Simulation gesellschaftlicher Medienwirkungsprozesse am Beispiel der Schweigespirale

Der Beitrag stellt mit der agentenbasierten Modellierung (ABM) eine Methode zur Diskussion, mit der sich dynamische Medienwirkungsprozesse auf mehreren Ebenen modellieren und simulieren lassen. Dazu wird das Mikro-Makro-Problem in der Medienwirkungsforschung genauer erläutert und aus Sicht der Komplexitätstheorie interpretiert. Die Methode der Computersimulation sozialer Prozesse, speziell mit-tels ABM, wird erläutert. Schließlich wird die ABM am Beispiel der Schweigespira-le vorgestellt, um ihre Eignung für die Untersuchung dynamischer, gesellschaftlicher Medienwirkungsprozesse zu demonstrieren. Hierzu werden die Annahmen der Schweigespirale nach Noelle- Neumann in einem Computermodell formalisiert und in ihrer Dynamik simuliert. Nach der Darstellung zentraler Simulationsergebnisse werden abschließend Chancen und Grenzen der Simulationsmethode für die Medi-enwirkungsforschung diskutiert

N,N'-1,2-Phenylenebis[4-(chloromethyl)benzamide]

4 páginas, 1 esquema.-- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license.N,N'-1,2-Phenylenebis[4-(chloromethyl)benzamide] (3) was obtained in 61% yield by nucleophilic acyl substitution of 4-(chloromethyl)benzoyl chloride (2) with 1,2-phenylenediamine (1) under basic conditions. The title compound was characterized by FT-IR, 1H NMR, 13C NMR, low- and high-resolution EI-MS, and melting point.Peer reviewe

LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning

The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows' congestion controls are not inherently fair. So far, each queue in the fair queuing system either has a fixed, static maximum size or is managed by an Active Queue Management (AQM) algorithm like CoDel. In this paper we design an AQM mechanism (Learning Fair Qdisc (LFQ)) that dynamically learns the optimal buffer size for each flow according to a specified reward function online. We show that our Deep Learning based algorithm can dynamically assign the optimal queue size to each flow depending on its congestion control, delay and bandwidth. Comparing to competing fair AQM schedulers, it provides significantly smaller queues while achieving the same or higher throughput

How News Audiences Allocate Trust in the Digital Age: A Figuration Perspective

The article enriches the understanding of trust in news at a time when mass and interpersonal communication have merged in the digital sphere. We propose disentangling individual-level patterns of trust allocation (i.e., trust figurations) across journalistic media, social media, and peers to reflect the multiplicity among modern news audiences. A latent class analysis of a representative survey among German young adults revealed four figurations: traditionalists, indifferentials, optimists, and cynics. Political characteristics and education corresponded with substantial heterogeneity in individuals’ trust in news sources, their inclination to differentiate between sources, and the ways of integrating trust in journalistic and non-journalistic sources

Cocoa: Congestion Control Aware Queuing

Recent model-based congestion control algorithms such as BBR use repeated measurements at the endpoint to build a model of the network connection and use it to achieve optimal throughput with low queuing delay. Conversely, applying this model-based approach to Active Queue Management (AQM) has so far received less attention. We propose the new AQM scheduler cocoa based on fair queuing, which adapts the buffer size depending on the needs of each flow without requiring active participation from the endpoint. We implement this scheduler for the Linux kernel and show that it interacts well with the most common congestion control algorithms and can significantly increase throughput compared to fair CoDel while avoiding overbuffering