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https://doi.org/10.4324/978131...
Part of book or chapter of book . 2018 . Peer-reviewed
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Coopetition and game theory 1

Authors: CARFI', David; Okura, Mahito;

Coopetition and game theory 1

Abstract

Many standard textbooks in microeconomic theory contain explanations of game theory, as it is a prominent section in microeconomics. However, there is one critical difference between game theory and traditional microeconomic theory. Traditional microeconomic theories, such as the perfect competition model, primarily focus on situations in which the decisions of one player (consumer and firm) never relate to the decisions of other players. However, this might not reflect in the real world. For example, the decision to lower prices in one supermarket might lead to decisions to lower prices in other supermarkets. In other words, one decision closely relates to other decisions. One attractive aspect of game theory is its ability to investigate such situations. Although it is well- known that game- theoretical models are useful for analyzing various situations and markets, game theory is seldom used to investigate coopetitive situations. Coopetition studies have a long history. It has been more than twenty years since Brandenburger and Nalebuff (1996) published their pioneering study on coopetition; however, only a small number of studies have built game- theoretical models for investigating coopetition, such as Dearden and Lilien (2001), Okura (2007, 2008, 2009), Ngo and Okura (2008), Carf ì and Schiliro (2012), Biondi and Giannoccolo (2012), Ohkita and Okura (2014), Arthanari et al. (2015), and Baglieri, Carf ì, and Dagnino (2016). There are three potential reasons why a game- theoretical model is seldom used in coopetition studies. First, most studies on coopetition are related to management rather than to economics. Moreover, game theory uses mathematical (and seemingly complex) models and equations based on (applied) microeconomics. Second, an important aim of many coopetition studies is to bring sophistication to the concept of coopetition and coopetition- related terms, such as value net. However, game theory seems unsuitable for this task. Third, case- study methods in coopetition studies are only moving towards quantitative methods as a common practice. These quantitative methods are based on observing situations and do not lend themselves readily to the game theoretical model, which is mainly based on qualitative methods. From this standpoint, the purpose of this study is to describe the advantages of game theory and how to use game- theoretical models in coopetition studies. To bridge the gap between coopetition studies and game- theoretical models, we describe the relationship between coopetition studies and game theory. Subsequently, we introduce the game- theoretical model of coopetition that appeared in the study by Okura (2009).

Country
Italy
Related Organizations
Keywords

coopetition, cooperation, game theory, normal-form games, extensive-form games, non-zero sum game.

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    citations
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    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
gold