Special Issue "Social Networks and Network Formation 2013"

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A special issue of Games (ISSN 2073-4336).

Deadline for manuscript submissions: closed (31 January 2014)

Special Issue Editor

Guest Editor
Dr.-Ing. Stephan Schosser

TU Kaiserslautern, Gottlieb-Daimler-Strasse, 67663 Kaiserslautern, Germany
Website | E-Mail
Phone: +49 631 205-2428

Special Issue Information

Dear Colleagues,

Both globalization and internet lead to an increased interest in networks and network formation. Today firms are globally linked via business relationships and consumers via online social networks. The (increased) practical relevance of network research, yielded additional interest in different disciplines. Today economics, sociology and the natural sciences investigate networks from their perspective resulting in a highly interdisciplinary research field.

Although interdisciplinary in nature, most research focuses on the strategic decisions of participants, be they human participants or computerized agents. New insights are found using game theory, simulations or empirical studies that reshape our views on human interaction. Network effects are relevant in our everyday lives and have applications such as trust networks, contributions to public goods, (job) search or
traffic control.

In 2010 the journal Games published a successful Special Issue on network research. Given the persisting importance of networks as an interdisciplinary field, Games will publish a follow-up Special Issue devoted to this topic. We welcome reviews and original papers, which touch upon the strategic aspect of networks and network formation. Contributions from other disciplines other than economics (e.g., sociology, physics or computer science) are welcome.

Dr.-Ing. Stephan Schosser
Guest Editor

Bernhard Voelkl
Article:
The ‘Hawk-Dove’ Game and the Speed of the Evolutionary Process in Small Heterogeneous Populations

Gilles Grandjean, Ana Mauleon and Vincent Vannetelbosch
Article: A Characterization of Farsightedly Stable Networks

Marco Tomassini and Enea Pestelacci
Article: Coordination Games on Dynamical Networks

Siegfried Berninghaus and Hans Haller
Article: Local Interaction on Random Graphs

Sven Van Segbroeck, Francisco C. Santos, Jorge M. Pacheco and Tom Lenaerts
Article: Coevolution of Cooperation, Response to Adverse Social Ties and Network Structure

Antonie Knigge and Vincent Buskens
Article: Coordination and Cooperation Problems in Network Good Production

Franz Wirl and Gustav Feichtinger
Article: Modelling Social Dynamics (of Obesity) and Thresholds

Antonio Guarino and Antonella Ianni
Article: Bayesian Social Learning with Local Interactions

Pascal Billand, Christophe Bravard and Sudipta Sarangi
Article: The Insider-Outsider Model Reexamined

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Games is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 500 CHF (Swiss Francs). English correction and/or formatting fees will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections. For further details see here.

Keywords

  • network formation
  • social networks
  • network experiments
  • local interaction games
  • non-cooperative games

Related Special Issue

Published Papers (1 paper)

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Research

Open AccessArticle Learning in Networks—An Experimental Study Using Stationary Concepts
Games 2014, 5(3), 140-159; doi:10.3390/g5030140
Received: 21 January 2014 / Revised: 8 July 2014 / Accepted: 9 July 2014 / Published: 31 July 2014
PDF Full-text (820 KB) | HTML Full-text | XML Full-text
Abstract
Our study analyzes theories of learning for strategic interactions in networks. Participants played two of the 2 × 2 games used by Selten and Chmura [1]. Every participant played against four neighbors. As a distinct aspect our experimental design allows players to choose
[...] Read more.
Our study analyzes theories of learning for strategic interactions in networks. Participants played two of the 2 × 2 games used by Selten and Chmura [1]. Every participant played against four neighbors. As a distinct aspect our experimental design allows players to choose different strategies against each different neighbor. The games were played in two network structures: a lattice and a circle. We analyze our results with respect to three aspects. We first compare our results with the predictions of five different equilibrium concepts (Nash equilibrium, quantal response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium, and impulse balance equilibrium) which represent the long-run equilibrium of a learning process. Secondly, we relate our results to four different learning models (impulse-matching learning, action-sampling learning, self-tuning EWA, and reinforcement learning) which are based on the (behavioral) round-by-round learning process. At last, we compare the data with the experimental results of Selten and Chmura [1]. One main result is that the majority of players choose the same strategy against each neighbor. As other results, we observe an order of predictive success for the equilibrium concepts that is different from the order shown by Selten and Chmura and an order of predictive success for the learning models that is only slightly different from the order shown in a recent paper by Chmura, Goerg and Selten [2]. Full article
(This article belongs to the Special Issue Social Networks and Network Formation 2013)

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