Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session E14: Physics of Social Interactions IIIFocus Live
|
Hide Abstracts |
Sponsoring Units: DBIO GSNP Chair: Orit Peleg, University of Colorado - Boulder; Greg Stephens, Vrije Univ (Free Univ) |
Tuesday, March 16, 2021 8:00AM - 8:36AM Live |
E14.00001: Early warning signals in motion inference Invited Speaker: Yuval Hart The ability to infer intention lies at the basis of many social interactions played out via motor actions. Here, we analyze data from experiments simulating an antagonistic game between an Attacker and a Blocker. Evidence shows early inference by Blockers of an Attacker move by ~100ms but the informational cues signaling the impending move remain unknown. We show that the transition to action has the hallmark of a critical transition that is accompanied by early warning signals. These early warning signals occur ~130 ms before motion ensues —showing a sharp rise in motion autocorrelation at lag-1 and a sharp rise in the autocorrelation decay time. These early warning signals correlate strongly with Blocker response times. We analyze the variance of the motion near the point of transition and find that it diverges in a manner consistent with the dynamics of a fold-transition. To test if humans can recognize and act upon these early warning signals, we simulate the dynamics of fold-transition events and ask people to recognize the onset of directional motion: participants react faster to fold-transition dynamics than to its uncorrelated counterpart. Together, our findings suggest that people can recognize the intent and onset of motion by inferring its early warning signals. |
Tuesday, March 16, 2021 8:36AM - 8:48AM Live |
E14.00002: Synchronization of Coupled Kuramoto Oscillators under Resource Constraints Keith A Kroma-Wiley, Peter J Mucha, Danielle Bassett A fundamental understanding of synchronized behavior in multi-agent systems can be acquired by studying analytically tractable Kuramoto models. However, such models typically diverge from many real systems whose dynamics evolve under non-negligible resource constraints. Here we construct a system of coupled Kuramoto oscillators that consume or produce resources as a function of their oscillation frequency. At high coupling, we observe strongly synchronized dynamics, whereas at low coupling we observe independent oscillator dynamics, as expected from standard Kuramoto models. For intermediate coupling, we empirically observe that (and theoretically explain why) the system can exist in either (i) a state in which the order parameter oscillates in time, or (ii) a state in which multiple synchronization states are simultaneously stable. Relevant for systems as varied as coupled neurons and social groups, our study lays important groundwork for future efforts to develop quantitative predictions of synchronized dynamics for systems embedded in environments marked by sparse resources. |
Tuesday, March 16, 2021 8:48AM - 9:00AM Live |
E14.00003: Information Propagation and Synchrony in Firefly Natural Swarms Raphael Sarfati, Orit Peleg Firefly synchronous flashing is a rare and mesmerizing natural phenomenon, but remains mysterious. Despite casual descriptions of collective flashing and hasty analogies with models of coupled oscillators, careful observations and quantitative analysis suggest that the underlying mechanisms of synchrony are complex and remain poorly understood. By using stereoscopic recordings of the collective flashing display of synchronous fireflies in their natural environment as well as in controlled experiments, we reconstruct flashing swarms in three dimensions and investigate local interactions and collective patterns. We show in particular that flashing information propagates across the swarm along a network of visual connections, and we search for signatures of heterogeneities that could suggest social differentiation. |
Tuesday, March 16, 2021 9:00AM - 9:12AM Live |
E14.00004: Ecological significance of imperfectly synchronized collective behaviors Ricardo Martinez Garcia, Fernando W Rossine, Allyson Sgro, Thomas Gregor, Corina E Tarnita Loners, individuals out-of-sync with a coordinated majority, occur frequently in nature. But are loners incidental byproducts of the large-scale coordinated behavior or are they part of a mosaic of life-history strategies? To address this question, we provide empirical evidence of naturally occurring heritable variation in loner behavior in the model social amoeba Dictyostelium discoideum. We propose that Dictyostelium loners—cells that do not join the multicellular life stage— arise from a dynamic population-partitioning process, the result of each cell making a stochastic, signal-based decision. Finally, we predict theoretically that when a pair of Dictyostelium strains differing in their partitioning behavior co-aggregate, cross-signaling impacts slime-mold diversity across spatiotemporal scales. Our findings suggest that loners could be critical to understanding collective and social behaviors, multicellular development, and ecological dynamics in D. discoideum. More broadly, across taxa, imperfect coordination of collective behaviors might be adaptive by enabling diversification of life-history strategies. |
Tuesday, March 16, 2021 9:12AM - 9:24AM Live |
E14.00005: Universal scaling laws of interaction time distribution in honeybee and human social networks Sang Hyun Choi, Vikyath D Rao, Tim Gernat, Adam Hamilton, Gene Robinson, Nigel Goldenfeld We report high-throughput automated measurements of trophallaxis and face-to-face event durations of honeybees. The distribution is heavy-tailed as in human face-to-face interactions. We derive the power-law form by viewing the termination of an interaction as a particle escaping over an energy barrier. The variability within the population is represented by the distribution of barrier heights determined by extreme value theory. We find a universal scaling law connecting the exponent in the interaction time distribution to that in the barrier height distribution, which is verified by both honeybee and human data. Although less prominent than in humans, individual differences in honeybee interactivity, which are usually overlooked, are confirmed. Our work shows how individual differences can lead to universal patterns of behavior that transcend species and specific mechanisms of social interactions. |
Tuesday, March 16, 2021 9:24AM - 9:36AM Live |
E14.00006: An Adaptive Voter Model in Heterogeneous Environments Olivia Chu, Marc Wiedermann, Jonathan Donges In human social systems, it is natural to assume that individuals’ opinions influence and are influenced by their interactions. Mathematically, it is common to represent such systems as networks, where nodes are individuals and edges between them denote a connection. Adaptive network models explore the dynamic relationship between node properties and network topology. In the context of opinion dynamics, these models often take the form of adaptive voter models, where there are two mechanisms through which network changes can take place. Through homophily, an edge forms between two individuals who already agree. Through social learning, an individual adopts a neighbor’s opinion. Central to these models is assortative mixing, the notion that individuals more frequently attach to those who are similar to them, thus facilitating the formation of sub-communities of like-minded individuals. However, it is not always the case that individuals want to cluster into homogeneous groups. Instead, they might attempt to surround themselves with those who both agree and disagree with them to attain a balance of inclusion and distinctiveness in their social environments. In this work, we explore the effects that such heterogeneous preferences have on the dynamics of the adaptive voter model. |
Tuesday, March 16, 2021 9:36AM - 9:48AM Live |
E14.00007: Catalyzing Collaborations: A Model for the Dynamics of Team Formation at Conferences Emma Zajdela, Daniel M Abrams, Richard Jay Wiener, Andrew Feig The COVID-19 pandemic has brought to the fore the importance of collaboration among scientists to address global challenges. One of the main ways that collaborations are catalyzed is by gathering scientists together at conferences. In the U.S. alone, conferences amount to billions of dollars per year in terms of travel expenses, organizing costs, and loss of research time. In this work, I present a dynamical model for predicting the formation of scientific collaborations at conferences, inspired by the process of catalysis. Specifically, the model tracks the probability that conference participants form collaborations given their level of interaction throughout the conference. Model predictions are tested using data from several multi-year series of interactive conferences known as Scialog Conferences, organized by the Research Corporation for Science Advancement over the period 2015-2020. We find that scientists who interact more intensely throughout the conference have a higher likelihood of forming a collaboration. Furthermore, we find that the likelihood of collaborating remains at a higher level even after the interaction between participants has ceased. |
Tuesday, March 16, 2021 9:48AM - 10:00AM Live |
E14.00008: Accurate Density-Functional Fluctuation Theory (DFFT) approach to forecasting ethnic composition of neighborhoods. Boris Barron, Yunus A Kinkhabwala, Matthew Hall, Itai Cohen, Tomas Alberto Arias Accurate predictions of human residential dynamics are invaluable to developing housing, transportation, and social policy. Although large-scale forecasts can be made by estimating birth, death, and migration rates; predictions at the neighborhood level (on the scale of ~1000 people) remain a challenge due to (1) the inherent complexity of the underlying interactions and (2) the difficulty of inferring the interactions from available data. Here, we demonstrate the power of Density-Functional Fluctuation Theory (DFFT) to address challenges (1) and (2) to produce novel forecasts of neighborhood-level composition changes. DFFT works by forming an energy-like landscape composed of regional interactions and density-dependent social interactions. Vitally, since these quantities capture cumulative interaction effects, they require minimal assumptions of human behavior. And, surprisingly, they can be determined directly from fluctuations in widely-available demographic data. We demonstrate the efficacy of our approach by forecasting the dynamics of neighborhood ethinic composition from the year 2000 to 2010 using US census data. |
Tuesday, March 16, 2021 10:00AM - 10:12AM Live |
E14.00009: Opinion dynamics under antagonistic influences Deepak Bhat, Sidney Redner We study the opinion dynamics of a generalized voter model in which N voters are additionally influenced by two antagonistic news sources. As the influence of these news sources is increased, the mean time to reach consensus scales N^z. The parameter z quantifies the influence of the news sources and increases without bound as the news sources become increasingly influential. The time to reach a politically polarized state, in which roughly equal fractions of the populations are in each opinion state, is generally short, and the steady-state opinion distribution exhibits a transition from near consensus to a politically polarized state as a function of z. |
Tuesday, March 16, 2021 10:12AM - 10:24AM Live |
E14.00010: Understanding mate choice signal-receiver dynamics using a phase space embedding approach Robert Etheredge, Greg Stephens, Alex Jordan Animal signals are selected to 1) maximize detection and 2) increase the likelihood of eliciting a response. While previous work has investigated visual and acoustic signals, postural movements represent a distinct and largely understudied signaling axis. Here we use high-resolution descriptions of animal movement to build a principled basis for investigating courtship signaling dynamics in guppies. We show that males and females occupy distinct behavioral subspaces. And during courtship these differences are further exaggerated as males produce stereotyped displays at the extremes of the behavioral space. States where behavioral trajectories become unstable, and are more likely to switch are disproportionately used both preceding (by males) and following courtship (by females). Coordination between males and females peaks immediately before courtship, and after courtship males show increased behavioral influence on females. Together, this provides the first quantitative evidence of signal maximization and receiver response dynamics in postural signaling interactions which are key predictions provided by signaling theory in animal communication, and exemplifies how quantitative, data-driven descriptions of system behavior may be used to directly inform ethological theory. |
Tuesday, March 16, 2021 10:24AM - 11:00AM Live |
E14.00011: Psychophysics of Musical Rhythms and the Riddle of Swing Invited Speaker: Theo Geisel Nonlinear dynamics offers a vast toolbox of techniques for |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700