Signaling Game Analyses on R&D and Economic Growth of Vertical Industrial Groups
|Course||Management Science and Engineering|
|Keywords||industrial group R&D economic growth signaling game principal-agent decision-making|
This dissertation develops a series of signaling game models on R&D and economic growth of vertical industrial groups, formulating the effects of R&D on groups’ economic growth and giving some optimal decision-makings for the member firms of groups, respectively. As a complementarity, the dissertation also sets up a principal-agent model between the member firms and the center for R&D, giving some optimal decision-makings, respectively. The main research work of the dissertation is outlined as follows: In Chapter I, the dissertation introduces some problems existing in the economic growth of industrial groups and reviews the related research works. In Chapter 2. firstly, it sets up the vertical group model in which vertical innovations affect economic growth. The model is a synthesized endogenous growth model that incorporates two category models in the new growth theory that are Capital-based models and Idea-based models. In the model, group-corporation ,4 is an upstream firm that takes on R&D activities and uses its technology to produce intermediate goods. The quality improvement of intermediate goods raises the productivity of final good production. Firm B is a downstream firm that produces final goods using intermediate goods and capital as input. Both firm A and B play signaling games with markets in the Cournot market. Secondly, it studies a class of signaling games in which a unique perfect Bayesian Nash equilibrium outcome satisfying intuitive criterion exists, it describes the models of signaling games in which the definition of the equilibrium and some basic assumptions are given, and it shows three basic theorems used to prove the main theorem giving sufficient conditions under which the signaling game has a unique equilibrium outcome. In fact, the process thai shows the main theorem gives the method solving the equilibrium outcome. Thirdly, it comes up with the algorithm solving the equilibrium outcome and an example is given to illustrate the algorithm of the unique equilibrium outcome. Finally, it shows that the method solving the basic model of downstream firm B is equivalent to the algorithm of ISGPBB. In Chapter 3, firstly, it solves the basic model of firm B by employing the algorithm of tSGPBE, giving optimal decision-makings for firm B. Secondly, it shows that the method solving the basic model of upstream firm A is equivalent to the algorithm of 1SGPRE. Thirdly; it solves the basic model of firm A by employing the algorithm of fSgPBE, giving optimal decision-makings for firm A. Fourthly, it studies the R&D choices of firm A. Finally, it comes up with an algorithm solving the basic model and provides an example to show the algorithm. In Chapter 4, it studies a three-level-group model that is the extension of the basic model, giving optimal decision-makings for the member firms of groups, respectively. Firstly, is describes the extended model. Secondly, it gives the utility function of firm C and analyzes it by employing signaling games, giving optimal decision-makings, if analyzes firm B and A as well. Thirdly, it sets up a principal-agent model between industrial group and center for R&D, giving optimal decision-makings for the member firms of group, respectively. Finally, it provides an example that shows an industrial group how to analyze problems and make decisions. In Chapter 5, firstly, it sets up a two-stage extended model on R&D and economic growth by employing signaling games, based on Schumpeter’s process of creative destruction, giving optimal decision-makings for the member firms of group, respectively. The model is a synthesized endogenous growth model that incorporates the two category models in the new growth theory. Secondly, it sets up a reputation model based on signaling game. The reputation model studies firms C and A of class L whether there are motives to set up reputations in stage one. Finally, it comes up with an algorithm solving the reputation model and provides an example to show the algorithm. In Chapter 6, it gives the conclusions and the original ideas of the dissertation and suggests its prospects for further studies.