THE CHAOTIC COST-PLUS PRICING MODEL

In this paper, it is examined one pricing practice often used by oligopolistic firm: cost-plus pricing. Cost-plus pricing refers to the setting of a price equal to average variable cost plus a markup. The pricing of airline tickets illustrates this concept presented in this paper as it is applied in a real-world oligopolistic market. It is important concept because the trend toward the formation of global oligopolies has accelerated as the world’s answer on the current financial crise. The basic aim of this paper is to construct a relatively simple chaotic cost-plus pricing model that is capable of generating stable equilibria, cycles, or chaos. A key hypothesis of this work is based on the idea that the coefficient,(eq in Abstract) plays a crucial role in explaining local stability of the oligopolistic firm’s output, where, d – the coefficient of the average variable cost function of the oligopolistic firm, b - the coefficient of the inverse demand function, r - the coefficient of price growth.

Estimating Cost Structures in the U.S. Nuclear Power Industry

With growing concerns about global warming and surging oil prices, nuclear power is now back on the U.S. energy policy agenda. This paper provides firm level analysis of the production technology and cost structures in the U.S. nuclear power generation industry. The paper applies an econometric approach into a dual restricted variable cost function within a “temporal equilibrium” framework. A panel data set of 32 nuclear power generations for major U.S. investor owned utilities over the period 1986–2002 is used. The major finding indicates that most electric utilities in the nuclear electricity generation industry over utilized capital in production. The estimated results show evidence of scale economies in the production of the electricity industry. The paper presents supporting evidence that nuclear technology is cheaper than other alternative technologies and fuels, and it can offer possible cost savings to other alternative technologies.

Real-Time Nonlinear Programming by Amplitude Modulation

Nonlinear Programming (NLP) is for optimization of nonlinear cost functions. In applications of NLP for real-time optimization, however, the estimation of the gradient of the cost function remains as a challenge. On the other hand, the Extremum-Seeking Control (ESC) optimizes the cost function in real-time, but it involves a complicated design of filters in multi-dimensional cases. In this paper, a new method that optimizes an arbitrary multi-variable cost function in real-time is proposed. In the proposed method, the variables are updated as in NLP while the gradient of the cost function is continuously estimated by the amplitude modulation as in ESC. The proposed method does not require design of any complicated filters. The performance is verified by simulations on time-varying and noisy cost functions as well as automatic controller tuning applications.