This paper addresses the problem of forming groups in peer-to-peer (P2P) systems and examines what dependability means in decentralized distributed systems. Much of the literature in this field assumes that the participants form a local picture of global state, yet little research has been done discussing how this state remains stable as nodes enter and leave the system. We assume that nodes remain in the system long enough to benefit from retaining state, but not sufficiently long that the dynamic nature of the problem can be ignored. We look at the components that describe a system's dependability and argue that next-generation decentralized systems must explicitly delineate the information dispersal mechanisms (e.g., probe, event-driven, broadcast), the capabilities assumed about constituent nodes (bandwidth, uptime, re-entry distributions), and distribution of information demands (needles in a haystack vs. hay in a haystack [13]). We evaluate two systems based on these criteria: Chord [22] and a heterogeneous-node hierarchical grouping scheme [11]. The former gives a failed request rate under normal P2P conditions and a prototype of the latter a similar rate under more strenuous conditions with an order of magnitude more organizational messages. This analysis suggests several methods to greatly improve the prototype.

This paper, that deals with the modelling of crowd dynamics, is the first one of a project finalized to develop a mathematical theory refereing to the modelling of the complex systems constituted by several interacting individuals in bounded and unbounded domains. The first part of the paper is devoted to scaling and related representation problems, then the macroscopic scale is selected and a variety of models are proposed according to different approximations of the pedestrian strategies and interactions. The second part of the paper deals with a qualitative analysis of the models with the aim of analyzing their properties. Finally, a critical analysis is proposed in view of further development of the modelling approach. Additional reasonings are devoted to understanding the conceptual differences between crowd and swarm modelling.

This contribution describes efforts to model the behavior of individual pedes-trians and their interactions in crowds, which generate certain kinds of self-organized patterns of motion. Moreover, this article focusses on the dynamics of crowds in panic or evacuation situations, methods to optimize building designs for egress, and factors potentially causing the breakdown of orderly motion. 2

We study how a swarm robotic system consisting of two different types of robots can solve a foraging task. The first type of robots are small wheeled robots, called foot-bots, and the second type are flying robots that can attach to the ceiling, called eye-bots. While the footbots perform the actual foraging, i.e. they move back and forth between a source and a target location, the eye-bots are deployed in stationary positions against the ceiling, with the goal of guiding the foot-bots. The key component of our approach is a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt the instructions they give. Through a simulation study, we show that this process allows the system to find a path for foraging in a cluttered environment. Moreover, it is able to converge onto the shortest of two paths, and spread over different paths in case of congestion. Since our approach involves mutual adaptation between two sub-swarms of different robots, we refer to it as cooperative self-

The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human reasoning into account. This approach has been the cornerstone of modern economic theory. We explain why this is so, extolling the virtues of equilibrium theory; then we present a critique and describe why this approach is inherently limited, and why economics needs to move in new directions if it is to continue to make progress. We stress that this shouldn’t be a question of dogma, and should be resolved empirically. There are situations where equilibrium models provide useful predictions and there are situations where they can never provide useful predictions. There are also many situations where the jury is still out,i.e.,where so far they fail to provide a good description of the world, but where proper extensions might change this. Our goal is to convince the skeptics that equilibrium models can be useful, but also to make traditional economists more aware of the limitations of equilibrium models.We sketch some alternative approaches and discuss why they should play an important role in

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We consider a decentralized bidirectional control of a platoon of N identical vehicles moving in a straight line. The control objective is for each vehicle to maintain a constant velocity and inter-vehicular separation using only the local information from itself and its two nearest neighbors. Each vehicle is modeled as a double integrator. To aid the analysis, we use continuous approximation to derive a partial differential equation (PDE) approximation of the discrete platoon dynamics. The PDE model is used to explain the progressive loss of closed-loop stability with increasing number of vehicles, and to devise ways to combat this loss of stability. If every vehicle uses the same controller, we show that the least stable closed-loop eigenvalue approaches zero as O ( 1 N 2) in the limit of a large number (N) of vehicles. We then show how to ameliorate this loss of stability margin by small amounts of “mistuning”, i.e., changing the controller gains from their nominal values. We prove that with arbitrary small amounts of mistuning, the asymptotic behavior of the least stable closed loop eigenvalue can be improved to O ( 1 N). All the conclusions drawn from analysis of the PDE model are corroborated via numerical calculations of the state-space platoon model.

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This paper considers a new model of individual displacement, based on fish motion, the so-called Persistent Turning Walker (PTW) model, which involves an Ornstein-Uhlenbeck process on the curvature of the particle trajectory. The goal is to show that its large time and space scale dynamics is of diffusive type, and to provide an analytic expression of the diffusion coefficient. Two methods are investigated. In the first one, we compute the large time asymptotics of the variance of the individual stochastic trajectories. The second method is based on a diffusion approximation of the kinetic formulation of these stochastic trajectories. The kinetic model is a Fokker-Planck type equation posed in an extended phase-space involving the curvature among the kinetic variables. We show that both methods lead to the same value of the diffusion constant. We present some numerical simulations to illustrate the theoretical results. Acknowledgements: The authors wish to thank Guy Théraulaz and Jacques Gautrais of the ’Centre de Recherches sur la Cognition Animale ’ in Toulouse, for introducing them to the model and for stimulating discussions.