Theory for correlation functions of processes driven by external colored noise

We study steady-state correlation functions of nonlinear stochastic processes driven by external colored noise. We present a methodology that provides explicit expressions of correlation functions approximating simultaneously short- and long-time regimes. The non-Markov nature is reduced to an effective Markovian formulation, and the nonlinearities are treated systematically by means of double expansions in high and low frequencies. We also derive some exact expressions for the coefficients of these expansions for arbitrary noise by means of a generalization of projection-operator techniques.

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory? (Commentary on Pothos and Busemeyer)

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

Auditory perceptual decision-making based on semantic categorization of environmental sounds.

Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception were the focus of the present study. We presented 250ms duration sounds of living and man-made objects while recording 160-channel electroencephalography (EEG). Subjects categorized each sound as that of a living, man-made or unknown item. We tested whether/when the brain discriminates between sound categories even when not transpiring behaviorally. We applied a single-trial classifier that identified voltage topographies and latencies at which brain responses are most discriminative. For sounds that the subjects could not categorize, we could successfully decode the semantic category based on differences in voltage topographies during the 116-174ms post-stimulus period. Sounds that were correctly categorized as that of a living or man-made item by the same subjects exhibited two periods of differences in voltage topographies at the single-trial level. Subjects exhibited differential activity before the sound ended (starting at 112ms) and on a separate period at ~270ms post-stimulus onset. Because each of these periods could be used to reliably decode semantic categories, we interpreted the first as being related to an implicit tuning for sound representations and the second as being linked to perceptual decision-making processes. Collectively, our results show that the brain discriminates environmental sounds during early stages and independently of behavioral proficiency and that explicit sound categorization requires a subsequent processing stage.

First-passage times for non-Markovian processes

First-passage time statistics for non-Markovian processes have heretofore only been developed for processes driven by dichotomous fluctuations that are themselves Markov. Herein we develop a new method applicable to Markov and non-Markovian dichotomous fluctuations and calculate analytic mean first-passage times for particular examples.

First-passage-time statistics for diffusion processes with an external random force

We present exact equations and expressions for the first-passage-time statistics of dynamical systems that are a combination of a diffusion process and a random external force modeled as dichotomous Markov noise. We prove that the mean first passage time for this system does not show any resonantlike behavior.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Mean first-passage time of continuous non-Markovian processes driven by colored noise

An equation for mean first-passage times of non-Markovian processes driven by colored noise is derived through an appropriate backward integro-differential equation. The equation is solved in a Bourret-like approximation. In a weak-noise bistable situation, non-Markovian effects are taken into account by an effective diffusion coefficient. In this situation, our results compare satisfactorily with other approaches and experimental data.

A fuzzy-based decision model application on strategic management

In this article, the objective is to demonstrate the effects of different decision styles on strategic decisions and likewise, on an organization. The technique that was presented in the study is based on the transformation of linguistic variables to numerical value intervals. In this model, the study benefits from fuzzy logic methodology and fuzzy numbers. This fuzzy methodology approach allows us to examine the relations between decision making styles and strategic management processes when there is uncertainty. The purpose is to provide results to companies that may help them to exercise the most appropriate decision making style for its different strategic management processes. The study is leaving more research topics for further studies that may be applied to other decision making areas within the strategic management process.

A new aggregation method for strategic decision making and its application in assignment theory

A new aggregation method for decision making is presented by using induced aggregation operators and the index of maximum and minimum level. Its main advantage is that it can assess complex reordering processes in the aggregation that represent complex attitudinal characters of the decision maker such as psychological or personal factors. A wide range of properties and particular cases of this new approach are studied. A further generalization by using hybrid averages and immediate weights is also presented. The key issue in this approach against the previous model is that we can use the weighted average and the ordered weighted average in the same formulation. Thus, we are able to consider the subjective attitude and the degree of optimism of the decision maker in the decision process. The paper ends with an application in a decision making problem based on the use of the assignment theory.

Weber's law in decision making: integrating behavioral data in humans with a neurophysiological model

Recent single-cell studies in monkeys (Romo et al., 2004) show that the activity of neurons in the ventral premotor cortex covaries with the animal's decisions in a perceptual comparison task regarding the frequency of vibrotactile events. The firing rate response of these neurons was dependent only on the frequency differences between the two applied vibrations, the sign of that difference being the determining factor for correct task performance. We present a biophysically realistic neurodynamical model that can account for the most relevant characteristics of this decision-making-related neural activity. One of the nontrivial predictions of this model is that Weber's law will underlie the perceptual discrimination behavior. We confirmed this prediction in behavioral tests of vibrotactile discrimination in humans and propose a computational explanation of perceptual discrimination that accounts naturally for the emergence of Weber's law. We conclude that the neurodynamical mechanisms and computational principles underlying the decision-making processes in this perceptual discrimination task are consistent with a fluctuation-driven scenario in a multistable regime.

The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses.

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging, overlooking any information about the time when decisions are made. Here, we carried out an electroencephalography (EEG) experiment, in order to detect the time when the brain generators responsible for these decisions have been sufficiently activated to lead to the next decision. Our analyses, based on a classification scheme, extract time-unlocked voltage topographies during reward presentation and use them to predict the type of decisions made on the subsequent trial. Classification accuracy, measured as the area under the Receiver Operator's Characteristic curve was on average 0.65 across 7 subjects. Classification accuracy was above chance levels already after 516 ms on average, across subjects. We speculate that decisions were already made before this critical period, as confirmed by a positive correlation with reaction times across subjects. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision-making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and the right supramarginal gyrus; areas responsible for modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis.

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) isapplied to the problem of blind (without training sequences) channel estimationand data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedureassumes the model (i.e., the channel response) to be static throughoutthe observation sequence. By means of introducing a parametric model fortime-varying channel responses, a version of the algorithm, which is moreappropriate for mobile channels [time-dependent Baum-Welch (TDBW)] isderived. Aiming to compare algorithm behavior, a set of computer simulationsfor a GSM scenario is provided. Results indicate that, in comparisonto other Baum–Welch (BW) versions of the algorithm, the TDBW approachattains a remarkable enhancement in performance. For that purpose, onlya moderate increase in computational complexity is needed.

Project Success through Effective Decisions: Case Studies on Project Goal Setting, Success Evaluation and Managerial Decision Making

The objective of the thesis is to structure and model the factors that contribute to and can be used in evaluating project success. The purpose of this thesis is to enhance the understanding of three research topics. The goal setting process, success evaluation and decision-making process are studied in the context of a project, business unitand its business environment. To achieve the objective three research questionsare posed. These are 1) how to set measurable project goals, 2) how to evaluateproject success and 3) how to affect project success with managerial decisions.The main theoretical contribution comes from deriving a synthesis of these research topics which have mostly been discussed apart from each other in prior research. The research strategy of the study has features from at least the constructive, nomothetical, and decision-oriented research approaches. This strategy guides the theoretical and empirical part of the study. Relevant concepts and a framework are composed on the basis of the prior research contributions within the problem area. A literature review is used to derive constructs of factors withinthe framework. They are related to project goal setting, success evaluation, and decision making. On the basis of this, the case study method is applied to complement the framework. The empirical data includes one product development program, three construction projects, as well as one organization development, hardware/software, and marketing project in their contexts. In two of the case studiesthe analytic hierarchy process is used to formulate a hierarchical model that returns a numerical evaluation of the degree of project success. It has its origin in the solution idea which in turn has its foundation in the notion of projectsuccess. The achieved results are condensed in the form of a process model thatintegrates project goal setting, success evaluation and decision making. The process of project goal setting is analysed as a part of an open system that includes a project, the business unit and its competitive environment. Four main constructs of factors are suggested. First, the project characteristics and requirements are clarified. The second and the third construct comprise the components of client/market segment attractiveness and sources of competitive advantage. Together they determine the competitive position of a business unit. Fourth, the relevant goals and the situation of a business unit are clarified to stress their contribution to the project goals. Empirical evidence is gained on the exploitation of increased knowledge and on the reaction to changes in the business environment during a project to ensure project success. The relevance of a successful project to a company or a business unit tends to increase the higher the reference level of project goals is set. However, normal performance or sometimes performance below this normal level is intentionally accepted. Success measures make project success quantifiable. There are result-oriented, process-oriented and resource-oriented success measures. The study also links result measurements to enablers that portray the key processes. The success measures can be classified into success domains determining the areas on which success is assessed. Empiricalevidence is gained on six success domains: strategy, project implementation, product, stakeholder relationships, learning situation and company functions. However, some project goals, like safety, can be assessed using success measures that belong to two success domains. For example a safety index is used for assessing occupational safety during a project, which is related to project implementation. Product safety requirements, in turn, are connected to the product characteristics and thus to the product-related success domain. Strategic success measures can be used to weave the project phases together. Empirical evidence on their static nature is gained. In order-oriented projects the project phases are oftencontractually divided into different suppliers or contractors. A project from the supplier's perspective can represent only a part of the ¿whole project¿ viewed from the client's perspective. Therefore static success measures are mostly used within the contractually agreed project scope and duration. Proof is also acquired on the dynamic use of operational success measures. They help to focus on the key issues during each project phase. Furthermore, it is shown that the original success domains and success measures, their weights and target values can change dynamically. New success measures can replace the old ones to correspond better with the emphasis of the particular project phase. This adjustment concentrates on the key decision milestones. As a conclusion, the study suggests a combination of static and dynamic success measures. Their linkage to an incentive system can make the project management proactive, enable fast feedback and enhancethe motivation of the personnel. It is argued that the sequence of effective decisions is closely linked to the dynamic control of project success. According to the used definition, effective decisions aim at adequate decision quality and decision implementation. The findings support that project managers construct and use a chain of key decision milestones to evaluate and affect success during aproject. These milestones can be seen as a part of the business processes. Different managers prioritise the key decision milestones to a varying degree. Divergent managerial perspectives, power, responsibilities and involvement during a project offer some explanation for this. Finally, the study introduces the use ofHard Gate and Soft Gate decision milestones. The managers may use the former milestones to provide decision support on result measurements and ad hoc critical conditions. In the latter milestones they may make intermediate success evaluation also on the basis of other types of success measures, like process and resource measures.

Expertise and decision making among clinicians in investigations of alleged child sexual abuse

The purpose of the present thesis was to explore different aspects of decision making and expertise in investigations of child sexual abuse (CSA) and subsequently shed some light on the reasons for shortcomings in the investigation processes. Clinicians’ subjective attitudes as well as scientifically based knowledge concerning CSA, CSA investigation and interviewing were explored. Furthermore the clinicians’ own view on their expertise and what enhances this expertise was investigated. Also, the effects of scientific knowledge, experience and attitudes on the decision making in a case of CSA were explored. Finally, the effects of different kinds of feedback as well as experience on the ability to evaluate CSA in the light of children’s behavior and base rates were investigated. Both explorative and experimental methods were used. The purpose of Study I was to investigate whether clinicians investigating child sexual abuse (CSA) rely more on scientific knowledge or on clinical experience when evaluating their own expertise. Another goal was to check what kind of beliefs the clinicians held. The connections between these different factors were investigated. A questionnaire covering items concerning demographic data, experience, knowledge about CSA, selfevaluated expertise and beliefs about CSA was given to social workers, child psychiatrists and psychologists working with children. The results showed that the clinicians relied more on their clinical experience than on scientific knowledge when evaluating their expertise as investigators of CSA. Furthermore, social workers possessed stronger attitudes in favor of children than the other groups, while child psychiatrists had more negative attitudes towards the criminal justice system. Male participants held less strong beliefs than female participants. The findings indicate that the education of CSA investigators should focus more on theoretical knowledge and decision making processes as well as the role of beliefs In Study II school and family counseling psychologists completed a Child Sexual Abuse Attitude and Belief Scale. Four CSA related attitude and belief subscales were identified: 1. The Disclosure subscale reflecting favoring a disclosure at any cost, 2. The Pro-Child subscale reflecting unconditional belief in children's reports, 3. The Intuition subscale reflecting favoring an intuitive approach to CSA investigations, and 4. The Anti Criminal Justice System subscale reflecting negative attitudes towards the legal system. Beliefs that were erroneous according to empirical research were analyzed separately. The results suggest that some psychologists hold extreme attitudes and many erroneous beliefs related to CSA. Some misconceptions are common. Female participants tended to hold stronger attitudes than male participants. The more training in interviewing children the participants have, the more erroneous beliefs and stronger attitudes they hold. Experience did not affect attitudes and beliefs. In Study III mental health professionals’ sensitivity to suggestive interviewing in CSA cases was explored. Furthermore, the effects of attitudes and beliefs related to CSA and experience with CSA investigations on the sensitivity to suggestive influences in the interview were investigated. Also, the effect of base rate estimates of CSA on decisions was examined. A questionnaire covering items concerning demographic data, different aspects of clinical experience, self-evaluated expertise, beliefs and knowledge about CSA and a set of ambiguous material based on real trial documents concerning an alleged CSA case was given to child mental health professionals. The experiment was based on a 2 x 2 x 2 x 2 (leading questions: yes vs no) x (stereotype induction: yes vs no) x (emotional tone: pressure to respond vs no pressure to respond) x (threats and rewards: yes vs no) between-subjects factorial design, in which the suggestiveness of the methods with which the responses of the child were obtained were varied. There was an additional condition in which the material did not contain any interview transcripts. The results showed that clinicians are sensitive only to the presence of leading questions but not to the presence of other suggestive techniques. Furthermore, the clinicians were not sensitive to the possibility that suggestive techniques could have been used when no interview transcripts had been included in the trial material. Experience had an effect on the sensitivity of the clinicians only regarding leading questions. Strong beliefs related to CSA lessened the sensitivity to leading questions. Those showing strong beliefs on the belief scales used in this study were even more prone to prosecute than other participants when other suggestive influences than leading questions were present. Controversy exists regarding effects of experience and feedback on clinical decision making. In Study IV the impact of the number of handled cases and of feedback on the decisions in cases of alleged CSA was investigated. One-hundred vignettes describing cases of suspected CSA were given to students with no experience with investigating CSA. The vignettes were based on statistical data about symptoms and prevalence of CSA. According to the theoretical likelihood of CSA the children described were categorized as abused or not abused. The participants were asked to decide whether abuse had occurred. They were divided into 4 groups: one received feedback on whether their decision was right or wrong, one received information about cognitive processes involved in decision making, one received both, and one did not receive feedback at all. The results showed that participants who received feedback on their performance made more correct positive decisions and participants who got information about decision making processes made more correct negative decisions. Feedback and information combined decreased the number of correct positive decisions but increased the number of correct negative decisions. The number of read cases had in itself a positive effect on correct positive decision.

Improving the Potentials of Logistics Processes: Identification and Solutions

The importance of logistics for companies is a well known and justified issue. Today, enterprises are developing their logistics processes in order to match their products and services to the requirements of the most important customers. Therefore there is a need for developing analysing tools for logistics and especially for analysing the significance of various customer service elements. The aim of this paper is to propose analytic tools for supporting strategic level logistics decision making by emphasizing service level elements on two levels: (1) to introduce and propose approaches to categorize the developing efforts of logistics and (2) to introduce and/or propose approaches for solving some customer service related strategic level logistics problems. This study consists of two parts. In the first part an overview of the work is presented, and the second part comprises eight research papers on the topic of the study. The overview includes an introduction, where strategic and tactical level logistics problems are discussed and the relation of logistics to marketing and customer service issues is presented. In the first part of the study the objectives, the structure, the research strategy and the contribution of the research are described, and the challenges for future research are discussed. In the second part the three first papers deal with the identification of objectives for logistics while the remaining five papaers concentrate on solving customer service related strategic level logistics problems.