A Multiclass Cumulative Prospect Theory-Based Stochastic User Equilibrium Model with Path Constraints in Degradable Transport Networks

The limited driving range and the unavailability or insufficiency of battery charging/swapping stations cause the so-called range anxiety issue for traffic assignment involving battery electric vehicle (BEV) users. In addition, expected utility theory-based stochastic user equilibrium (EUT-SUE) model generates the perfectly rational issue when the travellers make route choice decisions. To tackle these two problems, this article improves the cumulative prospect theory-based stochastic user equilibrium (CPT-SUE) model in a degradable transport network through incorporating the constraints of multiple user classes and distance limit. In this degradable network, the travellers experience stochastic travel times due to network link capacity degradations. For this improved CPT-SUE model, the equivalent variational inequality (VI) model and associated method of successive averages (MSA) based solution are provided. The improved CPT-SUE model is tested and compared with the EUT-SUE model with distance limit, with results showing that the improved CPT-SUE model can handle jointly the range anxiety issue and the perfectly rational issue

When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans

In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $100 or$130 with certainty. But in real world scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty--and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or$130 only 80% of the time, people tend to make the risk-averse choice--even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.Comment: ACM/IEEE International Conference on Human-Robot Interactio

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Dynamic traffic assignment: model classifications and recent advances in travel choice principles

Dynamic Traffic Assignment (DTA) has been studied for more than four decades and numerous reviews of this research area have been conducted. This review focuses on the travel choice principle and the classification of DTA models, and is supplementary to the existing reviews. The implications of the travel choice principle for the existence and uniqueness of DTA solutions are discussed, and the interrelation between the travel choice principle and the traffic flow component is explained using the nonlinear complementarity problem, the variational inequality problem, the mathematical programming problem, and the fixed point problem formulations. This paper also points out that all of the reviewed travel choice principles are extended from those used in static traffic assignment. There are also many classifications of DTA models, in which each classification addresses one aspect of DTA modeling. Finally, some future research directions are identified.postprin

Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C

Whole-economy scenarios for limiting global warming to 1.5C suggest that direct carbon emissions in the buildings sector should decrease to almost zero by 2050, but leave unanswered the question how this could be achieved by real-world policies. We take a modelling-based approach for simulating which policy measures could induce an almost-complete decarbonisation of residential heating, the by far largest source of direct emissions in residential buildings. Under which assumptions is it possible, and how long would it take? Policy effectiveness highly depends on behavioural decision- making by households, especially in a context of deep decarbonisation and rapid transformation. We therefore use the non-equilibrium bottom-up model FTT:Heat to simulate policies for a transition towards low-carbon heating in a context of inertia and bounded rationality, focusing on the uptake of heating technologies. Results indicate that the near-zero decarbonisation is achievable by 2050, but requires substantial policy efforts. Policy mixes are projected to be more effective and robust for driving the market of efficient low-carbon technologies, compared to the reliance on a carbon tax as the only policy instrument. In combination with subsidies for renewables, near-complete decarbonisation could be achieved with a residential carbon tax of 50-200Euro/tCO2. The policy-induced technology transition would increase average heating costs faced by households initially, but could also lead to cost reductions in most world regions in the medium term. Model projections illustrate the uncertainty that is attached to household behaviour for prematurely replacing heating systems

Laws and Limits of Econometrics

We start by discussing some general weaknesses and limitations of the econometric approach. A template from sociology is used to formulate six laws that characterize mainstream activities of econometrics and the scientific limits of those activities, we discuss some proximity theorems that quantify by means of explicit bounds how close we can get to the generating mechanism of the data and the optimal forecasts of next period observations using a finite number of observations. The magnitude of the bound depends on the characteristics of the model and the trajectory of the observed data. The results show that trends are more elusive to model than stationary processes in the sense that the proximity bounds are larger. By contrast, the bounds are of smaller order for models that are unidentified or nearly unidentified, so that lack or near lack of identification may not be as fatal to the use of a model in practice as some recent results on inference suggest, we look at one possible future of econometrics that involves the use of advanced econometric methods interactively by way of a web browser. With these methods users may access a suite of econometric methods and data sets online. They may also upload data to remote servers and by simple web browser selections initiate the implementation of advanced econometric software algorithms, returning the results online and by file and graphics downloads.Activities and limitations of econometrics, automated modeling, nearly unidentified models, nonstationarity, online econometrics, policy analysis, prediction, quantitative bounds, trends, unit roots, weak instruments

The disposition effect, dual process theory and emotion regulation

Research from the behavioural finance paradigm has detected bias in investors' decision making. One such bias, the disposition effect, shows that investors are reluctant to sell investments at a loss, yet are eager to sell investments at a gain. Investors vary in the extent to which they exhibit the disposition effect and research to date has found that an investor's level of sophistication and amount of experience can somewhat predict their susceptibility to this bias. Despite the disposition effect arising out of the nature of human psychology, few studies have empirically investigated psychological based explanations for susceptibility to this bias. I address this gap by applying two psychological theories to predict the susceptibility to the disposition effect: dual process theory and a model of the role of emotions and their regulation. The thesis contains two studies on the disposition effect of UK investors, a country where investors have not previously been researched for this bias. The first study involves using survival analysis to analyse the transactions made by 4,328 UK investors from July 2006 to December 2009. The second study is a subsample ofthe first, where 261 investors completed an online questionnaire to measure the psychological variables. I show that the average UK investor in this sample is susceptible to the disposition effect. contribute to existing knowledge about the disposition effect by showing that investor sophistication and experience attenuates, but does not eliminate, this bias. I extend knowledge on the disposition effect by showing that through the use of stop loss strategies, investors can inoculate against the disposition effect. In relation to the psychological variables, I find that investors who report higher levels of intuitive ability exhibit this bias to greater extent and investors who report a preference towards analytical cognition exhibit this bias to a lesser extent. Finally, the results tentatively show that investors who reappraise their emotions while investing, exhibit this bias to a lesser extent

Modelling Train Station Choice under Uncertainty for Park and Ride Users

This research develops a novel theoretical framework for modelling train station choice under uncertainty for park and ride users. Three uncertain factors, travel time to station, parking search time and crowding on trains, are modelled to estimate station choice probabilities, the risk attitudes of respondents and the preference heterogeneity of individuals. This study may support planning decisions on the location, price and capacity of P&R facilities, and provide evidence for evaluating P&R investment decisions