Willingness to lay down one’s life for a group of non-kin, well documented historically and ethnographically, represents an evolutionary puzzle. Building on research in social psychology, we develop a mathematical model showing how conditioning cooperation on previous shared experience can allow individually costly pro-group behavior to evolve. The model generates a series of predictions that we then test empirically in a range of special sample populations (including military veterans, college fraternity/sorority members, football fans, martial arts practitioners, and twins). Our empirical results show that sharing painful experiences produces “identity fusion” – a visceral sense of oneness – which in turn can motivate self-sacrifice, including willingness to fight and die for the group. Practically, our account of how shared dysphoric experiences produce identity fusion helps us better understand such pressing social issues as suicide terrorism, holy wars, sectarian violence, gang-related violence, and other forms of intergroup conflict.

 

The evolution of extreme cooperation via shared dysphoric experiences
Harvey Whitehouse, Jonathan Jong, Michael D. Buhrmester, Ángel Gómez, Brock Bastian, Christopher M. Kavanagh, Martha Newson, Miriam Matthews, Jonathan A. Lanman, Ryan McKay & Sergey Gavrilets

Scientific Reports 7, Article number: 44292 (2017)
doi:10.1038/srep44292

Source: www.nature.com

Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across  300 European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.

 

Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
Adrian Albert, Jasleen Kaur, Marta Gonzalez

Source: arxiv.org

Random Item Generation tasks (RIG) are commonly used to assess high cognitive abilities such as inhibition or sustained attention. They also draw upon our approximate sense of complexity. A detrimental effect of ageing on pseudo-random productions has been demonstrated for some tasks, but little is as yet known about the developmental curve of cognitive complexity over the lifespan. We investigate the complexity trajectory across the lifespan of human responses to five common RIG tasks, using a large sample (n = 3429). Our main finding is that the developmental curve of the estimated algorithmic complexity of responses is similar to what may be expected of a measure of higher cognitive abilities, with a performance peak around 25 and a decline starting around 60, suggesting that RIG tasks yield good estimates of such cognitive abilities. Our study illustrates that very short strings of, i.e., 10 items, are sufficient to have their complexity reliably estimated and to allow the documentation of an age-dependent decline in the approximate sense of complexity.

 

Original article: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005408

 An animated video from the authors show the approach taken to conduct the experiment: https://www.youtube.com/watch?v=E-YjBE5qm7c

Media covering the article:

• https://www.newscientist.com/article/2127995-our-ability-to-think-in-a-random-way-peaks-at-25-then-declines/
• http://mashable.com/2017/04/13/ability-to-be-random-peaks-at-25/#h7ySevuaIaqw
• https://www.sciencedaily.com/releases/2017/04/170413141122.htm
• https://phys.org/news/2017-04-golden-age-ability-random-choices.html

Complexity science has spread from its origins in the physical sciences into biological and social sciences (1). Increasingly, the social sciences frame policy problems from the financial system to the food system as complex adaptive systems (CAS) and urge policy-makers to design legal solutions with CAS properties in mind. What is often poorly recognized in these initiatives is that legal systems are also complex adaptive systems (2). Just as it seems unwise to pursue regulatory measures while ignoring known CAS properties of the systems targeted for regulation, so too might failure to appreciate CAS qualities of legal systems yield policies founded upon unrealistic assumptions. Despite a long empirical studies tradition in law, there has been little use of complexity science. With few robust empirical studies of legal systems as CAS, researchers are left to gesture at seemingly evident assertions, with limited scientific support. We outline a research agenda to help fill this knowledge gap and advance practical applications.

 

Harnessing legal complexity
J. B. Ruhl, Daniel Martin Katz, Michael J. Bommarito II

Science  31 Mar 2017:
Vol. 355, Issue 6332, pp. 1377-1378
DOI: 10.1126/science.aag3013

Source: science.sciencemag.org

For decades, archaeologists thought democratic republics such as classical Athens and medieval Venice were a purely European phenomenon. Conventional wisdom held that in premodern, non-Western societies, despots simply extracted labor and wealth from their subjects. But archaeologists have identified several societies in pre-Columbian Mesoamerica that upend that model. They argue that societies such as Tlaxcallan in the central Mexican highlands and Tres Zapotes along the Mexican gulf coast were organized collectively, meaning that rulers shared power and commoners had a say in the government that presided over their lives. These societies were not necessarily full democracies in which citizens cast votes, but they were radically different from the autocratic, inherited rule found—or assumed—in most ancient societies. Archaeologists now say that these collective societies left telltale traces in their material culture and urban planning, such as repetitive architecture, an emphasis on public space over palaces, reliance on local production over exotic trade goods, and a narrowing of wealth gaps between elites and commoners.

 

Unearthing democracy’s roots
Lizzie Wade

Science  17 Mar 2017:
Vol. 355, Issue 6330, pp. 1114-1118
DOI: 10.1126/science.355.6330.1114

Source: science.sciencemag.org