A Dynamic Model of Collective Bargaining
We use high-low search algorithms to compute equilibria in a multi-period model of collective bargaining. In this model, a group of workers bargains collectively with a firm which knows the per period incremental value v of each worker. The workers don't know their individual values, but use high-low search to learn them, given the known distribution of values in the group. In the first period the workers make a common wage demand w1 and then the firm chooses a cutoff level y for the workers they will hire (i.e., those with v≥ y). Since y is observable through numbers hired, workers may update their beliefs about their values, based on whether or not they were hired. In later periods workers with common beliefs make common wage demands. The main question addressed in the paper is how the firm can choose the hiring cutoff level to hinder the workers' learning process. We find (Theorem 1) that the firm's equilibrium strategy is to (i) hire some workers at a loss, and also (ii) to forego some profitable hirings. However (Theorem 2) the equilibrium outcome involves only the first kind of strategic loss by the firm. We also find (Theorem 3) that the firm's profit is less than when the worker optimizes against a predictable myopic firm (which chooses y=w1). Thus the firm would wish to commit to the myopic hiring strategy if it were possible.
A High–Low search game on the unit interval
We consider the following two-person zero-sum game on the closed interval [0,1]. The hider chooses any real number h in [0,1]. The searcher successively chooses real numbers ξ1ξ2… in [0,1], where at each choice ξi he is told whether h = ξih < ξ1 or h > ξi and he may choose ξi+1 in the light of this information. The payoff (to the hider) is the sum of the distances of the searcher from the hider at each of the moves, that is .
Search for point in interval, with high–low feedback
A point H is known to lie on a given bounded interval ℋ. A searcher wishes to locate H by making successive guesses g1, g2, …, each with the knowledge of whether the previous guesses were too high or low, or exactly right. Under these circumstances it is easy to devise a search strategy which ensures the convergence of the gi to H. One such strategy is the ‘halving’ strategy which always guesses the midpoint of the interval on which H is currently known to lie. The problem becomes well defined, and more difficult, if the searcher has to minimize a given cost function which in some way measures the speed of convergence of the gi to H.