An Error Corrected Almost Ideal Demand System for Major Cereals in Kenya

Despite significant progress in theory and empirical methods, the analysis of food consumption patterns in developing countries, particularly those in Sub Saharan Africa has received very limited attention. An attempt is made in this article to estimate an Error Corrected Almost Ideal Demand System for four major cereals consumed in Kenya employing annual data from 1963 to 2005. The model performs well both on theoretical and empirical grounds. All own-price elasticities are negative and statistically significant and all cereals are necessities both in the short-run and in the long-run in Kenya.Error Correction Model, AIDS, Cereal Consumption, Kenya, Demand and Price Analysis,

Who are the Real Gainers of Trade Liberalization in Kenya's Maize Sector?

In Kenya, trade policy reforms in the cereals sector were initiated as a key component of the economy-wide structural adjustment programmes (SAPs) during the mid 1980s. The SAPs were later strengthened and made irreversible by Kenya’s commitments at the multilateral trade negotiations. However, the welfare effects of these trade policy reforms remain controversial. This paper to quantifies the market and welfare impacts of trade liberalization in Kenya’s maize sector using a partial equilibrium model with market interrelationships at the farm, wholesale and retail levels. The model is calibrated to simulate a 24 percent reduction in maize import tariffs and a complete abolition of tariffs. The simulations results suggest that tariff reductions yield price decreases across the three market levels. The declining prices increase maize consumption but reduce domestic production. Consequently, consumer surplus increases while producer surplus decreases. However, the gain in consumer surplus is not sufficient to compensate the loss in producer surplus. Thus, the implementation of the multilateral agricultural trade agreement is likely to leave Kenya’s maize sector worse off and cannot be considered as a viable policy based on the compensation principle.Trade liberalization, maize, partial equilibrium analysis, welfare effects, Crop Production/Industries, International Relations/Trade, F14, F16, I32, C68, O24, Q12,

Spin-Charge Separation and Kinetic Energy in the t-J Model

I show that spin-charge separation in 2-D t-J model leads to an increase of kinetic energy. Using a sum rule, I derive an exact expression for the lowest possible KE (E_{bound}) for any state without doubly occupied sites. KE of relevant slave-boson and Schwinger-boson mean-field states -- which exhibit complete spin-charge separation -- are found to be much larger than E_{bound}. Examination of n(k) shows that the large increse in KE is due to excessive depletion of electrons from the bottom of the band (Schwinger boson) and of holes from the top (slave boson). To see whether the excess KE is simply due to poor treatment of the constraints, I solve the constraint problem analytically for the Schwinger boson case in the J = 0 limit. This restores gauge invariance, incorrectly violated in MF theories. The result is a generalized Hartree-Fock state of the Hubbard model, but one that includes spin waves. Even after constraints are imposed correctly, the KE remains much larger than E_{bound}. These results support the notion, advanced earlier [PRB 61, 8663 (2000)] that spin-charge separation in the MF state costs excessive KE, and makes the state unstable toward recombination processes which lead to superconductivity in d = 2 and a Fermi liquid state in higher dimensions.Comment: 13 pages, LateX plus three figures. To appear in Phys Rev B Typos correcte

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

Use of statistical outlier detection method in adaptive evolutionary algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case

Strategic Positioning in Tactical Scenario Planning

Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain