New Economics Papers
on Computational Economics
Issue of 2005‒11‒12
three papers chosen by

  1. Modeling the Firm as an Artificial Neural Network By Jason Barr; Francesco Saraceno
  2. Merger simulation analysis : an academic perspective By Damme,E. van; Pinkse,J.
  3. A simulation analysis of interactions between errors in costing system design By E. LABRO; M. VANHOUCKE

  1. By: Jason Barr; Francesco Saraceno
    Abstract: The purpose of this chapter is two-fold: (1) to make the case that a standard backward propagation artificial neural network (ANN) can be used as a general model of the information processing activities of the firm, and (2) to present a synthesis of Barr and Saraceno (BS) (2002, 2004, 2005), who offer various models of the firm as an artificial neural network.
    Keywords: neural networks, information processing, firm learning, agent-based
    JEL: C63 D21 D83 L13
    Date: 2005–10
  2. By: Damme,E. van; Pinkse,J. (TILEC (Tilburg Law and Economics Center))
    Date: 2005
    Abstract: The academic accounting literature has established that the conditions under which costing systems in general and Activity Based Costing (ABC) in particular provide accurate costs are very stringent. Less is known, however, about the nature, level and bias of costing errors and their interactions, when these conditions are not met. The main problem to overcome to enable us to learn about these is the notion of the unobservable true cost benchmark to which to compare the costing system approximation. This paper presents a simulation method to deal with this problem, allowing a variety of research questions in this research area to be addressed with more generalizable answers. Using our methodology, we test a variety of hypotheses on the interaction between various errors in costing system design that were developed in the previous analytical, empirical, and practitioner literature. We also provide some interesting new insights on interactions between errors that were previously not discussed in the literature. This paper presents new results on (1) conditions under which partial refinement in costing systems does or does not work to improve overall accuracy, (2) the contexts in which it is most effective to correct a particular type of error in terms of improving overall accuracy and (3) indicators of robustness or sensitivity of costing system designs to errors. In doing so, we also provide insights relevant to practitioners, costing system designers and users of costing information alike.
    Keywords: costing system design, costing accuracy, simulation, costing errors
    Date: 2005–09

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