In search of efficient network structures: the needle in the haystack

Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of non-trivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced by Jackson and Wolinsky (1996), for which the efficient networks are known over the whole state space of the parameters’ values. This approach allows us to obtain new exploratory results in the case of the linear-spatialized connections model proposed by Johnson and Gilles (Rev Econ Des 5:273–299, 2000), for which the efficient allocation of bilateral connections is driven by contradictory forces that push either for a centralized structure around a coordinating agent, or for only locally and evenly distributed connections. Murat Yıldızoğlu gratefully acknowledges the support of the CCRDT program of Aquitaine Region.     

On economic applications of the genetic algorithm: a model of the cobweb type

This paper explores the idea of using artificial adaptive agents in economic theory. In particular, we use Genetic Algorithms (GAs) to model the learning behavior of a population of adaptive and boundedly rational agents interacting in an economic system. We analyze the behavior of a GA in two versions of a model of the cobweb-type, one in which firms make only quantity choices, and the other one in which firms first decide to exit or to stay in the market, and subsequently decide how much to produce. We present simulations with different coding schemes and interpret the rather surprising differences between the results for different setups by employing the mathematical theory for GAs with state-dependent fitness functions. In particular, we explain the relationship between coding and convergence properties of GAs.     

论人类基因资料库研究中知情同意制度的完善

传统生物医学研究的知情同意保护制度无法应对人类基因资料库研究集体性、开放性、长期性带来的挑战,面临同意表达、同意获取和同意变更困境。基于利益保护与技术发展平衡理念,知情同意制度的完善措施应当包括： 第一,坚持以个人为知情同意主体,根据不同的研究对象差异化认定群体意见效果,以应对基因资料采集集体性带来的群体同意表达问题。第二,依托信息技术构建以广泛同意为基础、与动态同意相结合的知情同意模式,应对基因资料应用开放性带来的再次同意获取问题。第三,充分考虑基因提供者知情同意能力变化时的同意选择及撤回同意效力,应对基因资料留存的长期性带来的同意变更问题。     

深空天线组阵相关合成算法的Simulink仿真与分析

为对深空网天线组阵中几种相关合成算法进行分析比较,构建了一种新的Simulink仿真模型。将其应用于某测控站天线组阵试验数据,验证了模型的可行性。在此仿真模型下,对Simple、Sumple和Matrix-free 算法进行了频标同源/频标不同源、弱信号/强信号、2/3/6个天线等组阵情况下的仿真分析。三种算法在频标同源情况下的合成效率均优于不同源的情况;强信号组阵情况下,三种算法的信噪比合成性能基本相当;Simple算法在6天线情况下,信噪比合成性能下降;Sumple算法在组阵的天线数目很少时,合成信噪比较低且不稳定,在天线数目较多时性能良好;Matrix-free算法性能稳健,合成效率始终大于95%。该Simulink仿真模型对于进行天线组阵信号相关算法的分析具有一定的价值。     

物流系统优化问题研究方法概述

结合物流系统的特点,分析了物流系统中存在的优化问题。并针对一些关键技术问题对近年来国内外有关文献进行梳理和研究。文章首先综述了物流系统中四类组合优化问题以及此类问题的传统确定性算法及智能优化算法,同时分析这些方法在处理不同的问题时各自的优缺点。最后对物流系统优化问题的研究方法及研究方向做了分析和展望。     

Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression

Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents’ expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents’ expectations to anticipate economic growth after the crisis in all countries except Germany.     

GP algorithm versus hybrid and mixed neural networks

In the current paper, we present an integrated genetic programming (GP) environment called java GP modelling. The java GP modelling environment is an implementation of the steady-state GP algorithm. This algorithm evolves tree-based structures that represent models of inputs and outputs. The motivation of this paper is to compare the GP algorithm with neural network (NN) architectures when applied to the task of forecasting and trading the ASE 20 Greek Index (using autoregressive terms as inputs). This is done by benchmarking the forecasting performance of the GP algorithm and six different autoregressive moving average model (ARMA) NN combination designs representing a Hybrid, Mixed Higher Order Neural Network (HONN), a Hybrid, Mixed Recurrent Neural Network (RNN), a Hybrid, Mixed classic Multilayer Perceptron with some traditional techniques, either statistical such as a an ARMA or technical such as a moving average convergence/divergence model, and a naïve trading strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on ASE 20 time-series closing prices over the period 2001–2008, using the last one and a half years for out-of-sample testing. We use the ASE 20 daily series as many financial institutions are ready to trade at this level, and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the GP model does remarkably well and outperforms all other models in a simple trading simulation exercise. This is also the case when more sophisticated trading strategies using confirmation filters and leverage are applied, as the GP model still produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.     

Optimal hedge fund portfolios under liquidation risk

We use an expected utility framework to integrate the liquidation risk of hedge funds into portfolio allocation problems. The introduction of realistic investment constraints complicates the determination of the optimal solution, which is solved using a genetic algorithm that mimics the mechanism of natural evolution. We analyse the impact of the liquidation risk, of the investment constraints and of the agent's degree of risk aversion on the optimal allocation and on the optimal certainty equivalent of hedge fund portfolios. We observe, in particular, that the portfolio weights and their performance are significantly affected by liquidation risk. Finally, tight portfolio constraints can only provide limited protection against liquidation risk. This approach is of special interest to fund of hedge fund managers who wish to include the hedge fund liquidation risk in their portfolio optimization scheme.     

Maximizing Profits for a Multi-Category Catalog Retailer

It is a common trend in the retail industry for catalog retailers to mail multiple catalogs, each promoting different product categories. The existing catalog mailing models do not address the issue of optimizing multi-category catalog mailing. We address this research gap by introducing a model that integrates the when and what components of a customer's purchase decision into the how much component (number of catalogs) of a firm's cross-selling strategy. In addition to comparing the impact of category-specific versus full product catalogs in generating sales in a specific category, the study also finds relative impacts of various category-specific catalogs. We jointly estimate the probability of purchase and purchase amounts in multiple product categories by using multivariate proportional hazard model (MVPHM) and a regression based purchase amount model in a Hierarchical Bayesian framework. The model accounts for unobserved heterogeneity, and uses a control function (CF) approach to account for endogeneity in catalog mailing. The results from the Genetic Algorithm (GA) based optimization suggest that the catalog mailing policy as per the proposed model would be able to generate 38.4 percent more customer lifetime value (CLV) from a sample of 10 percent of the households as compared to the current catalog mailing policy of the retailer by reallocation of the catalogs across customers and mailing periods based on their propensity to buy.     

Testing assumptions underlying economic research on transgenic food crops for Third World farmers: Evidence from Cuba, Guatemala and Mexico

Transgenic crop varieties (TGVs) are being promoted as essential for improving small-scale Third World (SSTW) agriculture. Most economic research on this topic makes critical, untested assumptions, including that farmers will choose TGVs over other varieties because TGVs are economically optimal and because farmers are risk neutral profit maximizers. We tested these assumptions using data from a survey of 334 farmers in 6 communities in Cuba, Guatemala and Mexico in which farmers ranked 4 real and hypothetical maize varieties for eating and sowing. Our results did not support these assumptions. Most farmers preferred farmer varieties for sowing and especially for eating, avoiding TGVs, a preference associated with being risk averse and with non-monetary preferences. Farmers more integrated into modern agriculture were more likely to choose TGVs. These results suggest that farmers most in need of support and most important for conserving genetic diversity are least favorable toward TGVs, and that alternative ways of improving SSTW agriculture should receive more attention.