1.
Money burning
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Money burning or burning money is the purposeful act of destroying money. In the prototypical example, banknotes are destroyed by literally setting them on fire, burning money decreases the wealth of the owner without directly enriching any particular party. However, according to the quantity theory of money, because it reduces the supply of money it increases by the amount the collective wealth of everyone else who holds money. Money is usually burned to communicate a message, either for artistic effect, as a form of protest, in some games, a player can sometimes benefit from the ability to burn money. Burning money is illegal in some jurisdictions, for the purposes of macroeconomics, burning money is equivalent to removing the money from circulation, and locking it away forever, the salient feature is that no one may ever use the money again. Burning money shrinks the money supply, and is therefore a special case of monetary policy that can be implemented by anyone. In the usual case, the central bank money from circulation by selling government bonds or foreign currency. The difference with money burning is that the bank does not have to exchange any assets of value for the money burnt. Money burning is thus equivalent to gifting the money back to the central bank, if the economy is at full employment equilibrium, shrinking the money supply causes deflation, increasing the real value of the money left in circulation. Assuming that the money is paper money with negligible intrinsic value, no real goods are destroyed. Instead, all surviving money slightly increases in value, everyone gains wealth in proportion to the amount of money they already hold, Economist Steven Landsburg proposes in The Armchair Economist that burning ones fortune is a form of philanthropy more egalitarian than deeding it to the United States Treasury. In 1920, Thomas Nixon Carver wrote that money into the sea is better for society than spending it wastefully. Central banks routinely collect and destroy worn-out coins and banknotes in exchange for new ones and this does not affect the money supply, and is done to maintain a healthy population of usable currency. The practice raises an interesting possibility, if an individual can steal the money before it is incinerated, the effect is the opposite of burning money, the thief is enriched at the expense of the rest of society. One such incident at the Bank of England inspired the 2001 TV movie Hot Money, another, more common near-opposite is the creation of counterfeit money. Another way to analyze the cost of forgery is to consider the effects of a banks monetary policy. The interest earnings on those bonds is turned over to the US Treasury, so any lost interest must be made up by U. S. taxpayers, behaviorally speaking, burning money is usually seen as a purely negative act. The cognitive impact of burning money can even be a motivational tool
2.
Extensive-form game
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Extensive-form games also allow representation of incomplete information in the form of chance events encoded as moves by nature. Whereas the rest of this article follows this approach with motivating examples. This general definition was introduced by Harold W. Kuhn in 1953, each players subset of nodes is referred to as the nodes of the player. Each node of the Chance player has a probability distribution over its outgoing edges, at any given non-terminal node belonging to Chance, an outgoing branch is chosen according to the probability distribution. A pure strategy for a player thus consists of a selection—choosing precisely one class of outgoing edges for every information set, in a game of perfect information, the information sets are singletons. Its less evident how payoffs should be interpreted in games with Chance nodes and these can be made precise using epistemic modal logic, see Shoham & Leyton-Brown for details. A perfect information two-player game over a tree can be represented as an extensive form game with outcomes. Examples of such games include tic-tac-toe, chess, and infinite chess, a game over an expectminimax tree, like that of backgammon, has no imperfect information but has moves of chance. For example, poker has both moves of chance, and imperfect information, the numbers by every non-terminal node indicate to which player that decision node belongs. The numbers by every terminal node represent the payoffs to the players, the labels by every edge of the graph are the name of the action that edge represents. The initial node belongs to player 1, indicating that player 1 moves first, play according to the tree is as follows, player 1 chooses between U and D, player 2 observes player 1s choice and then chooses between U and D. The payoffs are as specified in the tree, there are four outcomes represented by the four terminal nodes of the tree, and. The payoffs associated with each outcome respectively are as follows, if player 1 plays D, player 2 will play U to maximise his payoff and so player 1 will only receive 1. However, if player 1 plays U, player 2 maximises his payoff by playing D, player 1 prefers 2 to 1 and so will play U and player 2 will play D. This is the perfect equilibrium. An advantage of representing the game in this way is that it is clear what the order of play is, the tree shows clearly that player 1 moves first and player 2 observes this move. However, in some games play does not occur like this, one player does not always observe the choice of another. An information set is a set of decision nodes such that, in extensive form, an information set is indicated by a dotted line connecting all nodes in that set or sometimes by a loop drawn around all the nodes in that set
3.
Game theory
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Game theory is the study of mathematical models of conflict and cooperation between intelligent rational decision-makers. Game theory is used in economics, political science, and psychology, as well as logic, computer science. Originally, it addressed zero-sum games, in one persons gains result in losses for the other participants. Today, game theory applies to a range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals. Modern game theory began with the idea regarding the existence of equilibria in two-person zero-sum games. Von Neumanns original proof used Brouwer fixed-point theorem on continuous mappings into compact convex sets and his paper was followed by the 1944 book Theory of Games and Economic Behavior, co-written with Oskar Morgenstern, which considered cooperative games of several players. The second edition of this provided an axiomatic theory of expected utility. This theory was developed extensively in the 1950s by many scholars, Game theory was later explicitly applied to biology in the 1970s, although similar developments go back at least as far as the 1930s. Game theory has been recognized as an important tool in many fields. With the Nobel Memorial Prize in Economic Sciences going to game theorist Jean Tirole in 2014, John Maynard Smith was awarded the Crafoord Prize for his application of game theory to biology. Early discussions of examples of two-person games occurred long before the rise of modern, the first known discussion of game theory occurred in a letter written by Charles Waldegrave, an active Jacobite, and uncle to James Waldegrave, a British diplomat, in 1713. In this letter, Waldegrave provides a mixed strategy solution to a two-person version of the card game le Her. James Madison made what we now recognize as an analysis of the ways states can be expected to behave under different systems of taxation. In 1913 Ernst Zermelo published Über eine Anwendung der Mengenlehre auf die Theorie des Schachspiels and it proved that the optimal chess strategy is strictly determined. This paved the way for more general theorems, the Danish mathematician Zeuthen proved that the mathematical model had a winning strategy by using Brouwers fixed point theorem. In his 1938 book Applications aux Jeux de Hasard and earlier notes, Borel conjectured that non-existence of mixed-strategy equilibria in two-person zero-sum games would occur, a conjecture that was proved false. Game theory did not really exist as a field until John von Neumann published a paper in 1928. Von Neumanns original proof used Brouwers fixed-point theorem on continuous mappings into compact convex sets and his paper was followed by his 1944 book Theory of Games and Economic Behavior co-authored with Oskar Morgenstern
4.
Ambiguity
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Ambiguity is a type of uncertainty of meaning in which several interpretations are plausible. It is thus an attribute of any idea or statement whose intended meaning cannot be resolved according to a rule or process with a finite number of steps. The concept of ambiguity is contrasted with vagueness. In ambiguity, specific and distinct interpretations are permitted, whereas with information that is vague, context may play a role in resolving ambiguity. For example, the piece of information may be ambiguous in one context. The lexical ambiguity of a word or phrase pertains to its more than one meaning in the language to which the word belongs. Meaning here refers to whatever should be captured by a good dictionary, for instance, the word bank has several distinct lexical definitions, including financial institution and edge of a river. Another example is as in apothecary, one could say I bought herbs from the apothecary. This could mean one actually spoke to the apothecary or went to the apothecary, the context in which an ambiguous word is used often makes it evident which of the meanings is intended. If, for instance, someone says I buried $100 in the bank, however, some linguistic contexts do not provide sufficient information to disambiguate a used word. Lexical ambiguity can be addressed by algorithmic methods that automatically associate the meaning with a word in context. The use of multi-defined words requires the author or speaker to clarify their context, the goal of clear concise communication is that the receiver have no misunderstanding about what was meant to be conveyed. Ambiguity is a tool of political science. More problematic are words whose senses express closely related concepts, good, for example, can mean useful or functional, exemplary, pleasing, moral, righteous, etc. I have a daughter is not clear about which sense is intended. The various ways to apply prefixes and suffixes can also create ambiguity, syntactic ambiguity arises when a sentence can have two different meanings because of the structure of the sentence—its syntax. This is often due to an expression, such as a prepositional phrase. He ate the cookies on the couch, for example, could mean that he ate those cookies that were on the couch, to get in, you will need an entrance fee of $10 or your voucher and your drivers license
5.
Coordination game
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In game theory, coordination games are a class of games with multiple pure strategy Nash equilibria in which players choose the same or corresponding strategies. If this game is a game, then the following inequalities in payoffs hold for player 1, A > B, D > C, and for player 2. In this game the strategy profiles and are pure Nash equilibria and this setup can be extended for more than two strategies, as well as for a game with more than two players. A typical case for a game is choosing the sides of the road upon which to drive. In a simplified example, assume that two drivers meet on a dirt road. Both have to swerve in order to avoid a head-on collision, if both execute the same swerving maneuver they will manage to pass each other, but if they choose differing maneuvers they will collide. In the payoff matrix in Fig.2, successful passing is represented by a payoff of 10, in this case there are two pure Nash equilibria, either both swerve to the left, or both swerve to the right. In this example, it doesnt matter which side both players pick, as long as they pick the same. This is not true for all games, as the pure coordination game in Fig.3 shows. Pure coordination is the game where the players prefer the same Nash equilibrium outcome, here both players prefer partying over both staying at home to watch TV. The outcome Pareto dominates the outcome, just as both Pareto dominate the two outcomes, and. This is different in type of coordination game commonly called battle of the sexes. In this game both players prefer engaging in the activity over going alone, but their preferences differ over which activity they should engage in. Player 1 prefers that they both party while player 2 prefers that they stay at home. Finally, the stag hunt game in Fig.5 shows a situation in both players can benefit if they cooperate. However, cooperation might fail, because each hunter has an alternative which is safer because it not require cooperation to succeed. This example of the conflict between safety and social cooperation is originally due to Jean-Jacques Rousseau. Coordination games also have mixed strategy Nash equilibria, since d > b and d-b < a+d-b-c, p is always between zero and one, so existence is assured
6.
Jean Tirole
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Jean Tirole is a French professor of economics. He focuses on industrial organization, game theory, banking and finance, in 2014 he was awarded the Nobel Memorial Prize in Economic Sciences for his analysis of market power and regulation. Tirole received engineering degrees from the École Polytechnique in Paris in 1976 and he graduated as a member of the elite Corps of Bridges, Waters and Forests. Tirole pursued graduate studies at the Paris Dauphine University and was awarded a DEA degree in 1976, in 1981, he received a Ph. D. in economics from the Massachusetts Institute of Technology for his thesis titled Essays in economic theory, under the supervision of Eric Maskin. After receiving his doctorate from MIT in 1981, he worked as a researcher at the École nationale des ponts et chaussées until 1984, from 1984–1991, he worked as Professor of Economics at MIT. From 1994 to 1996 he was a Professor of Economics at the École Polytechnique, Tirole was involved with Jean-Jacques Laffont in the project of creating a new School of Economics in Toulouse. He was president of the Econometric Society in 1998 and of the European Economic Association in 2001, Tirole has been a member of the Académie des Sciences morales et politiques since 2011, the Conseil danalyse économique since 2008 and the Conseil stratégique de la recherché since 2013. In the early 2010s, he showed that generally tend to take short-term risks. Tirole was awarded the Nobel Memorial Prize in Economic Sciences in 2014 for his analysis of market power and he is a foreign honorary member of the American Academy of Arts and Sciences and of the American Economic Association. He has also been a Sloan Fellow and a Guggenheim Fellow and he was a fellow of the Econometric Society in 1986 and an Economic Theory Fellow in 2011. In 2007 he was awarded the highest award of the French CNRS and he is among the most influential economists in the world according to IDEAS/RePEc. His research covers industrial organization, regulation, game theory, banking and finance, psychology and economics, international finance, the Theory of Industrial Organization, MIT Press,1988. Dynamic Models of Oligopoly (avec Drew Fudenberg, Harwood Academic Publishers GMbH,1986, the Theory of Industrial Organization, MIT Press,1988. A Theory of Incentives in Regulation and Procurement, MIT Press,1993, the Prudential Regulation of Banks, MIT Press,1994. Competition in Telecommunications, MIT Press,1999, financial Crises, Liquidity and the International Monetary System, Princeton University Press,2002. The Theory of Corporate Finance, Princeton University Press,2005, Association of American Publishers 2006 Award for Excellence. Balancing the Banks, Princeton University Press,2010, inside and Outside Liquidity, MIT Press,2011
7.
Preference (economics)
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In economics and other social sciences, preference is the ordering of alternatives based on their relative utility, a process which results in an optimal choice. The character of the preferences is determined purely by taste factors, independent of considerations of prices, income. With the help of the scientific method many practical decisions of life can be modelled, in 1926 Ragnar Frisch developed for the first time a mathematical model of preferences in the context of economic demand and utility functions. Up to then, economists had developed a theory of demand that omitted primitive characteristics of people. This omission ceased when, at the end of the 19th, because binary choices are directly observable, it instantly appealed to economists. The search for observables in microeconomics is taken further by revealed preference theory. Since the pioneer efforts of Frisch in the 1920s, one of the issues which has pervaded the theory of preferences is the representability of a preference structure with a real-valued function. This has been achieved by mapping it to the mathematical index called utility, von Neumann and Morgenstern 1944 book Games and Economic Behaviour treated preferences as a formal relation whose properties can be stated axiomatically. Even though the economics of choice can be examined either at the level of utility functions or at the level of preferences, suppose the set of all states of the world is X and an agent has a preference relation on X. It is common to mark the weak preference relation by ⪯, the symbol ∼ is used as a shorthand to the indifference relation, x ∼ y ⟺, which reads the agent is indifferent between y and x. The symbol ≺ is used as a shorthand to the preference relation, x ≺ y ⟺. In everyday speech, the statement x is preferred to y is generally understood to mean that someone chooses x over y, however, decision theory rests on more precise definitions of preferences given that there are many experimental conditions influencing peoples choices in many directions. Suppose a person is confronted with an experiment that she must solve with the aid of introspection. She is offered apples and oranges, and is asked to choose one of the two. A decision scientist observing this event would be inclined to say that whichever is chosen is the preferred alternative. Under several repetitions of experiment, if the scientist observes that apples are chosen 51% of the time it would mean that x ≻ y. If half of the oranges are chosen, then x ∼ y. Finally, if 51% of the time she chooses oranges it means that y ≻ x, preference is here being identified with a greater frequency of choice
8.
Simultaneous game
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In game theory, a simultaneous game is a game where each player chooses his action without knowledge of the actions chosen by other players. Normal form representations are used for simultaneous games. Rock-Paper-Scissors, a widely played game, is a real life example of a simultaneous game. Both make a decision at the time, randomly, without prior knowledge of the opponents decision. There are two players in game and each of them has 3 different strategies to make decision. We will display Player 1’s strategies as rows and Player 2’s strategies as columns, in the table, the numbers in red represent the payoff to Player 1, the numbers in blue represent the payoff to Player 2. Hence, the pay off for a 2 player game in Rock-Paper-Scissors will look like this, In game theory terms, Prisoner dilemma is an example of simultaneous game
9.
Nash equilibrium
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The Nash equilibrium is one of the foundational concepts in game theory. The reality of the Nash equilibrium of a game can be tested using experimental economics methods, Game theorists use the Nash equilibrium concept to analyze the outcome of the strategic interaction of several decision makers. The simple insight underlying John Nashs idea is that one cannot predict the result of the choices of multiple decision makers if one analyzes those decisions in isolation, instead, one must ask what each player would do, taking into account the decision-making of the others. Nash equilibrium has been used to analyze hostile situations like war and arms races and it has also been used to study to what extent people with different preferences can cooperate, and whether they will take risks to achieve a cooperative outcome. It has been used to study the adoption of technical standards, the Nash equilibrium was named after John Forbes Nash, Jr. A version of the Nash equilibrium concept was first known to be used in 1838 by Antoine Augustin Cournot in his theory of oligopoly, in Cournots theory, firms choose how much output to produce to maximize their own profit. However, the best output for one firm depends on the outputs of others, a Cournot equilibrium occurs when each firms output maximizes its profits given the output of the other firms, which is a pure-strategy Nash equilibrium. Cournot also introduced the concept of best response dynamics in his analysis of the stability of equilibrium, however, Nashs definition of equilibrium is broader than Cournots. It is also broader than the definition of a Pareto-efficient equilibrium, the modern game-theoretic concept of Nash equilibrium is instead defined in terms of mixed strategies, where players choose a probability distribution over possible actions. The concept of the mixed-strategy Nash equilibrium was introduced by John von Neumann and Oskar Morgenstern in their 1944 book The Theory of Games, however, their analysis was restricted to the special case of zero-sum games. They showed that a mixed-strategy Nash equilibrium will exist for any game with a finite set of actions. The key to Nashs ability to prove far more generally than von Neumann lay in his definition of equilibrium. According to Nash, a point is an n-tuple such that each players mixed strategy maximizes his payoff if the strategies of the others are held fixed. Thus each players strategy is optimal against those of the others, since the development of the Nash equilibrium concept, game theorists have discovered that it makes misleading predictions in certain circumstances. They have proposed many related solution concepts designed to overcome perceived flaws in the Nash concept, one particularly important issue is that some Nash equilibria may be based on threats that are not credible. In 1965 Reinhard Selten proposed subgame perfect equilibrium as a refinement that eliminates equilibria which depend on non-credible threats, other extensions of the Nash equilibrium concept have addressed what happens if a game is repeated, or what happens if a game is played in the absence of complete information. Informally, a profile is a Nash equilibrium if no player can do better by unilaterally changing his or her strategy. To see what this means, imagine that each player is told the strategies of the others
10.
Pareto efficiency
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The concept is named after Vilfredo Pareto, Italian engineer and economist, who used the concept in his studies of economic efficiency and income distribution. The concept has applications in fields such as economics, engineering. The Pareto frontier is the set of all Pareto efficient allocations, an allocation is defined as Pareto efficient or Pareto optimal when no further Pareto improvements can be made. The notion of Pareto efficiency can also be applied to the selection of alternatives in engineering, each option is first assessed under multiple criteria and then a subset of options is identified with the property that no other option can categorically outperform any of its members. Pareto optimality is a defined concept used to determine when an allocation is optimal. If there is a transfer that satisfies this condition, the reallocation is called a Pareto improvement, when no further Pareto improvements are possible, the allocation is a Pareto optimum. A formal definition for an economy is as follows, Consider an economy with i agents and j goods. Here in this economy, feasibility refers to an allocation where the total amount of each good that is allocated sums to no more than the total amount of the good in the economy. It is important to note that a change from a generally inefficient economic allocation to an efficient one is not necessarily a Pareto improvement, even if there are overall gains in the economy, if a single agent is disadvantaged by the reallocation, the allocation is not Pareto optimal. For instance, if a change in economic policy eliminates a monopoly and that subsequently becomes competitive. However, since the monopolist is disadvantaged, this is not a Pareto improvement, thus, in practice, to ensure that nobody is disadvantaged by a change aimed at achieving Pareto efficiency, compensation of one or more parties may be required. However, in the world, such compensations may have unintended consequences. They can lead to incentive distortions over time as agents anticipate such compensations, under certain idealized conditions, it can be shown that a system of free markets, also called a competitive equilibrium, will lead to a Pareto efficient outcome. This is called the first welfare theorem and it was first demonstrated mathematically by economists Kenneth Arrow and Gérard Debreu. However, the result only holds under the assumptions necessary for the proof. In the absence of information or complete markets, outcomes will generally be Pareto inefficient. In addition to the first welfare theorem linking the concepts of Pareto optimal allocations and free markets, the second welfare theorem is essentially the reverse of the first welfare theorem. It states that under similar ideal assumptions, any Pareto optimum can be obtained by some competitive equilibrium, or free market system, a weak Pareto optimum is an allocation for which there are no possible alternative allocations whose realization would cause every individual to gain
11.
Graphical game theory
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In game theory, the common ways to describe a game are the normal form and the extensive form. The graphical form is a compact representation of a game using the interaction among participants. Consider a game with n players with m strategies each and we will represent the players as nodes in a graph in which each player has a utility function that depends only on him and his neighbors. As the utility depends on fewer other players, the graphical representation would be smaller. Each node i in G has a function u i, d i +1 → R, U i specifies the utility of player i as a function of his strategy as well as those of his neighbors. For a general n players game, in each player has m possible strategies. The size of the representation for this game is O where d is the maximal node degree in the graph. If d ≪ n, then the graphical representation is much smaller. In case where each players utility function depends only on one player, The maximal degree of the graph is 1. So, the size of the input will be n m 2. Finding Nash equilibrium in a game takes exponential time in the size of the representation, if the graphical representation of the game is a tree, we can find the equilibrium in polynomial time. In the general case, where the degree of a node is 3 or more. In Vazirani, Vijay V. Nisan, Noam, Roughgarden, Tim, Tardos, Michael Kearns, Michael L. Littman and Satinder Singh Graphical Models for Game Theory