Learning and cooperation in network experiments
نویسندگان
چکیده
In this paper we study learning and cooperation in repeated prisoners’ dilemmas experiments. We compare interaction neighbourhoods of different size and structure, we observe choices under different information conditions, and we estimate parameters of a learning model. We test robustness of the estimator. We find that naive imitation, although a driving force in many models of spatial evolution, may be negligible in the experiment. Naive imitation predicts more cooperation in spatial structures than in spaceless ones—regardless whether interaction neighbourhoods have the same or different sizes in both structures. We find that with some interaction neighbourhoods even the opposite may hold. JEL-Classification: C72, C92, D74, D83, H41, R12
منابع مشابه
Modeling Cooperation between Nodes in Wireless Networks by APD Game
Cooperation is the foundation of many protocols in wireless networks. Without cooperation, the performance of a network significantly decreases. Hence, all nodes in traditional networks are required to cooperate with each other. In this paper, instead of traditional networks, a network of rational and autonomous nodes is considered, which means that each node itself can decide whe...
متن کاملModeling Cooperation between Nodes in Wireless Networks by APD Game
Cooperation is the foundation of many protocols in wireless networks. Without cooperation, the performance of a network significantly decreases. Hence, all nodes in traditional networks are required to cooperate with each other. In this paper, instead of traditional networks, a network of rational and autonomous nodes is considered, which means that each node itself can decide whe...
متن کاملA Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem
Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...
متن کاملDetecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملتولید خودکار الگوهای نفوذ جدید با استفاده از طبقهبندهای تک کلاسی و روشهای یادگیری استقرایی
In this paper, we propose an approach for automatic generation of novel intrusion signatures. This approach can be used in the signature-based Network Intrusion Detection Systems (NIDSs) and for the automation of the process of intrusion detection in these systems. In the proposed approach, first, by using several one-class classifiers, the profile of the normal network traffic is established. ...
متن کاملOn the effect of low-quality node observation on learning over incremental adaptive networks
In this paper, we study the impact of low-quality node on the performance of incremental least mean square (ILMS) adaptive networks. Adaptive networks involve many nodes with adaptation and learning capabilities. Low-quality mode in the performance of a node in a practical sensor network is modeled by the observation of pure noise (its observation noise) that leads to an unreliable measurement....
متن کامل