Example Of Game Theory Essay

In this paper, we present an optimal attack framework that can take down a sensing system even with truth discovery empowered. Compared with the aforementioned native attack strategy, the proposed optimal attack strategy makes the malicious workers behave ``smarter''. They can successfully disguise themselves as normal workers. If there is little hope to achieve the attack goal on some objects, they will tend to agree with the normal workers on those objects to gain higher weights, and in turn, can exert stronger impact on other

The two strategies I played against are to defect or cooperate when playing the Flip-a-coin strategy and the Massive Retaliatory Strike. In both cases when I cooperated one-hundred percent, I had a huge loss to the computer, my score for the flip coin toss was 21 to the computers 86 when I choose just to cooperate. In the massive retaliatory strike when I cooperated all the time my score was 60 and the machine was 60, so in this case, no one won, but we tied or it is a win-win scenario. In the flip-coin toss when I defected, I won, my score was 76 against the computer’s score of 6. When I defected all the time in the massive retaliatory strike, my score was 24 to the computer’s score of 16. When I played the game tit for tat and defected all

Fig 4 defines the efficiency curve of the d i s c n m i n a n t variable. The weaker the dlscriminant power of this variable, the nearest to the bissectmg line its efficiency line. A perfect discrimmant variable has a triangular efhciency x y z . The set of all the detectors determines a set of values for the game. The highest value v* for the insurer is reached when the p a y o f f line is horizontal. This can be roughly seen as follows (for a more rigorous proof see LUCE and RAIFFA (1957, pp. 394-396)): the critical value, m o v i n g from left to right, generates a family of hnes with decreasing slope. If .Pat chooses a d e t e c t o r with

Game Trees, as mentioned earlier, are very useful in determining what outcomes are most likely to occur, in other words, the most rational and logical decision. For example, Poundstone describes a dilemma between a thief trying to sell a priceless item and a questionable man with few scruples. The scenario is a perfect example of the “Prisoner’s Dilemma” and can be analyzed using a Game Tree. The most logical decision for both parties is to cheat, or select Traitor. Yet this decision undermines the “common good” of mutual cooperation which benefits both parties. Logic is what blocks the best outcome, simply due to the fact that rationality dictates a prime strategy of cheating and defection. The trend in such situations, similar to a business

The attacker (botmaster) achieves the benefits from botnets are information dispersion, information harvesting, and information processing [32]. The goal of information dispersion includes sending out spam, creating denial of service attacks (DoS), providing false information from illegally controlled sources, etc. The information harvesting aims at obtaining identity data, financial data, password data, relationship data (i.e., email addresses of friends), and any other type of data available on the host. The purpose of information processing is to process data such as cracking a password stored as a MD5 hash.

One company (Company A) will have a higher profit if it sends out two fleets ($12 000) rather than $10 000) if the other company (Company B) has only one fleet. If Company B sends out two fleets, Company A will have a higher profit if it also sends out two fleets ($ 7 500 rather than $4 000). The same thinking will affect Company B’s best strategy which is to send out two fleets regardless if company A will send out one or two. They will only have a profit of $7 500 instead of having a profit of $10 000 if they will both choose to send out two fleets instead of one only

“The Nash Equilibrium is a concept of game theory where the optimal outcome of a game is one where no player has an incentive to deviate from his chosen strategy after considering an opponent 's choice” (Investopedia). The Nash equilibrium is said to exist if a player has no incentive to independently change his course of action in the game. This is a point where both players

After playing through the stock market, I have more experience for playing in the future. Over time I have learned to be patience and intelligence in handling the situation. I have been lost several times but that’s okay because I have learned lessons for the next time. I play the stock through the guidance of the teacher or through information technology such as watching television, reading newspapers or magazines. And I also understand this game

In this game tree, the two player’s state of minds and actions are displayed. Consider the case scenario of Boeing and General Electric (GE) playing a sequential game, GE makes the first move in the process followed by Boeing based on their interpretation of GE’s

The scientific field of game theory is the usage of mathematics to predict and analyze “conflict, cooperation, and behaviors of thinking people” and the different strategies that can be applied in each case. Using mathematical fields such as calculus, statistics, and others, people who study game theory are able to formulate models and find the outcomes of a variety of different strategies. More importantly, game theory is not limited to games such as chess or poker, but a wide variety of fields including economics, computer science, biology and even political science. To understand why game theory underlies these other fields, one must first understand what a game is.

As mention before, it is difficult for the players to achieved complete randomization on the moves. Players will follow some sort of pattern, unconsciously, that the opponents might observe. Therefore, they can observe the opponents’ strategy or action history or own payoff history. As a player, we can observe the pattern being used by our opponents and develop a strategy to counteract the moves. Of course, this could lead to another learning process for our opponents, for we ourselves, in the end, will develop the pattern to beat the opponents’ pattern. Opponents may realize this and change their pattern.

Game Theory and Economic Analysis presents the wide range of current contributions of game theory to economics. The chapters fall broadly into two

According to Shmaya (2006) suggest that player one should arrive with an overview of the implantations payoff to determine the game structure, Player one opposition is to determine the actions of player two. Also, recognizing that player one does not have the required information, whereas, one player have an advantage because the level of information is distribute evenly between the two players. Therefore, giving player two the advantage of achieving the necessary systematic outcome he would gain wealth that is equivalent to player one loss, and this consider a stochastic signals (Shmaya, 2006, p., 156-157).

John Von Neumann (mathematician) and Oskar Morgenstern (economist) introduced game theory in 1950s for the first time as a mathematical instrument, used to define and solve games, []. It provides a set of quantitative and analytical tools for

The internet is an integral way of conducting daily business from government agencies to entertainers. Protection of attack, damage, or unauthorized access is necessary with the increase of mobile users, digital applications and data networks. A cyber-security problem can be viewed as a conflict-resolution scenario that typically consists of a security system and at least two decision makers (e.g. attacker and defender) that can each have competing objectives. For instance, the defender may be interested in ensuring that the system performs at or above a certain acceptable level. The attacker’s objective may be to disrupt the system and degrade it. Game theory is a well-established tool that can be used to analyze such problems. In this work, we present a brief survey of how game theory can be used to find appropriate strategies for both the attacker and the administrator (defender). We model the interactions between them as a stochastic game. Various formulations of game theory will be presented to deal with different cyber security situations. We will present mathematical models of security systems to analyze the system’s performance and to predict the likely behavior of key decision makers that influence/control the system.