Bottleneck Model Research Articles

Psychopathy is associated with an exaggerated attention bottleneck: EEG and behavioral evidence from a dual-task paradigm

Psychopathy is a personality disorder associated with a chronic disregard for the welfare of others. The attention bottleneck model of psychopathy asserts that the behavior of individuals higher on psychopathy is due to an exaggerated attention bottleneck that constrains all information processing, regardless of the information's potential goal-relevance. To date, the majority of research on the attention bottleneck model of psychopathy conceptually applied the tenets of the model but did not implement methods that directly test an exaggeration of the bottleneck in psychopathy. Accordingly, the presence of an exaggerated bottleneck, the exact expression of that bottleneck, and its potential mechanistic relevance for behavior in individuals higher on psychopathy remains untested. To address these gaps, a sample of 78 male community members, evaluated for psychopathic traits using the Self-Report Psychopathy-III scale, completed an EEG-based dual-task paradigm examining short stimulus onset asynchrony (SOA; 300 ms), long SOA (1,100 ms), and single-task baseline conditions. Additionally, participants were asked about their frequency of real-world risky, impulsive, and antisocial behaviors. Psychopathy was associated with slower reaction times to second targets (T2s) presented during the dual-task conditions, relative to the baseline condition. Psychopathy also was associated with blunted P300 responses, a neural index of stimulus evaluation, across all types of T2 events. Finally, bottleneck-related interference during the short SOA events mediated the relationship between psychopathy and real-world behavior. These findings suggested that individuals higher on psychopathy exhibit an exaggerated bottleneck which produces intense and long-lasting interference, impacting all information processing and partially contributing to their maladaptive behavior.

Pricing strategy for logistics service platforms with competition and user distance preference

AbstractIn practice, the demanders (customers) and providers (sellers) of logistics service platforms not only care about prices but also are sensitive to the spatial distance between each other (because longer distance would lead to longer service waiting time). We study the impacts of spatial distance preference on two competing logistics platforms' pricing strategies. Two groups of agents (demanders and providers) are required to pay the logistics service platform a fixed (registration) fees to gain access. We analyze two commonly seen scenarios in practice: (i) the single‐homing model—both demanders and providers can only join one of the platforms; (ii) the competitive bottleneck model—demanders (providers) can join only one of the platforms while providers (demanders) are allowed to join both platforms. We find that under the single‐homing setting, an increase in demanders' (providers') distance preference would lead to higher but lower (lower but higher) prices charged to demanders and providers, respectively, and higher profits of both platforms. Under the competitive bottleneck model, however, the price charged to demanders (providers) might decrease (increase) in demanders' (providers') distance preference. Finally, compared to the case in which both sides single homing, we find that allowing providers or demanders to join both platforms would decrease the profits of both platforms if users' distance preference is sufficiently low or high.

Optimization Scheme of Fine Toll and Bus Departure Quantity for Bottleneck Congestion Management

This paper chooses car travel and bus travel as the research objects, establishes a dual-mode equilibrium model based on the bottleneck model, and compares the travel characteristics of the no-toll and fine-toll schemes. We find that the fine-toll scheme can eliminate the queuing time at the bottleneck, but it also increases the congestion risk cost of bus travel. In order to eliminate the queuing time at the bottleneck and reduce the congestion risk cost of bus travel without increasing the car travel cost and bus travel cost, we propose an optimization scheme of fine toll and bus departure quantity and analyze its travel characteristics theoretically. Through the numerical example, we calculate and analyze the equilibrium results of no-toll scheme, fine-toll scheme, and optimization scheme of fine toll and bus departure quantity. The results indicate that the optimization scheme of fine toll and bus departure quantity can help travelers to choose a reasonable travel mode and travel time to travel in the rush hour.

Commuter behavior under travel time uncertainty

We investigate the impact of random deviations in planned travel times using an extension of Vickrey’s celebrated bottleneck model. The model is motivated by the fact that in real life, users can neither exactly plan the time at which they depart from home, nor the delay they experience before they join a particular congestion bottleneck under investigation. We show that the strategy advocated by the Nash equilibrium in Vickrey’s model is not a user equilibrium in the model with travel time uncertainty. We then investigate the existence of a user equilibrium for the latter and show that in general such an equilibrium can neither be a pure Nash equilibrium, nor a mixed equilibrium with a continuous density. Our results imply that when random distortions influence user decisions, the dynamics of deterministic bottleneck models are inadequate to describe this more complex situation. We illustrate with numerical analysis how the mechanics of a bottleneck with delayed arrivals are unstable for any continuous strategy, thus shedding more light on the non-existence result. Alternatively, we formulate a logit model for the day-to-day dynamic adjustment in response to previous travel experiences. We further investigate the conditions for convergence of the dynamic choice model and numerically illustrate the resulting equilibrium strategy.

Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics

AbstractModeling and simulations are a pillar in the development of complex technical systems. However, for time‐critical applications a conduction of high‐fidelity simulations is not always feasible. To mitigate this computational bottleneck model order reduction (MOR) can be applied. For nonlinear models, linear MOR approaches are only practicable to a limited extend. Nonlinear approaches, on the contrary, often require deep interventions in the used simulation code. If access is not possible, non‐intrusive nonlinear model order reduction can be the key to success. The goal of this work is to implement a non‐intrusive approach using classical model order reduction along with machine learning algorithms. It relies on the idea to learn the dynamics in the reduced space. The approach aims to learn the temporal evolution of the reduced dynamics of a system. By applying the method to a problem arising from the field of structural dynamics, accurate surrogate models are received. They can speed up the simulation time significantly, while still providing high‐quality state approximations.

Parking management in the morning commute problem with ridesharing

This paper extends the existing bottleneck model with ridesharing by considering commuters' parking competition. Each driver prefers to park closer and walk shorter to the workplace. We present a new equilibrium model by involving ridesharing and parking behaviour. The equilibrium solution implies that the parking density and parking price can significantly influence commuters’ ridesharing choices and arrival times. The optimal parking density is presented to enhance the parking supply. A parking fee discount for ridesharing (PDR) scheme is proposed to encourage ridesharing and hence mitigate congestion. The optimal PDR scheme can save 50% of the system cost at most.

A reinforcement learning-based dynamic congestion pricing method for the morning commute problems

A reinforcement learning-based dynamic congestion pricing method for morning commute problems is proposed. In this method, tolls are iteratively updated day by day based on observable information such as traffic volume. The advantage of the method is two fold. First, the method does not require travelers personal preferences such as value of time. Second, the method does not require manually designed toll update scheme; instead, the method automatically find the most efficient toll by a data-driven manner. Results of numerical experiments show that the proposed method properly decreased traffic congestion in various types of networks. Especially, in the experiment with a single bottleneck model, total waiting time decreases to nearly zero in about 200 days. One of future issues is more stable coordination among the time zones.

The impact of road pricing on travel time variability under elastic demand

This paper analyzes the impact of road pricing for the bottleneck model with endogenous random delay. We extend the widely used scheduling model by integrating trip scheduling, endogenous traffic congestion, travel time variability (TTV), and road pricing into one modeling framework to analyze the role of road pricing in addressing TTV. This paper investigates the departure profiles and examines the social surplus under TTV and derives analytical results to illustrate properties of the model. It demonstrates the impact of road pricing and TTV on the departure profile of travelers and the system performance. For travelers, the presence of TTV leads to a smooth departure profile, hence a reduction in queuing cost. As for the system, TTV reduces the social surplus. This study shows that road pricing can be used to compensate or mitigate the social surplus loss due to TTV.

Future of TCP on Wi-Fi 6

The Linux TCP/IP stack contributions have recently pointed all in the same direction: maximize the available throughput while maintaining low latency. These activities started by mitigating the buffebloat phenomenon at the network bottleneck as much as possible. So far, the deployed solutions have been designed by considering standard models for the bottleneck that could be either wireless or wired. The introduction of Wi-Fi 6 is dislocating the bottleneck of standard home and office WLAN from the radio access point to the 1 Gbps interface, the wired interface that points to the internet service provider; this bottleneck migration leads to a new real-case bottleneck model, which is hybrid. Such an environment embraces new technologies and provides new challenges for the old TCP protocol when applied to hybrid bottlenecks and operating in conjunction with the TCP side-modules, which are now part of the novel Linux kernels. This paper aims to highlight the TCP performance considering the new TCP modules and novel scenarios opened by Wi-Fi 6 with real-case hybrid bottlenecks.

The impact of autonomous vehicles on commute ridesharing with uncertain work end time

The uncertainty in work end time can prevent ridesharing between two commuters with identical home and work locations. This effect can be alleviated by autonomous vehicles (AV): when two commuters share a ride from home to work, if the realized work end times result in a long wait between the two, an additional AV can be easily repositioned from home to work, whereas with regular human-driven vehicles one has to either bear the long wait or request expensive taxi service. This observation implies that AVs have great potential for increasing commute ridesharing. To study this effect, we develop evening commute bottleneck models with uncertain work end time and establish mode choice equilibrium for the round-trip commute. We find that, with regular vehicles, because of the expected waiting cost caused by work end time uncertainty, commuters typically do not share rides even though they have the same home and work locations. With AVs, commuters always share rides for the morning commute, and they also share rides for the evening commute if their work end times turn out to be close; otherwise, they reposition an additional AV to pick one of them up. Our numerical examples show that AVs can encourage commute ridesharing and significantly reduce the expected round-trip travel costs for commuters.

An activity-based bottleneck model with stochastic capacity

This study investigated commuters’ departure time choice with stochastic capacity. It has typically been solved by using Vickrey’s bottleneck model which doesn’t take the impact of commuters’ activity on decision-making into account. We added activity utility parameters to Vickrey’s bottleneck model. Travel costs became negative utility. The model considered home-activity utility before work and work-activity utility in the morning rush. Simultaneously, the capacity of the bottleneck changed randomly between a designed value (good condition) and a degraded one(bad condition). We derived six reasonable equilibrium patterns by maximizing the commuters’ activity utility and closed form solutions to all possible equilibrium patterns. The departure rate curve for each equilibrium pattern was made within a reasonable parameter range.

The Conditional Entropy Bottleneck.

Much of the field of Machine Learning exhibits a prominent set of failure modes, including vulnerability to adversarial examples, poor out-of-distribution (OoD) detection, miscalibration, and willingness to memorize random labelings of datasets. We characterize these as failures of robust generalization, which extends the traditional measure of generalization as accuracy or related metrics on a held-out set. We hypothesize that these failures to robustly generalize are due to the learning systems retaining too much information about the training data. To test this hypothesis, we propose the Minimum Necessary Information (MNI) criterion for evaluating the quality of a model. In order to train models that perform well with respect to the MNI criterion, we present a new objective function, the Conditional Entropy Bottleneck (CEB), which is closely related to the Information Bottleneck (IB). We experimentally test our hypothesis by comparing the performance of CEB models with deterministic models and Variational Information Bottleneck (VIB) models on a variety of different datasets and robustness challenges. We find strong empirical evidence supporting our hypothesis that MNI models improve on these problems of robust generalization.

Fifty years of the bottleneck model: A bibliometric review and future research directions

The bottleneck model introduced by Vickrey in 1969 has been recognized as a benchmark representation of the peak-period traffic congestion due to its ability to capture the essence of congestion dynamics in a simple and tractable way. This paper aims to provide a 50th anniversary review of the bottleneck model research since its inception. A bibliometric analysis approach is adopted for identifying the distribution of all journal publications, influential papers, top contributing authors, and leading topics in the past half century. The literature is classified according to recurring themes into travel behavior analysis, demand-side strategies, supply-side strategies, and joint strategies of demand and supply sides. For each theme, typical extended models developed to date are surveyed. Some potential directions for further studies are discussed.

Travel cost budget based user equilibrium in a bottleneck model with stochastic capacity

This paper studies a bottleneck model in which the capacity of the bottleneck is constant within a day but changes stochastically from day-to-day between a designed value (good condition) and a degraded one (bad condition). The study relates the travel cost variability due to stochastic capacity with commuters' departure time choice behaviors. We postulate that commuters acquire the variability of travel cost based on past experiences and factor such variability into their departure time choice consideration by minimizing their travel cost budget (TCB), defined as a weighted average of mean travel cost and standard deviation of travel cost. We show that the consideration of TCB yields seven possible equilibrium patterns. Closed form solutions to all possible equilibrium patterns and their corresponding parameter ranges are derived. The rationality of the patterns has been investigated. Dependence of travel cost and the duration of peak hours on the commuters' risk attitude has also been derived in each equilibrium pattern. Finally, numerical studies have been conducted to illustrate the properties.

The two-mode problem with bottleneck queuing and transit crowding: How should congestion be priced using tolls and fares?

This paper proposes a bi-modal bottleneck model with time-varying scheduling preferences for investigating the two-mode problem with bottleneck queuing and transit crowding. In the proposed model, the activity scheduling utilities of commuters at home and at work vary by the time of day, and two kinds of congestion externalities (i.e., traffic congestion among vehicles at the bottleneck and in-bus passenger crowding discomfort) are considered. Commuters choose their travel modes and departure times from home to work during the morning commute to minimize their own generalized travel cost. The properties of the proposed bi-modal bottleneck model are analytically explored. Particularly, the relationship between the departure intervals of auto and bus commuters is revealed. The time-varying charging schemes and step charging schemes for reducing the congestion externalities are also investigated. A critical condition for passenger shift from one mode to the other mode with congestion charging schemes is identified, and the effects of congestion charging schemes with and without internalizing the bus-related congestion externalities (including the bottleneck congestion externality induced by auto and bus interaction and the in-bus passenger crowding externality) on the bottleneck system are compared.

On the commute travel pattern with compressed work schedule

Abstract Compressed work schedule (CW) is one of the widely known flexible work hours arrangements (FWAs). In the literature, it has been clearly demonstrated that CW can significantly reduce commuting trips per pay period and thereby relieve peak hour congestion. Without policy intervention, how many firms will choose CW? Is it socially beneficial to encourage all commuters to switch to CW? This paper mainly addresses these questions. A bi-level economic model encapsulating employers’ and employees’ choices on their work schedule is then developed. Firstly, the short-run daily and periodic commute patterns of employees are investigated in a modified activity-based bottleneck model. On top of that, the effects of CW on firms’ productivity are incorporated into our long-run analysis. At long-run equilibrium, no employers and employees can benefit by unilaterally switching their schedule type or changing their schedule design. Multiple equilibria are found with the different number of CW users, depending on the agglomeration rate of productivity and the initial status. The social optimums to maximize total net utility are also determined. At a low rate of agglomeration, it is socially beneficial to introduce CW and the system can achieve the social optimum naturally at long-run equilibrium with positive CW users, under mild assumptions. However, when the agglomeration rate is high, the social optimum is no longer achieved, and workers would stick to the normal schedule at equilibrium if the initial case without CW is considered. This can partially explain why CW is not commonly adopted in practice.

Kinetics of escape of ssDNA molecules from α-hemolysin nanopores: a dynamic disorder study

Experimental studies of the escape events of single-stranded DNA (ssDNA) molecules from the membrane embedded α-hemolysin nanopores exhibited the non-exponential kinetics. The data for homopolymers of adenine (dA27) and cytosine (dC27) revealed the profound influence of the sequence dependent DNA–pore interactions and the applied potentials on the escape kinetics. The non-exponential behavior is a signature of the presence of disorder in biochemical reactions. Here, we rationalize the experimental results with a dynamic disorder approach based on Zwanzig’s fluctuating bottleneck (FB) model as a general mechanism where the rate of the reaction is controlled by the passage of a molecule through the cross-sectional area of the vestibule of the α-hemolysin channel (the bottleneck). The stochastic fluctuations of the radius of the latter is represented by the fluctuations of the intersegment distance of a Rouse chain, the dynamics of which is described using a non-Markovian generalized Langevin equation with a memory kernel and Gaussian colored noise. Our analytical results essentially capture the effects of underlying energetics due to the sequence-dependent nature and applied potentials in reproducing the measured survival probabilities for dA27 and dC27 in an excellent way, thereby validating the application of a simple coarse-grained theory to address the thermal escape of biopolymers from bionanopores.

Parallel and serial task processing in the PRP paradigm: a drift\u2013diffusion model approach

Even after a long time of research on dual-tasking, the question whether the two tasks are always processed serially (response selection bottleneck models, RSB) or also in parallel (capacity-sharing models) is still going on. The first models postulate that the central processing stages of two tasks cannot overlap, producing a central processing bottleneck in Task 2. The second class of models posits that cognitive resources are shared between the central processing stages of two tasks, allowing for parallel processing. In a series of three experiments, we aimed at inducing parallel vs. serial processing by manipulating the relative frequency of short vs. long SOAs (Experiments 1 and 2) and including no-go trials in Task 2 (Experiment 3). Beyond the conventional response time (RT) analyses, we employed drift–diffusion model analyses to differentiate between parallel and serial processing. Even though our findings were rather consistent across the three experiments, they neither support unambiguously the assumptions derived from the RSB model nor those derived from capacity-sharing models. SOA frequency might lead to an adaptation to frequent time patterns. Overall, our diffusion model results and mean RTs seem to be better explained by participant’s time expectancies.

Mechanical Unfolding of Single Polyubiquitin Molecules Reveals Evidence of Dynamic Disorder.

Mechanical unfolding of single polyubiquitin molecules subjected to a constant stretching force showed nonexponentiality in the measured probability density of unfolding (waiting time distribution) and the survival probability of the folded state during the course of the measurements. These observations explored the relevance of disorder present in the system under study with implications for a static disorder approach to rationalize the experimental results. Here, an approach for dynamic disorder is presented based on Zwanzig’s fluctuating bottleneck (FB) model, in which the rate of the reaction is controlled by the passage through the cross-sectional area of the bottleneck. The radius of the latter undergoes stochastic fluctuations that in turn is described in terms of the end-to-end distance fluctuations of the Rouse-like dynamics using a non-Markovian generalized Langevin equation with a memory kernel and Gaussian colored noise. Our results are comprised of analytical expressions for the survival probability and waiting time distribution, which show excellent agreement with the experimental data throughout the range of the applied forces. In addition, by fitting the survival probabilities at different stretching forces, we quantify two system parameters, namely, the average free energy ΔGav and the average distance to the transition state Δxav, both perfectly recovered the experimental estimates. These agreements validate the present model of polymer dynamics, which captures the very essence of dynamic disorder in single-molecule pulling experiments.

Item-specific proportion congruency (ISPC) modulates, but does not generate, the backward crosstalk effect.

When both tasks in a psychological refractory period (PRP) paradigm have compatible manual responses, a compatibility benefit in RT can often be observed on Task1 performance, in apparent violation of a strict traditional response selection bottleneck model. This compatibility-based backward crosstalk effect (BCE) has been generally attributed to automatic activation of Task2 response information, in parallel with attended Task1 performance. This paper tests a potential alternative mechanism of the BCE. Item-specific proportion congruency (ISPC) effects are previously well demonstrated, where learning of associations between stimuli and task conflict (e.g., that particular Stroop items are typically incongruent) allows rapidly and automatically elicited control adjustments in performance. Similar proportion manipulations have recently been shown to modulate the BCE in dual-task performance. If participants could similarly learn associations between particular pairs of stimuli and resulting response conflict in a PRP task, this kind of mechanism could produce relative speeding versus slowing of Task1 RT on response compatible versus incompatible trials. This pattern of data directly describes the BCE, and represents a potential alternative mechanism that does not require any response crosstalk, and would reinforce a stricter view of the response selection bottleneck model, if true. Over two experiments, we demonstrate that while the BCE is sensitive to ISPC-like effects based on Task1 conflict contingencies, the BCE is insensitive to relationships between particular pairs of stimuli and associated conflict. While ISPC effects can modulate the BCE, they do not generate the BCE. These findings reinforce the current Task2 parallel response activation account of the BCE.