Game Theoretic Modeling of Economic Systems and the European Debt Crisis

A game theoretic model of six kinds of players is analyzed, i.e. countries, central banks, banks, firms, households, and financial inter-governmental organizations. Each player has a strategy set, with strategies such as setting interest rates, lending, borrowing, producing, consuming, investing, importing, exporting, defaulting, and penalizing default. Markets for goods, debt, and capital are modeled endogenously. This conceptualization of strategic opportunities for as many as six types of players is richer than anything that has been attempted earlier. 2005–2011 empirical data for Greece is used to analyze how utility is impacted by public consumption and lump sum transfers, and negative productivity shocks, and to analyze equilibrium over several time periods with and without the possibility of default. 2007–2008 empirical data for Greece and Germany is used to determine how the two countries’ utilities depend on Greece’s public 2007 consumption, with and without negative productivity shocks. Greece’s high debt burden is shown to make default optimal when productivity shocks are large and the default penalty is small. We find that Germany has limited ability, through its available strategies, to prevent a Greek default, and may need to resort to unconventional tools such as debt forgiveness and changing the default penalty.     

Game-theoretic Evolutionary Algorithm Based on Behavioral Expectation and its Performance Analysis

Game theory has been widely recognized as an important tool in many fields, which provides general mathematical techniques for analyzing situations in which two or more individuals make decisions that will influence one another’s welfare. This paper presents a game-theoretic evolutionary algorithm based on behavioral expectation, which is a type of optimization approach based on game theory. A formulation to estimate the payoffs expectation is given, which is a mechanism of trying to master the player’s information so as to facilitate the player becoming the rational decision maker. GameEA has one population (players set), and generates new offspring only by the imitation operator and the belief learning operator. The imitation operator is used to learn strategies and actions from other players to improve its competitiveness and applies it into the future game, namely that one player updates its chromosome by strategically copying some segments of gene sequences from the competitor. Belief learning refers to models in which a player adjusts its own strategies, behavior or chromosome by analyzing current history information with respect to an improvement of solution quality. The experimental results on various classes of problems using real-valued representation show that GameEA outperforms not only the standard genetic algorithm (GA) but also other GAs having additional mechanisms of accuracy enhancement. Finally, we compare the convergence of GameEA with different numbers of players to determine whether this parameter has a significant effect on convergence. The statistical results show that at the 0.05 significance level, the number of players has a crucial impact on GameEA's performance. The results suggest that 50 or 100 players will provide good results with unimodal functions, while 200 players will provide good results for multimodal functions.     

篮球视频中球员的分割与跟踪算法

分割与跟踪一直是视频图像处理的一个热门问题。针对篮球视频中激烈运动的球员队属和跟踪问题,实现了一个实时的球员分割与跟踪算法。首先,不同于一般的分割方法,采用基于两个级别（像素级和帧级）实时分割和背景更新的背景减除方法,实现了对球员的分割;接着,通过将球员与模板进行马式距离比较来辨识球员队属;然后,使用基于核的目标跟踪方法,实现了在激烈比赛中对球员的准确跟踪。实验结果表明,提出的算法能够很好地辨别各个球员的队属问题并在不完全遮挡的情况下跟踪多个球员,而且在球员形态变化较大时也能稳定地跟踪。     

Graph-Based Multiplayer Detection and Tracking in Broadcast Soccer Videos

In this paper, we propose a graph-based approach for detecting and tracking multiple players in broadcast soccer videos. In the first stage, the position of the players in each frame is determined by removing the non player regions. The remaining pixels are then grouped using a region growing algorithm to identify probable player candidates. A directed weighted graph is constructed, where probable player candidates correspond to the nodes of the graph while each edge links candidates in a frame with the candidates in next two consecutive frames. Finally, dynamic programming is applied to find the trajectory of each player. Experiments with several sequences from broadcasted videos of international soccer matches indicate that the proposed approach is able to track the players reasonably well even under varied illumination and ground conditions.      

Controlling the order of moves in voting by veto. I. Conditions for making the given decision

Collective decision making in complex systems using voting by veto is considered. A dedicated player is assumed to exist in the collective and be able to set sequential voting (the order of moves) for the players. The problem of searching for the control strategy for the order of moves so that to ensure the choice of the alternative of the dedicated player is stated. The game-theory formalization of the problem with the first player as the leader is proposed. Sufficient conditions are obtained for players’ preferences for the game to have the required order of moves. These conditions show that the voting procedure involved does not result in the worst decision made for any player or the decision chosen out of the last preferences of the group of participants. The explicit form is found for control strategies for the order of moves for the cases of the sufficient conditions met when the partners of the first player have his alternative at the end of their preference list-it is no higher than the third preference from the end for everybody and may be one place higher for one player. We proved that when controlling the order of moves, the move of the first player is sufficient to be left first and that the order of moves can be formed beforehand, prior to voting.     

Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud

The exponential growth in the demands of users to access various resources during mobility has led to the popularity of Vehicular Mobile Cloud. Vehicular users may access various resources on road from the cloud which acts as a service provider for them. Most of the existing proposals on vehicular cloud use unicast sender-based data forwarding, which results in an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay, and reliable data transmission. Most of the applications for vehicular cloud have tight upper bounds with respect to reliable transmission. In view of the above, in this paper, we formulate the problem of reliable data forwarding as a Bayesian Coalition Game (BCG) using Learning Automata concepts. Learning Automata (LA) are assumed as the players in the game stationed on the vehicles. For taking adaptive decisions about reliable data forwarding, each player observes the moves of the other players in the game. For this purpose, a coalition game is formulated among the players of the game for taking adaptive decisions. For each action taken by a player in the game, it gets a reward or a penalty from the environment, and accordingly, it updates its action probability vector. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) is also proposed. The proposed scheme is evaluated in different network scenarios with respect to parameters such as message overhead, throughput, and delay by varying the density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics.     

Interactive strategy sets in multiple payoff games

In game theory, it is usually assumed that each player has only one payoff function and the strategy set of the game is composed of the topological product of individual players’ strategy sets. In real business and system design or control problems, however, players’ strategy sets may be interactive and each player may have more than one payoff function. This paper, investigates the more general situation of multiple payoff and multiple person games in a normal form. In this paper, each player has several payoff functions which are dominated by certain convex cones, and the feasible strategy set of each player may be interactive with those of the other players. This new model is applied to a classical example without requiring variational and quasi-variational inequalities, or point-to-set mappings.     

基于DSP的便携式综合音频播放器的实现

目前各种便携式媒体播放器比较流行 ,数字音频广播 DAB也正在发展。本文主要介绍了以 DSP数据处理技术为基础 ,如何把媒体音频播放 ,数字音频广播 DAB集成在一起 ,组成新的便携式综合音频播放器。该便携式综合音频播放器将会有良好的市场前景     

Competitive facility location: the Voronoi game

We consider a competitive facility location problem with two players. Players alternate placing points, one at a time, into the playing arena, until each of them has placed n points. The arena is then subdivided according to the nearest-neighbor rule, and the player whose points control the larger area wins. We present a winning strategy for the second player, where the arena is a circle or a line segment. We permit variations where players can play more than one point at a time, and show that the first player can ensure that the second player wins by an arbitrarily small margin.     

VisuaLeague: Player performance analysis using spatial-temporal data

In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

     

Hint,Extortion, and Guessing Games in the Best Choice Problem

The best choice problem (also known as “the secretary problem”) is one of the classical in stochastic optimization. In this paper, we consider a modification of the classical secretary problem by adding the second player, who can either help the first player to find the best element by a hint or precludes him by imposing some restrictions on the search. Nash equilibrium has been found in the explicit form of mixed strategies for three different types of the game. The asymptotic behavior of diverse numerical quantities associated with the optimal strategies for both players, as the number of objects tends to infinity, has been studied.     

Multistage network games with perfect information

We consider multistage network games with perfect information. At each time instant, a current network structure connecting the players is specified. It is assumed that each edge has some utility value (player’s benefit from being linked to another player), and players can change the network structure at each stage. We propose a method for finding optimal behavior for players in games of this type.     

体育视频中的运动员检测与分割

运动员的动作行为分析是体育视频高级语义分析的直接途径,检测和分割视频中的运动员是分析运动员的动作行为的基础.利用体育视频的领域规则和中层特征块的性质,提出半监督的方法挖掘运动员的中层特征块,针对不同类型的镜头分别训练基于中层特征块的运动员检测分类器,实现运动员检测.利用运动员检测结果标记超像素,结合Grab Cut分割算法实现运动员分割.实验结果表明,基于中层特征块的运动员区域检测算法能够快速挖掘训练所需样本,从而训练得到检测分类器,检测结果具有较高的准确度,获得的运动员区域能够有效用于运动员分割,简化了分割计算过程.     

Automatic initialization for 3D soccer player tracking

As a special application of computer vision, automatic sports video analysis has been studied by some researchers. This sports video analysis via computer vision is a moderately challenging problem: it is more difficult than analyzing a video of a few laboratory members acting as in a simple scenario and is easier than analyzing a video of crowded people at a subway station. So the success of an analysis heavily depends on how much one can exploit the prior information on the sport and setting. The most challenging and important part would be the tracking of players (and ball). With a multi-camera system, 3D tracking is feasible which is much more meaningful than 2D tracking for the analysis. As an initial step of 3D player tracking from multi-view soccer videos, this paper deals with automatic initialization of player positions. Initial 3D positions can be estimated by exploiting some conditions of a soccer match. To make it robust, prior knowledge on the features of players is learnt by support vector machines (SVM). Experimental results show that the proposed system is efficient for general soccer sequences.     

A review of: “HUMANIZED INPUT” Tom Gilb and Gerald M. Weinberg Winthrop Computer Series,Winthrop Publishing,Cambridge, Mass. 1977 383 pages $4.00 ISBN 0-87626-345-7

Kayles is a nonpartizan two-person game. In such games the moves available to both the players are the same and either the player on move wins or the previous player wins. In the “normal form” of the game, the last player who can make a move is declared the winner. In the “misere form” of the game, the player who makes the last move is declared the loser. The complete analysis of the normal form of the game of Kayles has been described by Berlekamp, Conway, and Guy. This paper completes their analysis of the misere form of Kayles. This is done on the basis of certain properties of what have been called “wild” positions of misere games.     

Collaborate with Strangers to Find Own Preferences

We consider a model with n players and m objects. Each player has a “preference vector” of length m, that models his grades for all objects. The grades are initially unknown to the players. A player can learn his grade for an object by probing that object, but performing a probe incurs cost. The goal of a player is to learn his preference vector with minimal cost, by adopting the results of probes performed by other players. To facilitate communication, we assume that players collaborate by posting their grades for objects on a shared billboard: reading from the billboard is free. We consider players whose preference vectors are popular, i.e., players whose preferences are common to many other players. We present a sequential and a parallel algorithm to solve the problem with logarithmic cost overhead. An extended abstract of this work appeared in the 17th Ann. ACM Symp. on Parallelism in Algorithms and Architecture, Las Vegas, Nevada, July 2005. Research of B. Awerbuch supported by NSF grant ANIR-0240551 and NSF grant CCR-0311795. Research of Y. Azar supported in part by the German-Israeli Foundation and by the Israel Science Foundation. Research of B. Patt-Shamir supported in part by Israel Ministry of Science and Technology and by the Israel Science Foundation.     

Exploration of Player Behaviours from Broadcast Badminton Videos

Understanding an opposing player's behaviours and weaknesses is often the key to winning a badminton game. This study presents a system to extract game data from broadcast badminton videos, and visualize the extracted data to help coaches and players develop effective tactics. Specifically, we apply state-of-the-art machine learning methods to partition a broadcast video into segments, in which each video segment shows a badminton rally. Next, we detect players' feet in each video frame and transform the player positions into the court coordinate system. Finally, we detect hit frames in each rally, in which the shuttle moves towards the opposite directions. By visualizing the extracted data, our system conveys when and where players hit the shuttle in historical games. Since players tend to smash or drop shuttles under a specific location, we provide users with interactive tools to filter data and focus on the distributions conditioned by player positions. This strategy also reduces visual clutter. Besides, our system plots the shuttle hitting distributions side-by-side, enabling visual comparison and analysis of player behaviours under different conditions. The results and the use cases demonstrate the feasibility of our system.     

Lost at the Edge of Uncertainty: Measuring Player Uncertainty in Digital Games

Uncertainty has previously been identified as an important ingredient of engaging games. Design in games can create different levels of uncertainty in players that they can recognize and describe as being either attributable to external forces, such as chance or hidden information, or internal to their own understanding of what to do in relation to their own goals. While it appears that uncertainty can contribute both positive and negative play experiences, there is little work in trying to operationalize and measure this concept as a component of player experience. Reported in this article is an analysis of data from over 700 players using modern bi-factor analysis techniques resulting in a five factor psychometric scale which captures the broad feelings of players about uncertainty in games. Three of these specific factors appear to point toward a single generic factor of uncertainty that is internal to the players, one captures experiences relating to external uncertainty, with the final factor relating to player’s experience of exploring the game to resolve uncertainty. In order to further validate the scale, we conducted an experiment with a commercial puzzle game manipulating the duration of play with predicted outcomes on the different specific factors of the scale. Overall the scale shows promise with good statistical reliability and construct validity of the separate factors and so will be a useful tool for further investigating player experiences in digital games.     

基于重复博弈参与者有权重的秘密共享方案

在大多数参与者有权重的秘密共享方案中,各参与者子秘密份额数量的不同会导致秘密重构阶段产生不公平问题。为此,提出一个基于重复博弈的理性秘密共享方案。在参与者原有份额的基础上,为其构造数量差不超过1的有效子秘密份额,利用重复博弈使每个参与者可以获得其他参与者的全部份额,进而重构出秘密。分析结果表明,该方案可以使理性参与者始终遵守协议,完成秘密重构,且具有较高的安全性和良好的可扩展性。     

Is cheating a human function? The roles of presence,state hostility,and enjoyment in an unfair video game

In sports and board games, when an opponent cheats, the other players typically greet it with disdain, anger, and disengagement. However, work has yet to fully address the role of the computer cheating in video games. In this study, participants played either a cheating or a non-cheating version of a modified open-source tower-defense game. Results indicate that when a computer competitor cheats, players perceive the opponent as being more human. Cheating also increases player aggravation and presence, but does not affect enjoyment of the experience. Additionally, players that firmly believed that their opponent was controlled by the computer exhibited significantly less state hostility compared to players that were less certain of the nature of their competitor. Game designers can integrate subtle levels of cheating into computer opponents without any real negative responses from the players. The results indicate that minor levels of cheating might also increase player engagement with video games.