29,129 research outputs found
Initiating a Mexican wave: An instantaneous collective decision with both short and long range interactions
An interesting example for collective decision making is the so-called
Mexican wave during which the spectators in a stadium leap to their feet with
their arms up and then sit down again following those to their left (right)
with a small delay. Here we use a simple, but realistic model to explain how
the combination of the local and global interactions of the spectators produces
a breaking of the symmetry resulting in the replacement of the symmetric
solution -- containing two propagating waves -- by a single wave moving in one
of the two possible directions. Our model is based on and compared to the
extensive observations of volunteers filling out the related questionnaire we
have posted on the Internet. We find that, as a function of the parameter
controlling the strength of the global interactions, the transition to the
single wave solution has features reminiscent of discontinuous transitions.
After the spontaneous symmetry breaking the two directions of propagation are
still statistically equivalent. We investigate also how this remaining symmetry
is broken in real stadia by a small asymmetrical term in the perception of
spectators.Comment: Main text: 12 pages, 3 figures. Appendices: 18 pages (incl. answers
from online survey on Mexican waves). Supplementary website:
http://angel.elte.hu/localgloba
Symmetry restoring bifurcation in collective decision-making.
How social groups and organisms decide between alternative feeding sites or shelters has been extensively studied both experimentally and theoretically. One key result is the existence of a symmetry-breaking bifurcation at a critical system size, where there is a switch from evenly distributed exploitation of all options to a focussed exploitation of just one. Here we present a decision-making model in which symmetry-breaking is followed by a symmetry restoring bifurcation, whereby very large systems return to an even distribution of exploitation amongst options. The model assumes local positive feedback, coupled with a negative feedback regulating the flow toward the feeding sites. We show that the model is consistent with three different strains of the slime mold Physarum polycephalum, choosing between two feeding sites. We argue that this combination of feedbacks could allow collective foraging organisms to react flexibly in a dynamic environment
Multi-agent decision-making dynamics inspired by honeybees
When choosing between candidate nest sites, a honeybee swarm reliably chooses
the most valuable site and even when faced with the choice between near-equal
value sites, it makes highly efficient decisions. Value-sensitive
decision-making is enabled by a distributed social effort among the honeybees,
and it leads to decision-making dynamics of the swarm that are remarkably
robust to perturbation and adaptive to change. To explore and generalize these
features to other networks, we design distributed multi-agent network dynamics
that exhibit a pitchfork bifurcation, ubiquitous in biological models of
decision-making. Using tools of nonlinear dynamics we show how the designed
agent-based dynamics recover the high performing value-sensitive
decision-making of the honeybees and rigorously connect investigation of
mechanisms of animal group decision-making to systematic, bio-inspired control
of multi-agent network systems. We further present a distributed adaptive
bifurcation control law and prove how it enhances the network decision-making
performance beyond that observed in swarms
Rational Group Decision Making. A random field Ising model at T=0
A modified version of a finite random field Ising ferromagnetic model in an
external magnetic field at zero temperature is presented to describe group
decision making. Fields may have a non-zero average. A postulate of minimum
inter-individual conflicts is assumed. Interactions then produce a group
polarization along one very choice which is however randomly selected. A small
external social pressure is shown to have a drastic effect on the polarization.
Individual bias related to personal backgrounds, cultural values and past
experiences are introduced via quenched local competing fields. They are shown
to be instrumental in generating a larger spectrum of collective new choices
beyond initial ones. In particular, compromise is found to result from the
existence of individual competing bias. Conflict is shown to weaken group
polarization. The model yields new psycho-sociological insights about consensus
and compromise in groups.Comment: 25 pages, late
Modulating interaction times in an artificial society of robots
In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots' motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming
experiments. Exploiting an analogy with human decision making, we capture the
dynamics of the model by a low-dimensional system of equations permitting
analytical investigation. We find that the model reproduces several
characteristic features of swarms, including spontaneous symmetry breaking,
noise- and density-driven order-disorder transitions that can be of first or
second order, and intermittency. Reproducing these experimental observations
using a non-spatial model suggests that spatial geometry may have a lesser
impact on collective motion than previously thought.Comment: 8 pages, 3 figure
Adaptive network models of collective decision making in swarming systems
We consider a class of adaptive network models where links can only be
created or deleted between nodes in different states. These models provide an
approximate description of a set of systems where nodes represent agents moving
in physical or abstract space, the state of each node represents the agent's
heading direction, and links indicate mutual awareness. We show analytically
that the adaptive network description captures the phase transition to
collective motion in swarming systems and that the properties of this
transition are determined by the number of states (discrete heading directions)
that can be accessed by each agent.Comment: 8 pages, 5 figure
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