Network Game Research Articles

Running Away with the Pokémon Go Gang: Three Consumption Needs

ABSTRACT Pokémon Go, the study case of the current research, is one of the most popular augmented reality games today. The importance of the study lies in characterizing Pokémon Go players according to demographic groups and their motivations for playing AR games. The data were collected via a web-based survey open for Pokémon Go Players, who currently play or had recently played Pokémon Go all over Israel. The data were collected via a web-based survey which was distributed in Pokémon GO forums and was answered by 488 players. (N = 488, male = 67%, female = 33%, age mean 25.05, age SD = 8, age range = 14–73) with diverse demographic characteristics in order to understand their activity in relation to Yee’s model of motivationsand while relying on the motivational questionnaire for MOGQ network games. It was found that there are more Pokémon Go players among young, secular men from central Israel, who are highly educated and possess middle-income and above. It was found that Pokémon Go provides players with a number of needs: Escapism, the central and leading motive; Achievement; and Relationships. In light of these results, it is evident that Pokémon Go provides added value to players. In the future, this technology will probably be assimilated into additional games and bridge social gaps, providing significant added value to the rest of society by satisfying various needs.

Off-policy Q-learning: Solving Nash equilibrium of multi-player games with network-induced delay and unmeasured state

In the framework of adaptive dynamic programming combined with Q-learning, this paper investigates networked multi-player games, in which the common state of the plant is transmitted to all players via a network, for finding the Nash equilibrium solution without requiring the system matrices to be known, even though there exists network-induced delay and system state cannot be directly measured. By adding an observer and a virtual Smith predictor for estimating system state and predicting system state, the control policies of players can be successfully designed. Then, a novel off-policy Q-learning algorithm is proposed to learn the Nash equilibrium solution via solving the coupled algebraic Riccati equations using available data, followed by the rigorous proof of convergence of the proposed algorithm. Finally, an example is given to show the effectiveness of the proposed method.

Modeling and optimization for networked evolutionary games with player exit mechanism: Semi-tensor product of matrices method

This study investigates a class of networked evolutionary games (NEGs) with player exit mechanisms through the matrices semi-tensor product (STP) method for the first time. In these NEGs, the behavior of players changing strategies at the same time is similar to cellular automata. Firstly, the player’s exit is identified as a “virtual” strategy. This strategy will be chosen by players whose payoff is below a threshold and will not be changed. Based on this identification, a new strategy updating rule is proposed. Secondly, by using the method of STP, the NEGs with player exit mechanisms are formulated as an iterative matrix equation. Furthermore, a control sequence is designed to minimize the number of exiting players by analyzing the structure of the iterative matrix. Finally, the validity of our new results is illustrated by an example of the opinion evolution.

Asynchronous Networked Aggregative Games

We propose a fully asynchronous networked aggregative game (Asy-NAG) where each player minimizes a local cost function that depends on its own action and the aggregate of all players’ actions. In sharp contrast to the existing NAGs, each player in our Asy-NAG can compute an estimate of the aggregate action at any wall-clock time by only using (possibly stale) information from neighboring players of a directed network. Such an asynchronous update does not require any coordination among players. Moreover, we design a novel distributed algorithm with an aggressive mechanism for each player to adaptively adjust its optimization stepsize per update. Particularly, the slow players in terms of updating their estimates smartly increase their stepsizes to catch up with the fast ones. Then, we adopt an augmented network approach to address the asynchronicity and the information delays between players, and rigorously show the convergence to a Nash equilibrium of the Asy-NAG via a perturbed coordinate algorithm which is also of independent interest. Finally, we evaluate the performance of the distributed algorithm through numerical simulations.

A caching strategy based on many-to-many matching game in D2D networks

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device (D2D) networks. We model the D2D User Equipments (DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy. (1) It can cache other contents besides high popularity contents. (2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used (LRU), Equal Probability Random Cache (EPRC), and the Most Popular Content Cache (MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

Optimal Estimation of Link Delays Based on End-to-End Active Measurements

Current IP-based networks support a wide range of delay-sensitive applications such as live video streaming of network gaming. Providing an adequate quality of experience to these applications is of paramount importance for a network provider. The offered services are often regulated by tight Service Level Agreements (SLAs) that needs to be continuously monitored. Since the first step to guarantee a metric is to measure it, delay measurement becomes a fundamental operation for a network provider. In many cases, the operator needs to measure the delay on all network links. We refer to the collection of all link delays as the Link Delay Vector (LDV). Typical solutions to collect the LDV impose a substantial overhead on the network. In this paper, we propose a solution to measure the LDV in real-time with a low-overhead approach. In particular, we inject some flows into the network and infer the LDV based on the delay of those flows. To this end, the monitoring flows and their paths should be selected minimizing the network monitoring overhead. In this respect, the challenging issue is to select a proper combination of flows such that by knowing their delay it is possible to solve a set of linear equations and obtain a unique LDV. This combination of monitoring flows should be optimal according to some criteria and should meet some feasibility constraints. We first propose a mathematical formulation to select the optimal combination of flows, in form of an Integer Linear Programming (ILP) problem. Then we develop a heuristic algorithm to overcome the high computational complexity of existing ILP solvers. As a further step, we propose a meta-heuristic algorithm to solve the above-mentioned equations and infer the LDV. The challenging part of this step is the volatility of link delays. The proposed solution is evaluated over real-world emulated network topologies using the Mininet network emulator. Emulation results show the accuracy of the proposed solution with a negligible networking overhead in a real-time manner.

Study of multinational currency co-movement and exchange rate stability base on network game

Complex network analysis of the correlation of exchange rate fluctuations shows that the co-movement of global exchange rate fluctuations is a small-world network. Through the income function of currency issuance and circulation, we build a two countries’ currency network model, and then extend it to multi-countries model, within the evolution network game simulation method. We find that multi-countries exchange rate co-movement gradually shapes an stabilize exchange rate co-movement region. The more random the currency co-movement relationship among multi-currency countries is, the easier it is to stabilize exchange rate co-movement relationships; and the higher the proportion of investment income to total income is, the easier it is to shape a multi-currency co-movement exchange rate stability relationship.

A social interaction model with both in-group and out-group effects

ABSTRACT This paper studies social interaction models with both in-group and out-group effects. The in-group effect follows the standard setup in the literature, while the out-group effect is introduced by assuming the economic outcome also depends on its out-group average value. We present a network game with limited information of outside groups that rationalizes the econometric model. We show that both effects are identified under a set of mild regularity conditions. We propose to estimate the model using the two-stage least squares (2SLS) method and establish the asymptotic normality of the estimators. The finite sample performance of the estimators is investigated through Monte Carlo simulations.

Adaptation strategies and collective dynamics of extraction in networked commons of bistable resources

When populations share common-pool resources (CPRs), individuals decide how much effort to invest towards resource extraction and how to allocate this effort among available resources. We investigate these dual aspects of individual choice in networked games where resources undergo regime shifts between discrete quality states (viable or depleted) depending on collective extraction levels. We study the patterns of extraction that emerge on various network types when agents are free to vary extraction from each CPR separately to maximize their short-term payoffs. Using these results as a basis for comparison, we then investigate how results are altered if agents fix one aspect of adaptation (magnitude or allocation) while letting the other vary. We consider two constrained adaptation strategies: uniform adaptation, whereby agents adjust their extraction levels from all CPRs by the same amount, and reallocation, whereby agents selectively shift effort from lower- to higher-quality resources. A preference for uniform adaptation increases collective wealth on degree-heterogeneous agent-resource networks. Further, low-degree agents retain preferences for these constrained strategies under reinforcement learning. Empirical studies have indicated that some CPR appropriators ignore—while others emphasize—allocation aspects of adaptation; our results demonstrate that structural patterns of resource access can determine which behavior is more advantageous.

An online learning algorithm to play discounted repeated games in wireless networks

Discounted repeated games are currently being used to model the conflicts that arise between the nodes in a wireless network, such as distributed resource allocation, interference management or defending the network against attacks. In current literature, it is frequent that authors devise a specific strategy that performs well only for their concrete problem, thus, it would be desirable to have a generic algorithm that allows learning strategies for such games. However, current learning algorithms focus on average payoff repeated games, and we show analytically that there are important differences that prevent us from using such algorithms for discounted repeated games. In this work, we aim to fill this gap and we propose LEWIS, a lightweight, online learning algorithm specifically designed for these games, that deals with imperfect and incomplete information and is able to return a good payoff. We test LEWIS on two settings based on current literature problems to show that it has a good performance, hence, being a promising method to learn how to play a discounted repeated game in wireless networks.

The role of network embeddedness across multiple social networks: Evidence from mobile social network games

This article examines peer influences from network relationships within a social network game (i.e., embeddedness) and across such games (i.e., multiplexity). Drawing on social influence theory, we develop a bivariate Poisson model of users’ repeated visits and latent attrition that accommodates peer interaction after controlling for homophily. We estimate the model using data from two social network games with considerable overlap among network members. We find that friends who are only multiplex across games exert greater peer influence on users’ game visits than members who are embedded within a single game. We also determined that ignoring network multiplexity across games may lead firms to mistarget users due to biased peer influences of embedded friends. This result provides an unresearched explanation—strength of peer influence—for the mixed findings in previous literature on network embeddedness. We utilized our results to conduct several scenario analyses to demonstrate how firms can effectively manage users’ engagement and target users in multiple social network games.

Strategy optimization of weighted networked evolutionary games with switched topologies and threshold

In real life, whether individuals or enterprises there have a lot of objects which are related to them, but they will choose the objects they trust to refer to their information and play games with them. In this study, a model is established named the weighted networked evolutionary games(NEGs) with switched topologies, which describes that each player can choose the networks he is eager to attend at different moments, and in the selected network each player will choose the real neighborhood players called trusters, according to his own criteria. Using the semi-tensor product(STP) of matrices, the algebraic representation of the new model’s evolution process is obtained. It is known that players have a minimum payoff threshold to survive, so the strategy optimization algorithm is designed through the method of state feedback control so that players’ strategy choices can reach the threshold as they evolve. An example is given to illustrate the effectiveness of algorithms.

Equilibria and Systemic Risk in Saturated Networks

We undertake a fundamental study of network equilibria modeled as solutions of fixed-point equations for monotone linear functions with saturation nonlinearities. The considered model extends one originally proposed to study systemic risk in networks of financial institutions interconnected by mutual obligations. It is one of the simplest continuous models accounting for shock propagation phenomena and cascading failure effects. This model also characterizes Nash equilibria of constrained quadratic network games with strategic complementarities. We first derive explicit expressions for network equilibria and prove necessary and sufficient conditions for their uniqueness, encompassing and generalizing results available in the literature. Then, we study jump discontinuities of the network equilibria when the exogenous flows cross certain regions of measure 0 representable as graphs of continuous functions. Finally, we discuss some implications of our results in the two main motivating applications. In financial networks, this bifurcation phenomenon is responsible for how small shocks in the assets of a few nodes can trigger major aggregate losses to the system and cause the default of several agents. In constrained quadratic network games, it induces a blow-up behavior of the sensitivity of Nash equilibria with respect to the individual benefits.

Correlational study between online friendship network and internet game disorder among university students.

PurposeThe purpose of this study was to provide basic information about the analysis of the correlation between online friendship network and internet game disorder among university students.MethodsStudy subjects were 77 university students. For analysis of online friendship among them, social network analysis was performed and the analysis of degree, closeness, and betweenness centrality was measured. Assessment of internet game disorder was done using the Korean version of Internet Gaming Disorder Scale.ResultsAs per the results, the positive correlation showed between in‐closeness centrality and internet gaming disorder (IGD) and the negative correlation showed between out‐closeness centrality and internet gaming disorder.ConclusionsOnline friendship is considered to contribute to the changes of internet game disorder level. Thus, in the relationship between online friendship and internet game disorder, closeness centrality should be considered, and causal relation analysis should be performed in further studies.

A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism.

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

Coalition formation resource sharing games in networks

Cooperative game theory deals with systems where players want to cooperate to improve their payoffs. But players may choose coalitions in a non-cooperative manner, leading to a coalition-formation game. We consider such a game with several players (willing to cooperate) and a possible adamant player (unwilling to cooperate) involved in resource-sharing. Here, the strategy of a player is the set of players with whom it wants to form a coalition. Given a strategy profile, an appropriate partition of coalitions is formed; players in each coalition maximize their collective utilities leading to a non-cooperative resource-sharing game among the coalitions, the (unique) utilities at the resulting equilibrium are shared via Shapley-value; these shares define the utilities of players for the given strategy profile in the coalition-formation game. We also consider the utilitarian solution to derive the price of anarchy. With a sufficient number of almost similar players, no collaborative (players working together) partition emerges at equilibrium. But the scenario reverses when the players are significantly different: the system is in a ‘lethargic state’ where almost all partitions are stable against unilateral deviations. In the intermediate cases, the higher and lower influential players have more affinity to form successful collaborations. Grand coalition is the utilitarian solution and is also stable against coalitional deviations under certain conditions. The price of anarchy increases as the number of significant players increases or as the relative strength of adamant player increases/decreases.

The effects of reputation on inequality in network cooperation games.

In the last several decades, ample evidence from across evolutionary biology, behavioural economics and econophysics has solidified our knowledge that reputation can promote cooperation across different contexts and environments. Higher levels of cooperation entail higher final payoffs on average, but how are these payoffs distributed among individuals? This study investigates how public and objective reputational information affects payoff inequality in repeated social dilemma interactions in large groups. I consider two aspects of inequality: excessive dispersion of final payoffs and diminished correspondence between final payoff and cooperative behaviour. I use a simple heuristics-based agent model to demonstrate that reputational information does not always increase the dispersion of final payoffs in strategically updated networks, and actually decreases it in randomly rewired networks. More importantly, reputational information almost always improves the correspondence between final payoffs and cooperative behaviour. I analyse empirical data from nine experiments of the repeated Trust, Helping, Prisoner's Dilemma and Public Good games in networks of ten or more individuals to provide partial support for the predictions. Our research suggests that reputational information not only improves cooperation but may also reduce inequality.This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling'.

A Comprehensive Survey on STP Approach to Finite Games

Nowadays the semi-tensor product (STP) approach to finite games has become a promising new direction. This paper provides a comprehensive survey on this prosperous field. After a brief introduction for STP and finite (networked) games, a description for the principle and fundamental technique of STP approach to finite games is presented. Then several problems and recent results about theory and applications of finite games via STP are presented. A brief comment about the potential use of STP to artificial intelligence is also proposed.

Network Game of Student Training Quality Assessment System Based on KOHONEN Neural Network

Objectives: In recent years, with the continuous improvement of the requirements of student training quality, the evaluation results of the existing evaluation system of student training quality are mostly unsatisfactory. Therefore, by integrating c-mean algorithm and Kohonen clustering algorithm, a non-sequential artificial neural network is obtained, a student training quality evaluation system based on KOHONEN neural network is designed by automatically adjusting the size of the objective function nodes of the non-sequential artificial neural network. Then the evaluation system is applied to the expected evaluation of the training quality of students in two science classes of Xinghua Middle School in Shenyang, Liaoning Province. The comparison between the test result data and the expected results of the model after the experiment confirms that the evaluation results obtained by using the evaluation system based on KOHONEN neural network have high accuracy.

Spatial competition, strategic R&D and the structure of innovation networks

We provide a network game to analyze the innovation network structure by incorporating endogenous R&D spillovers and diffusion effects, reconciling the limited types of innovation alliance in practice. In our network model, enterprises can actively choose to invest in R&D or establish links with others to absorb R&D spillovers. We characterize the network structure of enterprises' strategic R&D decisions in different types of spatial equilibrium. We further extend the theoretical framework to generate industry-specific implications under cooperative R&D, location choice, spatial agglomeration, and social capital allocation in innovation networks.