Socio-economical dynamics as a solvable spin system on co-evolving networks

Biely, Christoly; Hanel, Rudolf; Thurner, Stefan

doi:10.1140/epjb/e2008-00390-7

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…In recent years, statistical physicists have turned their attention to the analysis of such systems and found applications in many academic disciplines outside the traditional confines of physics. These include sociology [5], economics [6], complex networks [7] as well as in more exotic areas [8]. Each of these disciplines involve cooperative phenomena emerging from the interactions between individual units.…”

Section: The Relationship Between Quality and Quantitymentioning

confidence: 99%

Critical mass and the dependency of research quality on group size

Kenna

Berche

2010

Scientometrics

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Academic research groups are treated as complex systems and their cooperative behaviour is analysed from a mathematical and statistical viewpoint. Contrary to the naive expectation that the quality of a research group is simply given by the mean calibre of its individual scientists, we show that intra-group interactions play a dominant role. Our model manifests phenomena akin to phase transitions which are brought about by these interactions, and which facilitate the quantification of the notion of critical mass for research groups. We present these critical masses for many academic areas. A consequence of our analysis is that overall research performance of a given discipline is improved by supporting medium-sized groups over large ones, while small groups must strive to achieve critical mass.

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Section: The Relationship Between Quality and Quantitymentioning

confidence: 99%

Critical mass and the dependency of research quality on group size

Kenna

Berche

2010

Scientometrics

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“…wealth) as a function of their local social networks, and vice versa, their social evolution as a function of their economic network, can be extracted from the data. The theoretical literature on co-evolving networks is relatively sparse (Biely et al, 2007(Biely et al, , 2009; to our knowledge there exist practically no measurements on this issue so far.…”

Section: Introductionmentioning

confidence: 99%

Measuring social dynamics in a massive multiplayer online game

2010

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Quantification of human group-behavior has so far defied an empirical, falsifiable approach. This is due to tremendous difficulties in data acquisition of social systems. Massive multiplayer online games (MMOG) provide a fascinating new way of observing hundreds of thousands of simultaneously socially interacting individuals engaged in virtual economic activities. We have compiled a data set consisting of practically all actions of all players over a period of three years from a MMOG played by 300,000 people. This large-scale data set of a socio-economic unit contains all social and economic data from a single and coherent source. Players have to generate a virtual income through economic activities to 'survive' and are typically engaged in a multitude of social activities offered within the game. Our analysis of high-frequency log files focuses on three types of social networks, and tests a series of social-dynamics hypotheses. In particular we study the structure and dynamics of friend-, enemy-and communication networks. We find striking differences in topological structure between positive (friend) and negative (enemy) tie networks. All networks confirm the recently observed phenomenon of network densification. We propose two approximate social laws in communication networks, the first expressing betweenness centrality as the inverse square of the overlap, the second relating communication strength to the cube of the overlap. These empirical laws provide strong quantitative evidence for the Weak ties hypothesis of Granovetter. Further, the analysis of triad significance profiles validates well-established assertions from social balance theory. We find overrepresentation (underrepresentation) of complete (incomplete) triads in networks of positive ties, and vice versa for networks of negative ties. Empirical transition probabilities between triad classes provide evidence for triadic closure with extraordinarily high precision. For the first time we provide empirical results for large-scale networks of negative social ties. Whenever possible we compare our findings with data from non-virtual human groups and provide further evidence that online game communities serve as a valid model for a wide class of human societies.With this setup we demonstrate the feasibility for establishing a 'socio-economic laboratory' which allows to operate at levels of precision approaching those of the natural sciences.All data used in this study is fully anonymized; the authors have the written consent to publish from the legal department of the Medical University of Vienna.

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“…The development of "new mathematical formalisms for the co-evolutionary dynamics of states and interactions" has been identified by Thurner, Hanel and Klimek as one of the key directions of future research in the Theory of Complex Systems (Chapter 7 of [52]). San Miguel, Eguíluz and others published on the co-evolution of dynamical states and interactions, addressing the question of network formation [53][54][55][56][57][58]. In this context, we have modeled and explored the co-evolution of link weights and knowledge levels among intelligent interacting agents.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Agents in Co-Evolving Knowledge Networks

2021

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We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.

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Socio-economical dynamics as a solvable spin system on co-evolving networks

Cited by 19 publications

References 17 publications

Critical mass and the dependency of research quality on group size

Critical mass and the dependency of research quality on group size

Measuring social dynamics in a massive multiplayer online game

Intelligent Agents in Co-Evolving Knowledge Networks

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