Author: cxdig

Networked Complexity: The Case of COVID-19. June 8-11, 2020

Close monitoring of the COVID-19 pandemic provides a blow by blow account of a spatio-temporal process percolating over complex (social)-networks. Efforts to contain the spread of the disease were and remain, for better or worse, explicitly informed by a rich tradition of mathematical models of such processes. This tradition was further enriched in the past couple of decades with the emergence of globally networked virtual societies, and the deployment of fine grained networks of sensors, both enabling the gathering of highly resolved data on the structure of complex networks, and flows over them.

Our online-conference is an occasion for expert reviews of this tradition, then presentations of work-in-progress on the gathering of epidemiological data (technical and ethical challenges), and its modeling (from the coarse grained compartmental, to the fine grained agent based models), with the urgency of COVID-19 mitigation in the air.

Taking place as it does at a cusp in a global pandemic, the meeting is for us at CAMS a timely intervention in a collaboration with the National Center for Remote Sensing (NCRS, CNRS-L) the principle aim of which is to harness big data analytics and complexity theory at the service of national and regional priorities. It draws on local expertise in concerned disciplines (in this case: physics, biology, epidemiology and sociology), and contributions by experts at leading international laboratories in data analytics, and complexity science (e.g. Multiscale and Quantum Physics, Aalto University, Finland; The Bartlett Center for Advanced Spatial Analysis, UCL, London; Center of Complexity Sciences (C3), UNAM, Mexico; The Alan Turing Institute, London; ICTP, Trieste, Italy; etc.).

Source: www.aub.edu.lb

Performing Complexity: Building Foundations for the Practice of Complex Thinking | Ana Teixeira de Melo

In the face of growing challenges, we need modes of thinking that allow us to not only grasp complexity but also perform it. In this book, the author approaches complexity from the standpoint of a relational worldview. The author recasts complex thinking as a mode of coupling between an observer and the world. Further, she explores the process and outcome of that coupling, namely, meaningful information that may have transformative effects and impact the management of change in the ‘real world’. The author presents a new framework for operationalising complex thinking in a set of dimensions and properties through which it may be enacted. This framework may inform the development and coordination of new tools and strategies to support the practice and evaluation of complex thinking across a variety of domains. Intended for a wide interdisciplinary audience of academics, practitioners and policymakers alike, the book is an invitation to pursue inter- and transdisciplinary dialogues and collaborations. 

Source: www.springer.com

Navigating the Landscape of Games

Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien Perolat, Bart De Vylder, Audrunas Gruslys, Remi Munos

 

Games are traditionally recognized as one of the key testbeds underlying progress in artificial intelligence (AI), aptly referred to as the "Drosophila of AI". Traditionally, researchers have focused on using games to build strong AI agents that, e.g., achieve human-level performance. This progress, however, also requires a classification of how ‘interesting’ a game is for an artificial agent. Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but also helps to determine what game an AI should address next as part of its training. Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes, characteristics, and complexities. We illustrate our findings in various domains, ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another. Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.

Source: arxiv.org

A framework for designing compassionate and ethical artificial intelligence and artificial consciousness

Soumya Banerjee

 

Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work, we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy, and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from fear of artificial intelligence towards designing machines that embed our cherished values. Consciousness, intelligence, and empathy would be worthy design goals that can be engineered in machines.

Source: indecs.eu

A network analysis of research productivity by country, discipline, and wealth

Jaffe K, ter Horst E, Gunn LH, Zambrano JD, Molina G (2020) A network analysis of research productivity by country, discipline, and wealth. PLoS ONE 15(5): e0232458. https://doi.org/10.1371/journal.pone.0232458

 

Introduction

Research productivity has been linked to a country’s intellectual and economic wealth. Further analysis is needed to assess the association between the distribution of research across disciplines and the economic status of countries.

Methods

By using 55 years of data, spanning 1962 to 2017, of Elsevier publications across a large set of research disciplines and countries globally, this manuscript explores the relationship and evolution of relative research productivity across different disciplines through a network analysis. It also explores the associations of those with economic productivity categories, as measured by the World Bank economic classification. Additional analysis of discipline similarities is possible by exploring the cross-country evolution of those disciplines.

Results

Results show similarities in the relative importance of research disciplines among most high-income countries, with larger idiosyncrasies appearing among the remaining countries. This group of high-income countries shows similarities in the dynamics of the relative distribution of research productivity over time, forming a stable research productivity cluster. Lower income countries form smaller, more independent and evolving clusters, and differ significantly from each other and from higher income countries in the relative importance of their research emphases. Country-based similarities in research productivity profiles also appear to be influenced by geographical proximity.

Conclusions

This new form of analyses of research productivity, and its relation to economic status, reveals novel insights to the dynamics of the economic and research structure of countries. This allows for a deeper understanding of the role a country’s research structure may play in shaping its economy, and also identification of benchmark resource allocations across disciplines for developing countries.

Source: journals.plos.org