Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information

In this paper we explore how specific aspects of market transparency and agents' behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with electronic book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or "foregone" payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents' orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared with to outcomes with Zero-Intelligent traders.

Ethnic Identity and discrimination among children

Cataloged from PDF version of article.We engaged over 430 Canadian children in a series of activities designed to reveal their evaluations of three ethnic groups (White, East Asian and South Asian), their identification with these groups, and their behavior towards them in a dictator game. Our experiments took place at the children’s schools during their normal school day, allowing us to evaluate the salience and effects of ethnic identities on economically relevant behavior in an important natural setting. We find that children from the dominant White category have a clear sense of White ethnic identity, and tend to favor White recipients in the dictator game relative to East Asian or South Asian recipients. Minority East Asian children reveal a more complex ethnic identity; they perceive themselves to be equally similar to White and East Asian children. Unlike Whites, East Asian children do not favor recipients from their own East Asian category, nor do they favor recipients with whom they tend to identify. If anything, East Asian children show out-group favoritism. (C) 2012 Elsevier B.V. All rights reserved

Using genetic algorithms to select architecture of a feedforward artificial neural network

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search for the global optimum of an arbitrary function as the output of a feedforward network model. Second, we allow the genetic algorithm to evolve the type of inputs, the number of hidden units and the connection structure between the inputs and the output layers. Third, we study how introduction of a local elitist procedure which we call the election operator affects the algorithm's performance. We conduct a Monte Carlo simulation to study the sensitiveness of the global approximation properties of the studied genetic algorithm. Finally, we apply the proposed methodology to the daily foreign exchange returns

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

<p>Abstract</p> <p>Background</p> <p>Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.</p> <p>Results</p> <p>PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.</p> <p>Conclusions</p> <p>The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.</p