Abstract

This paper models the organization of the firm as a type of artificial neural network in a duopoly setting. The firm plays a repeated Prisoner’s Dilemma type game, and must also learn to map environmental signals to demand parameters and to its rival’s willingness to cooperate. We study the prospects for cooperation given the need for the firm to learn the environment and its rival’s output. We show how profit and cooperation rates are affected by the sizes of both firms, their willingness to cooperate, and by environmental complexity. In addition, we investigate equilibrium firm size and cooperation rates.

Abstract

Recent evidence suggests that firms’ environments are becoming more complex and uncertain. This paper investigates the relationship between the complexity of a firm’s activities, environmental uncertainty and organizational structure. We assume agents are arranged hierarchically, but decisions can be made at different levels. We model a firm’s activity set as a modified NK landscape. Via simulations, we find that centralized decision making generates a higher payoff in more complex and uncertain environments, and that a flatter structure is better for the organization with centralized decision making, provided the cost of information processing is low enough.

Abstract

The paper studies the dynamics of firm size in a repeated Cournot game with unknown demand function. We model the firm as a type of artificial neural network. Each period it must learn to map environmental signals to both a demand parameter and its rival’s output choice. However, this learning game is in the background, as we focus on the endogenous adjustment of network size. We investigate the long-run evolution of firm/network size as a function of profits, rival’s size, and the type of adjustment rules used.

Abstract

We model +rms’ output decisions in a repeated duopoly framework, focusing on three interrelated issues: (1) the role of learning in the adjustment process toward equilibrium, (2) the role of organizational structure in the +rm’s decision making, and (3) the role of changing environmental conditions on learning and output decisions. We characterize the +rm as a type of arti+cial neural network, which must estimate its optimal output decision based on signals it receives from the economic environment (which in4uences the demand function). Via simulation analysis we show: (1) how organizations learn to estimate the optimal output over time as a function of the environmental dynamics, (2) which networks are optimal for each level of environmental complexity, and (3) the equilibrium industry structure

Abstract 

This paper proposes using computational learning theory (CLT) as a framework for analyzing the information processing behavior of firms; we argue that firms can be viewed as learning algorithms. The costs and benefits of processing information are linked to the structure of the firm and its relationship with the environment. We model the firm as a type of artificial neural network (ANN). By a simulation experiment, we show which types of networks maximize the net return to computation given different environments.