|Name, Ort/Land:||Michael Zaiser, Edinburgh, UK|
|E-Mail:||m.zaiser ed.ac.uk ('@' entfernt -- Spam-Vermeidung!)|
The University of Edinburgh, King's Buildings, Sanderson Building
Co-Editor, SISSA Journal of Statistical Physics - Theory and Experiment (JSTAT)
Sheen S. Levine|
|Veranstaltungsdaten:||21. Mai / 17:00 / 3 Std. / Track A / Raum 3B / englisch|
Sheen S. Levine: Linux, Napster, and Sobig: A Framework for Understanding Open Collective Innovation
The actions of collectives who primarily meet on-line have recently captured the attention of the media, general public, business executives, and academics. File-sharing, open-source, and computer viruses are all carried out by loosely bounded collectives, rather than by firms or other formal organizations. These collectives operate in concert to accomplish innovation goals that may have great economic significance. Despite their importance, empirical work is scarce, and theoretical work has taken either the self-interest or the communal view to explain contributions. We point out the logical deficiencies and continue empirically through original data on a collective devoted to the sharing of digital music, which does not fit neatly in either explanation. To account for the survival and effectiveness of these collectives, we offer a four legged framework that draws on research in economic sociology and behavioral economics. We argue that the survival of cooperation despite known free riding has to do with the nature of the exchanged good economic goods that are non-rival are more likely to be exchanged in an open system, and the (non) identifiability of (defectors) beneficiaries encourages cooperative behavior and discourages costly punishments. Further, we argue that the efficiency in production is achieved through the mode of exchange generalized exchange is more conducive to innovative work than direct exchange, and by institutional mechanisms allow these collectives to enjoy some of the benefits of formal organizations, while preserving their unique advantages. The suggested framework fits more phenomenon than previous explanations and can easily produce refutable propositions.
Michael Zaiser: Self-organized networks in science - Science of self-organized networks
Recently scientists (mainly statistical physicists, but also from other communities) have become interested in the self-organized formation of social and cybernetic networks. Self-organized meaning: there is no master plan of the network and its operation, but links evolve through the capacity of the network itself to act as a information carrier. Thinking of software production - a person is known in the community (word is spread that he is an expert on this or that problem), so someone with a related problem is likely to ask that person and thereby establish a collaborative link. A condition for the formation of self-organized networks (of which the Internet/WWW has served as a kind of paradigm) is the possibility to establish communicative links irrespective of geographical vicinity. These networks tend to develop complex, self-similar connectivity patterns which lead to a remarkable communication efficiency, resiliency against attack and stability against fluctuations in membership. Recently also the formation of hierarchies in such networks in view of access and distribution of information and in view of decisionmaking has come into the focus of interest.
The scientific community investigating self-organized networks is in itself an example of such a network. The formation of this network has been greatly aided by the acceleration of non-proprietary information circulation through electronic archives (in the case of self-organized networks, mainly through the cond-mat archive www.arXiv.org/cond-mat). Remarkably, other communities (e.g. in the the engineering sciences), which rely much more heavily on concepts of intellectual property, exhibit a different structure due to the prevalence of 'territorial' obstacles to information exchange and collaboration.
The proposal is to give a non-mathematical introduction into scientists views of self-organized networks and to discuss the operation of scientific communities as an example.