A Passion for Cooperation: Adventures of a Wide-Ranging Scientist, By Robert Axelrod

A Passion for Cooperation is the exciting autobiography of Robert Axelrod, one of the most acclaimed and wide-ranging scientists of the last fifty years. After being recognized by President Kennedy for being a promising young scientist while in high school, Axelrod built a career dedicated to collaborating with business school professors, international relations scholars, political scientists, computer scientists, and even evolutionary biologists and cancer researchers. Fifty years later, he was honored by President Obama with the National Medal of Science for scientific achievement and leadership and his work has been referred to as the gold standard of interdisciplinary research. 

Yet Axelrod’s autobiography is not just an account of his wide-ranging passion for cooperation. It reveals his struggles to overcome failures and experience the joys of gaining new insights into how to achieve cooperation. A Passion for Cooperation recounts Robert Axelrod’s adventures talking with the leader of the organization Hamas, the Prime Minister of Israel, and the Foreign Minister of Syria. Axelrod also shares stories of being hosted in Kazakhstan by senior Soviet retired generals and visiting China with well-connected policy advisors on issues of military aspects of cyber conflict. Through stories of the difficulties and rewards of interdisciplinary collaborations, readers will discover how Axelrod’s academic and practical work have enriched each other and demonstrated that opportunities for cooperation are much greater than generally thought. 

More at: press.umich.edu

SOCKS Undergraduate Internships in the science of online corpora, knowledge, and stories

Vermont EPSCoR is offering 12 summer research internship opportunities to join our cutting-edge NSF-funded research on Harnessing the Data Revolution for Vermont: The Science of Online Corpora, Knowledge, and Stories (SOCKS). We are initiating a five-year, large-scale, interdisciplinary, and groundbreaking data science effort to better understand and harness the power of stories. SOCKS revolves around stories as an essential part of how people comprehend, explain, predict, and seek to navigate the world. SOCKS supports the Digital Humanities by developing a powerful approach to quantifying both individual stories and ecologies of stories through massive data collection, natural language processing, and large language models—computer-based encodings of the meaningful connections between words and phrases.

The Undergraduate Research Internship Program offers students the opportunity to participate in current research conducted through the NSF EPSCoR award Harnessing the Data Revolution for Vermont: The Science of Online Corpora, Knowledge, and Stories SOCKS program. SOCKS Summer Undergraduate Interns (SSUI) will be matched with a research team working on the transdisciplinary SOCKS research program. At the end of the internship, research teams will meet together at a Symposium to share their discoveries through an oral presentation and a written report of their research. Students will do full-time, high-impact, authentic research for 10 weeks in one of the following areas:

(1) Indigenous voices in global environmental governance (mentored by Fuentes-George);
(2) Data ethics, privacy, and narrative bias (mentored by Harp and Lovato);
(3) Social and health narratives (mentored by SOCKS social or health teams)
(4) Analysis of local news stories and programs (mentored by Richard Watts, the Director of the Center for Community News at UVM)

More at: socks.w3.uvm.edu

Behavior-based dependency networks between places shape urban economic resilience

Takahiro Yabe, Bernardo Garcia Bulle Bueno, Morgan Frank, Alex Pentland, Esteban Moro

Urban economic resilience is intricately linked to how disruptions caused by pandemics, disasters, and technological shifts ripple through businesses and urban amenities. Disruptions, such as closures of non-essential businesses during the COVID-19 pandemic, not only affect those places directly but also influence how people live and move, spreading the impact on other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other in these situations. Leveraging large-scale human mobility data and millions of same-day visits in New York, Boston, Los Angeles, Seattle, and Dallas, we quantify dependencies between points-of-interest (POIs) encompassing businesses, stores, and amenities. Compared to places’ physical proximity, dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of POI categories. We show that using behavior-based dependency relationships improves the predictability of business resilience during shocks, such as the COVID-19 pandemic, by around 40% compared to distance-based models. Simulating hypothetical urban shocks reveals that neglecting behavior-based dependencies can lead to a substantial underestimation of the spatial cascades of disruptions on businesses and urban amenities. Our findings underscore the importance of measuring the complex relationships woven through behavioral patterns in human mobility to foster urban economic resilience to shocks.

Read the full article at: arxiv.org

Lake Como School on Complex Networks Theory, Methods, and Applications

May 27-31, 2024
Villa del Grumello,
Como, Italy

Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

Read the full article at: ntmh.lakecomoschool.org

Scaling deep learning for materials discovery

Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon & Ekin Dogus Cubuk 
Nature (2023)

Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing1,2,3,4,5,6,7,8,9,10,11. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation12,13,14. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies15,16,17, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity.

Read the full article at: www.nature.com