The aim of this article is to demonstrate that a firm may owe its continued existence to its attempts to conceal information from its competitors about the unknown characteristics of a certain factor, not just to its savings on market transaction costs, its team-working, risk-sharing or the encouragement of ex ante specific investment. This is because the existence of a firm contract severs the relationship between the factor market and the product market, thereby making it difficult for outsiders to observe the marginal contribution of the intermediate factor and make statistical inferences about the factor’s unknown characteristics. Furthermore, an optimal contract is determined by a trade-off not only between traditional risk-sharing and incentive, but also between the incentive and information concealing. Finally, we show that this latter kind of trade-off also affects the position of the optimal boundary of the firm.
No abstract is provided for this article.
The beauty of an analytical solution to a problem is that it is accurate and often provides some insight into the main process of interest. However, in most applications, no explicit forms of analytical solutions exist, and we have to use some approximations. In some cases, even the evaluation of a solution is not easy, and we have to use numerical methods to estimate solutions.
Partial differential equations are much more complicated compared with ordinary differential equations. There is no universal solution technique for nonlinear equations, even numerical simulations are usually not straightforward. Thus, we will mainly focus on the linear partial differential equations with the introduction of the most basic solution techniques.
Laplace transforms are an important tool with many applications in engineering such as control system and automation. This chapter introduces the fundamentals of Laplace transforms, their properties and applications in solving differential equations.
No abstract is provided for this article.
No abstract is provided for this article.
Real world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate numerous alternatives that provide dissimilar approaches to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the Firefly Algorithm can concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new approach is computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.
Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.
