8,845 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
Minimalistic Collective Perception with Imperfect Sensors
Collective perception is a foundational problem in swarm robotics, in which
the swarm must reach consensus on a coherent representation of the environment.
An important variant of collective perception casts it as a best-of-
decision-making process, in which the swarm must identify the most likely
representation out of a set of alternatives. Past work on this variant
primarily focused on characterizing how different algorithms navigate the
speed-vs-accuracy tradeoff in a scenario where the swarm must decide on the
most frequent environmental feature. Crucially, past work on best-of-
decision-making assumes the robot sensors to be perfect (noise- and
fault-less), limiting the real-world applicability of these algorithms. In this
paper, we derive from first principles an optimal, probabilistic framework for
minimalistic swarm robots equipped with flawed sensors. Then, we validate our
approach in a scenario where the swarm collectively decides the frequency of a
certain environmental feature. We study the speed and accuracy of the
decision-making process with respect to several parameters of interest. Our
approach can provide timely and accurate frequency estimates even in presence
of severe sensory noise.Comment: 7 pages, accepted into IROS2023. Current version incorporates minor
updates from reviewer comment
Identifying feasible operating regimes for early T-cell recognition: The speed, energy, accuracy trade-off in kinetic proofreading and adaptive sorting
In the immune system, T cells can quickly discriminate between foreign and
self ligands with high accuracy. There is evidence that T-cells achieve this
remarkable performance utilizing a network architecture based on a
generalization of kinetic proofreading (KPR). KPR-based mechanisms actively
consume energy to increase the specificity beyond what is possible in
equilibrium.An important theoretical question that arises is to understand the
trade-offs and fundamental limits on accuracy, speed, and dissipation (energy
consumption) in KPR and its generalization. Here, we revisit this question
through numerical simulations where we simultaneously measure the speed,
accuracy, and energy consumption of the KPR and adaptive sorting networks for
different parameter choices. Our simulations highlight the existence of a
'feasible operating regime' in the speed-energy-accuracy plane where T-cells
can quickly differentiate between foreign and self ligands at reasonable energy
expenditure. We give general arguments for why we expect this feasible
operating regime to be a generic property of all KPR-based biochemical networks
and discuss implications for our understanding of the T cell receptor circuit.Comment: 14 pages, 8 figure
Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making
Learning and decision making in the brain are key processes critical to
survival, and yet are processes implemented by non-ideal biological building
blocks which can impose significant error. We explore quantitatively how the
brain might cope with this inherent source of error by taking advantage of two
ubiquitous mechanisms, redundancy and synchronization. In particular we
consider a neural process whose goal is to learn a decision function by
implementing a nonlinear gradient dynamics. The dynamics, however, are assumed
to be corrupted by perturbations modeling the error which might be incurred due
to limitations of the biology, intrinsic neuronal noise, and imperfect
measurements. We show that error, and the associated uncertainty surrounding a
learned solution, can be controlled in large part by trading off
synchronization strength among multiple redundant neural systems against the
noise amplitude. The impact of the coupling between such redundant systems is
quantified by the spectrum of the network Laplacian, and we discuss the role of
network topology in synchronization and in reducing the effect of noise. A
range of situations in which the mechanisms we model arise in brain science are
discussed, and we draw attention to experimental evidence suggesting that
cortical circuits capable of implementing the computations of interest here can
be found on several scales. Finally, simulations comparing theoretical bounds
to the relevant empirical quantities show that the theoretical estimates we
derive can be tight.Comment: Preprint, accepted for publication in Neural Computatio
Collective decision-making
Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots
Collective decision-making
Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots
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