Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Application of Bayesian approach in geotechnical engineering

En esta Tesis Doctoral se emplean y desarrollan Métodos Bayesianos para su aplicación en análisis geotécnicos habituales, con un énfasis particular en (i) la valoración y selección de modelos geotécnicos basados en correlaciones empíricas; en (ii) el desarrollo de predicciones acerca de los resultados esperados en modelos geotécnicos complejos. Se llevan a cabo diferentes aplicaciones a problemas geotécnicos, como es el caso de: (1) En el caso de rocas intactas, se presenta un método Bayesiano para la evaluación de modelos que permiten estimar el módulo de Young a partir de la resistencia a compresión simple (UCS). La metodología desarrollada suministra estimaciones de las incertidumbres de los parámetros y predicciones y es capaz de diferenciar entre las diferentes fuentes de error. Se desarrollan modelos "específicos de roca" para los tipos de roca más comunes y se muestra cómo se pueden "actualizar" esos modelos "iniciales" para incorporar, cuando se encuentra disponible, la nueva información específica del proyecto, reduciendo las incertidumbres del modelo y mejorando sus capacidades predictivas. (2) Para macizos rocosos, se presenta una metodología, fundamentada en un criterio de selección de modelos, que permite determinar el modelo más apropiado, entre un conjunto de candidatos, para estimar el módulo de deformación de un macizo rocoso a partir de un conjunto de datos observados. Una vez que se ha seleccionado el modelo más apropiado, se emplea un método Bayesiano para obtener distribuciones predictivas de los módulos de deformación de macizos rocosos y para actualizarlos con la nueva información específica del proyecto. Este método Bayesiano de actualización puede reducir significativamente la incertidumbre asociada a la predicción, y por lo tanto, afectar las estimaciones que se hagan de la probabilidad de fallo, lo cual es de un interés significativo para los diseños de mecánica de rocas basados en fiabilidad. (3) En las primeras etapas de los diseños de mecánica de rocas, la información acerca de los parámetros geomecánicos y geométricos, las tensiones in-situ o los parámetros de sostenimiento, es, a menudo, escasa o incompleta. Esto plantea dificultades para aplicar las correlaciones empíricas tradicionales que no pueden trabajar con información incompleta para realizar predicciones. Por lo tanto, se propone la utilización de una Red Bayesiana para trabajar con información incompleta y, en particular, se desarrolla un clasificador Naïve Bayes para predecir la probabilidad de ocurrencia de grandes deformaciones (squeezing) en un túnel a partir de cinco parámetros de entrada habitualmente disponibles, al menos parcialmente, en la etapa de diseño. This dissertation employs and develops Bayesian methods to be used in typical geotechnical analyses, with a particular emphasis on (i) the assessment and selection of geotechnical models based on empirical correlations; on (ii) the development of probabilistic predictions of outcomes expected for complex geotechnical models. Examples of application to geotechnical problems are developed, as follows: (1) For intact rocks, we present a Bayesian framework for model assessment to estimate the Young’s moduli based on their UCS. Our approach provides uncertainty estimates of parameters and predictions, and can differentiate among the sources of error. We develop ‘rock-specific’ models for common rock types, and illustrate that such ‘initial’ models can be ‘updated’ to incorporate new project-specific information as it becomes available, reducing model uncertainties and improving their predictive capabilities. (2) For rock masses, we present an approach, based on model selection criteria to select the most appropriate model, among a set of candidate models, to estimate the deformation modulus of a rock mass, given a set of observed data. Once the most appropriate model is selected, a Bayesian framework is employed to develop predictive distributions of the deformation moduli of rock masses, and to update them with new project-specific data. Such Bayesian updating approach can significantly reduce the associated predictive uncertainty, and therefore, affect our computed estimates of probability of failure, which is of significant interest to reliability-based rock engineering design. (3) In the preliminary design stage of rock engineering, the information about geomechanical and geometrical parameters, in situ stress or support parameters is often scarce or incomplete. This poses difficulties in applying traditional empirical correlations that cannot deal with incomplete data to make predictions. Therefore, we propose the use of Bayesian Networks to deal with incomplete data and, in particular, a Naïve Bayes classifier is developed to predict the probability of occurrence of tunnel squeezing based on five input parameters that are commonly available, at least partially, at design stages.

Share the fame or share the blame? The reputational implications of partnerships

We use an adverse selection model to study the dynamics of firms' reputations when firms implement joint projects. We show that in contrast with projects implemented by a single firm, in the case of joint projects a firm's reputation does not necessarily increase following a success and does not necessarily decrease following a failure. We also study how reputation considerations affect firms ' decisions to participate in joint projects. We show that a high quality partner may not be preferable to a low quality partner, and that a high reputation partner is not necessarily preferable to a low reputation partner.

Candidate stability and voting correspondences

We study the incentives of candidates to enter or to exit elections in order to strategically affect the outcome of a voting correspondence. We extend the results of Dutta, Jackson and Le Breton (2000), who only considered single-valued voting procedures by admitting that the outcomes of voting may consist of sets of candidates. We show that, if candidates form their preferences over sets according to Expected Utility Theory and Bayesian updating, every unanimous and non dictatorial voting correspondence violates candidate stability. When candidates are restricted to use even chance prior distributions, only dictatorial or bidictatorial rules are unanimous and candidate stable. We also analyze the implications of using other extension criteria to define candidate stability that open the door to positive results.

Baysian Model Averaging, Learning and Model Selection

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

What is the Causal Effect of Information and Learning about a Public Good on Willingness to Pay?

In this study we elicit agents’ prior information set regarding a public good, exogenously give information treatments to survey respondents and subsequently elicit willingness to pay for the good and posterior information sets. The design of this field experiment allows us to perform theoretically motivated hypothesis testing between different updating rules: non-informative updating, Bayesian updating, and incomplete updating. We find causal evidence that agents imperfectly update their information sets. We also field causal evidence that the amount of additional information provided to subjects relative to their pre-existing information levels can affect stated WTP in ways consistent overload from too much learning. This result raises important (though familiar) issues for the use of stated preference methods in policy analysis.

The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Environmental Goods

This paper develop and estimates a model of demand estimation for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two environmental goods. We find that the predictions of the theoretical exercise that additional experience makes consumers more certain over their preferences in both mean and variance are supported in each case.

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Centralnotations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform.In this way very elaborated aspects of mathematical statistics can be understoodeasily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating,combination of likelihood and robust M-estimation functions are simple additions/perturbations in A2(Pprior). Weighting observations corresponds to a weightedaddition of the corresponding evidence.Likelihood based statistics for general exponential families turns out to have aparticularly easy interpretation in terms of A2(P). Regular exponential families formfinite dimensional linear subspaces of A2(P) and they correspond to finite dimensionalsubspaces formed by their posterior in the dual information space A2(Pprior).The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P.The discussion of A2(P) valued random variables, such as estimation functionsor likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Central notations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform. In this way very elaborated aspects of mathematical statistics can be understood easily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating, combination of likelihood and robust M-estimation functions are simple additions/ perturbations in A2(Pprior). Weighting observations corresponds to a weighted addition of the corresponding evidence. Likelihood based statistics for general exponential families turns out to have a particularly easy interpretation in terms of A2(P). Regular exponential families form finite dimensional linear subspaces of A2(P) and they correspond to finite dimensional subspaces formed by their posterior in the dual information space A2(Pprior). The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P. The discussion of A2(P) valued random variables, such as estimation functions or likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

Risk, ambiguity, and diversification

Attitudes toward risk influence the decision to diversify among uncertain options. Yet, because in most situations the options are ambiguous, attitudes toward ambiguity may also play an important role. I conduct a laboratory experiment to investigate the effect of ambiguity on the decision to diversify. I find that diversification is more prevalent and more persistent under ambiguity than under risk. Moreover, excess diversification under ambiguity is driven by participants who stick with a status quo gamble when diversification among gambles is not feasible. This behavioral pattern cannot be accommodated by major theories of choice under ambiguity.

Reworking the NAFTA: departures from traditional frameworks

This paper reviews the treatment of intellectual property rights in the North American Free Trade Agreement (NAFTA) and considers the welfare-theoretic bases for innovation transfer between member and nonmember states. Specifically, we consider the effects of new technology development from within the union and question whether it is efficient (in a welfare sense) to transfer that new technology to nonmember states. When the new technology contains stochastic components, the important issue of information exchange arises and we consider this question in a simple oligopoly model with Bayesian updating. In this context, it is natural to ask the optimal price at which such information should be transferred. Some simple, natural conjugate examples are used to motivate the key parameters upon which the answer is dependent

Diseño basado en técnicas de fiabilidad del tratamiento de mejora del terreno mediante columnas de grava

El proyecto geotécnico de columnas de grava tiene todas las incertidumbres asociadas a un proyecto geotécnico y además hay que considerar las incertidumbres inherentes a la compleja interacción entre el terreno y la columna, la puesta en obra de los materiales y el producto final conseguido. Este hecho es común a otros tratamientos del terreno cuyo objetivo sea, en general, la mejora “profunda”. Como los métodos de fiabilidad (v.gr., FORM, SORM, Monte Carlo, Simulación Direccional) dan respuesta a la incertidumbre de forma mucho más consistente y racional que el coeficiente de seguridad tradicional, ha surgido un interés reciente en la aplicación de técnicas de fiabilidad a la ingeniería geotécnica. Si bien la aplicación concreta al proyecto de técnicas de mejora del terreno no es tan extensa. En esta Tesis se han aplicado las técnicas de fiabilidad a algunos aspectos del proyecto de columnas de grava (estimación de asientos, tiempos de consolidación y aumento de la capacidad portante) con el objetivo de efectuar un análisis racional del proceso de diseño, considerando los efectos que tienen la incertidumbre y la variabilidad en la seguridad del proyecto, es decir, en la probabilidad de fallo. Para alcanzar este objetivo se ha utilizado un método analítico avanzado debido a Castro y Sagaseta (2009), que mejora notablemente la predicción de las variables involucradas en el diseño del tratamiento y su evolución temporal (consolidación). Se ha estudiado el problema del asiento (valor y tiempo de consolidación) en el contexto de la incertidumbre, analizando dos modos de fallo: i) el primer modo representa la situación en la que es posible finalizar la consolidación primaria, parcial o totalmente, del terreno mejorado antes de la ejecución de la estructura final, bien sea por un precarga o porque la carga se pueda aplicar gradualmente sin afectar a la estructura o instalación; y ii) por otra parte, el segundo modo de fallo implica que el terreno mejorado se carga desde el instante inicial con la estructura definitiva o instalación y se comprueba que el asiento final (transcurrida la consolidación primaria) sea lo suficientemente pequeño para que pueda considerarse admisible. Para trabajar con valores realistas de los parámetros geotécnicos, los datos se han obtenido de un terreno real mejorado con columnas de grava, consiguiendo, de esta forma, un análisis de fiabilidad más riguroso. La conclusión más importante, obtenida del análisis de este caso particular, es la necesidad de precargar el terreno mejorado con columnas de grava para conseguir que el asiento ocurra de forma anticipada antes de la aplicación de la carga correspondiente a la estructura definitiva. De otra forma la probabilidad de fallo es muy alta, incluso cuando el margen de seguridad determinista pudiera ser suficiente. En lo que respecta a la capacidad portante de las columnas, existen un buen número de métodos de cálculo y de ensayos de carga (tanto de campo como de laboratorio) que dan predicciones dispares del valor de la capacidad última de las columnas de grava. En las mallas indefinidas de columnas, los resultados del análisis de fiabilidad han confirmado las consideraciones teóricas y experimentales existentes relativas a que no se produce fallo por estabilidad, obteniéndose una probabilidad de fallo prácticamente nula para este modo de fallo. Sin embargo, cuando se analiza, en el contexto de la incertidumbre, la capacidad portante de pequeños grupos de columnas bajo zapatas se ha obtenido, para un caso con unos parámetros geotécnicos típicos, que la probabilidad de fallo es bastante alta, por encima de los umbrales normalmente admitidos para Estados Límite Últimos. Por último, el trabajo de recopilación sobre los métodos de cálculo y de ensayos de carga sobre la columna aislada ha permitido generar una base de datos suficientemente amplia como para abordar una actualización bayesiana de los métodos de cálculo de la columna de grava aislada. El marco bayesiano de actualización ha resultado de utilidad en la mejora de las predicciones de la capacidad última de carga de la columna, permitiendo “actualizar” los parámetros del modelo de cálculo a medida que se dispongan de ensayos de carga adicionales para un proyecto específico. Constituye una herramienta valiosa para la toma de decisiones en condiciones de incertidumbre ya que permite comparar el coste de los ensayos adicionales con el coste de una posible rotura y , en consecuencia, decidir si es procedente efectuar dichos ensayos. The geotechnical design of stone columns has all the uncertainties associated with a geotechnical project and those inherent to the complex interaction between the soil and the column, the installation of the materials and the characteristics of the final (as built) column must be considered. This is common to other soil treatments aimed, in general, to “deep” soil improvement. Since reliability methods (eg, FORM, SORM, Monte Carlo, Directional Simulation) deals with uncertainty in a much more consistent and rational way than the traditional safety factor, recent interest has arisen in the application of reliability techniques to geotechnical engineering. But the specific application of these techniques to soil improvement projects is not as extensive. In this thesis reliability techniques have been applied to some aspects of stone columns design (estimated settlements, consolidation times and increased bearing capacity) to make a rational analysis of the design process, considering the effects of uncertainty and variability on the safety of the project, i.e., on the probability of failure. To achieve this goal an advanced analytical method due to Castro and Sagaseta (2009), that significantly improves the prediction of the variables involved in the design of treatment and its temporal evolution (consolidation), has been employed. This thesis studies the problem of stone column settlement (amount and speed) in the context of uncertainty, analyzing two failure modes: i) the first mode represents the situation in which it is possible to cause primary consolidation, partial or total, of the improved ground prior to implementation of the final structure, either by a pre-load or because the load can be applied gradually or programmed without affecting the structure or installation; and ii) on the other hand, the second mode implies that the improved ground is loaded from the initial instant with the final structure or installation, expecting that the final settlement (elapsed primary consolidation) is small enough to be allowable. To work with realistic values of geotechnical parameters, data were obtained from a real soil improved with stone columns, hence producing a more rigorous reliability analysis. The most important conclusion obtained from the analysis of this particular case is the need to preload the stone columns-improved soil to make the settlement to occur before the application of the load corresponding to the final structure. Otherwise the probability of failure is very high, even when the deterministic safety margin would be sufficient. With respect to the bearing capacity of the columns, there are numerous methods of calculation and load tests (both for the field and the laboratory) giving different predictions of the ultimate capacity of stone columns. For indefinite columns grids, the results of reliability analysis confirmed the existing theoretical and experimental considerations that no failure occurs due to the stability failure mode, therefore resulting in a negligible probability of failure. However, when analyzed in the context of uncertainty (for a case with typical geotechnical parameters), results show that the probability of failure due to the bearing capacity failure mode of a group of columns is quite high, above thresholds usually admitted for Ultimate Limit States. Finally, the review of calculation methods and load tests results for isolated columns, has generated a large enough database, that allowed a subsequent Bayesian updating of the methods for calculating the bearing capacity of isolated stone columns. The Bayesian updating framework has been useful to improve the predictions of the ultimate load capacity of the column, allowing to "update" the parameters of the calculation model as additional load tests become available for a specific project. Moreover, it is a valuable tool for decision making under uncertainty since it is possible to compare the cost of further testing to the cost of a possible failure and therefore to decide whether it is appropriate to perform such tests.