Bounding the Inefficiency of the Multiclass, Multicriteria C-Logit Stochastic User Equilibrium in a Transportation Network

We derive the exact inefficiency upper bounds of the multiclass C-Logit stochastic user equilibrium (CL-SUE) in a transportation network. All travelers are classified on the basis of different values of time (VOT) into M classes. The multiclass CL-SUE model gives a more realistic path choice probability in comparison with the logit-based stochastic user equilibrium model by considering the overlapping effects between paths. To find efficiency loss upper bounds of the multiclass CL-SUE, two equivalent variational inequalities for the multiclass CL-SUE model, i.e., time-based variational inequality (VI) and monetary-based VI, are formulated. We give four different methods to define the inefficiency of the multiclass CL-SUE, i.e., to compare multiclass CL-SUE with multiclass system optimum, or to compare multiclass CL-SUE with multiclass C-Logit stochastic system optimum (CL-SSO), under the time-based criterion and the monetary-based criterion, respectively. We further investigate the effects of various parameters which include the degree of path overlapping (the commonality factor), the network complexity, degree of traffic congestion, the VOT of user classes, the network familiarity, and the total demand on the inefficiency bounds.

Global, Low Amplitude Cortical State Predicts Response Outcomes in a Selective Detection Task

Spontaneous neuronal activity strongly impacts stimulus encoding and behavioral responses. We sought to determine the effects of neocortical prestimulus activity on stimulus detection. We trained mice in a selective whisker detection task, in which they learned to respond (lick) to target stimuli in one whisker field and ignore distractor stimuli in the contralateral whisker field. During expert task performance, we used widefield Ca2+ imaging to assess prestimulus and post-stimulus neuronal activity broadly across frontal and parietal cortices. We found that lower prestimulus activity correlated with enhanced stimulus detection: lower prestimulus activity predicted response versus no response outcomes and faster reaction times. The activity predictive of trial outcome was distributed through dorsal neocortex, rather than being restricted to whisker or licking regions. Using principal component analysis, we demonstrate that response trials are associated with a distinct and less variable prestimulus neuronal subspace. For single units, prestimulus choice probability was weak yet distributed broadly, with lower than chance choice probability correlating with stronger sensory and motor encoding. These findings support a low amplitude, low variability, optimal prestimulus cortical state for stimulus detection that presents globally and predicts response outcomes for both target and distractor stimuli.

Plea Bargaining: A Test of Dual Discounting Preferences for Non-Monetary Losses

Judgment and decision-making research on discounting suggests that when humans are thinking about gains, they tend to prefer certain and immediate outcomes to uncertain and delayed outcomes. However, discounting has been studied primarily using monetary commodities and, until recently, by testing one feature of the binary forced-choice task at a time: delay, probability, or amount of money received/lost. The present research is the first test of a dual discounting task that combines probability and delay into a single, binary forced-choice task in a non-monetary loss context. The key findings, based on three studies, suggest that delay and probability discounting play a significant role in decisions including non-monetary loss commodities like plea bargaining. Future work should explore the boundary conditions of dual discounting based not only on the nature of the binary choice (probability and delay) but also on the nature of the commodity (amount, valence, and quantifiability).

Influence of Electronic-Docking Stations on China’s Dockless Bikesharing Programs: Evidence from Beijing

Electronic-docking stations have been proposed and tested by some Chinese cities to address the issue of disorderly parking of dockless bikesharing programs. We study the influence of electronic-docking stations on dockless bikesharing programs, and measure and explain riders’ preferences toward dockless bikesharing and electronic-docking stations. These research questions are addressed by a mixed logit model and regression models based on stated preference survey data collected in Beijing. We obtain some interesting empirical results which offer some guidance for governments and operators to facilitate the sustainable development of dockless bikesharing programs. For instance, we find that riders are sensitive to the density for electronic-docking stations, but somewhat indifferent to the rental fee of 1–2 yuan for each ride; the choice probability of riding a dockless shared bike drops by about 8–9% when access or egress time increases by 5 min; the fraction of riders interested in dockless bikesharing programs is close to 100%, while launching electronic-docking stations reduces this number to 88%; riders are willing to pay about 1.0–1.1 yuan to save access or egress time by 5 min; riders who live or work away from public transit stations or who often engage in physical exercises are interested in dockless bikesharing programs; and riders indifferent to the introduction of electronic-docking stations mainly include those who are above 26 years old and those with monthly income less than or equal to 8000 yuan.

Alternative Incentive Policies against Purchase Subsidy Decrease for Battery Electric Vehicle (BEV) Adoption

The purchase subsidy policy gives powerful support in battery electric vehicles’ (BEVs) market penetration. However, the purchase subsidy is also a huge financial burden for the government, so it can only be considered as a transitional measure and will be canceled gradually. This paper aims to investigate the impact of purchase subsidy phase-out on BEV adoptions and explore alternative incentive policies to continue stimulating BEV adoptions. A stated preference (SP) survey is conducted in Beijing, and a binary logit (BL) model is established to describe how various factors affect BEV adoption preferences. In addition to the factors related to vehicle techniques, the policies of license plate restrictions and driving restrictions are focused due to Beijing’s unique external policy environment. The vehicle use subsidy and bus line driving permit are tested as alternative incentive policies against the purchase subsidy decrease. The results show that incentive policies can significantly influence BEV adoption intentions. If the purchase subsidy policy is canceled in Beijing, the BEV choice probability will be reduced from 45.94% to 16.62%. In this case, the vehicle use subsidy needs to be set at the level of 4966 CNY/year (714.3 USD/year) to maintain the original BEV choice probability.

One-shot learning and behavioral eligibility traces in sequential decision making

In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here, we show one-shot learning of sequences. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without eligibility trace make qualitatively distinct predictions, we find direct behavioral (choice probability) and physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across multiple sensory modalities.