The herd moves? Emergence and self-organization in collective actors?

The puzzle about collective actors is in the focus of this contribution. The first section enters into the question of the adequateness and inadequateness of reductionist explanations for the description of entities. The considerations in this part do not draw on systems and hence not on principles of self-organisation, because this concept necessitates a systemic view. In other words, the first section discusses reductionism and holism on a very general level. The scope of these arguments goes far beyond self-organising systems. Pragmatically, these arguments will be discussed within the domain of corporative actors. Emergence is a concept embedded in system theory. Therefore, in the second part the previous general considerations about holism are integrated with respect to the concept “emergence”. In order to close the argument by exactly characterising self-organising systems and giving the conceptual link between self-organisation and emergence – which is done in the section four – the third section generally conceptualises systems. This conceptualisation is independent of whether these systems are self-organising or not. Feedback loops are specified as an essential component of systems. They establish the essential precondition of system-theoretic models where causes may also be effects and vice versa. System-theory is essential for dynamic models like ecological models and network thinking. In the fourth part mathematical chaos-theory bridges the gap between the presentation of systems in general and the constricted consideration of self-organising systems. The capability to behave or react chaotically is a necessary precondition of self-organisation. Nevertheless, there are striking differences in the answers given from theories of self-organisation in biology, economics or sociology on the question “What makes the whole more than the sum of its parts?” The fracture seems particularly salient at the borderline between formal-mathematical sciences like natural sciences including economy and other social sciences like sociology, for instance in the understanding and conceptualisation of “chaos” or “complexity”. Sometimes it creates the impression that originally well defined concepts from mathematics and natural science are metaphorically used in social sciences. This is a further reason why this paper concentrates on conceptualisations of self-organisation from natural sciences. The fifth part integrates the arguments from a system-theoretic point of view given in the three previous sections with respect to collective and corporative actors. Due to his prominence all five sections sometimes deal with the sociological system theory by Niklas Luhmann, especially in those parts with rigorous and important differences between his conception and the view given in this text. Despite Luhmann’s undoubted prominence in sociology, the present text strives for a more analytical and formal understanding of social systems and tries to find a base for another methodological approach.

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation

Modeling Multiple Occupant Behaviors in Buildings for increased Simulation Accuracy: An Agent-Based Modeling Approach

The dissertation addresses the limitation of current building energy simulation programs in accounting for occupant behaviors, which have been identified as having significant impact on the overall building energy performance. It introduces a new simulation methodology using an agent- based modeling approach that helps to both predict real-world occupant behaviors observed in an operating building and to calculate behavior impact on energy use and occupant comfort. A series of experiments has been conducted using the new methodology and yielded simulation results that not only distinguish themselves from current simulation practices, but also uncover emerging phenomena that enhance the insights on building dynamics

An agent-based approach for tourist planning

El turismo se comporta como un sistema complejo en evolución dinámica, que abarca numerosos factores y actividades que son interdependientes y cuyas relaciones pueden ser altamente no lineales (Baggio, 2008). Los sistemas de recomendación y los planificadores de rutas se utilizan con frecuencia para filtrar información que no es importante y, a su vez, ofrecen un servicio personalizado para los turistas (Noguera et. Al 2012). En este contexto, los modelos basados en agentes (ABM) son una herramienta apropiada para la toma de decisiones porque permiten representar sistemas complejos o situaciones con agentes autónomos en un escenario establecido (Nicholls et. Al, 2017). Además, ABM tiene la capacidad de modelar fenómenos emergentes, por lo tanto, fenómenos como las normas culturales que surgen en la sociedad debido a las interacciones entre los individuos y otros agentes, a veces incluso de manera contradictoria, y que no son bien captados por las técnicas de modelado tradicionales ( Nicholls et. Al., 2017). En esta perspectiva, este proyecto propone un ABM para simular el turismo en Bogotá. El objetivo principal es apoyar al turista en la planificación y realización de diferentes actividades turísticas, considerando variables cualitativas y cuantitativas que maximicen la experiencia de un turista que visita Bogotá (Colombia), que a su vez puede contribuir al desarrollo de este sector económico.Tourism behaves as a dynamic evolving complex system, encompassing numerous factors and activities that are interdependent and whose relationships might be highly nonlinear (Baggio, 2008). Recommendation systems and route planners are frequently used to filter information that is not important and in turn offer a personalized service for tourists (Noguera et. al 2012). In this context, Agent-Based Models (ABM) are an appropriate tool for decision making because they allow representing complex systems or situations with autonomous agents in an established scenario (Nicholls et. al, 2017). In addition, ABM has an ability for modeling emergent phenomena, thus phenomena such as cultural norms that come up into society because of interactions between individuals and other agents, sometimes even in a counterintuitive manner, and that are not well captured by traditional modeling techniques (Nicholls et. al, 2017). In this perspective, this project proposes an ABM to simulate tourism in Bogota. The main objetictive is to support the tourist in the planning and realization of different tourism activities, considering qualitative and quantitative variables that maximize the experience of a tourist visitingBogota (Colombia), which in turn may contribute to the development of this economic sector. In general, the results in several scenarios are positive according to the average level of satisfaction obtained, which means that the recommendations generated by the application are adequate. Therefore, the model has the potential to help current tourism platforms to better accomplish satisfactory recommendations for tourists and even be the basis for the development of a new app, so this would allow promoting the growth of tourism in La Candelaria, Bogota.Ingeniero (a) IndustrialPregrad

When ecology and philosophy meet: constructing explanation and assessing understanding in scientific practice

Philosophy of Science in Practice (PoSiP) has the “practice of science” as its object of research. Notwithstanding, it does not possess yet any general or specific methodology in order to achieve its goal. Instead of sticking to one protocol, PoSiP takes advantage of a set of approaches from different fields. This thesis takes as a starting point a collaborative and interdisciplinary research between two Ph.D. students from distinct areas: ecology and philosophy. This collaboration showed how a scientist could benefit from philosophy of science (in this case study the philosophical approach of. mechanistic explanation) to construct a model of his explanandum, by means of heuristics approach (heuristics as an instrument but also a methodological approach) and, also allowed philosophy of science take a closer look into the scientific practice to investigate how explanations are constructed and how scientific understanding is achieved (in this thesis, with a dialogue with the contextual theory of scientific understanding). As a result, it is asserted that (i) mechanistic explanation possess limitations but may work as epistemic instruments that mediate between theories, data, scientists, and models; (ii) explanation construction and scientific understanding deeply relies on intuition; (iii) scientific understanding is an instant, a moment, a temporary achievement, and its process may happen in degrees; (iv) philosophy of science, by means of heuristics process, may enhance scientists’ epistemic virtues, improving his academic skills, by means of self-evaluation. This research shows that interdisciplinarity and collaborative work can act, through heuristics, as a toolbox for PoSiP to achieve its goal of understanding how science is made. Despite its success, an analysis of this collaborative practice leads to some fundamental issues. First, philosophy of science in practice is a philosophy of past practice, in that the majority of examples used by mainstream PoSiP come from the final products of science. Second, is it philosophy of [science in practice] or philosophy of science [in practice]? How to practice philosophy of scientific practice and, how to practice interdisciplinarity in the philosophy of scientific practices simultaneously to its scientific activity? This research exposes the epistemic role heuristics and interdisciplinarity possess as methodological toolboxes for philosophy of science in practice. It is defended that other ways of constructing sciences would be through different dynamics such as collaborative networks and interdisciplinarity research contributing to the vision of Trading Zones from Peter Galison, in which bridges between specialized disciplines are created in order to exchange knowledge and information

Tools and methods in participatory modeling: Selecting the right tool for the job

© 2018 Elsevier Ltd Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process

Information gathering prior to emigration in house-hunting ants

Dans les sociétés animales, les décisions collectives résultent souvent de processus auto-organisés : des choix collectifs complexes émergent à partir d'interactions locales entre individus suivant des règles comportementales simples. Complexité cognitive et diversité des individus ne sont en général pas considérées nécessaires à l'ajustement fin des choix collectifs. La sélection d'un nouveau nid chez les abeilles et les fourmis du genre Temnothorax constitue un exemple classique de décision collective. Lors d'une émigration (déplacement de la colonie vers un nouveau site), les colonies de fourmis sont capables de choisir collectivement le meilleur site disponible par le biais de processus décentralisés. Ma thèse porte sur le rôle de la mémoire et de l'expérience des individus sur la sélection de nid par des colonies de fourmis Temnothorax albipennis. La première partie de ma thèse décrit l'impact sur la performance collective des colonies d'une familiarisation préalable avec certains sites. La deuxième partie décrit les mécanismes permettant l'exploitation collective des informations récoltées par les ouvrières avant l'émigration. D'après les données expérimentales, la collecte préalable d'informations sur des sites de bonne qualité permet aux colonies d'améliorer leurs performances collectives lors d'émigrations ultérieures (vitesse d'émigration, cohésion, et/ou précision du choix). De plus, les fourmis ajustent collectivement leurs critères de préférence en fonction de la qualité de leur propre nid et de celle des sites disponibles dans les environs. Cela permet aux colonies de prendre des décisions adaptées aux conditions environnementales. Une analyse détaillée révèle, en outre, que les ouvrières qui ont visité un site de bonne qualité mémorisent la position et la qualité de ce site et réutilisent ultérieurement les informations mémorisées, ce qui leur confère un rôle particulièrement important. Un transfert d'informations a également lieu au sein des sites familiers au cours de l'émigration, mettant en jeu phéromones et interactions sociales entre ouvrières. Dans l'ensemble, cette étude montre que les fourmis T. albipennis sont capables de réaliser des tâches cognitives complexes, puisqu'elles peuvent mémoriser certaines informations et les réutiliser au moment opportun. Enfin, il semble que certains individus ont une influence particulièrement importante sur les décisions du groupe. Ceci montre que les décisions collectives auto-organisées peuvent grandement bénéficier à la fois de la complexité cognitive des individus et d'un certain degré de diversité au sein des membres d'un même groupe.In animal societies, collective decisions are often self-organised: complex collective choices simply emerge from local interactions between group members following simple and relatively fixed behavioural rules. House-hunting by honeybees and ants of the genus Temnothorax is a classical example of collective decision-making. During an emigration (i.e. the relocation of a colony to a new nest site), Temnothorax colonies are able select the best available nest site through decentralised, self-organised processes. My PhD has the aim of investigating the role of individual memories and previous experience in nest site selection by the rock ant Temnothorax albipennis. In the first part, I present data on the influence of prior familiarisation with available nest sites on collective performance in emigrations. In the second part, I investigate the mechanisms underlying the collective exploitation of information previously gathered by individual workers. Experimental results show that familiarisation with high-quality nest sites leads to increased speed, higher cohesion and/or improved choice accuracy in later emigrations. Additionally, ants collectively adjust their preference and choice criteria according to the respective qualities of their home nest and of surrounding available nest sites. This allows colonies to tune collective decisions according to their environment. A detailed analysis of the underlying mechanisms reveals that informed individuals memorise the position and suitability of high-quality, available sites, and later retrieve and use that memorised information. Well informed individuals therefore play a key role in emigrations to good, familiar nest sites. Additionally, information transfer between individuals takes place inside familiar nest sites during emigrations: chemical cues (pheromones) and social interactions allow naïve individuals also to exploit the information previously gathered by their nestmates. The present study therefore indicates that T. albipennis ants have high cognitive abilities, as they are able to memorise information about available nest sites and retrieve that memory when required. Finally, it appears that some individuals have a disproportionate influence on collective choices. This suggests that self-organised collective decisions may actually greatly benefit from both individual cognitive complexity and inter-individual variability

Application of ant based routing and intelligent control to telecommunications network management

This thesis investigates the use of novel Artificial Intelligence techniques to improve the control of telecommunications networks. The approaches include the use of Ant-Based Routing and software Agents to encapsulate learning mechanisms to improve the performance of the Ant-System and a highly modular approach to network-node configuration and management into which this routing system can be incorporated. The management system uses intelligent Agents distributed across the nodes of the network to automate the process of network configuration. This is important in the context of increasingly complex network management, which will be accentuated with the introduction of IPv6 and QoS-aware hardware. The proposed novel solution allows an Agent, with a Neural Network based Q-Learning capability, to adapt the response speed of the Ant-System - increasing it to counteract congestion, but reducing it to improve stability otherwise. It has the ability to adapt its strategy and learn new ones for different network topologies. The solution has been shown to improve the performance of the Ant-System, as well as outperform a simple non-learning strategy which was not able to adapt to different networks. This approach has a wide region of applicability to such areas as road-traffic management, and more generally, positioning of learning techniques into complex domains. Both Agent architectures are Subsumption style, blending short-term responses with longer term goal-driven behaviour. It is predicted that this will be an important approach for the application of AI, as it allows modular design of systems in a similar fashion to the frameworks developed for interoperability of telecommunications systems