Arshed Nabeel, Danny Raj M
In the study of collective motion, it is common practice to collect movement information at the level of the group to infer the characteristics of the individual agents and their interactions. However, it is not clear whether one can always correctly infer individual characteristics from movement data of the collective. We investigate this question in the context of a composite crowd with two groups of agents, each with its own desired direction of motion. A simple observer attempts to classify an agent into its group based on its movement information. However, collective effects such as collisions, entrainment of agents, formation of lanes and clusters, etc. render the classification problem non-trivial, and lead to misclassifications. Based on our understanding of these effects, we propose a new observer algorithm that infers, based only on observed movement information, how the local neighborhood aids or hinders agent movement. Unlike a traditional supervised learning approach, this algorithm is based on physical insights and scaling arguments, and does not rely on training-data. This new observer improves classification performance and is able to differentiate agents belonging to different groups even when their motion is identical. Data-agnostic approaches like this have relevance to a large class of real-world problems where clean, labeled data is difficult to obtain, and is a step towards hybrid approaches that integrate both data and domain knowledge.

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Denis Sh. Sabirov and Igor S. Shepelevich

Entropy 2021, 23(10), 1240

Basic applications of the information entropy concept to chemical objects are reviewed. These applications deal with quantifying chemical and electronic structures of molecules, signal processing, structural studies on crystals, and molecular ensembles. Recent advances in the mentioned areas make information entropy a central concept in interdisciplinary studies on digitalizing chemical reactions, chemico-information synthesis, crystal engineering, as well as digitally rethinking basic notions of structural chemistry in terms of informatics.

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Heiko Hamann; Julian Schwarzat; Ingo Thomsen and Sven Tomforde

Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems – VEHITS

Platooning is an approach to coordinate the driving behaviour of vehicles on major roads such as motorways. The aim is to take advantage of, e.g., slipstream effects to reduce cost. We present an approach to transfer the platooning concept to urban road networks of cities. The reduced slipstream effect is compensated by integration with the signalisation infrastructure to dynamically allow for prioritisation of platoons using progressive signal systems (i.e., “green waves”). We define the scenario and derive a research road map towards fully self-organised platoon operations and integrated coordination with self-adaptive and self-organising urban traffic control systems. Starting from both directions, that is, self-organised urban platooning as well as self-organised progressive signal systems in urban road networks, we define the scenario, identify main challenges, and present first results to demonstrate the feasibility of our research agenda.

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Brennan Klein, View ORCID ProfileNicholas Generous, View ORCID ProfileMatteo Chinazzi, Zarana Bhadricha, Rishab Gunashekar, Preeti Kori, Bodian Li, View ORCID ProfileStefan McCabe, View ORCID ProfileJon Green, View ORCID ProfileDavid Lazer, View ORCID ProfileChristopher R. Marsicano, View ORCID ProfileSamuel V. Scarpino, View ORCID ProfileAlessandro Vespignani

Brennan Klein, Nicholas Generous, Matteo Chinazzi, Zarana Bhadricha, Rishab Gunashekar, Preeti Kori, Bodian Li, Stefan McCabe, Jon Green, David Lazer, Christopher R. Marsicano, Samuel V. Scarpino, Alessandro Vespignani

With a dataset of testing and case counts from over 1,400 institutions of higher education (IHEs) in the United States, we analyze the number of infections and deaths from SARS-CoV-2 in the counties surrounding these IHEs during the Fall 2020 semester (August to December, 2020). We used a matching procedure designed to create groups of counties that are aligned along age, race, income, population, and urban/rural categories—socio-demographic variables that have been shown to be correlated with COVID-19 outcomes. We find that counties with IHEs that remained primarily online experienced fewer cases and deaths during the Fall 2020 semester; whereas before and after the semester, these two groups had almost identical COVID-19 incidence. Additionally, we see fewer deaths in counties with IHEs that reported conducting any on-campus testing compared to those that reported none. We complement the statistical analysis with a case study of IHEs in Massachusetts—a rich data state in our dataset—which further highlights the importance of IHE-affiliated testing for the broader community. The results in this work suggest that campus testing can itself be thought of as a mitigation policy and that allocating additional resources to IHEs to support efforts to regularly test students and staff would be beneficial to mitigating the spread of COVID-19 in the general population.

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Fernando P. Santos, Jorge M. Pacheco and Francisco C. Santos

Phil. Trans. Roy. Soc.

Indirect reciprocity (IR) is a key mechanism to understand cooperation among unrelated individuals. It involves reputations and complex information processing, arising from social interactions. By helping someone, individuals may improve their reputation, which may be shared in a population and change the predisposition of others to reciprocate in the future. The reputation of individuals depends, in turn, on social norms that define a good or bad action, offering a computational and mathematical appealing way of studying the evolution of moral systems. Over the years, theoretical and empirical research has unveiled many features of cooperation under IR, exploring norms with varying degrees of complexity and information requirements. Recent results suggest that costly reputation spread, interaction observability and empathy are determinants of cooperation under IR. Importantly, such characteristics probably impact the level of complexity and information requirements for IR to sustain cooperation. In this review, we present and discuss those recent results. We provide a synthesis of theoretical models and discuss previous conclusions through the lens of evolutionary game theory and cognitive complexity. We highlight open questions and suggest future research in this domain.

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