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Subject:	Latest Complexity Digest Posts
Date:	Mon, 15 Jun 2020 11:02:31 +0000
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Learn about the latest and greatest related to complex systems research. More at https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d9748e1f1c&e=55e25a0e3e

Building the New Economy ·

https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=31a72d25ba&e=55e25a0e3e

Edited by Alex Pentland, Alexander Lipton, and Thomas Hardjono

With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and governments. Today's pandemic, joined with the tsunami of data, crypto and AI technologies, is such a crisis. Consequently the critical question for today is: what sort institutions should we be creating both to help us past this crisis and to make us less vulnerable to the next crisis? This book lays out a vision of what we should build, covering not only how to reforge our societies' social contract but also how institutions, systems, infrastructure, and law should change in support of this new order. We invite your comments and suggestions on both the ideas and the presentation, preferably by June 1, 2020 when we will move to make the book more widely available.

Source: wip.mitpress.mit.edu (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=75c716f76a&e=55e25a0e3e)

Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)

Jie Sun, Abd AlRahman AlMomani, Erik Bollt

Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field and in the finance industry. Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex. In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches. Building on the recently developed optimal causation entropy principle (oCSE), that we proved can correctly infer networks distinguishing between direct versus indirect connections, we develop here an efficient algorithm that furthermore infers a Boolean network (including both its structure and function) based
on data observed from the evolving states at nodes. We call this new inference method, Boolean optimal causation entropy (BoCSE), which we will show that our method is both computationally efficient and also resilient to noise. Furthermore, it allows for selection of a set of features that best explains the process, a statement that can be described as a networked Boolean function reduced order model. We highlight our method to the feature selection in several real-world examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) risk causality analysis of loans in default status. Our proposed method is effective and efficient in all examples.

Source: arxiv.org (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9cea45760f&e=55e25a0e3e)

Uncovering the social interaction network in swarm intelligence algorithms

https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ff95389bed&e=55e25a0e3e

Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020)

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study
distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.

Source: appliednetsci.springeropen.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e0e4361aaf&e=55e25a0e3e)

Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks

Masaaki Inoue, Thong Pham, Hidetoshi Shimodaira

Journal of Informetrics
Volume 14, Issue 3, August 2020, 101042

• Transitivity and preferential attachment exist jointly in two co-authorship networks.

• Neither alone could describe the networks well.

• Their functional forms deviate substantially from the conventional power-law form.

• Transitivity greatly dominated preferential attachment in both networks.

Source: www.sciencedirect.com (https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=06711bbf09&e=55e25a0e3e)

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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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