scholarly journals A Population Game Model for the Expansion of Airbnb in the City of Venice

2021 ◽  
Vol 13 (7) ◽  
pp. 3829
Author(s):  
Sophia Arbara ◽  
Roberto D’Autilia
Keyword(s):  
Game Theory ◽  
Urban Areas ◽  
Evolutionary Game ◽  
Game Model ◽  
Usage Patterns ◽  
Population Game ◽  
Accommodation Sector ◽  
Potential Equilibrium ◽  
The City ◽  
Housing Units

The emergence of Airbnb along with an increase in urban tourism has intensified the pressure on urban areas while adding a new dimension to the dynamics of housing distribution, especially in historic cities. These dynamics affect local economies and significantly alter the characteristics of urban spaces, hence the necessity to not only create policies that foster sustainable tourism development but also to advance urban models that explore the relation between Airbnb and the traditional rental and accommodation sector. Through the case of Venice, the present study sheds light on the potential evolution of Airbnb housing in comparison to the traditional rental and homeowner market. In particular, we sought to understand whether a potential equilibrium between these uses exists and if so, at which point in regard to this equilibrium the historic center of Venice is. To tackle this question, methods derived from the field of game theory and specifically evolutionary game theory were used. With the agents (players) being the housing units, the designed theoretical model explored the population dynamics of the housing units in Venice given the three options of homeownership or long-term renting (residential); short term renting or Airbnb (airbnb); and no use (vacant). The findings of our theoretical population game model were validated and discussed with a dataset describing the usage patterns in the city of Venice during the past 20 years. A verification of the outcome through further case studies could eventually provide insights into the future behavior of tourism’s pressure in historic urban areas.

scholarly journals A Population Game Model for the Expansion of Airbnb in the City of Venice

2021 ◽  
Author(s):  
Sophia Arbara ◽  
Roberto D'Autilia
Keyword(s):  
Game Theory ◽  
Urban Space ◽  
Urban Areas ◽  
Evolutionary Game ◽  
Game Model ◽  
Population Game ◽  
Accommodation Sector ◽  
Potential Equilibrium ◽  
The City ◽  
Housing Units

The emergence of Airbnb along with an increase in urban tourism has intensified the pressure on urban areas while adding a new dimension in the dynamics of the housing distribution especially in historic cities. These dynamics affect both local economies and alter significantly the characteristics of urban space arising the necessity to create not only policies that foster sustainable tourism development but also to advance urban models that explore the relation between Airbnb and the traditional rental and accommodation sector. Through the case of Venice, the present study sheds light on the potential evolution of Airbnb housing in comparison to the traditional rental and homeowner market. In particular, it seeks to understand whether a potential equilibrium between these uses exists and if so, at which point in regards to this equilibrium the historic center of Venice is now. To tackle this question, methods deriving from the field of game theory and specifically evolutionary game theory are used. With the agents (players) being the housing units, the designed theoretical model explores the population dynamics of the housing units in Venice given the three options of homeownership or long-term rental (residential), short term rental over Airbnb (airbnb) or no use (vacant). The findings of our theoretical population game model are validated and discussed against a dataset describing the use patterns in the city of Venice during the past 20 years. A verification of the outcome through further case studies could eventually provide insights on future behavior of tourism pressure in historic urban areas.

scholarly journals A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Mode Choice ◽  
Evolutionary Game ◽  
Payoff Matrix ◽  
Game Model ◽  
Stable States ◽  
Taxi Service ◽  
Service Mode ◽  
The Impact

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

Behavior Analysis of Together-Conspired Bidding Based on the Evolutionary Game

2012 ◽  
Vol 209-211 ◽  
pp. 1513-1516
Author(s):  
Qian Li
Keyword(s):  
Game Theory ◽  
Behavior Analysis ◽  
Evolutionary Game Theory ◽  
Theoretical Basis ◽  
Evolutionary Game ◽  
Research Work ◽  
Game Model ◽  
Stable Strategy ◽  
Periodic Fluctuation ◽  
Construction Market

Based on the “replication dynamics” ideas, the paper establishes asymmetric evolutionary game model of together-conspired bidding using evolutionary game theory, and obtains its evolutionary stable strategy under the present governmental supervision that surround-bidder and accompanying-bidder’s proportion is periodic fluctuation of the center stability, explains the reason why together-conspired bidding is difficult to be prevented effectively. In order to find the decisive factor of the evolutionary drift, further investigation shows that the evolutionary drift is converged to the different evolutionary stable properties when evolution conditions change, such as the supervision target, supervision strength. Through the analysis to the punishment extent on surround-bidder and accompanying-bidder, the conclusion is arrived that the strength of punishment and execution on the surround-bidder can effectively control together-conspired bidding, which provides the theoretical basis to governmental supervision department for the management and research work on together-conspired bidding in the construction market.

Flocking Task Research for Multiple Mobile Robots Based on Evolutionary Game Model

2011 ◽  
Vol 201-203 ◽  
pp. 1845-1848
Author(s):  
Ye Ye ◽  
Neng Gang Xie ◽  
Yu Wan Cen ◽  
Qing Yun Liu
Keyword(s):  
Game Theory ◽  
Mobile Robots ◽  
Evolutionary Game ◽  
Planning Problem ◽  
Game Model ◽  
Multiple Robots ◽  
Multi Objective Optimization ◽  
Multiple Mobile Robots ◽  
Coordinated Path Planning ◽  
Path Planning Problem

For flocking task of multiple mobile robots (MMR for short), the paper establishes a multi-objective optimization model and studies a solving method based on game theory. According to evolutionary game theory and taking the dynamic variability of gaming behaviors into account, it proposes a method based on evolutionary game model by using evolutionary rules “In success, commit oneself to the welfare of the society; in distress, maintain one‘s own integrity ”. Then, the paper performs researches on path coordination and obtains the optimum non-collision coordinated paths of flocking task for MMR. The simulation results show that the evolutionary game method can effectively solve coordinated path planning problem for multiple robots. By contrast with Nash equilibrium game model and coalition cooperative game model through computation results, the paper illustrates that the evolutionary game model is the best.

scholarly journals Research on Promotion of Consumers’ Application of After Service in Online Shopping Based on Evolutionary Game Theory-- Introduction of Smart Contract

2021 ◽  
Vol 233 ◽  
pp. 01074
Author(s):  
Hongzhen Lei ◽  
Di Lu ◽  
HongHong Zhang
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Online Shopping ◽  
Evolutionary Game ◽  
Game Model ◽  
Strategic Choices ◽  
Smart Contract ◽  
Service Guarantee ◽  
Relationship Of ◽  
The Relationship

In order to research how to promote online shopping consumers’ application of after service, build an evolutionary game model of both consumers and e-stores. This paper introduces the variables of supervision and punishment, tries to introduce the smart contract as a powerful service guarantee, and analyzes the influence relationship of variables between the two players and their strategic choices. This paper analyzes the ESS of the system when the relationship among smart contract, revenue, supervision and punishment meets 8 different conditions. Finally, giving suggestions to optimize the after service in online shopping according to the results.

scholarly journals An Evolutionary Game Theory Study for Construction and Demolition Waste Recycling Considering Green Development Performance under the Chinese Government’s Reward–Penalty Mechanism

2020 ◽  
Vol 17 (17) ◽  
pp. 6303 ◽  
Author(s):  
Hongyu Long ◽  
Hongyong Liu ◽  
Xingwei Li ◽  
Longjun Chen
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Construction And Demolition Waste ◽  
Decision Making Process ◽  
Game Model ◽  
Demolition Waste ◽  
Green Development ◽  
Evolutionary Paths

The low efficiency of the closed-loop supply chain in construction and demolition waste (CDW) recycling has restricted the green development of China’s construction industry. Additionally, the government’s reward–penalty mechanism has a huge influence on green development. This study aimed to investigate the effect of green development performance (GDP) and the government’s reward–penalty mechanism on the decision-making process of production and recycling units, as well as to reveal the optimal strategies under different conditions. Therefore, the strategies’ evolutionary paths of production and recycling units were investigated by using evolutionary game theory. Firstly, an evolutionary game model between production units and recycling units was proposed under the government’s reward–penalty mechanism. Then, the evolutionary stability strategies in different scenarios were discussed. Finally, the effects of the relevant parameters on the evolutionary paths of the game model were analyzed using numerical simulations. The main conclusions are as follows. (1) When the range of GDP changes, the evolutionary stable strategy changes accordingly. GDP plays a positive role in promoting the high-quality development of the CDW recycling supply chain, but an increase in GDP can easily lead to the simultaneous motivation of free-riding. (2) The government’s reward–penalty mechanism effectively regulates the decision-making process of production and recycling units. An increase in the subsidy rate and supervision probability helps to reduce free-riding behavior. Moreover, the incentive effect of the subsidy probability on recycling units is more obvious, while the effect of the supervision probability on improving the motivation of active participation for production units is more remarkable. This paper not only provides a decision-making basis to ensure production and recycling units to make optimal strategy choices under different conditions but also provides a reference for the government to formulate a reasonable reward–penalty mechanism that is conducive to a macro-control market.

scholarly journals Research on franchised store chain operation based on evolutionary game theory

2021 ◽  
Vol 275 ◽  
pp. 03022
Author(s):  
Siyuan Deng
Keyword(s):  
Game Theory ◽  
Business Model ◽  
Evolutionary Game Theory ◽  
Brand Management ◽  
Evolutionary Game ◽  
Case Analysis ◽  
Game Model ◽  
Policy Recommendations ◽  
Chain Operation ◽  
Selection Of

Franchised store chain is the most popular business model today. The franchisor and the franchisees share the same brand, but the value of the entire brand will be degraded once one side pursues self-interests in brand management. From the perspective of franchised store chain, this paper develops an evolutionary game model between franchisor and franchisees under the assumption of bounded rationality. The strategic selection of franchisor and franchisees includes cooperation and no-cooperation. In the end, the corresponding policy recommendations are proposed in the foundation of case analysis.

scholarly journals Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yanhua Liu ◽  
Hui Chen ◽  
Hao Zhang ◽  
Ximeng Liu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Dynamic Network ◽  
Game Model ◽  
Strategy Selection ◽  
Network Attack ◽  
Learning Mechanism ◽  
Defense Strategy ◽  
Attack And Defense

Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM.

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