1,147,853 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
Individual differences in causal learning and decision making
This is an accepted author manuscript of an article subsequently published by Elsevier. The final published version can be found here: http://dx.doi.org/10.1016/j.actpsy.2005.04.003In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people s accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DP), they tend to misperceive base-rate information (e.g., the cause density effect). The present study investigates the relationship between causal learning and decision making by asking whether people weight base-rate information in the same way when estimating causal strength and when making judgments or inferences about the likelihood of an event. The results suggest that people differ according to the weight they place on base-rate information, but the way individuals do this is consistent across causal and decision making tasks. We interpret the results as reflecting a tendency to differentially weight base-rate information which generalizes to a variety of tasks. Additionally, this study provides evidence that causal learning and decision making share some component processes
Designing Information Markets for Decision Making
People often make important decisions based on information elicited from experts with uncertain preferences. We provide a theoretical rationale for the use of information markets in decision making tasks. Specifically, we show that markets for claims on decision-relevant variables can be efficient incentive schemes for eliciting information. Our model shows decision makers will subsidize liquidity in illiquid decision markets to gather valuable information. Our model also shows that the mere act of linking the decision to the market price will typically enhance liquidity in the market. Overall, our results highlight the potential for using information markets in diverse decision making tasks.
Task Release Control for Decision Making Queues
We consider the optimal duration allocation in a decision making queue.
Decision making tasks arrive at a given rate to a human operator. The
correctness of the decision made by human evolves as a sigmoidal function of
the duration allocated to the task. Each task in the queue loses its value
continuously. We elucidate on this trade-off and determine optimal policies for
the human operator. We show the optimal policy requires the human to drop some
tasks. We present a receding horizon optimization strategy, and compare it with
the greedy policy.Comment: 8 pages, Submitted to American Controls Conference, San Francisco,
CA, June 201
A control theory model for human decision making
The optimal control model for pilot-vehicle systems has been extended to handle certain types of human decision tasks. The model for decision making incorporates the observation noise, optimal estimation, and prediction concepts that form the basis of the model for control behavior. Experiments are described for the following task situations: (1) single decision tasks; (2) two decision tasks; and (3) simultaneous manual control and decision tasks. Using fixed values for model parameters, single-task and two-task decision performance scores to within an accuracy of 10 percent can be predicted. The experiment on simultaneous control and decision indicates the presence of task interference in this situation, but the results are not adequate to allow a conclusive test of the predictive capability of the model
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