What are your “odds-of-success”? Reflecting on the role of adaptive leadership in Leicester City’s (2015/16) English Football Premier League Title win

Purpose: This article utilizes the concept of adaptive leadership to explore how Leicester City, a small, provincial football club, defied odds of 5000-1 to became only the sixth winners of the English Premier League. It examines two research questions: 1. can adaptive leadership be used to explain how the club developed the conditions for the team’s success? and; 2. what practical lessons can be learned from this? Design/methodology/approach: This case study utilizes secondary material, published from 2011-2019, including interviews with players and staff, recordings of press conferences, club announcements, match programmes, books, magazine and newspaper articles, television reports, and social media coverage. Findings: Adaptive leadership provides a mechanism for understanding the organizational change necessary for Leicester City’s title victory. Three core elements of adaptive leadership are identified: 1. the “change leader’s” deliberate decision to engage others across the organization in a process of “intelligent reflection,” to identify the required approach to address an identified organizational objective; 2. an organization-wide focus on building leadership capacity, to promote continuous improvement through personal and organizational learning; 3. a long term commitment by the most senior organizational leader to elements of the change process, thereby ensuring new ways of working became normalized over the longer term. Originality/value: While theoretically well-developed, the practice of adaptive leadership remains under-researched (Yukl and Mahsud, 2010). Leicester City’s Premier League victory illustrates several key aspects of adaptive leadership in action, in a way that many people can easily relate to. The efficacious and team learning aspects of Leicester City’s success story are important for organizational development scholars and practitioners alike. In summary, the key findings and lessons within this article can be metaphorically transferred to other team-based learning organization, i.e. including and beyond the world of sport!.</p

Adaptive self-organization in a realistic neural network model

Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.Comment: 6 pages, 4 figure

Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally re-distributed. We claim that all steps related to testing need to become self-adaptive to match the capabilities of the self-adaptive system-under-test. Otherwise, the adaptive system's behavior might elude traditional variants of quality assurance. We thus propose the paradigm of scenario coevolution, which describes a pool of test cases and other constraints on system behavior that evolves in parallel to the (in part autonomous) development of behavior in the system-under-test. Scenario coevolution offers a simple structure for the organization of adaptive testing that allows for both human-controlled and autonomous intervention, supporting software engineering for adaptive systems on a procedural as well as technical level.Comment: 17 pages, published at ISOLA 201

Digital adaptive controllers for VTOL vehicles. Volume 2: Software documentation

The VTOL approach and landing test (VALT) adaptive software is documented. Two self-adaptive algorithms, one based on an implicit model reference design and the other on an explicit parameter estimation technique were evaluated. The organization of the software, user options, and a nominal set of input data are presented along with a flow chart and program listing of each algorithm

Adaptive Technology HR organization

У статті наводиться сутність і технологія адаптивного управління (АУ). Розглядається використання моніторингу ділових, особистісних і професійних якостей персоналу на основі кваліметричного підходу в системі АУ.В статье автор раскрывает сущность и технологию адаптивного управления (АУ). Рассматривается использование мониторинга деловых, личностных и профессиональных качеств персонала на основе квалиметрического подхода в системе АУ.The article considers the essence of adaptive management and technology (AM). The use of monitoring of business, personal and professional qualities of staff based on the qualimetry approach in the adaptive management is considered

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Planning assistance for the NASA 30/20 GHz program. Network control architecture study.

Network Control Architecture for a 30/20 GHz flight experiment system operating in the Time Division Multiple Access (TDMA) was studied. Architecture development, identification of processing functions, and performance requirements for the Master Control Station (MCS), diversity trunking stations, and Customer Premises Service (CPS) stations are covered. Preliminary hardware and software processing requirements as well as budgetary cost estimates for the network control system are given. For the trunking system control, areas covered include on board SS-TDMA switch organization, frame structure, acquisition and synchronization, channel assignment, fade detection and adaptive power control, on board oscillator control, and terrestrial network timing. For the CPS control, they include on board processing and adaptive forward error correction control