29,177 research outputs found
Collective Decision-Making in Ideal Networks: The Speed-Accuracy Tradeoff
We study collective decision-making in a model of human groups, with network
interactions, performing two alternative choice tasks. We focus on the
speed-accuracy tradeoff, i.e., the tradeoff between a quick decision and a
reliable decision, for individuals in the network. We model the evidence
aggregation process across the network using a coupled drift diffusion model
(DDM) and consider the free response paradigm in which individuals take their
time to make the decision. We develop reduced DDMs as decoupled approximations
to the coupled DDM and characterize their efficiency. We determine high
probability bounds on the error rate and the expected decision time for the
reduced DDM. We show the effect of the decision-maker's location in the network
on their decision-making performance under several threshold selection
criteria. Finally, we extend the coupled DDM to the coupled Ornstein-Uhlenbeck
model for decision-making in two alternative choice tasks with recency effects,
and to the coupled race model for decision-making in multiple alternative
choice tasks.Comment: to appear in IEEE TCN
Interactive robots in experimental biology
Interactive robots have the potential to revolutionise the study of social behaviour because they provide several methodological advances. In interactions with live animals, the behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that different morphologies and behavioural strategies can be combined), behaviour can be manipulated in complex interaction sequences and models of behaviour can be embodied by the robot and thereby be tested. Furthermore, robots can be used as demonstrators in experiments on social learning. As we discuss here, the opportunities that robots create for new experimental approaches have far-reaching consequences for research in fields such as mate choice, cooperation, social learning, personality studies and collective behaviour. © 2011 Elsevier Ltd
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
The evolution of leadership in migratory populations depends not only on
costs and benefits of leadership investments but also on the opportunities for
individuals to rely on cues from others through social interactions. We derive
an analytically tractable adaptive dynamic network model of collective
migration with fast timescale migration dynamics and slow timescale adaptive
dynamics of individual leadership investment and social interaction. For large
populations, our analysis of bifurcations with respect to investment cost
explains the observed hysteretic effect associated with recovery of migration
in fragmented environments. Further, we show a minimum connectivity threshold
above which there is evolutionary branching into leader and follower
populations. For small populations, we show how the topology of the underlying
social interaction network influences the emergence and location of leaders in
the adaptive system. Our model and analysis can describe other adaptive network
dynamics involving collective tracking or collective learning of a noisy,
unknown signal, and likewise can inform the design of robotic networks where
agents use decentralized strategies that balance direct environmental
measurements with agent interactions.Comment: Submitted to Physica D: Nonlinear Phenomen
- …