Pareto efficiency

Pareto efficiency, or Pareto optimality, is a state of allocation of resources in which it is impossible to make any one individual better off without making at least one individual worse off. The term is named after Vilfredo Pareto (1848–1923), an Italian economist who used the concept in his studies of economic efficiency and income distribution.[citation needed] The concept has applications in academic fields such as economics and engineering. For example, suppose there are two consumers A & B and only one resource X. Suppose X is equal to 20. Let us assume that the resource has to be distributed equally between A and B and thus can be distributed in the following way: (1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (10,10). Pareto efficiency in short A production-possibility frontier is an example of a Pareto Efficient Frontier, or Pareto-Optimal Front. It is commonly accepted[by whom?] In real-world practice, such compensations have unintended consequences.

Self-confirming equilibrium Consistent self-confirming equilibrium is a refinement of self-confirming equilibrium that further requires that each player correctly predicts play at all information sets that can be reached when the player's opponents, but not the player herself, deviate from their equilibrium strategies. Consistent self-confirming equilibrium is motivated by learning models in which players are occasionally matched with "crazy" opponents, so that even if they stick to their equilibrium strategy themselves, they eventually learn the distribution of play at all information sets that can be reached if their opponents deviate.

Best response Best response correspondence Figure 1. Reaction correspondence for player Y in the Stag Hunt game. , for each player from the set of opponent strategy profiles into the set of the player's strategies. So, for any given set of opponent's strategies represents player i 's best responses to Win-Win / Win-Lose / Lose-Lose Situations The Basics Win-win, win-lose, and lose-lose are game theory terms that refer to the possible outcomes of a game or dispute involving two sides, and more importantly, how each side perceives their outcome relative to their standing before the game. For example, a "win" results when the outcome of a negotiation is better than expected, a "loss" when the outcome is worse than expected.

Writing a Business Case A business case is intended to convince key decision-makers of the merits of a particular course of action. It is a key part of your project documentation: if a project brief describes what needs doing, and a project plan explains how, the business case sets out why. A good business case will explain the problem, identify all the possible options to address it, and allow decision-makers to decide which course of action will be best for the organisation. It will also allow any changes to the scope or timescale of the project to be assessed against the original purpose. What has Already Happened? Before you write a business case, you should have carried out a fair amount of research into the problem and possible solutions.

Prisoner's dilemma The prisoners' dilemma is a canonical example of a game analyzed in game theory that shows why two individuals might not cooperate, even if it appears that it is in their best interests to do so. It was originally framed by Merrill Flood and Melvin Dresher working at RAND in 1950. Albert W. Tucker formalized the game with prison sentence rewards and gave it the name "prisoner's dilemma" (Poundstone, 1992), presenting it as follows: Collective intentionality Collective intentionality demonstrated in a human formation. In philosophy, collective intentionality characterizes the phenomenon that occurs when two or more individuals undertake a task together. Examples include two individuals carrying a heavy table up a flight of stairs or dancing a tango. This phenomenon is approached from both psychological and normative perspectives, among others. Prominent philosophers working in the psychological manner are Raimo Tuomela, Kaarlo Miller, John R.

yEd - Graph Editor yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams. Create diagrams manually, or import your external data for analysis. Our automatic layout algorithms arrange even large data sets with just the press of a button. yEd is freely available and runs on all major platforms: Windows, Unix/Linux, and Mac OS X. Nash equilibrium In game theory, the Nash equilibrium is a solution concept of a non-cooperative game involving two or more players, in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only their own strategy.[1] If each player has chosen a strategy and no player can benefit by changing strategies while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitutes a Nash equilibrium. The reality of the Nash equilibrium of a game can be tested using experimental economics method. Stated simply, Amy and Will are in Nash equilibrium if Amy is making the best decision she can, taking into account Will's decision while Will's decision remains unchanged, and Will is making the best decision he can, taking into account Amy's decision while Amy's decision remains unchanged.

Collective intelligence Types of collective intelligence Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications.

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