El risc dinàmic: concepte, mesura i determinants econòmics

Aquesta tesi té la intenció de realitzar una contribució metodològica en el camp de la direcció estratègica, per mitjà de tres objectius: la revisió del concepte de risc ex post o realitzat per l'àmbit de la direcció estratègica; la concreció d'aquest concepte en una mesura de risc vàlida; i l'exploració de les possibilitats i l'interès de la descomposició del risc en diferents determinants que puguin explicar-ne la seva naturalesa. El primer objectiu es du a terme prenent com a base el concepte intuïtiu de risc i revisant la literatura en els camps més afins, especialment en la teoria comportamental de la decisió i la direcció estratègica. L'anàlisi porta a formular el risc ex post d'una activitat com el grau en què no s'han assolit els objectius per a aquesta activitat. La concreció d'aquesta definició al camp de la direcció estratègica implica que els objectius han de portar a l'obtenció de l'avantatge competitiu sostenible, el que descobreix l'interès de realitzar la mesura del risc a curt termini, és a dir, estàticament, i a llarg termini, és a dir, dinàmicament, pel que es defineix una mesura de Risc Estàtic i una altra de Risc dinàmic, respectivament. En l'anàlisi apareixen quatre dimensions conceptuals bàsiques a incorporar en les mesures: sign dependence, relativa, longitudinal i path dependence. Addicionalment, la consideració de que els resultats puguin ser cardinals o ordinals justifica que es formulin les dues mesures anteriors per a resultats cardinals i, en segon lloc, per a resultats ordinals. Les mesures de risc que es proposen sintetitzen els resultats ex post obtinguts en una mesura de centralitat relativa dels resultats, el Risc Estàtic, i una mesura de la tendència temporal dels resultats, el Risc Dinàmic. Aquesta proposta contrasta amb el plantejament tradicional dels models esperança-variància. Les mesures desenvolupades s'avaluen amb un sistema de propietats conceptuals i tècniques que s'elaboren expressament en la tesi i que permeten demostrar el seu gra de validesa i el de les mesures existents en la literatura, destacant els problemes de validesa d'aquestes darreres. També es proporciona un exemple teòric il·lustratiu de les mesures proposades que dóna suport a l'avaluació realitzada amb el sistema de propietats. Una contribució destacada d'aquesta tesi és la demostració de que les mesures de risc proposades permeten la descomposició additiva del risc si els resultats o diferencials de resultats es descomponen additivament. Finalment, la tesi inclou una aplicació de les mesures de Risc Estàtic i Dinàmic cardinals, així com de la seva descomposició, a l'anàlisi de la rendibilitat del sector bancari espanyol, en el període 1987-1999. L'aplicació il·lustra la capacitat de les mesures proposades per a analitzar la manifestació de l'avantatge competitiu, la seva evolució i naturalesa econòmica. En les conclusions es formulen possibles línees d'investigació futures.

Linking Canadian Harvested Juvenile American Black Ducks to Their Natal Areas Using Stable Isotope (δD, δ13C, and δ15N) Methods

Understanding source-sink dynamics of game birds is essential to harvest and habitat management but acquiring this information is often logistically and financially challenging using traditional methods of population surveys and banding studies. This is especially true for species such as the American Black Duck (Anas rubripes), which have low breeding densities and extensive breeding ranges that necessitate extensive surveys and banding programs across eastern North America. Despite this effort, the contribution of birds fledged from various landscapes and habitat types within specific breeding ranges to regional harvest is largely unknown but remains an important consideration in adaptive harvest management and targeted habitat conservation strategies. We investigated if stable isotope (δD, δ13C, δ15N) could augment our present understanding of connectivity between breeding and harvest areas and so provide information relevant to the two main management strategies for black ducks, harvest and habitat management. We obtained specimens from 200 hatch-year Black Duck wings submitted to the Canadian Wildlife Service Species Composition Survey. Samples were obtained from birds harvested in Western, Central, and Eastern breeding/harvest subregions to provide a sample representative of the range and harvest rate of birds harvested in Canada. We sampled only hatch-year birds to provide an unambiguous and direct link between production and harvest areas. Marine origins were assigned to 12%, 7%, and 5% of birds harvested in the Eastern, Central, and Western subregions, respectively. In contrast, 32%, 9%, and 5% of birds were assigned, respectively, to agricultural origins. All remaining birds were assigned to nonagricultural origins. We portrayed probability of origin using a combination of Bayesian statistical and GIS methods. Placement of most eastern birds was western Nova Scotia, eastern New Brunswick, Prince Edward Island, and southern Newfoundland. Agricultural birds from the Central region were consistent with the Saguenay region of Québec and the eastern claybelt with nonagricultural birds originating in the boreal. Western nonagricultural birds were associated with broad boreal origins from southern James Bay to Lake of the Woods and east to Cochrane, Ontario. Our work shows that the geographic origins, landscape, and habitat associations of hatch-year Black Ducks can be inferred using this technique and we recommend that a broad-scale isotopic study using a large sample of Canadian and US harvested birds be implemented to provide a continental perspective of source-sink population dynamics.

Chronology for the Aegean Late Bronze Age 1700-1400 BC

Radiocarbon (carbon-14) data from the Aegean Bronze Age 1700-1400 B.C. show that the Santorini (Thera) eruption must have occurred in the late 17th century B.C. By using carbon-14 dates from the surrounding region, cultural phases, and Bayesian statistical analysis, we established a chronology for the initial Aegean Late Bronze Age cultural phases (Late Minoan IA, IB, and II). This chronology contrasts with conventional archaeological dates and cultural synthesis: stretching out the Late Minoan IA, IB, and II phases by similar to 100 years and requiring reassessment of standard interpretations of associations between the Egyptian and Near Eastern historical dates and phases and those in the Aegean and Cyprus in the mid-second millennium B.C.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Universality of rain event size distributions

We compare rain event size distributions derived from measurements in climatically different regions, which we find to be well approximated by power laws of similar exponents over broad ranges. Differences can be seen in the large-scale cutoffs of the distributions. Event duration distributions suggest that the scale-free aspects are related to the absence of characteristic scales in the meteorological mesoscale.

Judgments, forecasts and decisions: an analysis of fund managers over time

Decision theory is the study of models of judgement involved in, and leading to, deliberate and (usually) rational choice. In real estate investment there are normative models for the allocation of assets. These asset allocation models suggest an optimum allocation between the respective asset classes based on the investors’ judgements of performance and risk. Real estate is selected, as other assets, on the basis of some criteria, e.g. commonly its marginal contribution to the production of a mean variance efficient multi asset portfolio, subject to the investor’s objectives and capital rationing constraints. However, decisions are made relative to current expectations and current business constraints. Whilst a decision maker may believe in the required optimum exposure levels as dictated by an asset allocation model, the final decision may/will be influenced by factors outside the parameters of the mathematical model. This paper discusses investors' perceptions and attitudes toward real estate and highlights the important difference between theoretical exposure levels and pragmatic business considerations. It develops a model to identify “soft” parameters in decision making which will influence the optimal allocation for that asset class. This “soft” information may relate to behavioural issues such as the tendency to mirror competitors; a desire to meet weight of money objectives; a desire to retain the status quo and many other non-financial considerations. The paper aims to establish the place of property in multi asset portfolios in the UK and examine the asset allocation process in practice, with a view to understanding the decision making process and to look at investors’ perceptions based on an historic analysis of market expectation; a comparison with historic data and an analysis of actual performance.

Disentangling multi-level systems: averaging, correlations and memory

We consider two weakly coupled systems and adopt a perturbative approach based on the Ruelle response theory to study their interaction. We propose a systematic way of parameterizing the effect of the coupling as a function of only the variables of a system of interest. Our focus is on describing the impacts of the coupling on the long term statistics rather than on the finite-time behavior. By direct calculation, we find that, at first order, the coupling can be surrogated by adding a deterministic perturbation to the autonomous dynamics of the system of interest. At second order, there are additionally two separate and very different contributions. One is a term taking into account the second-order contributions of the fluctuations in the coupling, which can be parameterized as a stochastic forcing with given spectral properties. The other one is a memory term, coupling the system of interest to its previous history, through the correlations of the second system. If these correlations are known, this effect can be implemented as a perturbation with memory on the single system. In order to treat this case, we present an extension to Ruelle's response theory able to deal with integral operators. We discuss our results in the context of other methods previously proposed for disentangling the dynamics of two coupled systems. We emphasize that our results do not rely on assuming a time scale separation, and, if such a separation exists, can be used equally well to study the statistics of the slow variables and that of the fast variables. By recursively applying the technique proposed here, we can treat the general case of multi-level systems.

On the mass distribution of neutron stars

The distribution of masses for neutron stars is analysed using the Bayesian statistical inference, evaluating the likelihood of the proposed Gaussian peaks by using 54 measured points obtained in a variety of systems. The results strongly suggest the existence of a bimodal distribution of the masses, with the first peak around 1.37 M(circle dot) and a much wider second peak at 1.73 M(circle dot). The results support earlier views related to the different evolutionary histories of the members for the first two peaks, which produces a natural separation (even if no attempt to `label` the systems has been made here). They also accommodate the recent findings of similar to M(circle dot) masses quite naturally. Finally, we explore the existence of a subgroup around 1.25 M(circle dot), finding weak, if any, evidence for it. This recently claimed low-mass subgroup, possibly related to the O-Mg-Ne core collapse events, has a monotonically decreasing likelihood and does not stand out clearly from the rest of the sample.

A new scale-invariant ratio and finite-size scaling for the stochastic susceptible-infected-recovered model

The critical behavior of the stochastic susceptible-infected-recovered model on a square lattice is obtained by numerical simulations and finite-size scaling. The order parameter as well as the distribution in the number of recovered individuals is determined as a function of the infection rate for several values of the system size. The analysis around criticality is obtained by exploring the close relationship between the present model and standard percolation theory. The quantity UP, equal to the ratio U between the second moment and the squared first moment of the size distribution multiplied by the order parameter P, is shown to have, for a square system, a universal value 1.0167(1) that is the same for site and bond percolation, confirming further that the SIR model is also in the percolation class.

Aging and stationary properties of non-equilibrium symmetrical three-state models

We consider a non-equilibrium three-state model whose dynamics is Markovian and displays the same symmetry as the three-state Potts model, i.e. the transition rates are invariant under the cyclic permutation of the states. Unlike the Potts model, detailed balance is, in general, not satisfied. The aging and the stationary properties of the model defined on a square lattice are obtained by means of large-scale Monte Carlo simulations. We show that the phase diagram presents a critical line, belonging to the three-state Potts universality class, that ends at a point whose universality class is that of the Voter model. Aging is considered on the critical line, at the Voter point and in the ferromagnetic phase.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

Critical behavior of a contact process with aperiodic transition rates

We performed Monte Carlo simulations to investigate the steady-state critical behavior of a one-dimensional contact process with an aperiodic distribution of rates of transition. As in the presence of randomness, spatial fluctuations can lead to changes of critical behavior. For sufficiently weak fluctuations, we give numerical evidence to show that there is no departure from the universal critical behavior of the underlying uniform model. For strong spatial fluctuations, the analysis of the data indicates a change of critical universality class.

Shared information in stationary states at criticality

We consider bipartitions of one-dimensional extended systems whose probability distribution functions describe stationary states of stochastic models. We define estimators of the information shared between the two subsystems. If the correlation length is finite, the estimators stay finite for large system sizes. If the correlation length diverges, so do the estimators. The definition of the estimators is inspired by information theory. We look at several models and compare the behaviors of the estimators in the finite-size scaling limit. Analytical and numerical methods as well as Monte Carlo simulations are used. We show how the finite-size scaling functions change for various phase transitions, including the case where one has conformal invariance.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.