康尼島（英語：），又译科尼島，是位於美國紐約市布魯克林區的半島，原本為一座海島，其面向大西洋的海灘是美國知名的休閒娛樂區域。居民大多集中位於半島的西側，約有六萬人左右，範圍西至希捷社區，東至布萊登海灘和曼哈頓海灘，而北至葛瑞福山德社區。 二十世紀前葉在美國極為知名的太空星際樂園即是以康尼島作為主要的腹地，該樂園在二次大戰後開始衰退，並持續荒廢了許久。在最近幾年，康尼島因為凱斯班公園的開幕而重新繁榮起來，凱斯班公園是職棒小聯盟球隊布魯克林旋風的主要球場。旋風隊在當地十分受到歡迎，每季開賽時都會吸引許多球迷到場觀戰。 ..

Anjos da guarda são os anjos que segundo as crenças cristãs, Deus envia no nosso nascimento para nos proteger durante toda a nossa vida. Argumenta-se que a Bíblia sustenta em algumas ocasiões a crença do anjo da guarda: "Vou enviar um anjo adiante de ti para ..

Altay Cumhuriyeti (Rusça: Респу́блика Алта́й / Respublika Altay; Altay Türkçesi: Алтай Республика / Altay Respublika), Rusya'nın en güneyinde yer alan, federasyona bağlı bir özerk cumhuriyet. Orta Asya'da Asya kıtasının coğrafî merkezinin hemen kuzeyinde ve ..

沙羅週期長度為18年11天，本週期包含70次日食，其中公元3000年以前有49次。 註：下表各項數據均為食分最大地點的情況。寬度指該地點食甚時刻月球的本影（全食時）或偽本影（環食時）落在地表的寬度，持續時間指該地點食既到生光的時間，即全食或環食的持續時間，全環食（亦稱混合食）發生時，食分最大處為全食。最後兩項參數不適用於偏食。 本周期最終結束於3378年6月17日。

希西家王 (希伯來語：，英語：）是猶大末年的君主，也是猶大國歷史中極尊重上帝的君王，在位29年。終年54歲。他在位的年份有兩種說法：其一是前715年-前687年；另一種是前716年-前687年。他的德行在其前後的猶大列王中，没有一個能及他。其希伯來名字的意思是“被神加力量”。 希西家的父親亞哈斯是一個背逆上帝的君王。因此在希西家當政之初的猶大國，無論政治，宗教上都极其黑暗。根據《聖經》記載，因为北國以色列被亞述攻滅，亞述王可以趁勢来攻打猶大國；又猶大的先王亞哈斯曾封鎖了聖殿之路，引導舉國崇拜偶像，大大得罪上帝。若非上帝的憐憫，為了堅定向大衛家所說的應許，猶大國的暫得幸存。希西家在二十五歲就登基作王，且正在國家危急之秋，由於行耶和華上帝眼中看為正的事，因而得上帝的憐憫，得以成功脫離亞述大軍的攻擊和一場致死的大病。他樂於聽從當代先知以賽亞的指導，使他為上帝大發熱心。 ..

The OnePlus 2 (also abbreviated as OP2) is a smartphone designed by OnePlus. It is the successor to the OnePlus One. OnePlus revealed the phone on 28 July 2015 via virtual reality, using Google's Cardboard visor and their own app. OnePlus sold out 30,000 units ..

兴隆街镇，是中华人民共和国四川省内江市资中县下辖的一个乡镇级行政单位。 兴隆街镇下辖以下地区： 兴隆街社区、兴松村、玄天观村、三元村、金星村、三皇庙村、双桥村、红庙子村、华光村、高峰村、芦茅湾村、篮家坝村、五马村和解放村。

Национальная и университетская библиотека (словен. Narodna in univerzitetna knjižnica, NUK), основанная в 1774 году, — один из важнейших образовательных и культурных учреждений Словении. Она располагается в центре столицы Любляна, между улицами Турьяшка (Turjaška ..

Mauser M1924 (или M24) — серия винтовок компании Mauser, использовавшихся в армиях Бельгии и Югославии. Внешне напоминают чехословацкие винтовки vz. 24, в которых использовались стандартный открытый прицел, патроны калибра 7,92×57 мм (или 8×57 мм), укороченные ..

第三条道路（英語：），又称新中间路线（Middle Way），是一种走在自由放任资本主义和传统社会主义中间的一种政治经济理念的概称。它由中间派所倡导，是社会民主主义的一个流派，英国工党称其为「现代化的社会民主主义」。它的中心思想是既不主张纯粹的自由市场，也不主张纯粹的社會主義，主张在两者之间取折衷方案。 第三条道路不只单单是走在中间，或只是一种妥协或混合出来的东西，第三条道路的提倡者看到了社会主义和资本主义互有不足之处，所以偏向某一极端也不是一件好事，第三条道路正正是揉合了双方主义的优点，互补不足而成的政治哲学。 ..

H. Peyton Young | |
---|---|

Born | March 9, 1945 |

Nationality | American |

Alma mater | North Shore Country Day School Harvard University University of Michigan |

Known for | Evolutionary Game Theory Social Dynamics Learning in Games Social Norms Distributive justice Applications of Game Theory to Finance |

Awards | George Hallett Award, American Political Science Association Lester R. Ford Award, Mathematical Association of America |

Scientific career | |

Fields | Economics, Game Theory, Finance |

Institutions | London School of Economics University of Oxford Nuffield College, Oxford U.S. Department of the Treasury |

Doctoral advisor | Thomas Frederick Storer Jack Edmonds |

**Hobart Peyton Young** (born March 9, 1945) is an American game theorist and economist known for his contributions to evolutionary game theory and its application to the study of institutional and technological change, as well as the theory of learning in games. He is currently centennial professor at the London School of Economics, James Meade Professor of Economics Emeritus at the University of Oxford, professorial fellow at Nuffield College Oxford, and research principal at the Office of Financial Research at the U.S. Department of the Treasury.

Peyton Young was named a fellow of the Econometric Society in 1995, a fellow of the British Academy in 2007, and a fellow of the American Academy of Arts and Sciences in 2018. He served as president of the Game Theory Society from 2006–08. He has published widely on learning in games, the evolution of social norms and institutions, cooperative game theory, bargaining and negotiation, taxation and cost allocation, political representation, voting procedures, and distributive justice.

In 1966, he graduated cum laude in general studies from Harvard University. He completed a PhD in Mathematics at the University of Michigan in 1970, where he graduated with the Sumner B. Myers thesis prize for his work in combinatorial mathematics.

His first academic post was at the graduate school of the City University of New York as assistant professor and then associate professor, from 1971 to 1976. From 1976 to 1982, Young was research scholar and deputy chairman of the Systems and Decision Sciences Division at the Institute for Applied Systems Analysis, Austria. He was then appointed professor of Economics and Public Policy in the School of Public Affairs at the University of Maryland, College Park from 1992 to 1994. Young was Scott & Barbara Black Professor of Economics at the Johns Hopkins University from 1994, until moving to Oxford as James Meade Professor of Economics in 2007. He has been centennial professor at the London School of Economics since 2015 and remains a professorial fellow Of Nuffield College, Oxford.

Conventional concepts of dynamic stability, including the *evolutionarily stable strategy* concept, identify states from which small once-off deviations are self-correcting. These stability concepts are not appropriate for analyzing social and economic systems which are constantly perturbed by idiosyncratic behavior and mistakes, and individual and aggregate shocks to payoffs. Building upon Freidlin and Wentzell's (1984) theory of large deviations for continuous time-processes, Dean Foster and Peyton Young (1990) developed the more powerful concept of *stochastic stability*: "The stochastically stable set [SSS] is the set of states such that, in the long run, it is nearly certain that the system lies within every open set containing S as the noise tends slowly to zero" [p. 221]. This solution concept created a major impact in economics and game theory after Young (1993) developed a more tractable version of the theory for general finite-state Markov chains. A state is stochastically stable if it attracts positive weight in the stationary distribution of the Markov chain. Young develops powerful graph-theoretic tools for identifying the stochastically stable states.

In an influential book, *Individual Strategy and Social Structure*, Young provides a clear and compact exposition of the major results in the field of stochastic evolutionary game theory, which he pioneered. He introduces his model of social interactions called 'adaptive play.' Agents are randomly selected from a large population to play a fixed game. They choose a myopic best response, based upon a random sample of past plays of the game. The evolution of the (bounded) history of play is described by a finite Markov chain. Idiosyncratic behavior or mistakes constantly perturb the process, so that every state is accessible from every other. This means that the Markov chain is ergodic, so there is a unique stationary distribution which characterizes the long-run behavior of the process. Recent work by Young and coauthors finds that evolutionary dynamics of this and other kinds can transit rapidly to scholastically stable equilibria from locally stable ones, when perturbations are small but nonvanishing (Arieli and Young 2016, Kreindler and Young 2013, Kreindler and Young 2014).

The theory is used to show that in 2x2 coordination games, the risk-dominant equilibrium will be played virtually all the time, as time goes to infinity. It also yields a formal proof of Thomas Schelling's (1971) result that residential segregation emerges at the social level even if no individual prefers to be segregated. In addition, the theory "demonstrates how high-rationality solution concepts in game theory can emerge in a world populated by low-rationality agents" [p. 144]. In bargaining games, Young demonstrates that the Nash (1950) and Kalai-Smorodinsky (1975) bargaining solutions emerge from the decentralized actions of boundedly rational agents without common knowledge.

Whereas evolutionary game theory studies the behavior of large populations of agents, the theory of *learning in games* focuses on whether the actions of a small group of players end up conforming to some notion of equilibrium. This is a challenging problem, because social systems are self-referential: the act of learning changes the thing to be learned. There is a complex feedback between a player's beliefs, their actions and the actions of others, which makes the data-generating process exceedingly non-stationary. Young has made numerous contributions to this literature. Foster and Young (2001) demonstrate the failure of Bayesian learning rules to learn mixed equilibria in games of uncertain information. Foster and Young (2003) introduce a learning procedure in which players form hypotheses about their opponents' strategies, which they occasionally test against their opponents' past play. By backing off from rationality in this way, Foster and Young show that there are natural and robust learning procedures that lead to Nash equilibrium in general normal form games.

The recent literature on learning in games is elegantly reviewed in Young's 2004 book, *Strategic Learning and its Limits*.

In a series of papers, Young has applied the techniques of stochastic evolutionary game theory to the study of social norms (see Young 2015 for a review). The theory identifies four key features of norm dynamics.

(1) *Persistence*: once norms are in place, they persist for long periods of time despite changing external conditions.

(2) *Tipping*: when norms change, they do so suddenly. Deviations from an established norm may occur incrementally at first. Once a critical mass of deviators forms, however, the process tips and a new norm spreads rapidly through the population.

(3) *Compression*: norms imply that behavior (e.g. retirement ages, cropsharing contracts) exhibits a higher degree of conformity and lower responsiveness to economic conditions than predicted by standard economic models.

(4) *Local conformity/global diversity*: A norm is one of many possible equilibria. Compression implies that individuals who are closely connected conform fairly closely to a particular norm. At the same time, the presence of multiple equilibria implies that less closely connected individuals in the population could arrive at a very different norm.

These predictions are borne out in empirical work. Several regularities were uncovered in Young and Burke's (2001) study of cropsharing contracts in Illinois, which made use of detailed information on the terms of contracts on several thousand farms from different parts of the state. Firstly, there was considerable compression in the contract terms: 98% of all contracts involved 1/2-1/2, 2/5-3/5 or 1/3-2/3 splits. Secondly, when splitting the sample into farms from Northern and Southern Illinois, Young and Burke discovered a high degree of uniformity in contracts within each region, but significant variance across regions---evidence of the local conformity/global diversity effect. In Northern Illinois, the customary share was 1/2-1/2. In Southern Illinois, it was 1/3-2/3 or 2/5-3/5.

Young has also made significant applied contributions to understanding the diffusion of new ideas, technologies and practices in a population. The spread of particular social norms can be analyzed within the same framework. In the course of several papers (Young 2003, Young 2011, Kreindler and Young 2014), Young has showed how the topology of a social network affects the rate and nature of diffusion under particular adoption rules at the individual level.

In an influential 2009 paper, Young turned attention to the diffusion dynamics that can result from different adoption rules in a well-mixed population. In particular, he distinguished between three different classes of diffusion model:

(1) *Contagion*: Individuals adopt an innovation (a new idea, product or practice) following contact with existing adopters.

(2) *Social Influence*: Individuals are likely to adopt an innovation when a critical mass of individuals in their group has adopted it.

(3) *Social Leaning*: Individuals observe the payoffs of adopters and adopt the innovation when these payoffs are sufficiently high.

The third adoption process is most closely related to optimizing behavior and thus standard approaches in economics. The first two processes are, however, the ones focused on by the vast sociological and marketing literature on the subject.

Young characterized the mean dynamic of each of these processes under general forms of heterogeneity in individual beliefs and preferences. While each of the dynamics yields a familiar S-shaped adoption curve, Young showed how the underlying adoption process can be inferred from the aggregate adoption curve. It turns out that each process leaves a distinct footprint. Turning to data on hybrid corn adoption in the United States, Young presented evidence of superexponential acceleration in the early stages of adoption, a hallmark of social learning.

Young (1985) has contributed an axiomatization of the Shapley value. It is regarded as a key piece^{[1]} for understanding the relationship between the marginality principle and the Shapley value. Young shows that the Shapley value is the only symmetric and efficient solution concept that is solely computed from a player's marginal contributions in a cooperative game. Consequently, the Shapley value is the only efficient and symmetric solution that satisfies monotonicity which requires that whenever a player's contribution to all coalitions weakly increases, then this player's allocation should also weakly increase. This justifies the Shapley value as *the* measure of a player's productivity in a cooperative game and makes it particularly appealing for cost allocation models.^{[2]}^{[3]}

The **Kemeny–Young method** is a voting system that uses preferential ballots and pairwise comparison counts to identify the most popular choices in an election. It is a Condorcet method because if there is a Condorcet winner, it will always be ranked as the most popular choice.

The Kemeny–Young method was developed by John Kemeny in 1959. Young and Levenglick (1978) showed that this method was the unique neutral method satisfying reinforcement and the Condorcet criterion. In other papers (Young 1986, 1988, 1995, 1997), Young adopted an epistemic approach to preference-aggregation: he supposed that there was an objectively 'correct', but unknown preference order over the alternatives, and voters receive noisy signals of this true preference order (cf. Condorcet's jury theorem). Using a simple probabilistic model for these noisy signals, Young showed that the Kemeny–Young method was the maximum likelihood estimator of the true preference order. Young further argues that Condorcet himself was aware of the Kemeny-Young rule and its maximum-likelihood interpretation, but was unable to clearly express his ideas.

- J. Kemeny, "Mathematics without numbers",
*Daedalus*,**88**(1959), 577–591. - H. P. Young and A. Levenglick, "A Consistent Extension of Condorcet's Election Principle",
*SIAM Journal on Applied Mathematics***35**, no. 2 (1978), 285–300. - H. P. Young, "Optimal ranking and choice from pairwise comparisons", in
*Information pooling and group decision making*edited by B. Grofman and G. Owen (1986), JAI Press, 113–122. - H.P Young, "Monotonic solutions of cooperative games",
*International Journal of Game Theory*,**14**, No. 2 (1985), 65–72. - H. P. Young, "Condorcet's Theory of Voting",
*American Political Science Review***82**, no. 2 (1988), 1231–1244. - D. Foster and H.P Young, "Stochastic Evolutionary Game Dynamics",
*Theoretical Population Biology*,**38**(1990), 219–232. - H.P Young, "The Evolution of Conventions",
*Econometrica*,**61**(1993), 57–84. - H.P Young, "An Evolutionary Model of Bargaining",
*Journal of Economic Theory*,**59**(1993), 145–168. - H. P. Young, "Optimal Voting Rules",
*Journal of Economic Perspectives***9**, no.1 (1995), 51–64. - H. P. Young, "Group choice and individual judgements", Chapter 9 of
*Perspectives on public choice: a handbook*, edited by Dennis Mueller (1997) Cambridge UP., pp. 181–200. - D. Foster and H.P Young, "On the Impossibility of Predicting the Behavior of Rational Agents",
*Proceedings of the National Academy of Sciences of the USA*,**98**, no. 22 (2001), 12848–12853. - H.P Young and M.A. Burke, "Competition and Custom in Economic Contracts: A Case Study of Illinois Agriculture",
*American Economic Review*,**91**(2001), 559–573. - D. Foster and H.P Young, "Learning, Hypothesis Testing, and Nash Equilibrium",
*Games and Economic Behavior*,**45**(2003), 73–96. - H.P Young, "The Diffusion of Innovations in Social Networks” in
*The Economy as a Complex Evolving System*, vol. III, Lawrence E. Blume and Steven N. Durlauf, eds. Oxford University Press, (2003). - H.P Young, "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence and Social Learning",
*American Economic Review*,**99**(2009), 1899–1924. - H.P Young, "Learning by Trial and Error",
*Games and Economic Behavior*,**65**(2009), 626–643. - D. Foster and H.P Young, "Gaming Performance Fees by Portfolio Managers",
*Quarterly Journal of Economics*,**125**(2010), 1435–1458. - H.P Young, "The Dynamics of Social Innovation”,
*Proceedings of the National Academy of Sciences*,**108**, No. 4 (2011), 21285–21291. - B.S.R. Pradelski and H.P Young, "Learning Efficient Nash Equilibria in Distributed Systems",
*Games and Economic Behavior*,**75**(2012), 882–897. - G. Kreindler and H.P Young, "Fast Convergence in Evolutionary Equilibrium Selection",
*Games and Economic Behavior*,**80**(2013), 39–67. - G. Kreindler and H.P Young, "Rapid Innovation Diffusion in Social Networks",
*Proceedings of the National Academy of Sciences*,**111 Suppl 3**(2014), 10881–10888. - H.P Young, "The Evolution of Social Norms",
*Annual Review of Economics*,**7**(2015), 359–87. - I. Arieli and H.P Young, "Stochastic Learning Dynamics and Speed of Convergence in Population Games",
*Econometrica*,**84**(2016), 627–676.

- H. Peyton Young (2004).
*Strategic Learning and Its Limits*. Oxford UK: Oxford University Press. Contents and introduction. - _____ (2001).
*Fair Representation*, 2nd edition (with M. L. Balinski). Washington, D. C.: The Brookings Institution. Contents and introduction.^{[permanent dead link]} - _____ (1998).
*Individual Strategy and Social Structure: An Evolutionary Theory of Institutions*. Princeton, NJ: Princeton University Press. Contents and introduction.^{[permanent dead link]} - _____ (1994).
*Equity: In Theory and Practice*. Princeton NJ: Princeton University Press. Contents and introduction.^{[permanent dead link]}

- ↑ Geoffroy De Clippel Roberto Serrano (2008). "Marginal Contributions and Externalities in the Value".
*Econometrica*.**76**(6): 1413–1436. CiteSeerX 10.1.1.388.1120. doi:10.3982/ECTA7224. - ↑ Casajus, André; Huettner, Frank (2014). "Weakly monotonic solutions for cooperative games".
*Journal of Economic Theory*.**154**: 162–172. doi:10.1016/j.jet.2014.09.004. - ↑ Nagarajan, Mahesh; Sošić, Greys (2008). "Game-theoretic analysis of cooperation among supply chain agents: Review and extensions".
*European Journal of Operational Research*.**187**(3): 719–745. doi:10.1016/j.ejor.2006.05.045. ISSN 0377-2217.

- Young's page at the University of Oxford with his CV and full list of publications.
- Peyton Young at the Mathematics Genealogy Project

© 2019 raptorfind.com. Imprint, All rights reserved.