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Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

Multi-criteria methods represent an adequate tool for solving complex decision problems that provide real support to the decision maker in the choice process. This paper analyzes a decision problem that recurs over time using one of the newer methods as the Parsimonious AHP.

In this paper we integrated the P-AHP with: (1) the weighted average which takes into account the objectivity of the data; (2) ordered weighted average (OWA) aggregation operators that address the subjective nature of the data; (3) the Choquet integral and (4) the Sugeno integral which also considers the uncertain nature of the final ranking as it is defined on a fuzzy measure.

The present paper proves that variations in the final ranking, due to the different mathematical properties of the selected aggregators, are fundamental to select the best alternative without neglecting any characteristic of the input data. In fact, it is discussed and underlined how and why the best alternative is one that never excels but has very good positions with respect to all aggregation operator rankings.

The aim and innovation presented in this work is the use of the Parsimonious AHP (P-AHP) method in a dynamic way with the use of different aggregation techniques.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

Serious games (SGs) are virtual systems that allow the reconstruction of the laws governing the behavior of complex adaptive systems such as urban transportation and social interaction. Unlike stated preference-based studies, improved visualization, feedback, and scores mediate players’ learning through experience. SG’s potential to understand users’ preferences regarding shared automated vehicles (SAVs) is developed. The investigation focused on three innovative, entirely automated commuting options: shared rides, shared cars, and automated transit. The research involved 10 participants actively involved in a competitive mode selection exercise, which emulated 50 workdays and was conducted in 10 separate sessions. The players aimed to maximize their overall score influenced by their mode choice, punctuality, and the other players’ choices. SG-obtained data was used to estimate a game-based discrete choice model. The sustainability policy implications of game-based methods on the future adoption of SAVs and impacts on other modes are further discussed.

This paper aims to explore the outcomes of an analysis on day by day task – journey planning conduct of senior citizens by using a modern dynamic model and a family unit travel overview, gathered in Bhubaneswar, Odisha, of India in 2018. The task-journey planning display assumes an unique time–space-constrained planning development.

The main commitment of this paper is to reveal day by day task – journey planning conduct through a comprehensive dynamic framework. Numerous behavioural subtleties are revealed by the subsequent empirical model. These incorporate the role that income plays in directing outside time consumption decisions of senior citizens. Senior citizens in the most elevated and least salary classes will in general have minor varieties in time consumption decisions than those in middle pay classifications. Generally speaking, the time consumption decisions become progressively steady with expanding age, demonstrating that more task durations and lower task recurrence become progressively predominant with increasing age.

Day by day task-type and area decisions reveal a reasonable irregular utility-amplifying level headed conduct of senior residents. Unmistakably expanding spatial availability to different task areas is an urgent factor in characterizing every day outside task interest of senior residents. It is likewise evident that the assorted variety of outside task-type decisions decreases with rise in age and senior citizens are major touchy to auto journey hour than to travel or non-mechanized journey hour.

The fundamental constraint to the dynamic structure is that the mode decision model was viewed as exogenic to the demonstrating framework. The essential purpose behind this supposition that was that senior citizens in the Bhubaneswar are overwhelmingly customers of the local car. Coordination of the mode decision display part inside this structure would deliver a full task-based journey request model that could catch trip age, starting times, outing circulation and mode decision using a solitary demonstrating framework.

The purpose of this study is to investigate the influence of ambidextrous strategy on supply chain resilience and its impact on firm performance, employing the Dynamic Capabilities View.

Through a survey of 215 supply chain professionals, the research employs a structural equation modeling analysis to examine the relationships between ambidexterity, agile operations, resilience, and performance.

The findings demonstrate that the ambidextrous strategy significantly enhances both agile operations and supply chain resilience. In turn, agile operations and resilience positively impact firm performance. The study also reveals that agile operations and supply chain resilience partially mediate the relationship between ambidextrous strategy and firm performance.

This research contributes to the supply chain management literature by highlighting the importance of an ambidextrous approach in fostering agile operations and resilience, thereby improving firm performance. It extends the dynamic capabilities view framework by elucidating how ambidexterity acts as a pivotal mechanism for adapting to disruptions and securing competitive advantage in volatile markets. Finally, measurements of ambidextrous strategy and resilience are provided to further enhance practitioners’ understanding of building these important components in networks.

The paper aims to reveal the attitude of the Russian competition authorities towards cross-border mergers involving foreign buyers. The study addresses the following question: Is the probability of Russian competition authorities' intervention significantly different when a foreign buyer takes part in the merger? This is the key test to reveal whether competition authorities gravitate towards “economic nationalism” or “promotion of foreign investments”.

The discrete choice model is applied to the dataset of 7,607 merger cases investigated by the Russian competition authorities between 2012 and 2017. The probability of competition authorities' intervention, such as merger correction by using remedies or deal rejection, is used as a measure of special attention.

The study finds out favoritism patterns of the regulator with regard to foreign companies. In particular, the deals involving a foreign buyer had less chance of intervention, i.e. imposition of remedies, from national competition authorities. The sanctions period does not moderate the probability of approval of a cross-border merger with foreign buyers by the Russian competition authorities.

The paper contributes to merger control literature by addressing the political economy issues. It discovers that, besides regulation by the law, there are hidden motives, such as protectionism or favoritism of foreign companies, which could drive the regulator's decision. Therefore, the studies of cross-border mergers provide an opportunity to investigate the political issues of merger control through the identification of a special attitude to foreign companies and analysis of regularities that might explain such a policy.