scholarly journals Coordinating Manipulation in Real-time Interactive Mechanism of College Admission: Agent-Based Simulations

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Lan Hou ◽  
Tao Jia ◽  
Xiangbing Wang ◽  
Tongkui Yu
Keyword(s):  
Social Welfare ◽  
Success Rate ◽  
Real Time ◽  
Inner Mongolia ◽  
Physical Space ◽  
College Admission ◽  
Stage Number ◽  
Agent Based ◽  
Agent Based Simulations ◽  
The University

The matching in college admission is a typical example of applying algorithms in cyberspace to improve the efficiency of the corresponding process in physical space. This paper studies the real-time interactive mechanism (RIM) recently adopted in Inner Mongolia of China, where students can immediately observe the provisional admission results for their applications and are allowed to modify the application before the deadline. Since the universities accept the applications according to the ranking of the scores, RIM is believed to make the competition more transparent. However, students may coordinate to manipulate this mechanism. A high-score student can perform a last-minute change on the university applied, opening a slot for a student with a much lower score. With agent-based simulations, we find that a large portion of students will choose to perform coordinating manipulation, which erodes the welfare and fairness of society. To cope with this issue, we investigate the Multistage RIM (MS-RIM), where students with different ranges of scores are given different deadlines for application modification. We find that the multistage policy reduces the chance of manipulation. However, the incentive to conduct manipulation is increased by a higher success rate of manipulation. Hence, the overall social welfare and fairness are further diminished under MS-RIM with a small number of stages, but are improved if the stage number is large.

scholarly journals Negotiation for Time Optimization in Construction Projects with Competitive and Social Welfare Preferences

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Qingfeng Meng ◽  
Zhen Li ◽  
Jianguo Du ◽  
Huimin Liu ◽  
Xiang Ding
Keyword(s):  
Social Welfare ◽  
Success Rate ◽  
Construction Projects ◽  
Revenue Sharing ◽  
Construction Time ◽  
Agent Based ◽  
Chinese Context ◽  
Time Optimization ◽  
The Subject ◽  
Negotiation Success

Construction time optimization is affected greatly by the negotiation between owners and contractors, whose progress is dictated by their desire to maximize system revenues. This paper builds an agent-based model and designs an experimental scenario in which the contractor has competitive and social welfare preferences relevant to the Chinese context; we subdivide competitive preference into greed and jealousy components and subdivide social welfare preference into generosity and sympathy components. We analyze the impacts of these different contractor preferences on the revenue-sharing coefficient, negotiation success rate, and negotiation time when negotiation reaches agreement. The results show that the jealousy component of competitive preference has an important influence on improving the income of the subject, while the greed component does not significantly enhance the revenue-sharing coefficient. The sympathy component of social welfare preference does not have an influence on the revenue-sharing coefficient no matter the strength of the generosity component. Increasing the greed component of competitive preference will lead to the extension of negotiation time and, to a certain extent, to the reduction of the negotiation success rate; the sympathy component of social welfare preference does not have an influence on negotiation time no matter the strength of the generosity preference.

scholarly journals Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter

2021 ◽  
Author(s):  
Kathrin Eismann
Keyword(s):  
Social Media ◽  
Agent Based ◽  
Social Media Networks ◽  
Agent Based Simulations

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.

scholarly journals A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 307
Author(s):  
Zhaoxiong Huang ◽  
Zhenhao Li ◽  
Chun Sing Lai ◽  
Zhuoli Zhao ◽  
Xiaomei Wu ◽  
...  
Keyword(s):  
Social Welfare ◽  
Real Time ◽  
Electric Vehicle ◽  
Peak Load ◽  
Market Mechanism ◽  
Double Auction ◽  
Energy Trading ◽  
Charging System ◽  
Electric Vehicle Charging ◽  
System Operator

This work presents a novel blockchain-based energy trading mechanism for electric vehicles consisting of day-ahead and real-time markets. In the day-ahead market, electric vehicle users submit their bidding price to participate in the double auction mechanism. Subsequently, the smart match mechanism will be conducted by the charging system operator, to meet both personal interests and social benefits. After clearing the trading result, the charging system operator uploads the trading contract made in the day-ahead market to the blockchain. In the real-time market, the charging system operator checks the trading status and submits the updated trading results to the blockchain. This mechanism encourages participants in the double auction to pursue higher interests, in addition to rationally utilize the energy unmatched in the auction and to achieve the improvement of social welfare. Case studies are used to demonstrate the effectiveness of the proposed model. For buyers and sellers who successfully participate in the day-ahead market, the total profit increase for buyer and seller are 22.79% and 53.54%, respectively, as compared to without energy trading. With consideration of social welfare in the smart match mechanism, the peak load reduces from 182 to 146.5 kW, which is a 19.5% improvement.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

An agent-based model for real-time bus stop-skipping and holding schemes

2020 ◽  
pp. 1-33 ◽  
Author(s):  
Lele Zhang ◽  
Jiangyan Huang ◽  
Zhiyuan Liu ◽  
Hai L. Vu
Keyword(s):  
Real Time ◽  
Agent Based Model ◽  
Agent Based ◽  
Bus Stop
Sign in / Sign up

Export Citation Format

Share Document