Influence of pricing on mode choice decision in Jakarta: A random regret minimization model
نویسندگان
چکیده
منابع مشابه
A New Model of Random Regret Minimization
A new choice model is derived, rooted in the framework of Random Regret Minimization (RRM). The proposed model postulates that when choosing, people anticipate and aim to minimize regret. Whereas previous regret-based discrete choice-models assume that regret is experienced with respect to only the best of foregone alternatives, the proposed model assumes that regret is potentially experienced ...
متن کاملThe application of the random regret minimization model to drivers’ choice of crash avoidance maneuvers
This study explores the plausibility of regret minimization as behavioral paradigm underlying the choice of crash avoidance maneuvers. Alternatively to previous studies that considered utility maximization, this study applies the random regret minimization (RRM) model while assuming that drivers seek to minimize their anticipated regret from their corrective actions. The model accounts for driv...
متن کاملRegret Minimization Algorithms for Pricing Lookback Options
In this work, we extend the applicability of regret minimization to pricing financial instruments, following the work of [10]. More specifically, we consider pricing a type of exotic option called a fixed-strike lookback call option. A fixed-strike lookback call option has a known expiration time, at which the option holder has the right to receive the difference between the maximal price of a ...
متن کاملPricing Exotic Derivatives Using Regret Minimization
We price various financial instruments, which are classified as exotic options, using the regret bounds of an online algorithm. In addition, we derive a general result, which upper bounds the price of any derivative whose payoff is a convex function of the final asset price. The market model used is adversarial, making our price bounds robust. Our results extend the work of [9], which used regr...
متن کاملRegret Minimization in Nonstationary Markov Decision Processes
We consider decision-making problems in Markov decision processes where both the rewards and the transition probabilities vary in an arbitrary (e.g., nonstationary) fashion to some extent. We propose online learning algorithms and provide guarantees on their performance evaluated in retrospect against stationary policies. Unlike previous works, the guarantees depend critically on the variabilit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Case Studies on Transport Policy
سال: 2019
ISSN: 2213-624X
DOI: 10.1016/j.cstp.2018.12.002