433 research outputs found

    Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

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    open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

    Managing stimulation of regional innovation subjects’ interaction in the digital economy

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    The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

    COMPLEXITY * SIMPLICITY * SIMPLEXITY

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    “In the midst of order, there is chaos; but in the midst of chaos, there is order”, John Gribbin wrote in his book Deep Simplicity (p.76). In this dialectical spirit, we discuss the generative tension between complexity and simplicity in the theory and practice of management and organization. Complexity theory suggests that the relationship between complex environments and complex organizations advanced by the well-known Ashby’s law, may be reconsidered: only simple organization provides enough space for individual agency to match environmental turbulence in the form of complex organizational responses. We suggest that complex organizing may be paradoxically facilitated by a simple infrastructure, and that the theory of organizations may be viewed as resulting from the interplay between simplicity and complexity. JEL codes:

    A multi-agent simulation approach to sustainability in tourism development

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    In the last decades the increasing facility in moving and the simultaneous fall of the transportation costs have strongly increased the tourist flows. As a consequence, different destinations, especially those which are rich of natural resources, unable or unready to sustain huge tourism flows, present serious problems of sustainability and Tourism Carrying Capacity (TCC). At the present, it is universally recognized that every tourist destination should plan effective and pro-reactive protection policies of its cultural, environmental and social resources. In order to facilitate policies definition it may be useful to measure the Tourist Carrying Capacity, but the literature has highlighted that this is not an easy task for different reasons: among the others, the complexity and the dynamicity of the concept, the absence of a universally accepted definition and the impossibility of assigning an objective scientific value and to apply a rigorous analysis. Thereby, more recently an alternative, or even complementary, interpretation of TCC has developed; it is called LAC, Limit of Acceptable Changes where the focus shifts from: ñ€ƓHow much use an area can tolerate?ñ€ to ñ€ƓHow much change is acceptable?ñ€, aiming at evaluating the costs and benefits from alternative management tourism actions. The aim of the paper is to present an innovative framework, based on the LAC approach - MABSiT, Mobile Agent Behavior Simulation in Tourism - developed by the authors, which is composed by five modules: elaboration data, DBMS, ad-hoc maps, agents and ontology. Its modular structure allows to easily study the interactions among the components in order to observe the behavior of the single agents. In an aggregate form, it is possible to define group dynamics, where one possible effect is the influence on the variation of agents’ satisfaction perception in comparison to the surroundings environment. The paper will be structured as follows: an introduction will be followed by a literature review; than the methodology and the framework will be presented and applied to a case study: Vieste, a known maritime destination of South of Italy, which is characterized by high problems of seasonality in the summer. Finally, some conclusions and policy recommendations will be drawn.

    EMPOWERING LEADERSHIP: EMBRACING ENDOGENOUS DYNAMICS

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    Purpose: The article introduces the kind of leadership that fosters bottom-up dynamics, empowering people, groups, teams and societies.Approach: It is documented through literature analysis and case studies.Findings: That this approach, called Empowering Leadership, can achieve success in business and, in the case of social entrepreneurship, a significant social impact. After reviewing the existing definitions of leadership, the complexity theory is delineated, with twelve core attributes defined. Next, case studies demonstrating a new kind of leadership enabling endogenous dynamics both in in the social arena as well as in business are presented and analyzed from the perspective of the complexity theory. Finally, a definition of Empowering Leadership is delineated.Implications: The presented Empowering Leadership is becoming critical in the growing world of multiplicity and unpredictability. It also enables achieving high impact though low investments. The paper is introducing ways of implementing presented Empowering Leadership in practice through building pre-conditions for the process of empowerment. The article concludes with a review of possible future areas of research.Value: The paper brings and in-depth analysis of the bottom-up approach to leadership with the premise of complexity theory, demonstrating that Empowering Leadership is focused on building preconditions for endogenous dynamics, rather than driving change from the top.

    Managing Complexity in Socio-Economic Systems

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    This contribution summarizes some typical features of complex systems such as non-linear interactions, chaotic dynamics, the 's'sbutterfly effect'', phase transitions, self-organized criticality, cascading effects, and power laws. These imply sometimes quite unexpected, counter-intuitive, or even paradoxical behaviors of socio-economic systems. A typical example is the faster-is-slower effect. Due to their tendency of self-organization, complex systems are often hard to control. Instead of trying to control their behavior, it would often be better to pursue the approach of guided self-organization, i.e. to use the driving forces of the system rather than to fight against them. This is illustrated by the example of hierarchical systems, which need to fulfill certain principles in order to be efficient and robust in an ever-changing environment. We also discuss the important role of fluctuations and heterogeneity for the adaptability, flexibility and robustness of complex systems. The presentation is enriched by a number of examples ranging from decision behavior up to production systems and disaster spreadin

    Effect of leader placement on robotic swarm control

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    Human control of a robotic swarm entails selecting a few in-fluential leaders who can steer the collective efficiently and robustly. However, a clear measure of influence with respect to leader position is not adequately studied. Studies with animal systems have shown that leaders who exert strong couplings may be located in front, where they provide energy benefits, or in the middle, where they can be seen by a larger section of the group. In this paper, we systematically vary number of leaders and leader positions in simulated robotic swarms of two different sizes, and assess their effect on steering effectiveness and energy expenditure. In particular, we analyze the effect of placing leaders in the front, middle, and periphery, on the time to converge and lateral acceleration of a swarm of robotic agents as it performs a single turn to reach the desired goal direction. Our results show that swarms with leaders in the middle and periphery take less time to converge than swarms with leaders in the front, while the lateral acceleration between the three placement strategies is not different. We also find that the time to converge towards the goal direction reduces with the increase in percentage of leaders in the swarm, although this value decays slowly beyond the percentage of leaders at 30%. As the swarm size is increased, we find that the leaders in the periphery become less effective in reducing the time to converge. Finally, closer analysis of leader placement and coverage reveals that front leaders within the swarm tend to expand their coverage and move towards the center as the maneuver is performed. Results from this study are expected to inform leader placement strategies towards more effective human swarm interaction systems

    Characterizing human perception of emergent swarm behaviors

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