Nobel laureate François Jacob wrote often about evolution as “tinkering” in which parts and processes alone or together in cells and organisms were co-opted for new functional purposes. Such behavior remains unexamined concerning how adaptive systems succeed in biology, supply networks, the economy, and beyond. In the presence of Unknown-Unknown events (Unk-Unks) that have no prior occurrences and are evident only in their realizations, the design of supply networks must allow for developing adaptive capabilities at the firm-level. When done right, such organic development in the supply network would mimic a biological phenomenon of tinkering and natural selection. We describe enabling such adaptive processes as jury rigging. We discuss how firms could design their supply networks and organize their supply network ex-ante that enables the network members to respond to Unk-Unks in an innovative way through jury rigging of their relationships. Development of such jury rigging capabilities requires integrative suppliers with deep embedded relationships, enabled through appropriate incentives that include incomplete contracts with the suppliers and sharing of unspecified decision rights.

 

Jury Rigging and Supply Network Design: Evolutionary “Tinkering” in the presence of Unknown-Unknowns
Stuart Kauffman, Surya D. Pathak, Pradyot K. Sen,
Thomas Choi

Journal of Supply Chain Management

doi: 10.1111/jscm.12146

Source: onlinelibrary.wiley.com

We propose a mapping of spreading dynamics to an ensemble of weighted networks, where edge weights represent propagation time delays. In this mapping, shortest paths in the weighted networks preserve the temporal causality of spreading. Our framework provides insights into the local and global spreading dynamics, enables efficient source detection, and helps to improve strategies for time-critical vaccination. Finally, we establish the connection of our mapping to bond percolation theory.

 

Mapping spreading dynamics: From time respecting shortest paths to bond percolation
Dijana Tolic, Kaj-Kolja Kleineberg, Nino Antulov-Fantulin

Source: arxiv.org

In this paper, we document lognormal distributions for spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas.
The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears as a functional property that is present everywhere. 
Secondly, we created a generic neural model to show that Hebbian learning will create and maintain lognormal distributions.
We could prove with the model that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This settles a long-standing question about the type of plasticity exhibited by intrinsic excitability.

Source: f1000research.com

It has long been assumed that grammar is a system of abstract rules, that the world’s languages follow universal patterns, and that we are born with a ‘language instinct’. But an alternative paradigm that focuses on how we learn and use language is emerging, overturning these assumptions and many more.

 

Towards an integrated science of language
Morten H. Christiansen & Nick Chater

Nature Human Behaviour 1, Article number: 0163 (2017)DOIdoi:10.1038/s41562-017-0163

 

Source: www.nature.com

Physicists are accustomed to dealing with large datasets, yet they are fortunate in that the quality of their experimental data is very good. The onset of big data has led to an explosion of datasets with a far more complex structure — a development that requires new tools and a different mindset.

 

The physics of data
Jeff Byers
Nature Physics 13, 718–719 (2017) doi:10.1038/nphys4202

Source: www.nature.com