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

Dynamic simulator for a grinding circuit

Thesis (M.S.) University of Alaska Fairbanks, 2017The grinding circuit is a primary and indispensable unit of a mineral processing plant. The product from a grinding circuit affects the recovery rate of minerals in subsequent downstream processes and governs the amount of concentrate produced. Because of the huge amount of energy required during the grinding operation, they contribute to a major portion of the concentrator cost. This makes grinding a crucial process to be considered for optimization and control. There are numerous process variables that are monitored and controlled during a grinding operation. The variables in a grinding circuit are highly inter-related and the intricate interaction among them makes the process difficult to understand from an operational viewpoint. Modeling and simulation of grinding circuits have been used by past researchers for circuit design and pre-flowsheet optimization in terms of processing capacity, recovery rate, and product size distribution. However, these models were solved under steady approximation and did not provide any information on the system in real time. Hence, they cannot be used for real time optimization and control purposes. Therefore, this research focuses on developing a dynamic simulator for a grinding circuit. The Matlab/Simulink environment was used to program the models of the process units that were interlinked to produce the flowsheet of a grinding circuit of a local gold mine operating in Alaska. The flowsheet was simulated under different operating conditions to understand the behavior of the circuit. The explanation for such changes has also been discussed. The dynamic simulator was then used in designing a neural network based controller for the semi-autogenous mill (SAG). A two-layer non-linear autoregressive (NARX) neural network with feed to the mill as exogenous input was designed using data generated by the simulator for a range of operating conditions. Levenberg-Marquardt (LM) and Bayesian Regularization (BR) training algorithms were used to train the network. Comparison of both algorithms showed LM performed better provided the number of parameters in the network were chosen in a prudent manner. Finally, the implementation of the controller for maintaining SAG mill power to a reference point is discussed

Explicit moments of decision times for single- and double-threshold drift-diffusion processes

We derive expressions for the first three moments of the decision time (DT) distribution produced via first threshold crossings by sample paths of a drift-diffusion equation. The "pure" and "extended" diffusion processes are widely used to model two-alternative forced choice decisions, and, while simple formulae for accuracy, mean DT and coefficient of variation are readily available, third and higher moments and conditioned moments are not generally available. We provide explicit formulae for these, describe their behaviors as drift rates and starting points approach interesting limits, and, with the support of numerical simulations, discuss how trial-to-trial variability of drift rates, starting points, and non-decision times affect these behaviors in the extended diffusion model. Both unconditioned moments and those conditioned on correct and erroneous responses are treated. We argue that the results will assist in exploring mechanisms of evidence accumulation and in fitting parameters to experimental data

Active oxygen involvement in developmental processes in Populus

In plants, oxidative stress is result of disruption of the cellular redox metabolism and is caused by a variety of stress conditions (abiotic and/or biotic). This leads to the induction of several mechanisms that protect against disruption of the redox balance, as well as mechanisms to assist in recovery from toxicity/damage caused by increased cellular levels of reactive oxygen species (ROS). The superoxide dismutase enzymes (SODs) are key component of the reactive oxygen species gene network and represent the first line of defense against ROS, by converting superoxide radicals (O2−) to hydrogen peroxide (H2O2) and water (H2O). Therefore, SODs play an important role in protection against oxidative stress in all aerobic organisms. In this thesis, I describe the characterization of an SOD isoform from Populus, hipI-SOD, which has a high iso-electric point. The global response to oxidative stress is also discussed. Different forms of hipI-SOD transcripts were found in vascular tissue, one of which was produced by alternative splicing. HipI-SOD proteins were found to be mainly localized extracellularly, in the primary and secondary cell walls of vascular tissues. These results together with analysis of transgenic Populus trees with suppressed expression of hipI-SOD strongly indicate roles for hipI-SOD in regulating ROS levels in vascular tissue. ROS are important regulators of plant stress responses. Nevertheless, oxidative stress often affects plants growth and development. In order to understand the basis of oxidative stress tolerance, the diversity of stress responses needs to be investigated. To achieve this we first developed an O2PLS-based multivariate methodology for the integration of multiple datasets originating from three different platforms (transcriptomics, proteomics and metabolomics). Subsequently this data integration method was utilized for a comprehensive study of the overall responses to oxidative stress in Populus. The findings may facilitate the development of stress-tolerant plants with improved survival rates and yields under stressed conditions