Complex Systems Studies – Telegram
Complex Systems Studies
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Dan Larremore and I are hiring a postdoc @ CUBoulder to work on Computational Social Science, the Science of Science, Statistical Inference, and Dynamical Systems. For best consideration, please apply by April 15; position to start in August: https://t.co/MiyCEL3qbY
Interested in scientific research in the field of #ComplexSystems?
IFISC announces 6 SURF@IFISC2019 summer research grants for undergraduates with the aim of introducing student fellows to cutting-edge research. Deadline: 31/03

https://t.co/uCpoTFE1jf
💶 Thanks to generous support from our sponsors and supporters we are able to offer a limited number of #NetSci_2019 registration waivers to students!

To apply for a waiver please visit the application form listed here
https://t.co/AL3Jn2DjpF @
💰 We currently have openings for 2x postdoc and 1x predoc in cancer data science. Come join us to work on projects ranging from cancer evolution, image analysis to molecular epidemiology in millions of individuals. https://t.co/MG14EPxT0o
💰 2-year postdoc position in cancer data science: Applications open until April 18. See https://t.co/ZLQQgWQCwF for details.
Construction and Analysis of Protein-Protein Interaction Network of Heroin Use Disorder

https://t.co/RSlhdD9JKG
🔸 "Higher-Order Interaction Networks: Dynamics, Structure, Data": https://t.co/76FT8cwrpf

Mathematical Institute, University of Oxford, 9–11 Sept 2019

Goal: Bring together researchers from different communities with distinct perspectives on network dynamics
Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions
Simon DeDeo

https://www.mdpi.com/1999-5903/8/3/31/htm

Abstract:
What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more than a decade of cooperation and conflict among its editors. Across a wide range of pages, we discover a bursty war/peace structure where the systems can become trapped, sometimes for months, in a computational subspace associated with significantly higher levels of conflict-tracking “revert” actions. Distinct patterns of behavior characterize the lower-conflict subspace, including tit-for-tat reversion. While a fraction of the transitions between these subspaces are associated with top-down actions taken by administrators, the effects are weak. Surprisingly, we find no statistical signal that transitions are associated with the appearance of particularly anti-social users, and only weak association with significant news events outside the system. These findings are consistent with transitions being driven by decentralized processes with no clear locus of control. Models of belief revision in the presence of a common resource for information-sharing predict the existence of two distinct phases: a disordered high-conflict phase, and a frozen phase with spontaneously-broken symmetry. The bistability we observe empirically may be a consequence of editor turn-over, which drives the system to a critical point between them.


Keywords:
conflict; cooperation; finite-state machine; tit-for-tat; critical transition; hidden Markov model; memory; social norms; knowledge commons; Wikipedia
Group Minds and the Case of Wikipedia
Simon DeDeo

https://arxiv.org/pdf/1407.2210

Abstract:
Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors, showing how some, but not all, effects of individual experience persist in the aggregate. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions, and attributions of cognitive states of the form "what the group believes" and "what the group values".
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Complex Time: A SFI/JSMF Research Theme

https://www.aparat.com/v/KXuQ3