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01. The network takeover , Nature Physics
Summary: Reductionism, as a paradigm, is expired, and
complexity, as a field, is
tired. Data-based mathematical models of complex systems are offering a fresh
perspective, rapidly developing into a new discipline: network science.
* [1] The network takeover, Albert-László Barabási, 2011/12/22, DOI:
10.1038/nphys2188, Nature Physics 8, 14–16
[1]
http://dx.doi.org/10.1038/nphys2188
For anyone with an interest in the history and philosophy of science, this has to be a must-read. Plus, the author is Barabasi. The writing is sure to be fun.
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02. Between order and chaos , Nature Physics
Abstract: What is a pattern? How do we come to recognize patterns never seen
before? Quantifying the notion of pattern and formalizing the process of pattern
discovery go right to the heart of physical science. Over the past few decades
physics’ view of nature’s lack of structure—its
unpredictability—underwent a major renovation with the discovery of
deterministic chaos, overthrowing two centuries of Laplace’s strict
determinism in classical physics. Behind the veil of apparent randomness,
though, many processes are highly ordered, following simple rules. Tools adapted
from the theories of information and computation have brought physical science
to the brink of automatically discovering hidden patterns and quantifying their
structural
complexity.
* [2] Between order and chaos, James Crutchfield, 2011/12/22, DOI:
10.1038/nphys2190, Nature Physics 8, 17–24
[2]
http://dx.doi.org/10.1038/nphys2190
Ditto. The anthropologists among us will also note that pattern recognition, a.k.a., the imposition of order on chaos, is something we think a lot about. What can mathematical tools add to our interpretations?
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03. Uninformed Individuals Promote Democratic Consensus in Animal Groups ,
Science
Abstract: Conflicting interests among group members are common when making
collective decisions, yet failure to achieve consensus can be costly. Under
these circumstances individuals may be susceptible to manipulation by a strongly
opinionated, or extremist, minority. It has previously been argued, for humans
and animals, that social groups containing individuals who are uninformed, or
exhibit weak preferences, are particularly vulnerable to such manipulative
agents. Here, we use theory and experiment to demonstrate that, for a wide range
of conditions, a strongly opinionated minority can dictate group choice, but the
presence of uninformed individuals spontaneously inhibits this process,
returning control to the numerical majority. Our results emphasize the role of
uninformed individuals in achieving democratic consensus amid internal group
conflict and informational constraints.
* [4] Uninformed Individuals Promote Democratic Consensus in Animal Groups, Iain
D. Couzin, Christos C. Ioannou, Güven Demirel, Thilo Gross, Colin J. Torney,
Andrew Hartnett, Larissa Conradt, Simon A. Levin, and Naomi E. Leonard,
2011/12/16, DOI: 10.1126/science.1210280, Science Vol. 334 no. 6062 pp.
1578-1580
[4]
http://dx.doi.org/10.1126/science.1210280
Given that it is a presidential election year in the U.S.A., this one raises all sorts of interesting questions. How do the activists among us get the ignorant to the polls?
Big topic. TED talk. Could be fun._________________________________________________________________
06. Communities, modules and large-scale structure in networks , Nature Physics
Abstract: Networks, also called graphs by mathematicians, provide a useful
abstraction of the structure of many complex systems, ranging from social
systems and computer networks to biological networks and the state spaces of
physical systems. In the past decade there have been significant advances in
experiments to determine the topological structure of networked systems, but
there remain substantial challenges in extracting scientific understanding from
the large quantities of data produced by the experiments. A variety of basic
measures and metrics are available that can tell us about small-scale structure
in networks, such as correlations, connections and recurrent patterns, but it is
considerably more difficult to quantify structure on medium and large scales, to
understand the ‘big picture’. Important progress has been made, however,
within the past few years, a selection of which is reviewed here.
* [9] Communities, modules and large-scale structure in networks, M. E. J.
Newman, 2011/12/22, DOI: 10.1038/nphys2162, Nature Physics 8, 25–31
[9]
http://dx.doi.org/10.1038/nphys2162
M.E.H. Newman. I try to read all his stuff
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14. The Diversity Paradox: How Nature Resolves an Evolutionary Dilemma , arXiv
Excerpt: Adaptation to changing environments is a hallmark of biological
systems. Diversity in traits is necessary for adaptation and can influence the
survival of a population faced with novelty. In habitats that remain stable over
many generations, stabilizing selection reduces trait differences within
populations, thereby appearing to remove the diversity needed for heritable
adaptive responses in new environments. Paradoxically, field studies have
documented numerous populations under long periods of stabilizing selection and
evolutionary stasis that have rapidly evolved under changed environmental
conditions. In this article, we review how cryptic genetic variation (CGV)
resolves this diversity paradox by allowing populations in a stable environment
to gradually accumulate hidden genetic diversity that is revealed as trait
differences when environments change. (…)
* [19] The Diversity Paradox: How Nature Resolves an Evolutionary Dilemma, James
M. Whitacre and Sergei P. Atamas, 2011/12/14, arXiv:1112.3115
[19]
http://arXiv.org/abs/1112.3115A fascinating idea. I wonder if there is a sociological equivalent to CGV.
