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Cournot Oligopoly with Randomly Arriving Producers Cournot oligopoly with randomly arriving producers Pierre Bernhard∗ and Marc Deschampsy February 26, 2017 Abstract Cournot model of oligopoly appears as a central model of strategic inter- action between competing firms both from a theoretical and applied perspec- tive (e.g antitrust). As such it is an essential tool in the economics toolbox and always a stimulus. Although there is a huge and deep literature on it and as far as we know, we think that there is a ”mouse hole” wich has not al- ready been studied: Cournot oligopoly with randomly arriving producers. In a companion paper [4] we have proposed a rather general model of a discrete dynamic decision process where producers arrive as a Bernoulli random pro- cess and we have given some examples relating to oligopoly theory (Cournot, Stackelberg, cartel). In this paper we study Cournot oligopoly with random entry in discrete (Bernoulli) and continuous (Poisson) time, whether time horizon is finite or infinite. Moreover we consider here constant and variable probability of entry or density of arrivals. In this framework, we are able to provide algorithmes answering four classical questions: 1/ what is the ex- pected profit for a firm inside the Cournot oligopoly at the beginning of the game?, 2/ How do individual quantities evolve?, 3/ How do market quantities evolve?, and 4/ How does market price evolve? Keywords Cournot market structure, Bernoulli process of entry, Poisson density of arrivals, Dynamic Programming. JEL code: C72, C61, L13 MSC: 91A25, 91A06, 91A23, 91A50, 91A60. ∗Biocore team, INRIA-Sophia Antipolis-Mediterran´ ee´ yCRESE EA3190, Univ. Bourgogne Franche-Comte´ F-25000 Besanc¸on. I thank BETA CNRS, GREDEG CNRS and OFCE Sciences Po for their financial and material support. 1 1 Introduction While it was ignored for many years it seems almost impossible today to think about competition in economics without considering the Cournot oligopoly model. As H. Demsetz said in his Economics of Business firms book in 1995 it is one of the ”safe harbors” of economic analysis, a statement also share by A. Daughety who considers that the Cournot oligopoly model ”over the recent decades has come to be an essential tool in many economist’s toolbox, and is likely to continue as such” in his New Plagrave Dictionnary notice on Cournot competition. In it classical form Cournot’s model is static, each producer’s strategy is the quantity of output she will produce in the market for a specific homogeneous good and when the number of identical producers goes to infinity the market price con- verges toward the marginal cost. For many decades economists have extend this classical form to a large extent including to asymetric producers, differenciated goods and dynamics. On this last topic economists have notably considered the Cournot model with such characteristics as several periods of production ([14], [7]), free entry ([11], [2]), as a repeated game form ([1]), as a stochastic game form ([9]), as a Poisson game ([12], [13]) and, more recently, as a mean field game ([6]), in continuous-time ([15]) or with intertemporal capacity constraints ([16]). But as far as we know, despite this huge and deep literature, there is a ”mouse hole” wich has not been already investigated: a Cournot oligopoly model with randomly arriving producers. To begin the study of this question we here consider a model where there is at the initial step a fix number of symmetric producers of an homogeneous good play- ing according to a complete information Cournot game with the common knowl- edge hypothesis that, at each next step, an identical producer can (or not) enter in the game and definitvely stay. We will use control theory and games differentials methods as in [8] and [10]. The paper is organized as follows: in the next section we present the structure of the general game dynamic model with randomly arriving players as we devel- opped in [4]. In Section 3, we present Cournot oligopoly with randomly arriving in discrete time with the use of a Bernouilli process in finite and infinite time hori- zon. Then in Section 4, using Poisson process, we analyse Cournot oligopoly with random arrivals in continuous time wether the game is finite or infinite. In each of these two sections we analyse the market structure with the hypothesis of constant (or variable) probability (discrete case) or density (continuous case) of entry and provide numerical results. Section 5 ends the paper by discussing conclusions and limits of our analysis. 1 2 General model In a companion paper [4], we investigated a rather general model of a discrete dynamic decision process where players arrive as a Bernoulli random process. We summarize here the results obtained there, simplified to fit our need in this article. Time t is an integer (we will consider further down the continuous limit). At time t1 one player is present, then players arrive as a Bernoulli process with a unit probability p, (we will later allow p to depend on the rank of the arriving player) player number m arriving at time tm. The game is played over an horizon T which may be finite or infinite. A sequence of (usually positive decreasing) numbers fπmg is given, denoting the reward of each player during one time period if there are m players present. We let m(t) be the number of players actually present at time t, a random variable. Therefore, at each period of time t, all players get a reward πm(t). Let finally r 2 (0; 1) be a discount factor. The reward of the n-th player arrived is T X t−tn Πn = r πm(t) ; t=tn e and we sought to evaluate its expectation Πn. Figure 1 illustrates that problem. m π 4 4 3 !!``` π3 ! ``` !! π3 `` 3 !! 2 a! ## aa # a π3 3 π π2 a 2 # aaa # ``` Legend: 2 ``` # π2 `` 2 # t: current time 1 # %c π : per stage payoff % m c π3 3 % c m: number of players c !2 ` ` % π1 !! `` c π2 ! π ```` π % c !! 2 2 1% ! cca! % 1 aa a π2 2 % π1 a % aaa 0 ``` 1 ``` s π1 `` 1 - 0 1 2 3 t Figure 1: The events tree 2 Concerning the sequence fπmg, we will use the following definitions: Definition 1 The sequence fπmg is said to be • bounded by π if there exists a positive number π such that 8m 2 N ; jπmj ≤ π ; • exponentially bounded by π if there exists a positive number π such that m 8m 2 N ; jπmj ≤ π : Notice that if the sequence fπmg is bounded by π, it is exponentially bounded by maxf1; πg, while if it is exponentially by π ≤ 1, it is bounded by π. We also need the following notation for a domain of the discrete plane, for any positive integer (a time interval) ν: 2 Dν = f(k; `) 2 N j 0 ≤ ` ≤ k ≤ νg : (1) The theorems proved in [4] can be simplified here, with the use of the combi- natorial coefficients k k! 8k ≥ ` 2 ; = N ` `!(k − `)! Theorem 1 (Bernhard and Deschamps [2016]) If T < 1, or if T = 1 and the sequence fπmg is bounded or exponentially bounded by $ < 1=r, the expected payoff of the n-th arrived player is 8 p ` > X k k > [(1 − p)r] πn+` if p < 1 ; <> 1 − p ` e (k;`)2DT −tn Πn = T −t (2) > Xn > rkπ if p = 1 : :> n+k k=0 3 Discrete time 3.1 Constant entry probability 3.1.1 Algorithm e We offer an alternative approach to theorem 1 to evaluate Πn, recovering an algo- rithm that can easily be derived from formula (2). Given any natural integer (time 3 interval) k, let q`(k) be the probability that ` players arrive during that time in- terval. In a Bernoulli process, only one player may arrive at each instant of time. Thus, there are only two incompatible ways to achieve exactly ` arrivals at time k: either there were ` − 1 arrivals at time k − 1 and one arrived at time k, or there were already ` arrivals at time k − 1 and none arrived at time k. Hence q`(k) = pq`−1(k − 1) + (1 − p)q`(k − 1) : (3) Now, it holds that T t−tn e X X Πn = E(πm(t) j tn) ; and E(πm(t) j tn) = q`(t − tn)πn+` : t=tn `=0 Therefore, using t − tn = k, T −tn k e X X k Πn = r q`(k)πn+` k=0 `=0 k We define w`(k) = r q`(k) to obtain T −tn k e X X X Πn = w`(k)πn+` = w`(k)πn+` ; (4) k=0 `=0 (k;`)2DT −tn and also the recursive formula, useful for numerical computations: T −tn e e X Πn(T ) = Πn(T − 1) + w`(T − tn)πn+` : `=0 The w`(k) can be computed according to the following recursion. (The first two lines may be seen as initialization tricks, while the third one directly derives from equation (3)) w0(0) = 1 ; 8k 2 N ; w−1(k) = wk(k − 1) = 0 ; 8` ≤ k ; w`(k) = rpw`−1(k − 1) + r(1 − p)w`(k − 1) ; We know from appendix A that the πm of interest here are uniformly bounded, and therefore, from theorem 1, that this can be extended to the case where T = 1. Indeed, that algorithm may be derived from formula (2) identifying k w (k) = rk(1 − p)k−`p` ` ` 4 and using the classical formula of the “Pascal triangle”: k k − 1 k − 1 = + : ` ` − 1 ` 3.1.2 Numerical results e Figure 2 provides a plot of Π1(T ) assuming t1 = 0, for T varying from 0 to 20, 2 and for p 2 f0;:1;:4;:7; 1g.
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