scholarly journals Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy

2019 ◽  
Author(s):  
Pengzhan Zhou ◽  
Xin Wei ◽  
Cong Wang ◽  
Yuanyuan Yang
Keyword(s):  
Resource Allocation ◽  
General Problem ◽  
Incentive Mechanism ◽  
Sharing Economy ◽  
Time Saving ◽  
Online Implementation ◽  
Cost Distribution ◽  
The Cost ◽  
Special Case ◽  
Budget Feasible

Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users' cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users' cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.

scholarly journals Budget Feasible Mechanisms on Matroids

Algorithmica ◽  
2020 ◽  
Author(s):  
Stefano Leonardi ◽  
Gianpiero Monaco ◽  
Piotr Sankowski ◽  
Qiang Zhang
Keyword(s):  
Polynomial Time ◽  
Private Information ◽  
Independent Set ◽  
Deterministic Algorithm ◽  
Matroid Intersection ◽  
Practical Applications ◽  
Incentive Compatible ◽  
The Cost ◽  
Special Case ◽  
Budget Feasible

AbstractMotivated by many practical applications, in this paper we study budget feasible mechanisms with the goal of procuring an independent set of a matroid. More specifically, we are given a matroid $${\mathcal {M}}=(E,{\mathcal {I}})$$ M = ( E , I ) . Each element of the ground set E is controlled by a selfish agent and the cost of the element is private information of the agent itself. A budget limited buyer has additive valuations over the elements of E. The goal is to design an incentive compatible budget feasible mechanism which procures an independent set of the matroid of largest possible value. We also consider the more general case of the pair $${\mathcal {M}}=(E,{\mathcal {I}})$$ M = ( E , I ) satisfying only the hereditary property. This includes matroids as well as matroid intersection. We show that, given a polynomial time deterministic algorithm that returns an $$\alpha $$ α -approximation to the problem of finding a maximum-value independent set in $${\mathcal {M}}$$ M , there exists an individually rational, truthful and budget feasible mechanism which is $$(3\alpha +1)$$ ( 3 α + 1 ) -approximated and runs in polynomial time, thus yielding also a 4-approximation for the special case of matroids.

scholarly journals An integer optimization model and algorithms to support the cost-revenue study and provisory designing warehouses or other storage objects

2018 ◽  
Vol 4 (21) ◽  
pp. 257-269
Author(s):  
Mikalai Miatselski ◽  
Bożena Staruch ◽  
Bogdan Staruch
Keyword(s):  
Exact Solution ◽  
General Problem ◽  
Optimization Model ◽  
Optimization Problem ◽  
System Analysis ◽  
Software Tools ◽  
Integer Optimization ◽  
The Cost ◽  
Special Case ◽  
Basic Constraint

An optimization model for the cost–revenue study at the stage of system analysis and preliminary designs of storage objects such as warehouses, containers, packs and similar objects are developed. Our assumptions motivated by warehouses design lead us to a nonlinear integer optimization problem with the only basic constraint. We present algorithmic methods for obtaining the exact solution to the general problem with emphasizing the special case when both the objective and the constraint functions are increasing. The results of the paper may be used in developing software tools intended for supporting designers.

A Digital Economy Model for Talent Prediction Data Analytic

2019 ◽  
Vol 2 (4) ◽  
pp. 260-266
Author(s):  
Haru Purnomo Ipung ◽  
Amin Soetomo
Keyword(s):  
Business Model ◽  
Industrial Revolution ◽  
Technology Innovation ◽  
Cost Effective ◽  
Sharing Economy ◽  
Token Economy ◽  
Economy Model ◽  
New Business ◽  
The Cost ◽  
Data Analytic

This research proposed a model to assist the design of the associated data architecture and data analytic to support talent forecast in the current accelerating changes in economy, industry and business change due to the accelerating pace of technological change. The emerging and re-emerging economy model were available, such as Industrial revolution 4.0, platform economy, sharing economy and token economy. Those were driven by new business model and technology innovation. An increase capability of technology to automate more jobs will cause a shift in talent pool and workforce. New business model emerge as the availabilityand the cost effective emerging technology, and as a result of emerging or re-emerging economic models. Both, new business model and technology innovation, create new jobs and works that have not been existed decades ago. The future workers will be faced by jobs that may not exist today. A dynamics model of inter-correlation of economy, industry, business model and talent forecast were proposed. A collection of literature review were conducted to initially validate the model.

scholarly journals A Financial Incentive Mechanism for Truthful Reporting Assurance in Online Crowdsourcing Platforms

2021 ◽  
Vol 16 (6) ◽  
pp. 2014-2030
Author(s):  
Alireza Mohammadi ◽  
Seyyed Alireza Hashemi Golpayegani
Keyword(s):  
Mechanism Design ◽  
Financial Incentive ◽  
Incentive Mechanism ◽  
High Volume ◽  
Effective Strategy ◽  
Budget Management ◽  
Management Mechanism ◽  
Price Management ◽  
Budget Feasible

In today’s world, crowdsourcing is regarded as an effective strategy to deal with a high volume of small issues whose solutions can have their own complexities in systems. Moreover, requesters are currently providing hundreds of thousands of tasks in online job markets and workers need to perform these tasks to earn money. Thus far, various aspects of crowdsourcing including budget management, mechanism design for price management, forcing workers to behave truthfully in bidding prices, or maximized gains of crowdsourcing have been considered in different studies. One of the main existing challenges in crowdsourcing is how to ensure truthful reporting is provided by contributing workers. Since the amount of pay to workers is directly correlated with the number of tasks performed by them over a period of time, it can be predicted that strong incentives encourage them to carry out more tasks by giving untruthful answers (providing the first possible answer without examining it) in order to increase the amount of pay. However, crowdsourcing requesters need to obtain truthful reporting as an output of tasks assigned to workers. In this study, a mechanism was developed whose implementation in crowdsourcing could ensure truthful reporting by workers. The mechanism provided in this study was evaluated as more budget feasible and it was also fairer for requesters and workers due to its well-defined procedure.

Optimal Order Picking in Carousels Storage System

2012 ◽  
Vol 601 ◽  
pp. 347-353
Author(s):  
Xiong Zhi Wang ◽  
Guo Qing Wang
Keyword(s):  
Approximation Algorithm ◽  
General Problem ◽  
Storage System ◽  
Order Picking ◽  
Total Order ◽  
Experimental Results ◽  
Optimal Order ◽  
Np Hard ◽  
Special Case

We study the order picking problem in carousels system with a single picker. The objective is to find a picking scheduling to minimizing the total order picking time. After showing the problem being strongly in NP-Hard and finding two characteristics, we construct an approximation algorithm for a special case (two carousels) and a heuristics for the general problem. Experimental results verify that the solutions are quickly and steadily achieved and show its better performance.

scholarly journals Examining Cross-Industry Collaboration in Sharing Economy Based On Social Exchange and Social Network Theories

2020 ◽  
pp. 29-41
Author(s):  
Cheng-Wen Lee ◽  
Hao-Yuan Yu
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Social Exchange ◽  
Service Providers ◽  
Secondary Data ◽  
Sharing Economy ◽  
Jel Classification ◽  
Industry Collaboration ◽  
Multiple Case ◽  
The Cost

Information technology and advanced online environments have reduced the cost of these exchange activities and triggered the emergence of the sharing economy. Con-sequently, public attitude toward the sharing economy has gradually shifted from re-luctance to acceptance. Moreover, the sharing economy has revolutionized the busi-ness models and viewpoints of conventional industries, and sharing service providers have gradually shifted from an independent to a collaborative stance, thereby affect-ing conventional economies. This study interprets the phenomenon of cross-industry collaboration in the sharing economy through social exchange and social network the-ories. A multiple-case research framework is used to examine tourism and service in-dustries. Secondary data of service providers and users on sharing platforms are ana-lyzed using content analysis, supplemented with a content analysis of the interview data of three hotel executives. The varying phenomena of the conventional and shar-ing economies on social exchange and social network were compared. Finally, this paper proposes conclusions and practical recommendations according to the analytical results. JEL classification numbers: D85, M31, L14. Keywords: Cross-Industry Collaboration, Sharing Economy, Social Exchange, Social Network.

scholarly journals Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

2019 ◽  
Vol 44 (4) ◽  
pp. 407-426
Author(s):  
Jedrzej Musial ◽  
Emmanuel Kieffer ◽  
Mateusz Guzek ◽  
Gregoire Danoy ◽  
Shyam S. Wagle ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Cloud Service ◽  
Optimal Choice ◽  
Cloud Services ◽  
Multi Objective Optimization ◽  
Problem Instances ◽  
The Cost ◽  
Special Case ◽  
Single Objective

Abstract Cloud computing has become one of the major computing paradigms. Not only the number of offered cloud services has grown exponentially but also many different providers compete and propose very similar services. This situation should eventually be beneficial for the customers, but considering that these services slightly differ functionally and non-functionally -wise (e.g., performance, reliability, security), consumers may be confused and unable to make an optimal choice. The emergence of cloud service brokers addresses these issues. A broker gathers information about services from providers and about the needs and requirements of the customers, with the final goal of finding the best match. In this paper, we formalize and study a novel problem that arises in the area of cloud brokering. In its simplest form, brokering is a trivial assignment problem, but in more complex and realistic cases this does not longer hold. The novelty of the presented problem lies in considering services which can be sold in bundles. Bundling is a common business practice, in which a set of services is sold together for the lower price than the sum of services’ prices that are included in it. This work introduces a multi-criteria optimization problem which could help customers to determine the best IT solutions according to several criteria. The Cloud Brokering with Bundles (CBB) models the different IT packages (or bundles) found on the market while minimizing (maximizing) different criteria. A proof of complexity is given for the single-objective case and experiments have been conducted with a special case of two criteria: the first one being the cost and the second is artificially generated. We also designed and developed a benchmark generator, which is based on real data gathered from 19 cloud providers. The problem is solved using an exact optimizer relying on a dichotomic search method. The results show that the dichotomic search can be successfully applied for small instances corresponding to typical cloud-brokering use cases and returns results in terms of seconds. For larger problem instances, solving times are not prohibitive, and solutions could be obtained for large, corporate clients in terms of minutes.

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