Dynamics of Spatial Game Theory Networks with Novel Modifications

Introduction: Advances in biotechnology have shed light on many biological processes. In biological networks, nodes are used to represent the function of individual entities within a system and have historically been studied in isolation. Network structure adds edges that enable communication between nodes. An emerging fieldis to combine node function and network structure to yield network function. One of the most complex networks known in biology is the neural network within the brain. Modeling neural function will require an understanding of networks, dynamics, andneurophysiology. It is with this work that modeling techniques will be developed to work at this complex intersection. Methods: Spatial game theory was developed by Nowak in the context of modeling evolutionary dynamics, or the way in which species evolve over time. Spatial game theory offers a two dimensional view of analyzingthe state of neighbors and updating based on the surroundings. Our work builds upon this foundation by studying evolutionary game theory networks with respect to neural networks. This novel concept is that neurons may adopt a particular strategy that will allow propagation of information. The strategy may therefore act as the mechanism for gating. Furthermore, the strategy of a neuron, as in a real brain, isimpacted by the strategy of its neighbors. The techniques of spatial game theory already established by Nowak are repeated to explain two basic cases and validate the implementation of code. Two novel modifications are introduced in Chapters 3 and 4 that build on this network and may reflect neural networks. Results: The introduction of two novel modifications, mutation and rewiring, in large parametricstudies resulted in dynamics that had an intermediate amount of nodes firing at any given time. Further, even small mutation rates result in different dynamics more representative of the ideal state hypothesized. Conclusions: In both modificationsto Nowak's model, the results demonstrate the network does not become locked into a particular global state of passing all information or blocking all information. It is hypothesized that normal brain function occurs within this intermediate range and that a number of diseases are the result of moving outside of this range.

The Dual Ownership Structure: a Model to Connect the Cooperative Model With Financial Markets

In business literature, the conflicts among workers, shareholders and the management have been studied mostly in the frame of stakeholder theory. The stakeholder theory recognizes this issue as an agency problem, and tries to solve the problem by establishing a contractual relationship between the agent and principals. However, as Marcoux pointed out, the appropriateness of the contract as a medium to reduce the agency problem should be questioned. As an alternative, the cooperative model minimizes the agency costs by integrating the concept of workers, owners and management. Mondragon Corporation is a successful example of the cooperative model which grew into the sixth largest corporation in Spain. However, the cooperative model has long been ignored in discussions of corporate governance, mainly because the success of the cooperative model is extremely difficult to duplicate in reality. This thesis hopes to revitalize the scholarly examination of cooperatives by developing a new model that overcomes the fundamental problem in the cooperative model: the limited access to capital markets. By dividing the ownership interest into financial and control interest, the dual ownership structure allows cooperatives to issue stock in the capital market by making a financial product out of financial interest.

Compensation, Incentives and Risk: Economic Theory and Russian Practise

Mr. Pechersky set out to examine a specific feature of the employer-employee relationship in Russian business organisations. He wanted to study to what extent the so-called "moral hazard" is being solved (if it is being solved at all), whether there is a relationship between pay and performance, and whether there is a correlation between economic theory and Russian reality. Finally, he set out to construct a model of the Russian economy that better reflects the way it actually functions than do certain other well-known models (for example models of incentive compensation, the Shapiro-Stiglitz model etc.). His report was presented to the RSS in the form of a series of manuscripts in English and Russian, and on disc, with many tables and graphs. He begins by pointing out the different examples of randomness that exist in the relationship between employee and employer. Firstly, results are frequently affected by circumstances outside the employee's control that have nothing to do with how intelligently, honestly, and diligently the employee has worked. When rewards are based on results, uncontrollable randomness in the employee's output induces randomness in their incomes. A second source of randomness involves the outside events that are beyond the control of the employee that may affect his or her ability to perform as contracted. A third source of randomness arises when the performance itself (rather than the result) is measured, and the performance evaluation procedures include random or subjective elements. Mr. Pechersky's study shows that in Russia the third source of randomness plays an important role. Moreover, he points out that employer-employee relationships in Russia are sometimes opposite to those in the West. Drawing on game theory, he characterises the Western system as follows. The two players are the principal and the agent, who are usually representative individuals. The principal hires an agent to perform a task, and the agent acquires an information advantage concerning his actions or the outside world at some point in the game, i.e. it is assumed that the employee is better informed. In Russia, on the other hand, incentive contracts are typically negotiated in situations in which the employer has the information advantage concerning outcome. Mr. Pechersky schematises it thus. Compensation (the wage) is W and consists of a base amount, plus a portion that varies with the outcome, x. So W = a + bx, where b is used to measure the intensity of the incentives provided to the employee. This means that one contract will be said to provide stronger incentives than another if it specifies a higher value for b. This is the incentive contract as it operates in the West. The key feature distinguishing the Russian example is that x is observed by the employer but is not observed by the employee. So the employer promises to pay in accordance with an incentive scheme, but since the outcome is not observable by the employee the contract cannot be enforced, and the question arises: is there any incentive for the employer to fulfil his or her promises? Mr. Pechersky considers two simple models of employer-employee relationships displaying the above type of information symmetry. In a static framework the obtained result is somewhat surprising: at the Nash equilibrium the employer pays nothing, even though his objective function contains a quadratic term reflecting negative consequences for the employer if the actual level of compensation deviates from the expectations of the employee. This can lead, for example, to labour turnover, or the expenses resulting from a bad reputation. In a dynamic framework, the conclusion can be formulated as follows: the higher the discount factor, the higher the incentive for the employer to be honest in his/her relationships with the employee. If the discount factor is taken to be a parameter reflecting the degree of (un)certainty (the higher the degree of uncertainty is, the lower is the discount factor), we can conclude that the answer to the formulated question depends on the stability of the political, social and economic situation in a country. Mr. Pechersky believes that the strength of a market system with private property lies not just in its providing the information needed to compute an efficient allocation of resources in an efficient manner. At least equally important is the manner in which it accepts individually self-interested behaviour, but then channels this behaviour in desired directions. People do not have to be cajoled, artificially induced, or forced to do their parts in a well-functioning market system. Instead, they are simply left to pursue their own objectives as they see fit. Under the right circumstances, people are led by Adam Smith's "invisible hand" of impersonal market forces to take the actions needed to achieve an efficient, co-ordinated pattern of choices. The problem is that, as Mr. Pechersky sees it, there is no reason to believe that the circumstances in Russia are right, and the invisible hand is doing its work properly. Political instability, social tension and other circumstances prevent it from doing so. Mr. Pechersky believes that the discount factor plays a crucial role in employer-employee relationships. Such relationships can be considered satisfactory from a normative point of view, only in those cases where the discount factor is sufficiently large. Unfortunately, in modern Russia the evidence points to the typical discount factor being relatively small. This fact can be explained as a manifestation of aversion to risk of economic agents. Mr. Pechersky hopes that when political stabilisation occurs, the discount factors of economic agents will increase, and the agent's behaviour will be explicable in terms of more traditional models.

Academic Intelligence is not Enough! WICS: An Expanded Model for Effective Practice in School and in Later Life

Is there a psychological basis for teaching and learning in the context of a liberal education, and if so, what might such a psychological basis look like? Traditional teaching and assessment often emphasize remembering facts and, to some extent, analyzing ideas. Such skills are important, but they leave out of the aspects of thinking that are most important not only in liberal education, but in life, in general. In this article, I propose a theory called WICS, which is an acronym for wisdom, intelligence, and creativity, synthesized. The basic idea underlying this theory is that, through liberal education, students need to acquire creative skills and attitudes to generate new ideas about how to adapt flexibly to a rapidly changing world, analytical skills and attitudes to ascertain whether these new ideas are good ones, practical skills and attitudes to implement the new ideas and convince others of their value, and wisdom-based skills and attitudes in order to ensure that the new ideas help to achieve a common good through the infusion of positive ethical values.

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

A Note on the Asymptotic Variance of Survival Function in the Semi-Markov Model

In Malani and Neilsen (1992) we have proposed alternative estimates of survival function (for time to disease) using a simple marker that describes time to some intermediate stage in a disease process. In this paper we derive the asymptotic variance of one such proposed estimator using two different methods and compare terms of order 1/n when there is no censoring. In the absence of censoring the asymptotic variance obtained using the Greenwood type approach converges to exact variance up to terms involving 1/n. But the asymptotic variance obtained using the theory of the counting process and results from Voelkel and Crowley (1984) on semi-Markov processes has a different term of order 1/n. It is not clear to us at this point why the variance formulae using the latter approach give different results.

Locally Efficient Estimation in Censored Data Models: Theory and Examples

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curve of dimensionality it is typically not possible to construct estimators that are asymptotically efficient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construction of one-step estimators that are efficient at a chosen submodel of the full-data model, are still well behaved off this submodel and can be chosen to always improve on a given initial estimator. These one-step estimators rely on good estimators of the censoring mechanism and thus will require a parametric or semiparametric model for the censoring mechanism. We present a general theorem that provides a template for proving the desired asymptotic results. We illustrate the general one-step estimation methods by constructing locally efficient one-step estimators of marginal distributions and regression parameters with right-censored data, current status data and bivariate right-censored data, in all models allowing the presence of time-dependent covariates. The conditions of the asymptotics theorem are rigorously verified in one of the examples and the key condition of the general theorem is verified for all examples.

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.

FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING

We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a functional mixed effect model is fitted to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

Studying Effects of Primary Care Physicians and Patients on the Trade-Off Between Charges for Primary Care and Specialty Care Using a Hierarchical Multivariate Two-Part Model

Objective. To examine effects of primary care physicians (PCPs) and patients on the association between charges for primary care and specialty care in a point-of-service (POS) health plan. Data Source. Claims from 1996 for 3,308 adult male POS plan members, each of whom was assigned to one of the 50 family practitioner-PCPs with the largest POS plan member-loads. Study Design. A hierarchical multivariate two-part model was fitted using a Gibbs sampler to estimate PCPs' effects on patients' annual charges for two types of services, primary care and specialty care, the associations among PCPs' effects, and within-patient associations between charges for the two services. Adjusted Clinical Groups (ACGs) were used to adjust for case-mix. Principal Findings. PCPs with higher case-mix adjusted rates of specialist use were less likely to see their patients at least once during the year (estimated correlation: –.40; 95% CI: –.71, –.008) and provided fewer services to patients that they saw (estimated correlation: –.53; 95% CI: –.77, –.21). Ten of 11 PCPs whose case-mix adjusted effects on primary care charges were significantly less than or greater than zero (p < .05) had estimated, case-mix adjusted effects on specialty care charges that were of opposite sign (but not significantly different than zero). After adjustment for ACG and PCP effects, the within-patient, estimated odds ratio for any use of primary care given any use of specialty care was .57 (95% CI: .45, .73). Conclusions. PCPs and patients contributed independently to a trade-off between utilization of primary care and specialty care. The trade-off appeared to partially offset significant differences in the amount of care provided by PCPs. These findings were possible because we employed a hierarchical multivariate model rather than separate univariate models.

Stochastic Models Based on Molecular Hybridization Theory for Short Oligonucleotide Microarrays

High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure offered by Affymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures. In this paper we demonstrate that the system producing the measured intensities is too complex to be fully described with these relatively simple physical models and we propose empirically motivated stochastic models that compliment the above mentioned molecular hybridization theory to provide a comprehensive description of the data. We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts.