Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

2020 ◽  
Author(s):  
Zhong-Zhong Jiang ◽  
Guangwen Kong ◽  
Yinghao Zhang
Keyword(s):  
System Performance ◽  
Financial Incentives ◽  
Laboratory Experiments ◽  
Policy Design ◽  
Working Hours ◽  
Problem Definition ◽  
Analytical Models ◽  
Behavioral Biases ◽  
On Demand ◽  
Regret Aversion

Problem definition: We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. Although these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers’ relocation decisions and ultimately the system performance. Academic/practical relevance: Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers’ relocation decisions from a behavioral perspective. A deeper understanding of workers’ behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. Methodology: We use a combination of behavioral modeling and controlled laboratory experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled laboratory experiments. Results: We find that regret aversion plays an important role in workers’ relocation decisions. Regret-averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system. Managerial implications: Our research emphasizes the importance and necessity of incorporating workers’ behavioral biases such as regret aversion into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers’ decision may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.

On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

scholarly journals Analisis dan Perancangan Sistem Karcis Elektronik Pada Gerbang Masuk Busway Dengan Menggunakan RFID

2010 ◽  
Vol 1 (2) ◽  
pp. 942
Author(s):  
Suryadiputra Liawatimena
Keyword(s):  
Response Time ◽  
Data Security ◽  
System Performance ◽  
Research Laboratory ◽  
Laboratory Experiments ◽  
Field Survey ◽  
The Internet ◽  
Literature Study ◽  
Online Systems ◽  
Minimum System

The aim of this study is to use Radio Frequency technology to facilitate human activities, especially used in Busway entrances. In this research methodologies used include field survey to the BP Transjakarta; literature study by reading manuals, text books, journals, and articles on the Internet, and conduct laboratory experiments on the Bina Nusantara University Hardware Research Laboratory in designing and making the minimum system . Based on the results of an experiment and taking data on the minimum system, it can be concluded in general the performance of the system is running well, but the response time was not optimal. Some improvements to the system needed to improve system performance, such as raising response time, improved data security, and online systems. 

Contextual Complications in Analytical Modeling: When the Problem is Not the Problem

2021 ◽  
Author(s):  
Michael F. Gorman
Keyword(s):  
Contextual Factors ◽  
Careful Consideration ◽  
Risk Tolerance ◽  
Problem Definition ◽  
Data Availability ◽  
Analytical Models ◽  
Individual Risk ◽  
Future Research ◽  
Solution Approach ◽  
Complicating Factors

In this essay, I describe 10 critical complicating factors that directly affect the six basic modeling components of problem definition, assumptions, decision variables, objective functions, constraints, and solution approach. The proposed 10 contextual complicating factors are (1) organization, (2) decision-making processes, (3) measures and key performance indicators, (4) rational and irrational biases, (5) decision horizon and interval, (6) data availability, accuracy, fidelity, and latency, (7) legacy and other computer systems, (8) organizational and individual risk tolerance, (9) clarity of model and method, and (10) implementability and sustainability of the approach. I hypothesize that the core analytical problem cannot be adequately described or usefully solved without careful consideration of these factors. I describe detailed examples of these contextual factors’ effects on modeling from six published applied prescriptive analytics projects and provide other examples from the literature. The complicating factors are pervasive in these projects, directly and dramatically affecting basic modeling components over half the time. Further, in the presence of these factors, 23 statistically significant correlations tend to form in three clusters, which I characterize as culture, decision, and project clusters. Unrecognized, these factors would have hampered the implementation and ongoing use of these analytical models; in a sense, the models themselves were wrong, absent consideration of these contextual considerations. With these insights, I hope to help practitioners identify the effects of these common complications and avoid project failure by incorporating these contextual factors into their modeling considerations. Future research could seek to better understand these factors and their effects on modeling.

Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

2020 ◽  
Author(s):  
Lai Wei ◽  
Roman Kapuscinski ◽  
Stefanus Jasin
Keyword(s):  
Economies Of Scale ◽  
Optimal Solution ◽  
Problem Definition ◽  
Shipment Consolidation ◽  
On Demand ◽  
Numerical Tests ◽  
Optimal Policies ◽  
Shipping Costs ◽  
Multiple Orders ◽  
Managerial Implications

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

scholarly journals INFLATION EXPECTATIONS AND MONETARY POLICY DESIGN: EVIDENCE FROM THE LABORATORY

2016 ◽  
Vol 22 (4) ◽  
pp. 1035-1075 ◽  
Author(s):  
Damjan Pfajfar ◽  
Blaž Žakelj
Keyword(s):  
Monetary Policy ◽  
Laboratory Experiments ◽  
Policy Design ◽  
New Keynesian ◽  
Inflation Expectations ◽  
Central Question ◽  
Policy Regime ◽  
Expectations Formation ◽  
Reaction Coefficient ◽  
Inflation Variability

Using laboratory experiments within a New Keynesian framework, we explore the interaction between the formation of inflation expectations and monetary policy design. The central question in this paper is how to design monetary policy when expectations formation is not perfectly rational. Instrumental rules that use actual rather than forecasted inflation produce lower inflation variability and reduce expectational cycles. A forward-looking Taylor rule where a reaction coefficient equals 4 produces lower inflation variability than rules with reaction coefficients of 1.5 and 1.35. Inflation variability produced with the latter two rules is not significantly different. Moreover, the forecasting rules chosen by subjects appear to vary systematically with the policy regime, with destabilizing mechanisms chosen more often when inflation control is weaker.

scholarly journals Changes in the quantity and quality of time use during the COVID-19 lockdowns in the UK: Who is the most affected?

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258917
Author(s):  
Ines Lee ◽  
Eileen Tipoe
Keyword(s):  
Young Children ◽  
Time Use ◽  
Policy Design ◽  
Leisure Activities ◽  
Working Hours ◽  
Diary Data ◽  
The Third ◽  
The Uk

We investigated changes in the quantity and quality of time spent on various activities in response to the COVID-19-induced national lockdowns in the UK. We examined effects both in the first national lockdown (May 2020) and the third national lockdown (March 2021). Using retrospective longitudinal time-use diary data collected from a demographically diverse sample of over 760 UK adults in both lockdowns, we found significant changes in both the quantity and quality of time spent on broad activity categories (employment, housework, leisure). Individuals spent less time on employment-related activities (in addition to a reduction in time spent commuting) and more time on housework. These effects were concentrated on individuals with young children. Individuals also spent more time doing leisure activities (e.g. hobbies) alone and conducting employment-related activities outside normal working hours, changes that were significantly correlated with decreases in overall enjoyment. Changes in quality exacerbated existing inequalities in quantity of time use, with parents of young children being disproportionately affected. These findings indicate that quality of time use is another important consideration for policy design and evaluation.

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