Hierarchical Population Game Models of Coevolution in Multi-Criteria Optimization Problems under Uncertainty

The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem of multi-criteria optimization under uncertainty. The principles of vector minimax and vector minimax risk are used as the basic principles of optimality for the problem of multi-criteria optimization under uncertainty. The concept of equilibrium of a hierarchical population game with the right of the first move is defined. The necessary conditions are formulated under which the equilibrium solution of a hierarchical population game is a discrete approximation of the set of optimal solutions to the multi-criteria optimization problem under uncertainty.

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

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.

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

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.

A Differential Privacy Mechanism that Accounts for Network Effects for Crowdsourcing Systems

In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore this network effect, we formulate the interaction among users regarding their participation decisions as a population game, because a user’s welfare from the interaction depends not only on his own participation decision but also the distribution of others’ decisions. We show that the Nash equilibrium of this game consists of a threshold strategy, where all users whose privacy sensitivity is below a certain threshold will participate and the remaining users will not. We characterize the existence and uniqueness of this equilibrium, which depends on the privacy guarantee, the reward provided by the initiator and the population size. Based on this equilibria analysis, we design the PINE (Privacy Incentivization with Network Effects) mechanism and prove that it maximizes the initiator’s payoff while providing participating users with a guaranteed degree of privacy protection. Numerical simulations, on both real and synthetic data, show that (i) PINE improves the initiator’s expected payoff by up to 75%, compared to state of the art mechanisms that do not consider this effect; (ii) the performance gain by exploiting the network effect is particularly good when the majority of users are flexible over their privacy attitudes and when there are a large number of low quality task performers.

Refinements of Equilibria for Population Games Based on Bounded Rationality of Agents

We consider stability of equilibria for population games against slight perturbation on the social state space. We provide a necessary and sufficient condition for the existence of Nash equilibria for perturbed population games, which is very important and interesting. Then, refinements of equilibria for population games are introduced. Equivalent characterizations of perfect equilibrium are given. At last, it is shown that each population game admits at least one perfect (proper, weakly proper, and robust) equilibrium.

Auditors in the Economy and the Impact of Rent-Seeking Behaviour and Penalties

Society often relies on information disclosed by enterprises and verified by auditors to decide on an efficient allocation of capital. Auditing sector serves as a means of verification to protect investors from making decisions based on inaccurate information. However, auditors can use their superior information for extracting additional rents. This study explores an economy where entrepreneurs choose their financial reporting quality considering incentives imposed by the society, and rent-seeking auditors may manipulate their reports to extract gains in the expense of public interest. The analysis captures the dynamics of strategy changes among different actors by introducing a population game framework. The steady-state equilibrium analysis shows that there is a pure state and mixed states whose stability is affected by policy parameters such as subsidies, taxes, competitive auditor fee, and rate of adjustment of different behavioral dynamics. It appears that corruption in auditing sector and poor quality in financial reporting may arise as a temporally persistent outcome.

Government Regulations on Closed-Loop Supply Chain with Evolutionarily Stable Strategy

The government plays a critical role in the promotion of recycling strategy among supply chain members. The purpose of this study is to investigate the optimal government policies on closed-loop supply chains and how these policies impact the market demand and the returning strategies of manufacturers and retailers. This paper presents a design of closed-loop supply chains under government regulation by considering a novel three-stage game theoretic model. Firstly, Stackelberg models are adopted to describe the one-shot game between the manufacturer and the retailer in a local market. Secondly, based on the Stackelberg equilibriums, a repeated and dynamic population game is developed. Thirdly, the government analyzes the population game to find the optimal tax and subsidy policies in the whole market. To solve the proposed model, the idea of backward induction is adopted. The results suggest that, by collecting tax and allocating subsidy, the government can influence the market demands and return rates. The centralized supply chain structure is always preferred for the government and the market. The government prefers to allocate subsidy to low-pollution, low-profit remanufactured products. The environmental attention of the government affects the subsidy policy.

Best experienced payoff dynamics and cooperation in the centipede game

We study population game dynamics under which each revising agent tests each of his strategies a fixed number of times, with each play of each strategy being against a newly drawn opponent, and chooses the strategy whose total payoff was highest. In the centipede game, these best experienced payoff dynamics lead to cooperative play. When strategies are tested once, play at the almost globally stable state is concentrated on the last few nodes of the game, with the proportions of agents playing each strategy being largely independent of the length of the game. Testing strategies many times leads to cyclical play.