to stimulate the next wave of research on organization learning. It provides a conceptual framework for research on the differences and similarities of learning by individuals, groups, and organizations.

It is suggested that the motion of pedestrians can be described as if they would be subject to ‘social forces’. These ‘forces ’ are not directly exerted by the pedestrians ’ personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically. Typeset using REVTEX 1 I.

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well. CONTENTS

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In a dynamic world organisations have to change often. To enable organisations to change, certain structures and capabilities are needed. As all processes, a change process has to be organised itself. In this paper it is shown how within a formal organisation modelling approach also organisation change processes can be modelled. A generic organisation model (covering both organisation structure and behaviour) for organisational change is presented and formally evaluated for a case study. This model takes into account different phases in a change process considered in Social Science literature, such as unfreezing, movement and refreezing. Moreover, at the level of individuals, the internal beliefs and their changes are incorporated in the model. In addition, a distinction is made between automated and non-automated (more conscious) role behaviour. For the latter case an internal mental model for (reflective) reasoning about expected role behaviour is included in the organisation model.

With the ever-increasing link speeds and traffic volumes of the Internet, monitoring and analyzing network traffic usage becomes a challenging but essential service for network administrators of large ISPs or institutions. There are two popular primitives for efficient analysis over massive data streams: heavy hitter detection and heavy change detection. Although numerous approaches have been proposed for efficient heavy hitter detection [1], [2], [3], [4], [5], the sketch-based scheme [6] is one of the very few that can detect heavy changes and anomalies over massive data streams at network traffic speeds. However, sketches do not preserve keys (e.g., source IP address) of the flows. Thus even if anomalies are detected, it is difficult to infer the culprit flows.

A key function for network traffic monitoring and analysis is the ability to perform aggregate queries over multiple data streams. Change detection is an important primitive which can be extended to construct many aggregate queries. The recently proposed sketches [1] are among the very few that can detect heavy changes online for high speed links, and thus support various aggregate queries in both temporal and spatial domains. However, it does not preserve the keys (e.g., source IP address) of flows, making it difficult to reconstruct the desired set of anomalous keys. In an earlier abstract we proposed a framework for a reversible sketch data structure that offers hope for efficient extraction of keys [2]. However, this scheme is only able to detect a single heavy change key and places restrictions on the statistical properties of the key space. To address these challenges, we propose an efficient reverse hashing scheme to infer the keys of culprit flows from reversible sketches. There are two phases. The first operates online, recording the packet stream in a compact representation with negligible extra memory and few extra memory accesses. Our prototype single FPGA board implementation can achieve a throughput of over 16 Gbps for 40-byte-packet streams (the worst case). The second phase identifies heavy changes and their keys from the representation in nearly real time. We evaluate our scheme using traces from large edge routers with OC-12 or higher links. Both the analytical and experimental results show that we are able to achieve online traffic monitoring and accurate change/intrusion detection over massive data streams on high speed links, all in a manner that scales to large key space size. To the best of our knowledge, our system is the first to achieve these properties simultaneously. I.

How do social networks evolve when both friendly and unfriendly relations exist? Here we propose a simple dynamics for social networks in which the sense of a relationship can change so as to eliminate imbalanced triads—relationship triangles that contains 1 or 3 unfriendly links. In this dynamics, a friendly link changes to unfriendly or vice versa in an imbalanced triad to make the triad balanced. Such networks undergo a dynamic phase transition from a steady state to “utopia”—all friendly links—as the amount of network friendliness is changed. Basic features of the long-time dynamics and the phase transition are discussed.

Abstract. In this contribution, dynamical models for decision making with and without temporal constraints are developed and applied to opinion formation, migration, game theory, the self-organization of behavioral conventions, etc. These models take into account the non-transitive and probabilistic aspects of decisions, i.e. they reflect the observation that individuals do not always take the decision with the highest utility or payoff. We will also discuss issues like the freedom of decision making, the red-busblue-bus problem, and effects of pair interactions such as the transition from individual to mass behavior. In the second part, the theory is compared with recent results of experimental games relevant to the route choice behavior of drivers. The adaptivity (“group intelligence”) with respect to changing environmental conditions and unreliable information is very astonishing. Nevertheless, we find an intermittent dynamical reaction to aggregate information similar to volatility clustering in stock market data, which leads to considerable losses in the average payoffs. It turns out that the decision behavior is not just driven by the potential gains in payoffs. To understand these findings, one has to consider reinforcement learning, which can also explain the empirically observed emergence of individual response patterns. Our results are highly significant for predicting decision behavior and reaching the optimal distribution of behaviors by means of decision support systems. These results are practically relevant for any information service provider. 1

Abstract — A key function for network traffic monitoring and analysis is the ability to perform aggregate queries over multiple data streams. Change detection is an important primitive which can be extended to construct many aggregate queries. The recently proposed sketches [1] are among the very few that can detect heavy changes online for high speed links, and thus support various aggregate queries in both temporal and spatial domains. However, it does not preserve the keys (e.g., source IP address) of flows, making it difficult to reconstruct the desired set of anomalous keys. To address this challenge, we propose the reversible sketch data structure along with reverse hashing algorithms to infer the keys of culprit flows. There are two phases. The first operates online, recording the packet stream in a compact representation with negligible extra memory and few extra memory accesses. Our prototype single FPGA board implementation can achieve a throughput of over 16 Gbps for 40-byte-packet streams (the worst case). The second phase identifies heavy changes and their keys from the representation in nearly real time. We evaluate our scheme using traces from large edge routers with OC-12 or higher links. Both the analytical and experimental results show that we are able to achieve online traffic monitoring and accurate change/intrusion detection over massive data streams on high speed links, all in a manner that scales to large key space size. To the best of our knowledge, our system is the first to achieve these properties simultaneously. I.