Phenotypic and social effects on behavioural trade-offs in Eurasian perch

Trading between conflicting demands is a fundamental part in how animals interact with its environment and social surrounding. Knowledge of what factors that are affecting behavioural decisions is central in our understanding of animal adaptation and ecology. This thesis summarizes a series of behavioural experiments investigating how animals compromise behaviours depending on environmental background and context. The focus is on within- and between-population variation in risk-taking and social trade-offs in young of the year and one year old Eurasian perch. Perch behaviour was quantified by observational studies in aquaria, using standardized assays that captured perch boldness and sociability. Perch from different predation backgrounds were compared in common garden experiments, as well as in multi-year inter-population comparisons, to study influence of predation experience on risk-taking phenotype. Results demonstrate predation as an important factor underlying how perch balance risk. Variation in risk-taking phenotype could to a large extent be explained by individual differences in experience of predation, rather than by fixed inherited responses caused by divergent selection. Experience of predation had long lasting effects on perch boldness, but perch were also able to quickly adjust phenotype in response to current conditions, indicating temporal flexibility in how experience shape behaviour. Social context influenced behaviour, with fish being bolder in larger group, and showing higher behavioural conformity. Occurrence of consistent individual variation in risk-taking and social behaviour could be established, confirming the existence of a personality dimension in perch behaviour. The thesis concludes that variation in how perch trade-off conflicting behaviours exists at multiple levels, from population to individual. Behavioural plasticity, even in strongly fitness related traits, is evident, although potential behavioural constraints in the form of consistent individuality is also present

Count Data Modelling and Tourism Demand

This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the INAR(1) model. The fourth paper studies the interaction between households' choice of number of leisure trips and number of overnight stays within a bivariate count data modelling framework. Paper[I] extends the basic INAR(1) model to enable more flexible and realistic empirical economic applications. The model is generalized by relaxing some of the model’s basic independence assumptions. Results are given in terms of first and second conditional and unconditional order moments. Extensions to general INAR(p), time-varying, multivariate and threshold models are also considered. Estimation by conditional least squares and generalized method of moments techniques is feasible. Monte Carlo simulations for two of the extended models indicate reasonable estimation and testing properties. An illustration based on the number of Swedish mechanical paper and pulp mills is considered. Paper[II] considers the robustness of a conventional Dickey-Fuller (DF) test for the testing of a unit root in the INAR(1) model. Finite sample distributions for a model with Poisson distributed disturbance terms are obtained by Monte Carlo simulation. These distributions are wider than those of AR(1) models with normal distributed error terms. As the drift and sample size, respectively, increase the distributions appear to tend to t(T-2) and standard normal distributions. The main results are summarized by an approximating equation that also enables calculation of critical values for any sample and drift size. Paper[III] utilizes the INAR(1) model to model the day-to-day movements in the number of guest nights in hotels. By cross-sectional and temporal aggregation an INARMA(1,1) model for monthly data is obtained. The approach enables easy interpretation and econometric modelling of the parameters, in terms of daily mean check-in and check-out probability. Empirically approaches accounting for seasonality by dummies and using differenced series, as well as forecasting, are studied for a series of Norwegian guest nights in Swedish hotels. In a forecast evaluation the improvements by introducing economic variables is minute. Paper[IV] empirically studies household's joint choice of the number of leisure trips and the total night to stay on these trips. The paper introduces a bivariate count hurdle model to account for the relative high frequencies of zeros. A truncated bivariate mixed Poisson lognormal distribution, allowing for both positive as well as negative correlation between the count variables, is utilized. Inflation techniques are used to account for clustering of leisure time to weekends. Simulated maximum likelihood is used as estimation method. A small policy study indicates that households substitute trips for nights as the travel costs increase.Time series; Count data; INARMA; Unit root; Aggregation; Forecasting; Tourism; Truncation; Inflation; Simulated maximum likelihood; Bivariate hurdle model.

Generalization Bounds via Information Density and Conditional Information Density

We present a general approach, based on an exponential inequality, to derive bounds on the generalization error of randomized learning algorithms. Using this approach, we provide bounds on the average generalization error as well as bounds on its tail probability, for both the PAC-Bayesian and single-draw scenarios. Specifically, for the case of subgaussian loss functions, we obtain novel bounds that depend on the information density between the training data and the output hypothesis. When suitably weakened, these bounds recover many of the information-theoretic available bounds in the literature. We also extend the proposed exponential-inequality approach to the setting recently introduced by Steinke and Zakynthinou (2020), where the learning algorithm depends on a randomly selected subset of the available training data. For this setup, we present bounds for bounded loss functions in terms of the conditional information density between the output hypothesis and the random variable determining the subset choice, given all training data. Through our approach, we recover the average generalization bound presented by Steinke and Zakynthinou (2020) and extend it to the PAC-Bayesian and single-draw scenarios. For the single-draw scenario, we also obtain novel bounds in terms of the conditional $\alpha$-mutual information and the conditional maximal leakage.Comment: Published in Journal on Selected Areas in Information Theory (JSAIT). Important note: the proof of the data-dependent bounds provided in the paper contains an error, which is rectified in the following document: https://gdurisi.github.io/files/2021/jsait-correction.pd

Algebraic curves for commuting elements in the q-deformed Heisenberg algebra

In this paper we extend the eliminant construction of Burchnall and Chaundy for commuting differential operators in the Heisenberg algebra to the q-deformed Heisenberg algebra and show that it again provides annihilating curves for commuting elements, provided q satisfies a natural condition. As a side result we obtain estimates on the dimensions of the eigenspaces of elements of this algebra in its faithful module of Laurent series.Comment: 18 pages, 2 figures, LaTeX. Final version with some improvements in presentation. To appear in Journal of Algebra

Forecasting based on Very Small Samples and Additional Non-Sample Information

Generalized method of moments estimation and forecasting is introduced for very small samples when additional non-sample information is available. Small simulation experiments are conducted for the linear model with errors-in-variables and for a Poisson regression model. Two empirical illustrations are included. One is based on Ukrainian imports and the other on private schools in a Swedish county.Generalized method of moments; additional information; forecasting; Ukrainian imports; private schools

The Impact of Stock Market Jumps on Time-Varying Return Correlations: Empirical Evidence from the Baltic Countries

In this paper we study the impact of market jumps on the time varying return correlations between stock market indices in the Baltic countries. An EARJI-EGARCH model facilitating direct modelling of the time varying return correlations is introduced. The empirical results indicate that there is a quite large number of identi…ed jumps in the emerging Baltic stock markets. The main …nding is that isolated market jumps in one of the markets generally have no or small e¤ects on the time-varying correlations. In contrast, simultaneous jumps of equal sign increase the average correlation, in some cases with as much as 100 percent.Correlated jumps; contagion

The Purchase by Railroads of Their Own Obligations

Recent studies from several authors show that it is possible to lower the fuel consumption for heavy trucks by utilizing information about the road topography ahead of the vehicle. The approach in these studies is receding horizon control where horizon length and residual cost are main topics. To approach these topics, fuel equivalents previously introduced based on physical intuition are given a mathematical interpretation in terms of Lagrange multipliers. Measures for the suboptimality, caused by the truncated horizon and the residual cost approximation, are defined and evaluated for different routes and parameters.Original Publication: Erik Hellström, Jan Åslund and Lars Nielsen, Horizon length and fuel equivalents for fuel-optimal look-ahead control, 2010, 6th IFAC Symposium Advances in Automatic Control. Copyright: INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL IFAC.</p

Uncertainty in the Generic Versus Brand Name Prescription Decision

This paper analyzes the impact of uncertainty concerning product quality of generic drugs on the substitution behavior of prescribing physicians. It is shown that uncertainty about the generic drug quality gives the physician a value of waiting for more information before switching to the generic version. In addition, it is shown that reducing the approval requirements for generic drugs, thereby increasing uncertainty about quality, may discourage physicians from prescribing such drugs. A small empirical study supports the theoretical findings and indicate that uncertainty about the quality of generic drugs do affect physician prescription behavior.Pharmaceutical industry; uncertainty; real options

Does the Open Limit Order Book Reveal Information About Short-run Stock Price Movements?

This paper empirically tests whether an open limit order book contains information about future short-run stock price movements. To account for the discrete nature of price changes, the integer-valued autoregressive model of order one is utilized. A model transformation has an advantage over conventional count data approaches since it handles negative integer-valued price changes. The empirical results reveal that measures capturing offered quantities of a share at the best bid- and ask-price reveal more information about future short-run price movements than measures capturing the quantities offered at prices below and above. Imbalance and changes in offered quantities at prices below and above the best bid- and ask-price do, however, have a small and significant effect on future price changes. The results also indicate that the value of order book information is short-term.Negative integer-valued data; time series; INAR; finance; stock price; open limit order book

Comparing Centralized and Decentralized Banking: A Study of the Risk-Return Profiles of Banks

This paper studies the risk-return profile of centralized and decentralized banks. We address the conditions that favor a particular lending regime while acknowledging the effects on lending and returns caused by the course of the business cycle. To analyze these issues, we develop a model which incorporates two stylized facts; (i) banks in which lending decisions are decentralized tend to have a lower cost associated with screening potential borrowers and (ii) decentralized decision-making may generate inefficient outcomes because of lack of coordination. Simulations are used to compare the two banking regimes. Among the results, it is found that asymmetric markets (in terms of the proportion of high ability entrepreneurs) tend to favor centralized banking while decentralized banks seem better at lending in the wake of an economic downturn (high probability of a recession). In addition, we find that even though a bank group where decisions are decentralized may end up with a portfolio of loans which is (relatively) poorly diversified between regions, the ability to effectively screen potential borrowers may nevertheless give a decentralized bank a lower overall risk in the lending portfolio than when decisions are centralized.lending; screening; business cycle; portfolio diversification; risk; organization; simulations