Revisiting How Perceived Uncertainty and Herd Behavior Influence Technology Choice

Understanding how herd behavior occurs in the information systems context is important because such behavior influences many choice decisions, is the reason for some decision anomalies, and explains the reasons behind the rise or collapse of technology trends. Perceived uncertainty is a critical factor that triggers herding, but despite its influential role, prior research has not adequately investigated this broad concept. This research contributes to the literature by decomposing perceived uncertainty to its dimensions and analyzing the influence of each one on triggering individuals’ herd behavior. Our findings show that unlike state uncertainty, only effect and response uncertainty are the triggers herd behavior.

Revisiting How Perceived Uncertainty and Herd Behavior Influence Technology Choice

Understanding how herd behavior occurs in the information systems context is important because such behavior influences many choice decisions, is the reason for some decision anomalies, and explains the reasons behind the rise or collapse of technology trends. Perceived uncertainty is a critical factor that triggers herding, but despite its influential role, prior research has not adequately investigated this broad concept. This research contributes to the literature by decomposing perceived uncertainty to its dimensions and analyzing the influence of each one on triggering individuals’ herd behavior. Our findings show that unlike state uncertainty, only effect and response uncertainty are the triggers herd behavior.

Neutron stars in Palatini $$R+\alpha R^2$$ and $$R+\alpha R^2+\beta Q$$ theories

We study solutions of the stellar structure equations for spherically symmetric objects in modified theories of gravity, where the Einstein-Hilbert Lagrangian is replaced by $$f(R)=R+\alpha R^2$$ f ( R ) = R + α R 2 and $$f(R,Q)=R+\alpha R^2+\beta Q$$ f ( R , Q ) = R + α R 2 + β Q , with R being the Ricci scalar curvature, $$Q=R_{\mu \nu }R^{\mu \nu }$$ Q = R μ ν R μ ν and $$R_{\mu \nu }$$ R μ ν the Ricci tensor. We work in the Palatini formalism, where the metric and the connection are assumed to be independent dynamical variables. We focus on stellar solutions in the mass-radius region associated to neutron stars. We illustrate the potential impact of the $$R^2$$ R 2 and Q terms by studying a range of viable values of $$\alpha $$ α and $$\beta $$ β . Similarly, we use different equations of state (SLy, FPS, HS(DD2) and HS(TMA)) as a simple way to account for the equation of state uncertainty. Our results show that for certain combinations of the $$\alpha $$ α and $$\beta $$ β parameters and equation of state, the effect of modifications of general relativity on the properties of stars is sizeable. Therefore, with increasing accuracy in the determination of the equation of state for neutron stars, astrophysical observations may serve as discriminators of modifications of General Relativity.

Model-Free Adaptive Control for Tank Truck Rollover Stabilization

Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled system. Next, the control objective of tank vehicle rollover stabilization is analyzed and the controlled variable is selected. Subsequently, differential braking and active front steering controller are designed by MFAC algorithm. Finally, the effectiveness of the designed controllers is verified by simulation, and difference between the controllers is analyzed. The controller designed by MFAC algorithm is proven to be adaptive to vehicle loading and driving states. The controlled system has great robustness.

Incorporating antenna detections into abundance estimates of fish

Autonomous passive integrated transponder (PIT) tag antennas are commonly used to detect fish marked with PIT tags but cannot detect unmarked fish, creating challenges for abundance estimation. Here we describe an approach to estimate abundance from paired physical capture and antenna detection data in closed and open mark-recapture models. Additionally, for open models, we develop an approach that incorporates uncertainty in fish size, because fish size changes through time (as fish grow bigger) but is unknown if fish are not physically captured (e.g., only detected on antennas). Incorporation of size uncertainty allows for estimation of size-specific abundances and demonstrates a generally useful method for obtaining state-specific abundances estimates under state uncertainty. Simulation studies comparing models with and without antenna detections illustrate that the benefit of our approach increases as a larger proportion of the population is marked. When applied to two field data sets, our approach to incorporating antenna detections reduced uncertainty in abundance substantially. We conclude that PIT antennas hold great potential for improving abundance estimation, despite the challenges they present.

Design and Optimal Control of a Multistable, Cooperative Microactuator

In order to satisfy the demand for the high functionality of future microdevices, research on new concepts for multistable microactuators with enlarged working ranges becomes increasingly important. A challenge for the design of such actuators lies in overcoming the mechanical connections of the moved object, which limit its deflection angle or traveling distance. Although numerous approaches have already been proposed to solve this issue, only a few have considered multiple asymptotically stable resting positions. In order to fill this gap, we present a microactuator that allows large vertical displacements of a freely moving permanent magnet on a millimeter-scale. Multiple stable equilibria are generated at predefined positions by superimposing permanent magnetic fields, thus removing the need for constant energy input. In order to achieve fast object movements with low solenoid currents, we apply a combination of piezoelectric and electromagnetic actuation, which work as cooperative manipulators. Optimal trajectory planning and flatness-based control ensure time- and energy-efficient motion while being able to compensate for disturbances. We demonstrate the advantage of the proposed actuator in terms of its expandability and show the effectiveness of the controller with regard to the initial state uncertainty.

Antibody Tests: They Are More Important Than We Thought

Antibody testing is a non-pharmaceutical intervention – not recognized so far in the literature – to prevent COVID-19 contagion. I show this in a simple economic model of an epidemic in which agents choose social activity under health state uncertainty. In the model, susceptible and asymptomatic agents are more socially active when they think they might be immune. And this increased activity escalates infections, deaths, and welfare losses. Antibody testing, however, prevents this escalation by revealing that those agents are not immune. Through this mechanism, I find that antibody testing prevents about 12% of COVID-19 related deaths within 12 months.

Using Machine Learning for Decreasing State Uncertainty in Planning

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.