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Stackelberg competition : ウィキペディア英語版
Stackelberg competition
The Stackelberg leadership model is a strategic game in economics in which the leader firm moves first and then the follower firms move sequentially. It is named after the German economist Heinrich Freiherr von Stackelberg who published ''Market Structure and Equilibrium (Marktform und Gleichgewicht)'' in 1934 which described the model.
In game theory terms, the players of this game are a ''leader'' and a ''follower'' and they compete on quantity. The Stackelberg leader is sometimes referred to as the Market Leader.
There are some further constraints upon the sustaining of a Stackelberg equilibrium. The leader must know ''ex ante'' that the follower observes its action. The follower must have no means of committing to a future non-Stackelberg follower action and the leader must know this. Indeed, if the 'follower' could commit to a Stackelberg leader action and the 'leader' knew this, the leader's best response would be to play a Stackelberg follower action.
Firms may engage in Stackelberg competition if one has some sort of advantage enabling it to move first. More generally, the leader must have commitment power. Moving observably first is the most obvious means of commitment: once the leader has made its move, it cannot undo it - it is committed to that action. Moving first may be possible if the leader was the incumbent monopoly of the industry and the follower is a new entrant. Holding excess capacity is another means of commitment.
== Subgame perfect Nash equilibrium ==

The Stackelberg model can be solved to find the subgame perfect Nash equilibrium or equilibria (SPNE), i.e. the strategy profile that serves best each player, given the strategies of the other player and that entails every player playing in a Nash equilibrium in every subgame.
In very general terms, let the price function for the (duopoly) industry be P; price is simply a function of total (industry) output, so is P(q_1+q_2) where the subscript 1 represents the leader and 2 represents the follower. Suppose firm i has the cost structure C_i(q_i). The model is solved by backward induction. The leader considers what the best response of the follower is, i.e. how it ''will'' respond once it has observed the quantity of the leader. The leader then picks a quantity that maximises its payoff, anticipating the predicted response of the follower. The follower actually observes this and in equilibrium picks the expected quantity as a response.
To calculate the SPNE, the best response functions of the follower must first be calculated (calculation moves 'backwards' because of backward induction).
The profit of firm 2 (the follower) is revenue minus cost. Revenue is the product of price and quantity and cost is given by the firm's cost structure, so profit is:
\Pi_2 = P(q_1+q_2) \cdot q_2 - C_2(q_2). The best response is to find the value of q_2 that maximises \Pi_2 given q_1, i.e. given the output of the leader (firm 1), the output that maximises the follower's profit is found. Hence, the maximum of \Pi_2 with respect to q_2 is to be found. First differentiate \Pi_2 with respect to q_2:
:\frac = \frac \cdot q_2 + P(q_1+q_2) - \frac.
Setting this to zero for maximisation:
:\frac = \frac \cdot q_2 + P(q_1+q_2) - \frac=0.
The values of q_2 that satisfy this equation are the best responses. Now the best response function of the leader is considered. This function is calculated by considering the follower's output as a function of the leader's output, as just computed.
The profit of firm 1 (the leader) is \Pi_1 = P(q_1+q_2(q_1)).q_1 - C_1(q_1), where q_2(q_1) is the follower's quantity as a function of the leader's quantity, namely the function calculated above. The best response is to find the value of q_1 that maximises \Pi_1 given q_2(q_1), i.e. given the best response function of the follower (firm 2), the output that maximises the leader's profit is found. Hence, the maximum of \Pi_1 with respect to q_1 is to be found. First, differentiate \Pi_1 with respect to q_1:
:\frac = \frac \cdot \frac \cdot q_1 +\frac \cdot q_1+ P(q_1+q_2(q_1)) - \frac.
Setting this to zero for maximisation:
:\frac = \frac \cdot \frac \cdot q_1+\frac \cdot q_1 + P(q_1+q_2(q_1)) - \frac=0.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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