Resilient help to switch and overlap hierarchical subsystems in a small human group

Abstract

Groups of social organisms in nature are resilient systems that can overcome unpredicted threats by helping its members. These social organisms are assumed to behave both autonomously and cooperatively as individuals, the helper, the helped and other part of a group depending on the context such as emergencies. However, the structure and function of these resilient actions, such as how helpers help colleagues and how the helper’s action is effective at multiple subsystem scales remain unclear. Here we investigated the behaviour of organised and efficient small human groups in a ballgame defence, and identified three principles of hierarchical resilient help when under attack. First, at a present high emergency level, the helper simply switched the local roles in the attacked subsystem with the helped. Second, at an intermediate emergency level, the helpers effectively acted in overlapping subsystems. Third, for the most critical emergency, the helpers globally switched the action on the overall system. These resilient actions to the benefit of the system were assumed to be observed in only humans, which help colleagues at flexibly switched and overlapped hierarchical subsystem. We suggest that these multi-layered helping behaviours can help to understand resilient cooperation in social organisms and human groups.

Effects of Agent-Environment Symmetry on the Coordination Dynamics of Triadic Jumping

“…The role of a player (e.g., step forward or away, offense or defense) alters accordingly (Kijima et al, 2012 ). Such role asymmetries are characteristic of the sports like soccer (Yamamoto and Yokoyama, 2011 ) and basketball (Fujii et al, 2016 ) and can depend on the skill of the players. For example, Yokoyama and Yamamoto ( 2011 ) asked four participants, including collegiate soccer players, to engage in a simplified three on one soccer game (monkey in the middle game) and found that the symmetry of the coordination patterns adopted by players were skill dependent realizations of behavioral coordination modes predicted by the symmetries of symmetric Hopf bifurcation theory (Golubitsky and Stewart, 1985 ).…”

Physically-interpretable classification of biological network dynamics for complex collective motions

“…However, a general quantitative validation method for the unknown dynamics is further required. Another is to reflect the more local interaction dynamics such as local competitive and cooperative play by the attackers and defenders [49,50,51,22], which can provides more practical information in the sport domain. Although the purpose of Graph DMD is to extract the underlying global dynamics of GDSs and we can obtain the interpretable local spectra in the DMD modes, there are other approaches for extracting the more specific local dynamics.…”

Prediction and classification in equation-free collective motion dynamics

“…Cohesive and attractive motion in groups of social organisms emerges from dynamic individual interactions. Collective motion has been understood mainly via microscopic modeling of individual behavior and macroscopic group statistics, which has attracted attention in broad research areas such as in biology [ 1 – 3 ], physics [ 4 – 6 ], and human behavior [ 7 – 9 ]. Most of researches elucidated that complex group motion can emerge from simple individual interaction rules [ 2 , 3 , 6 ] and shows a nonlinear change in global variables [ 2 , 8 ] or statistics [ 1 , 10 ], including phase transitions [ 6 ].…”

“…Generally, the problem has been well-known as an active matter or non-equilibrium many-body system problem [ 11 ], in which some physical laws such as fluctuation-dissipation theorem [ 5 ] do not hold owing to the external or self-propelled force. Fundamental questions also remain about the bridge between individual and functional collective motion in applied contexts e.g., in human behavior [ 7 – 9 ], which has been also simply modeled as artificial multi-agents [ 12 ] and as “social force” in non-reciprocal interaction [ 7 , 13 , 14 ]. However, the modeling has been sometimes too complicated owing to its higher-level sociality [ 9 , 15 ].…”

“…Fundamental questions also remain about the bridge between individual and functional collective motion in applied contexts e.g., in human behavior [ 7 – 9 ], which has been also simply modeled as artificial multi-agents [ 12 ] and as “social force” in non-reciprocal interaction [ 7 , 13 , 14 ]. However, the modeling has been sometimes too complicated owing to its higher-level sociality [ 9 , 15 ]. The collective motion problem requires a way to understand them in a data-driven and equation-free manner [ 16 ], because the standard parametric approach assumes that individual and statistical models are basically correct and sometimes lacks the flexibility to describe complex dynamics that occur in nature [ 17 ].…”