E. Dante Suarez

Epistemology of modeling and simulation

While ontology deals with the question of being or existence, epistemology deals with the question of gaining knowledge. This panel addresses the challenge of how we gain knowledge from modeling and simulation. What is the underlying philosophy of science of M&S? What are our canons of research for M&S? Is it sufficient to apply the foundational methods of the application domains, or do we need to address these questions from the standpoint of M&S as a discipline? The invited experts illuminate various facets from philosophical, mathematical, computational, and application viewpoints.

The Perfect Withdrawal Amount: A Methodology for Creating Retirement Account Distribution Strategies

We present a new way to develop withdrawal strategies from retirement portfolios. It is derived analytically, instead of from empirical testing, and it iterates always in the same manner. It is based on a new measure we develop, the Perfect Withdrawal Amount, for which we discuss how to construct a probability distribution and how to apply it sequentially. We also derive a new measure of sequencing risk. We present new strategies built with this framework.

Enforcing the Law of One Price: Nonlinear Mean Reversion in the ADR Market

This work presents evidence that cross‐isted stocks (ADRs) are traded in markets that are not completely integrated, and it is the presence of high frequency arbitrage activity that forces these stock pairs to be most commonly in relative equilibrium. A Threshold Autoregressive (TAR) model tests the hypothesis that the reversion to equilibrium of the price discrepancy series is a nonlinear function that has nontrivial thresholds, and that large price discrepancies are relatively short‐lived. The TAR specification models the neutralization of arbitrage forces with thresholds that separate outer regions where large discrepancies have a strong reversion to equilibrium from a central region where transaction costs significantly mitigate this reversion.

Fuzzy Models for Complex Social Systems Using Distributed Agencies in Poverty Studies

There are several ways to model a complex social system, as is the poverty of an entity, the object of this paper is to present a methodology consisting of several techniques that offers to solve complex social problems with soft computing.

Modeling the Employment Using Distributed Agenciesand Data Mining

There are several ways to model a complex social system, as is the employment. This work is motivated by need to establish a model for the study of social and economic systems in situations where conventional analysis is insufficient; this paper is to present a methodology applied to complex social problems with soft computing.

The use of Neural Networks for modeling nonlinear mean reversion: Measuring efficiency and integration in ADR markets

We propose the use of a Neural Network (NN) methodology for evaluating models of time series that exhibit nonlinear mean reversion, such as those stemming from equilibrium relationships that are affected by transaction costs or institutional rigidities. Given the vast array of such models found in the literature, the proposed NN procedure represents a useful graphical tool, providing the researcher with the ability to visualize the data before choosing the most appropriate approach for modeling mean-reversion dynamics with either a Threshold Autoregression (TAR), a Smooth Transition Autoregression (STAR), or any hybrid model. Our case study is involved with understanding the nature of cross-listed stocks (ADRs) and the degree of market integration and efficiency, as captured by the NN methodology. This is done through an analysis of the intradaily price discrepancies of cross-listed French, Mexican and American stocks. The results of the NN methodology are relevant in describing the arbitrage forces that maintain the Law of One Price in these ADR markets, and thus provide a more explicit insight on how these markets are integrated.