The n-Person War of Attrition

Haigh, John; Cannings, C.

doi:10.1007/978-94-009-2358-4_7

Cited by 25 publications

(46 citation statements)

References 7 publications

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“…In this case, a symmetric equilibrium does not exit. For the necessity of this randomization device in N -player games and discussion, see Haigh and Cannings (1989), footnote 31…”

Section: Setupmentioning

confidence: 99%

Estimating a War of Attrition: The Case of the US Movie Theater Industry

Takahashi

2015

American Economic Review

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This paper provides a tractable empirical framework to analyze …rm behavior in a dynamic oligopoly when demand is declining over time. I modify Fudenberg and Tirole (1986)'s model of exit in a duopoly with incomplete information to a model that can be used in an oligopoly, and combine this with an auxiliary entry model to address the initial conditions problem. I estimate this model with panel data on the U.S. movie theater industry from 1949 to 1955, using variations in TV di¤usion rates across households, market structure before the exit game starts, and other market characteristics to identify the parameters in the theater's payo¤ function and the distribution of unobservable …xed costs. Using the estimated model, I measure strategic delays in the exit process due to oligopolistic competition and incomplete information. The delay in exit that arises from strategic interaction is 2.7 years on average. Out of these years, 3.7% of this delay is accounted for by incomplete information, while the remaining 96.3% is explained by oligopolistic competition.

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“…In this case, a symmetric equilibrium does not exit. For the necessity of this randomization device in N -player games and discussion, see Haigh and Cannings (1989), footnote 31…”

Section: Setupmentioning

confidence: 99%

Estimating a War of Attrition: The Case of the US Movie Theater Industry

Takahashi

2015

American Economic Review

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“…However until recently they have been less common in evolutionary games. An extension of the classical idea of well-mixed populations of pairwise games to consider such populations with multiplayer games was first introduced with the work of Palm (1984) and followed by Haigh and Cannings (1989) ;Broom et al (1997); Bukowski and Miȩkisz (2004). More recently Hauert et al (2006), Gokhale and Traulsen (2010), Han et al (2012), Gokhale and Traulsen (2014) have developed the theory further.…”

Section: Discussionmentioning

confidence: 99%

“…We shall only consider multi-player matrix games (Broom et al, 1997) here. Note that another important example of a multi-player game is the multi-player war of attrition (Haigh and Cannings, 1989). For an extensive review of multiplayer evolutionary games, see Gokhale and Traulsen (2014).…”

Section: Multi-player Gamesmentioning

confidence: 99%

Nonlinear and Multiplayer Evolutionary Games

Broom

Rychtář

2016

Advances in Dynamic and Evolutionary Games

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract Classical evolutionary game theory has typically considered populations within which randomly selected pairs of individuals play games against each other, and the resulting payoff functions are linear. These simple functions have led to a number of pleasing results for the dynamic theory, the static theory of evolutionarily stable strategies, and the relationship between them. We discuss such games, together with a basic introduction to evolutionary game theory, in Section 1. Realistic populations, however, will generally not have these nice properties, and the payoffs will be nonlinear. In Section 2 we discuss various ways in which nonlinearity can appear in evolutionary games, including pairwise games with strategy-dependent interaction rates, and playing the field games, where payoffs depend upon the entire population composition, and not on individual games. In Section 3 we consider multiplayer games, where payoffs are the result of interactions between groups of size greater than two, which again leads to nonlinearity, and a breakdown of some of the classical results of Section 1. Finally in Section 4 we summarise and discuss the previous sections. Permanent repository link

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“…The game has a single ESS, namely to choose x drawn at random from an exponential distribution with mean V. Haigh and Cannings (1989) extended this model to a multiplayer setting, considering four different models, with different assumptions. This is the earliest example of a multiplayer game applied to a biological setting (though see Palm, 1984, for a discussion of a general notion of a multiplayer ESS).…”

Section: The N-player War Of Attritionmentioning

confidence: 99%

The Use of Multiplayer Game Theory in the Modeling of Biological Populations

Broom¹

2003

Comments� on Theoretical Biology

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Citation: Broom, M. (2003). The use of multiplayer game theory in the modeling of biological populations. Comments on Theoretical Biology, 8(2-3), pp. 103-123. This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThe use of game theory in modelling the natural world is widespread. However this modelling mainly involves two player games only, or 'playing the field' games where an individual plays against an entire (infinite) population. Game theoretic models are common in economics as well, but in this case the use of multiplayer games has not been neglected. This paper outlines where multiplayer games have been used in evolutionary modelling and the merits and limitations of these games. Finally we discuss why there has been so little use of multiplayer games in the biological setting and what developments might be useful.

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The n-Person War of Attrition

Cited by 25 publications

References 7 publications

Estimating a War of Attrition: The Case of the US Movie Theater Industry

Estimating a War of Attrition: The Case of the US Movie Theater Industry

Nonlinear and Multiplayer Evolutionary Games

The Use of Multiplayer Game Theory in the Modeling of Biological Populations

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