Bargaining for power and information : an experimental and theoretical analysis of the interaction between institutions, externalities and adverse selection

Bargaining is the building block of many economic interactions, ranging from bilateral to multilateral encounters and from situations in which the actors are individuals to negotiations between firms or countries. In all these settings, economists have been intrigued for a long time by the fact that some projects, trades or agreements are not realized even though they are mutually beneficial. On the one hand, this has been explained by incomplete information. A firm may not be willing to offer a wage that is acceptable to a qualified worker, because it knows that there are also unqualified workers and cannot distinguish between the two types. This phenomenon is known as adverse selection. On the other hand, it has been argued that even with complete information, the presence of externalities may impede efficient outcomes. To see this, consider the example of climate change. If a subset of countries agrees to curb emissions, non-participant regions benefit from the signatories’ efforts without incurring costs. These free riding opportunities give rise to incentives to strategically improve ones bargaining power that work against the formation of a global agreement. This thesis is concerned with extending our understanding of both factors, adverse selection and externalities. The findings are based on empirical evidence from original laboratory experiments as well as game theoretic modeling. On a very general note, it is demonstrated that the institutions through which agents interact matter to a large extent. Insights are provided about which institutions we should expect to perform better than others, at least in terms of aggregate welfare. Chapters 1 and 2 focus on the problem of adverse selection. Effective operation of markets and other institutions often depends on good information transmission properties. In terms of the example introduced above, a firm is only willing to offer high wages if it receives enough positive signals about the worker’s quality during the application and wage bargaining process. In Chapter 1, it will be shown that repeated interaction coupled with time costs facilitates information transmission. By making the wage bargaining process costly for the worker, the firm is able to obtain more accurate information about the worker’s type. The cost could be pure time cost from delaying agreement or cost of effort arising from a multi-step interviewing process. In Chapter 2, I abstract from time cost and show that communication can play a similar role. The simple fact that a worker states to be of high quality may be informative. In Chapter 3, the focus is on a different source of inefficiency. Agents strive for bargaining power and thus may be motivated by incentives that are at odds with the socially efficient outcome. I have already mentioned the example of climate change. Other examples are coalitions within committees that are formed to secure voting power to block outcomes or groups that commit to different technological standards although a single standard would be optimal (e.g. the format war between HD and BlueRay). It will be shown that such inefficiencies are directly linked to the presence of externalities and a certain degree of irreversibility in actions. I now discuss the three articles in more detail. In Chapter 1, Olivier Bochet and I study a simple bilateral bargaining institution that eliminates trade failures arising from incomplete information. In this setting, a buyer makes offers to a seller in order to acquire a good. Whenever an offer is rejected by the seller, the buyer may submit a further offer. Bargaining is costly, because both parties suffer a (small) time cost after any rejection. The difficulties arise, because the good can be of low or high quality and the quality of the good is only known to the seller. Indeed, without the possibility to make repeated offers, it is too risky for the buyer to offer prices that allow for trade of high quality goods. When allowing for repeated offers, however, at equilibrium both types of goods trade with probability one. We provide an experimental test of these predictions. Buyers gather information about sellers using specific price offers and rates of trade are high, much as the model’s qualitative predictions. We also observe a persistent over-delay before trade occurs, and this mitigates efficiency substantially. Possible channels for over-delay are identified in the form of two behavioral assumptions missing from the standard model, loss aversion (buyers) and haggling (sellers), which reconcile the data with the theoretical predictions. Chapter 2 also studies adverse selection, but interaction between buyers and sellers now takes place within a market rather than isolated pairs. Remarkably, in a market it suffices to let agents communicate in a very simple manner to mitigate trade failures. The key insight is that better informed agents (sellers) are willing to truthfully reveal their private information, because by doing so they are able to reduce search frictions and attract more buyers. Behavior observed in the experimental sessions closely follows the theoretical predictions. As a consequence, costless and non-binding communication (cheap talk) significantly raises rates of trade and welfare. Previous experiments have documented that cheap talk alleviates inefficiencies due to asymmetric information. These findings are explained by pro-social preferences and lie aversion. I use appropriate control treatments to show that such consideration play only a minor role in our market. Instead, the experiment highlights the ability to organize markets as a new channel through which communication can facilitate trade in the presence of private information. In Chapter 3, I theoretically explore coalition formation via multilateral bargaining under complete information. The environment studied is extremely rich in the sense that the model allows for all kinds of externalities. This is achieved by using so-called partition functions, which pin down a coalitional worth for each possible coalition in each possible coalition structure. It is found that although binding agreements can be written, efficiency is not guaranteed, because the negotiation process is inherently non-cooperative. The prospects of cooperation are shown to crucially depend on i) the degree to which players can renegotiate and gradually build up agreements and ii) the absence of a certain type of externalities that can loosely be described as incentives to free ride. Moreover, the willingness to concede bargaining power is identified as a novel reason for gradualism. Another key contribution of the study is that it identifies a strong connection between the Core, one of the most important concepts in cooperative game theory, and the set of environments for which efficiency is attained even without renegotiation.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Análisis de los factores determinantes de la repetición del proceso de compra en el comercio electrónico entre empresas y consumidores finales. Aplicación al mercado español

Las Tecnologías de la Información y la Comunicación en general e Internet en particular han supuesto una revolución en nuestra forma de comunicarnos, relacionarnos, producir, comprar y vender acortando tiempo y distancias entre proveedores y consumidores. A la paulatina penetración del ordenador, los teléfonos inteligentes y la banda ancha fija y/o móvil ha seguido un mayor uso de estas tecnologías entre ciudadanos y empresas. El comercio electrónico empresa–consumidor (B2C) alcanzó en 2010 en España un volumen de 9.114 millones de euros, con un incremento del 17,4% respecto al dato registrado en 2009. Este crecimiento se ha producido por distintos hechos: un incremento en el porcentaje de internautas hasta el 65,1% en 2010 de los cuales han adquirido productos o servicios a través de la Red un 43,1% –1,6 puntos porcentuales más respecto a 2010–. Por otra parte, el gasto medio por comprador ha ascendido a 831€ en 2010, lo que supone un incremento del 10,9% respecto al año anterior. Si segmentamos a los compradores según por su experiencia anterior de compra podemos encontrar dos categorías: el comprador novel –que adquirió por primera vez productos o servicios en 2010– y el comprador constante –aquel que había adquirido productos o servicios en 2010 y al menos una vez en años anteriores–. El 85,8% de los compradores se pueden considerar como compradores constantes: habían comprado en la Red en 2010, pero también lo habían hecho anteriormente. El comprador novel tiene un perfil sociodemográfico de persona joven de entre 15–24 años, con estudios secundarios, de clase social media y media–baja, estudiante no universitario, residente en poblaciones pequeñas y sigue utilizando fórmulas de pago como el contra–reembolso (23,9%). Su gasto medio anual ascendió en 2010 a 449€. El comprador constante, o comprador que ya había comprado en Internet anteriormente, tiene un perfil demográfico distinto: estudios superiores, clase alta, trabajador y residente en grandes ciudades, con un comportamiento maduro en la compra electrónica dada su mayor experiencia –utiliza con mayor intensidad canales exclusivos en Internet que no disponen de tienda presencial–. Su gasto medio duplica al observado en compradores noveles (con una media de 930€ anuales). Por tanto, los compradores constantes suponen una mayoría de los compradores con un gasto medio que dobla al comprador que ha adoptado el medio recientemente. Por consiguiente es de interés estudiar los factores que predicen que un internauta vuelva a adquirir un producto o servicio en la Red. La respuesta a esta pregunta no se ha revelado sencilla. En España, la mayoría de productos y servicios aún se adquieren de manera presencial, con una baja incidencia de las ventas a distancia como la teletienda, la venta por catálogo o la venta a través de Internet. Para dar respuesta a las preguntas planteadas se ha investigado desde distintos puntos de vista: se comenzará con un estudio descriptivo desde el punto de vista de la demanda que trata de caracterizar la situación del comercio electrónico B2C en España, poniendo el foco en las diferencias entre los compradores constantes y los nuevos compradores. Posteriormente, la investigación de modelos de adopción y continuidad en el uso de las tecnologías y de los factores que inciden en dicha continuidad –con especial interés en el comercio electrónico B2C–, permiten afrontar el problema desde la perspectiva de las ecuaciones estructurales pudiendo también extraer conclusiones de tipo práctico. Este trabajo sigue una estructura clásica de investigación científica: en el capítulo 1 se introduce el tema de investigación, continuando con una descripción del estado de situación del comercio electrónico B2C en España utilizando fuentes oficiales (capítulo 2). Posteriormente se desarrolla el marco teórico y el estado del arte de modelos de adopción y de utilización de las tecnologías (capítulo 3) y de los factores principales que inciden en la adopción y continuidad en el uso de las tecnologías (capítulo 4). El capítulo 5 desarrolla las hipótesis de la investigación y plantea los modelos teóricos. Las técnicas estadísticas a utilizar se describen en el capítulo 6, donde también se analizan los resultados empíricos sobre los modelos desarrollados en el capítulo 5. El capítulo 7 expone las principales conclusiones de la investigación, sus limitaciones y propone nuevas líneas de investigación. La primera parte corresponde al capítulo 1, que introduce la investigación justificándola desde un punto de vista teórico y práctico. También se realiza una breve introducción a la teoría del comportamiento del consumidor desde una perspectiva clásica. Se presentan los principales modelos de adopción y se introducen los modelos de continuidad de utilización que se estudiarán más detalladamente en el capítulo 3. En este capítulo se desarrollan los objetivos principales y los objetivos secundarios, se propone el mapa mental de la investigación y se planifican en un cronograma los principales hitos del trabajo. La segunda parte corresponde a los capítulos dos, tres y cuatro. En el capítulo 2 se describe el comercio electrónico B2C en España utilizando fuentes secundarias. Se aborda un diagnóstico del sector de comercio electrónico y su estado de madurez en España. Posteriormente, se analizan las diferencias entre los compradores constantes, principal interés de este trabajo, frente a los compradores noveles, destacando las diferencias de perfiles y usos. Para los dos segmentos se estudian aspectos como el lugar de acceso a la compra, la frecuencia de compra, los medios de pago utilizados o las actitudes hacia la compra. El capítulo 3 comienza desarrollando los principales conceptos sobre la teoría del comportamiento del consumidor, para continuar estudiando los principales modelos de adopción de tecnología existentes, analizando con especial atención su aplicación en comercio electrónico. Posteriormente se analizan los modelos de continuidad en el uso de tecnologías (Teoría de la Confirmación de Expectativas; Teoría de la Justicia), con especial atención de nuevo a su aplicación en el comercio electrónico. Una vez estudiados los principales modelos de adopción y continuidad en el uso de tecnologías, el capítulo 4 analiza los principales factores que se utilizan en los modelos: calidad, valor, factores basados en la confirmación de expectativas –satisfacción, utilidad percibida– y factores específicos en situaciones especiales –por ejemplo, tras una queja– como pueden ser la justicia, las emociones o la confianza. La tercera parte –que corresponde al capítulo 5– desarrolla el diseño de la investigación y la selección muestral de los modelos. En la primera parte del capítulo se enuncian las hipótesis –que van desde lo general a lo particular, utilizando los factores específicos analizados en el capítulo 4– para su posterior estudio y validación en el capítulo 6 utilizando las técnicas estadísticas apropiadas. A partir de las hipótesis, y de los modelos y factores estudiados en los capítulos 3 y 4, se definen y vertebran dos modelos teóricos originales que den respuesta a los retos de investigación planteados en el capítulo 1. En la segunda parte del capítulo se diseña el trabajo empírico de investigación definiendo los siguientes aspectos: alcance geográfico–temporal, tipología de la investigación, carácter y ambiente de la investigación, fuentes primarias y secundarias utilizadas, técnicas de recolección de datos, instrumentos de medida utilizados y características de la muestra utilizada. Los resultados del trabajo de investigación constituyen la cuarta parte de la investigación y se desarrollan en el capítulo 6, que comienza analizando las técnicas estadísticas basadas en Modelos de Ecuaciones Estructurales. Se plantean dos alternativas, modelos confirmatorios correspondientes a Métodos Basados en Covarianzas (MBC) y modelos predictivos. De forma razonada se eligen las técnicas predictivas dada la naturaleza exploratoria de la investigación planteada. La segunda parte del capítulo 6 desarrolla el análisis de los resultados de los modelos de medida y modelos estructurales construidos con indicadores formativos y reflectivos y definidos en el capítulo 4. Para ello se validan, sucesivamente, los modelos de medida y los modelos estructurales teniendo en cuenta los valores umbrales de los parámetros estadísticos necesarios para la validación. La quinta parte corresponde al capítulo 7, que desarrolla las conclusiones basándose en los resultados del capítulo 6, analizando los resultados desde el punto de vista de las aportaciones teóricas y prácticas, obteniendo conclusiones para la gestión de las empresas. A continuación, se describen las limitaciones de la investigación y se proponen nuevas líneas de estudio sobre distintos temas que han ido surgiendo a lo largo del trabajo. Finalmente, la bibliografía recoge todas las referencias utilizadas a lo largo de este trabajo. Palabras clave: comprador constante, modelos de continuidad de uso, continuidad en el uso de tecnologías, comercio electrónico, B2C, adopción de tecnologías, modelos de adopción tecnológica, TAM, TPB, IDT, UTAUT, ECT, intención de continuidad, satisfacción, confianza percibida, justicia, emociones, confirmación de expectativas, calidad, valor, PLS. ABSTRACT Information and Communication Technologies in general, but more specifically those related to the Internet in particular, have changed the way in which we communicate, relate to one another, produce, and buy and sell products, reducing the time and shortening the distance between suppliers and consumers. The steady breakthrough of computers, Smartphones and landline and/or wireless broadband has been greatly reflected in its large scale use by both individuals and businesses. Business–to–consumer (B2C) e–commerce reached a volume of 9,114 million Euros in Spain in 2010, representing a 17.4% increase with respect to the figure in 2009. This growth is due in part to two different facts: an increase in the percentage of web users to 65.1% en 2010, 43.1% of whom have acquired products or services through the Internet– which constitutes 1.6 percentage points higher than 2010. On the other hand, the average spending by individual buyers rose to 831€ en 2010, constituting a 10.9% increase with respect to the previous year. If we select buyers according to whether or not they have previously made some type of purchase, we can divide them into two categories: the novice buyer–who first made online purchases in 2010– and the experienced buyer: who also made purchases in 2010, but had done so previously as well. The socio–demographic profile of the novice buyer is that of a young person between 15–24 years of age, with secondary studies, middle to lower–middle class, and a non–university educated student who resides in smaller towns and continues to use payment methods such as cash on delivery (23.9%). In 2010, their average purchase grew to 449€. The more experienced buyer, or someone who has previously made purchases online, has a different demographic profile: highly educated, upper class, resident and worker in larger cities, who exercises a mature behavior when making online purchases due to their experience– this type of buyer frequently uses exclusive channels on the Internet that don’t have an actual store. His or her average purchase doubles that of the novice buyer (with an average purchase of 930€ annually.) That said, the experienced buyers constitute the majority of buyers with an average purchase that doubles that of novice buyers. It is therefore of interest to study the factors that help to predict whether or not a web user will buy another product or use another service on the Internet. The answer to this question has proven not to be so simple. In Spain, the majority of goods and services are still bought in person, with a low amount of purchases being made through means such as the Home Shopping Network, through catalogues or Internet sales. To answer the questions that have been posed here, an investigation has been conducted which takes into consideration various viewpoints: it will begin with a descriptive study from the perspective of the supply and demand that characterizes the B2C e–commerce situation in Spain, focusing on the differences between experienced buyers and novice buyers. Subsequently, there will be an investigation concerning the technology acceptance and continuity of use of models as well as the factors that have an effect on their continuity of use –with a special focus on B2C electronic commerce–, which allows for a theoretic approach to the problem from the perspective of the structural equations being able to reach practical conclusions. This investigation follows the classic structure for a scientific investigation: the subject of the investigation is introduced (Chapter 1), then the state of the B2C e–commerce in Spain is described citing official sources of information (Chapter 2), the theoretical framework and state of the art of technology acceptance and continuity models are developed further (Chapter 3) and the main factors that affect their acceptance and continuity (Chapter 4). Chapter 5 explains the hypothesis behind the investigation and poses the theoretical models that will be confirmed or rejected partially or completely. In Chapter 6, the technical statistics that will be used are described briefly as well as an analysis of the empirical results of the models put forth in Chapter 5. Chapter 7 explains the main conclusions of the investigation, its limitations and proposes new projects. First part of the project, chapter 1, introduces the investigation, justifying it from a theoretical and practical point of view. It is also a brief introduction to the theory of consumer behavior from a standard perspective. Technology acceptance models are presented and then continuity and repurchase models are introduced, which are studied more in depth in Chapter 3. In this chapter, both the main and the secondary objectives are developed through a mind map and a timetable which highlights the milestones of the project. The second part of the project corresponds to Chapters Two, Three and Four. Chapter 2 describes the B2C e–commerce in Spain from the perspective of its demand, citing secondary official sources. A diagnosis concerning the e–commerce sector and the status of its maturity in Spain is taken on, as well as the barriers and alternative methods of e–commerce. Subsequently, the differences between experienced buyers, which are of particular interest to this project, and novice buyers are analyzed, highlighting the differences between their profiles and their main transactions. In order to study both groups, aspects such as the place of purchase, frequency with which online purchases are made, payment methods used and the attitudes of the purchasers concerning making online purchases are taken into consideration. Chapter 3 begins by developing the main concepts concerning consumer behavior theory in order to continue the study of the main existing acceptance models (among others, TPB, TAM, IDT, UTAUT and other models derived from them) – paying special attention to their application in e–commerce–. Subsequently, the models of technology reuse are analyzed (CDT, ECT; Theory of Justice), focusing again specifically on their application in e–commerce. Once the main technology acceptance and reuse models have been studied, Chapter 4 analyzes the main factors that are used in these models: quality, value, factors based on the contradiction of expectations/failure to meet expectations– satisfaction, perceived usefulness– and specific factors pertaining to special situations– for example, after receiving a complaint justice, emotions or confidence. The third part– which appears in Chapter 5– develops the plan for the investigation and the sample selection for the models that have been designed. In the first section of the Chapter, the hypothesis is presented– beginning with general ideas and then becoming more specific, using the detailed factors that were analyzed in Chapter 4– for its later study and validation in Chapter 6– as well as the corresponding statistical factors. Based on the hypothesis and the models and factors that were studied in Chapters 3 and 4, two original theoretical models are defined and organized in order to answer the questions posed in Chapter 1. In the second part of the Chapter, the empirical investigation is designed, defining the following aspects: geographic–temporal scope, type of investigation, nature and setting of the investigation, primary and secondary sources used, data gathering methods, instruments according to the extent of their use and characteristics of the sample used. The results of the project constitute the fourth part of the investigation and are developed in Chapter 6, which begins analyzing the statistical techniques that are based on the Models of Structural Equations. Two alternatives are put forth: confirmatory models which correspond to Methods Based on Covariance (MBC) and predictive models– Methods Based on Components–. In a well–reasoned manner, the predictive techniques are chosen given the explorative nature of the investigation. The second part of Chapter 6 explains the results of the analysis of the measurement models and structural models built by the formative and reflective indicators defined in Chapter 4. In order to do so, the measurement models and the structural models are validated one by one, while keeping in mind the threshold values of the necessary statistic parameters for their validation. The fifth part corresponds to Chapter 7 which explains the conclusions of the study, basing them on the results found in Chapter 6 and analyzing them from the perspective of the theoretical and practical contributions, and consequently obtaining conclusions for business management. The limitations of the investigation are then described and new research lines about various topics that came up during the project are proposed. Lastly, all of the references that were used during the project are listed in a final bibliography. Key Words: constant buyer, repurchase models, continuity of use of technology, e–commerce, B2C, technology acceptance, technology acceptance models, TAM, TPB, IDT, UTAUT, ECT, intention of repurchase, satisfaction, perceived trust/confidence, justice, feelings, the contradiction of expectations, quality, value, PLS.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

Metagenomic Anlaysis of microsymbiont selection by the legume plant host

Rhizobium leguminosarum bv.viciae is able to establish nitrogen-fixing symbioses with legumes of the genera Pisum, Lens, Lathyrus and Vicia. Classic studies using trap plants (Laguerre et al., Young et al.) provided evidence that different plant hosts are able to select different rhizobial genotypes among those available in a given soil. However, these studies were necessarily limited by the paucity of relevant biodiversity markers. We have now reappraised this problem with the help of genomic tools. A well-characterized agricultural soil (INRA Bretennieres) was used as source of rhizobia. Plants of Pisum sativum, Lens culinaris, Vicia sativa and V. faba were used as traps. Isolates from 100 nodules were pooled, and DNA from each pool was sequenced (BGI-Hong Kong; Illumina Hiseq 2000, 500 bp PE libraries, 100 bp reads, 12 Mreads). Reads were quality filtered (FastQC, Trimmomatic), mapped against reference R. leguminosarum genomes (Bowtie2, Samtools), and visualized (IGV). An important fraction of the filtered reads were not recruited by reference genomes, suggesting that plant isolates contain genes that are not present in the reference genomes. For this study, we focused on three conserved genomic regions: 16S-23S rDNA, atpD and nodDABC, and a Single Nucleotide Polymorphism (SNP) analysis was carried out with meta / multigenomes from each plant. Although the level of polymorphism varied (lowest in the rRNA region), polymorphic sites could be identified that define the specific soil population vs. reference genomes. More importantly, a plant-specific SNP distribution was observed. This could be confirmed with many other regions extracted from the reference genomes (data not shown). Our results confirm at the genomic level previous observations regarding plant selection of specific genotypes. We expect that further, ongoing comparative studies on differential meta / multigenomic sequences will identify specific gene components of the plant-selected genotypes

Binocular vision in insects: How mantids solve the correspondence problem

Praying mantids use binocular cues to judge whether their prey is in striking distance. When there are several moving targets within their binocular visual field, mantids need to solve the correspondence problem. They must select between the possible pairings of retinal images in the two eyes so that they can strike at a single real target. In this study, mantids were presented with two targets in various configurations, and the resulting fixating saccades that precede the strike were analyzed. The distributions of saccades show that mantids consistently prefer one out of several possible matches. Selection is in part guided by the position and the spatiotemporal features of the target image in each eye. Selection also depends upon the binocular disparity of the images, suggesting that insects can perform local binocular computations. The pairing rules ensure that mantids tend to aim at real targets and not at “ghost” targets arising from false matches.

Murine hematopoietic reconstitution after tagging and selection of retrovirally transduced bone marrow cells

A major problem facing the effective treatment of patients with cancer is how to get the specific antitumor agent into every tumor cell. In this report we describe the use of a strategy that, by using retroviral vectors encoding a truncated human CD5 cDNA, allows the selection of only the infected cells, and we show the ability to obtain, before bone marrow transplantation, a population of 5-fluouraci-treated murine bone marrow cells that are 100% marked. This marked population of bone marrow cells is able to reconstitute the hematopoietic system in lethally irradiated mice, indicating that the surface marker lacks deleterious effects on the functionality of bone marrow cells. No gross abnormalities in hematopoiesis were detected in mice repopulated with CD5-expressing cells. Nevertheless, a significant proportion of the hematopoietic cells no longer expresses the surface marker CD5 in the 9-month-old recipient mice. This transcriptional inactivity of the proviral long terminal repeat (LTR) was accompanied by de novo methylation of the proviral sequences. Our results show that the use of the CD5 as a retrovirally encoded marker enables the rapid, efficient, and nontoxic selection in vitro of infected primary cells, which can entirely reconstitute the hematopoietic system in mice. These results should now greatly enhance the power of studies aimed at addressing questions such as generation of cancer-negative hematopoiesis.

Selection of transduced CD34+ progenitors and enzymatic correction of cells from Gaucher patients, with bicistronic vectors.

The gene transfer efficiency of human hematopoietic stem cells is still inadequate for efficient gene therapy of most disorders. To overcome this problem, a selectable retroviral vector system for gene therapy has been developed for gene therapy of Gaucher disease. We constructed a bicistronic retroviral vector containing the human glucocerebrosidase (GC) cDNA and the human small cell surface antigen CD24 (243 bp). Expression of both cDNAs was controlled by the long terminal repeat enhancer/promoter of the Molony murine leukemia virus. The CD24 selectable marker was placed downstream of the GC cDNA and its translation was enhanced by inclusion of the long 5' untranslated region of encephalomyocarditis virus internal ribosomal entry site. Virus-producing GP+envAM12 cells were created by multiple supernatant transductions to create vector producer cells. The vector LGEC has a high titer and can drive expression of GC and the cell surface antigen CD24 simultaneously in transduced NIH 3T3 cells and Gaucher skin fibroblasts. These transduced cells have been successfully separated from untransduced cells by fluorescence-activated cell sorting, based on cell surface expression of CD24. Transduced and sorted NIH 3T3 cells showed higher GC enzyme activity than the unsorted population, demonstrating coordinated expression of both genes. Fibroblasts from Gaucher patients were transduced and sorted for CD24 expression, and GC enzyme activity was measured. The transduced sorted Gaucher fibroblasts had a marked increase in enzyme activity (149%) compared with virgin Gaucher fibroblasts (17% of normal GC enzyme activity). Efficient transduction of CD34+ hematopoietic progenitors (20-40%) was accomplished and fluorescence-activated cell sorted CD24(+)-expressing progenitors generated colonies, all of which (100%) were vector positive. The sorted, CD24-expressing progenitors generated erythroid burst-forming units, colony-forming units (CFU)-granulocyte, CFU-macrophage, CFU-granulocyte/macrophage, and CFU-mix hematopoietic colonies, demonstrating their ability to differentiate into these myeloid lineages in vitro. The transduced, sorted progenitors raised the GC enzyme levels in their progeny cells manyfold compared with untransduced CD34+ progenitors. Collectively, this demonstrates the development of high titer, selectable bicistronic vectors that allow isolation of transduced hematopoietic progenitors and cells that have been metabolically corrected.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Isolation and characterization of integron-containing bacteria without antibiotic selection

The emergence of antibiotic resistance among pathogenic and commensal bacteria has become a serious problem worldwide. The use and overuse of antibiotics in a number of settings are contributing to the development of antibiotic-resistant microorganisms. The class 1 and 2 integrase genes (intI1 and intI2, respectively) were identified in mixed bacterial cultures enriched from bovine feces by growth in buffered peptone water (BPW) followed by integrase-specific PCR. Integrase-positive bacterial colonies from the enrichment cultures were then isolated by using hydrophobic grid membrane filters and integrase-specific gene probes. Bacterial clones isolated by this technique were then confirmed to carry integrons by further testing by PCR and DNA sequencing. Integron-associated antibiotic resistance genes were detected in bacteria such as Escherichia coli, Aeromonas spp., Proteus spp., Morganella morganii, Shewanella spp., and urea-positive Providencia stuartii isolates from bovine fecal samples without the use of selective enrichment media containing antibiotics. Streptomycin and trimethoprim resistance were commonly associated with integrons. The advantages conferred by this methodology are that a wide variety of integron-containing bacteria may be simultaneously cultured in BPW enrichments and culture biases due to antibiotic selection can be avoided. Rapid and efficient identification, isolation, and characterization of antibiotic resistance-associated integrons are possible by this protocol. These methods will facilitate greater understanding of the factors that contribute to the presence and transfer of integron-associated antibiotic resistance genes in bacterial isolates from red meat production animals.

The dynamics of a genetic algorithm for a simple learning problem

A formalism for describing the dynamics of Genetic Algorithms (GAs) using method s from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.