-oriented architecture based on resource virtualization. The self-organization model for complex computing and communication systems is applied to Very Large Sensor Networks (VLSNs). An algorithm for self-organization of anonymous sensor nodes called SFSN (Scale-free Sensor Networks) and an algorithm utilizing the Small-worlds principle called SWAS (Small-worlds of Anonymous Sensors) are introduced. The SFSN algorithm is designed for VLSNs consisting of a fairly large number of inexpensive sensors with limited resources. An important feature of the algorithm is the ability to interconnect sensors without an identity, or physical address used by traditional communication and coordination protocols. During the self-organization phase, the collision-free communication channels allowing a sensor to synchronously forward information to the members of its proximity set are established and the communication pattern is followed during the activity phases. Simulation study shows that the SFSN ensures the scalability, limits the amount of communication and the complexity of coordination. The SWAS algorithm is further improved from SFSN by applying the Small-worlds principle. It is unique in its ability to create a sensor network with a topology approximating small-world networks. Rather than creating shortcuts between pairs of diametrically positioned nodes in a logical ring, we end up with something resembling a double-stranded DNA. By exploiting Small-worlds principle we combine two desirable features of networks, namely high clustering and small path length.

This thesis aims to evaluate four artificial neural network architectures, each of which implements the sensory-motor mapping in an embodied, situated, and autonomous agent set up to reach a goal area in one out of six systematically varied T-maze environments. In order to reach the goal the agent has to turn either to the left or to the right in each junction in the environment, depending on the placement of previously encountered light sources. The evaluation is broken down into (i) measuring the reliability of the agents' capacity to repeatedly reach the goal area, (ii) analyzing how the agents work, and (iii) comparing the results to related work on the problem. Each T-maze constitutes an instance of a broad class of problems known as delayed response tasks, which are characterized by a significant (and typically varying) delay between a stimulus and the corresponding appropriate response. This thesis expands this notion to include, besides simple tasks, repeated and multiple delayed response tasks. In repeated tasks, the agent faces several stimulus-delay-response sequences after each other. In multiple tasks, the agent faces several stimuli before the delay and the corresponding appropriate responses. Even if simple at an abstract level, these tasks raise some of the fundamental issues within cognitive science and artificial intelligence such as whether or not an internal objective world model is necessary and/or suitable to achieve the appropriate behavior. For such reasons, these problems also constitute an interesting base for evaluating alternative ideas within these fields. The work leads to several interesting insights. Firstly, purely reactive controllers (as represented by a feed-forward network) may be sufficient, in interaction with the environment, to solve both simple and repeated delayed response tasks. Secondly, an extended sequential cascaded network that selectively replaces its own sensory-motor mapping achieves significantly better performance than the other networks. This indicates that selective replacement of the sensory-motor mapping may be more powerful than both modulation (as represented by a simple recurrent network) and replacement in each step (as represented by a standard sequential cascaded network). Thirdly, this thesis demonstrates that even reactive controllers may contribute to behavior, which, from an observer's point of view, may seem to require an internal rational capacity, i.e. the ability to represent and explore alternatives internally.

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This dissertation is a primarily theoretical study of self-organization, through which a constellation of organizations can control the process of strategic adaptation in the paradigm shift. Curiously, existing theories about the control mechanism seem to be insufficient to address the problems of control seriously in the pre-paradigmatic state. The dissertation includes three main chapters of literature review, theoretical analysis, and case study. The extensive literature review provides a fertile foundation to support this study. The chapter of theoretical analysis aims to theorize about self-organization as a new control mechanism. The case study chapter seeks to address the new conceptual model and its application to the phenomenon. Consequently, the dissertation not only presents a detailed comparison of existing theories but also elaborates the new model of control mechanism. This study considers the phenomenon of manufacturing without a factory as the evidence of the controlled system without governance structure in the paradigm shift. Through the content analysis method, the case study analyzes how differently IBM and Apple, Inc. have controlled manufacturing processes entirely outsourced to external partners and why IBM failed the challenges while Apple could succeed. The theoretical analysis addresses why firms must manage external adjustment processes and be agile with the high capacity of information processing as a constellation of organizations grows. This study will shed lights on uncovering the management of postmodern organizations in the twenty-first century. / Business Administration/Strategic Management

Modeling or simulating Complex Adaptive Systems (CASs) is both important and challenging. As the name suggests, CASs are systems consisting of large numbers of interacting adaptive compartments. They are studied across a wide range of disciplines and have unique properties. They model such systems as multicellular organisms, ecosystems, social networks, and many more. They are complex, in the sense that they are dynamical, nonlinear, and heterogeneous systems that cannot be simply scaled up/down. However, they are self-organized, in the sense that they can evolve into specific structures/patterns without guidance from outside sources. Modeling/Simulating CASs is challenging, not only because of the high complexity, but also because of the difficulty in explaining the underlying mechanism behind self-organization. The goal of this research is to provide a modeling framework as well as a simulation platform to advance the study of CASs. We argue that there are common principles behind self-organization processes of different systems across different domains. We explore, analyze, and perform experiments into these principles. We propose and implement modeling templates such as short-term and long-term adaptivity. We incorporate techniques from systems theory, employing computing paradigms, including multi-agent system and asynchronous message passing. We also consider an application from the biological domain to model and simulate under our framework, treating it as a CAS for validation purposes. / Ph. D.