Game Theory in Economics

Game theory is a concept of decision making that considers more elements beyond just benefits minus costs. Specifically, it includes the interaction between participants. In economics, the theory attempts to predict the participants’ optimal decisions. It has found a core place in economic decision-making and policy-making for its inherent ability to predict reactions in resource allocation, business negotiation, and other economic aspects. Game theory is mostly associated with decision-theory and other contexts such as cooperation and negotiations.
From its function, it is evident that the game theory is largely used in the study of the human decision making processes. In psychology, its equivalent is known as the theory of social situations. In economics, however, game theory tends to focus on sets of outcomes known as equilibrium that represent the most rational solutions to each situation. Game theory emanates from the complexity of human interactions; thus, in a situation where an individual is dealing with an inanimate object such as a tree, he or she does not expect the tree to fight back or respond (Layton-Brown and Sham 51).
The environment can also be considered neutral to what is done to the tree, at least in direct and rational response. In human interactions, however, each action by an actor emanates from a situation and elicits a response. Each actor must thus recognize how his of her interaction with other rational actors works so as to foster cooperation and stem conflict. Every game is primarily dependent on a core strategic interdependence. Strategic interdependence is defined by two major types, namely simultaneous and sequential ones.

In simultaneous strategy, the players act at the same time or at different times UT without perfect knowledge-essentially what the other players are doing. In the latter strategy, however, the players move in sequence, with knowledge of all the past actions by the other players. Sequential-strategy games allow each player to review the past moves and rationalize his next move with more information (Osborne 30). He or she can also use that information to anticipate the responses to his or her move as well as how he or she will have to respond after that.
In practice, this information comes together during the process to select the best solution as each player must IEEE the board from several different angles, including how the other player views all his or her moves. Sequence-strategy games are different as they are based on a logical circle. Although either player in the Prisoner’s Dilemma, for example, is unaware of the other’s actions and vice versa, he or she knows that the other prisoner has been presented with the same choices in the same situation. The game is thus a logical circle that requires the individual actor to review his position without sufficient information.
A game in strategic form possesses no temporal component. Instead, each player must choose his or her strategy without knowing the other players choice or move. As a matter of fact, this is the central tenet of the Prisoner’s Dilemma. In a game with accurate information, however, there are no simultaneous moves as each player moves at a time. Where the game is in extensive form, each player is at any particular point during play also aware of all the previous choices of commitment game tree as represented below. Figure 1: Quality choice with commitment game tree Any player a move makes is represented above as the next node.
The tree starts at the root and ends at the terminal node where players’ playoffs can be determined. In the example above, Player I is the service provider who makes the first move by choosing to provide either high or low quality service. Player II is the customer who is then aware of that choice and can thus whether buy or not buy in either case (Osborne 70). The payoffs can then be determined by the choice made. The backward induction technique is best suited to fully analyze such extensive games with perfect information since the players and thus the nodes move in sequence.
Thus, one only needs consider the last possible choice. In the game tree, Player I makes his decision with the rational knowledge of what Player II might choose. The ideal solution is hence one where the service provider offers high-quality service and Player II buys it. The complication that emanates from the use of backward induction technique emanates when/if one player is indifferent to some more than one move at a single node. The complexity makes rational prediction less accurate and means that instead of Player Xi’s decision affecting Player Sis’s choices, the reverse happens.
Backward induction, however, defines the extent of the game by defining the complete plan for each player as it works from root of the tree to the outcome, providing a step-by-step overview of what should be done and when (Giants 89). A critical point in the development of game theory was John Anna’s demonstration in 1950 that all finite games have an equilibrium point (Pyongyang 105). At this optimal point, each player must make decisions that are best for them while considering their opponents choices. The example best given for this is the prisoners’ dilemma.
In the analogy, the decision of either broker has implications that are dependent on what the other person chooses. In the scenario, the authorities arrest two fraudsters and take them to different interrogation rooms. They offer them an incentive, that each will have their prison sentence reduced if they betray the other. In practice, this places each prisoner in a dilemma where the only choices are to cooperate or defect. The payoff in this case is a reduction in the years to be spent in prison. The most significant element in this game is that it is a non-zero-sum game where both players eave imperfect information.
In this context, neither player knows what the other will do until both of them have made their moves. The prisoner’s dilemma illustrates the conundrum that multiple agents have in a game. According to single-agent decision theory, all that one has to do is to assess the choices and determine the optimal strategy. The theory encourages the agent to improve and maximize his or her payoff in the environment. In essence, this analogy is used in political theory and economic contexts such as union negotiations where collective bargaining is the strategy of hospice.
In multiple agent contexts, the game theory normally arrives to three major outcomes: Nash equilibrium Parent optimality Dominant strategy equilibrium In game theory, the Nash equilibrium is an example of a non-cooperative game that defines a strategy for each player that that player cannot improve upon alone. Since recommendation. To illustrate the Nash equilibrium, it is necessary to consider the example below that shows two companies selling items at the price of one, two, or three per item. Items B p=l p=2 p=3 Items A 40,-20 -10,50 20,20 90,10 -20,40 10,90 50,50
The payoffs for such companies are profits which are determined by subtracting costs of all kinds from the price of each (Harrington 92). The rationale is that the company that sells them at the least price gets more customers than the other, and thus, given the economies of scale, more profits. From p=3, it is clear that either competitor can benefit from reducing their price even if the other players does nothing. Specifically, this is an example of a situation that is not Nash equilibrium. In B (p=2)/A (p=3), A charges $3 and B charges $2.
A can benefit from cutting its price to 1, a price strategy that can be applied across the board except for the upper left corner where both companies have a non-Nash equilibrium. In mixed strategy games, the payoff should be the average of that that can be obtained through all the other strategies. German economist Reinhardt Selene refined the Nash equilibrium to include the small probability that either or both players have trembling hands (Layton-Brown and Sham 60). Essentially, a tremble’ is an n error in Judgment or reason.
In particular, this refinement is applied in macroeconomic theory to account or the protection of one player from another mistake. Parent optimality is the situation in a game where no player can be made better off without making another player worse off. In resource allocation, this is known as Parent efficiency if it is not possible to allocate resources in such a way that making some people better off will not cost others. The assumption is that there is a defined number of goods in a society. Where there is only one good then each allocation becomes Parent efficient.
However, where there are two people and two goods the rationale is more difficult to e unless each prefers either of the goods. It is important to note that every game has a Parent optimal profile in at least one context. In economics, specifically business contexts, Parent efficiency is where one party makes profit at the expense of the other. At Parent optimum, the economic efficiency is the point where no one can make more profit at the expense of the other (Osborne 30). The exchange or reallocation will therefore not harm the other party. These conditions would hold if the economy had perfect competition with no externalities.
In fact, this makes it hard o apply it in real-life economic policymaking because of the presence of externalities. In Nash equilibrium, a player’s best choice is dependent on whether the other players keep to their stipulated strategies (Pyongyang 59). However, there are some situations when there is a dominant strategy such that a player’s best choice is the same no matter what the others do. The antonym of this is the dominated strategy-a uniformly bad choice for each player. In economic policymaking, dominant strategy makes it easier to decide on resource allocation and other such policies by seeking the dominant strategy.
Unlike other equilibrium and contexts, the dominant Specifically, this consideration makes it such that selecting the good does not affect and is not itself affected by the choices of other players. As discussed in preceding sections, selecting the best strategy is often a rational process that requires information and observation of what the other player might do. A significant concept in game theory is that it might be in the player’s interest to place him in a situation where he will have less freedom of action in future.
When Polaroid Corporation’s monopoly on the instant photography market was threatened by Kodak, for example, Polaroid decided not to diversify to fill in the gap (Osborne 20). Instead, it fought to maintain its monopoly, dragging Kodak through a fourteen year billion-dollar lawsuit. In conclusion, the application of game theory in economics is wide and varied. It covers many aspects related to economic decision making and resource allocation. In practice, the theoretical tenets of assessing how one player’s moves affects and is affected by those of others makes it possible to effectively select the best choice in ACH situation.
The four tenets of game theory are thus competition, conflict, cooperation, and interdependence. These form the foundation of game theory where players have to employ rational thinking and strategy under specified rules to arrive to a conclusion. Non-zero-sum games and other many agent contexts where cooperation is the most effective solution are the most used to study economic behavior. Works Cited Giants, Herbert. Game Theory Evolving: A Problem-centered Introduction to Modeling Strategic Behavior. Princeton: Princeton University Press, 2000. Print.