12 resultados para R15 - Econometric and Input Output Models

em Helda - Digital Repository of University of Helsinki


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Tutkimuksen tavoitteena on tuottaa uutta tietoa Suomen kansantalouden rakenteesta ja lyhyen aikavälin kehityksestä 1920- ja 1930-luvulla. Tutkimus toteutettiin laatimalla kansantaloutta kuvaava panos-tuotostaulu vuodelle 1928 sekä sen laajennus, panos-tuotosmalli. Aineiston avulla kuvataan kansantalouden rakenteellisia riippuvuuksia, tuotannon avaintoimialoja sekä näiden vaikutusta kansantalouteen. Lisäksi tutkimuksessa tarkastellaan kansantalouden tuontiriippuvuutta sekä tuontitullien vaikutusta hintoihin 1930-luvun laman aikana. Tutkimuksen perusteella voitiin identifioida Suomen kansantalouden avaintoimialat vuonna 1928: maatalous, metsätalous, elintarviketeollisuus, puuteollisuus, paperiteollisuus ja rakennustoiminta. Erityisesti elintarviketeollisuuden vahva rooli kansantaloudessa oli kenties yllättävää, erityisesti kun huomioidaan kuinka vähän toimiala on saanut huomiota osakseen taloushistorian tutkimuksessa. Tutkimus osoitti, että Suomen vienti oli pääomavaltaisempaa kuin tuonti. Vaikka tämän tuloksen tulkinta on varauksellinen, tutkimus pystyi osoittamaan ja kvantifioimaan toimialojen työ- ja pääomapanoksen osuuden tuotoksesta yksityiskohtaisesti. Panos-tuotosmallilla arvioitiin puuteollisuuden, paperiteollisuuden ja rakennustoiminnan ajanjaksona 1928-32 tapahtuneen loppukäytön muutoksen vaikutusta kansantalouteen. Merkittävä havainto on, että rakennustoiminnan loppukäytön muutoksella oli erittäin suuri kasvua vähentävä vaikutus koko kansantaloudessa. Talonrakennusinvestointien romahtaminen aiheutti lähes 13 prosentin tuotannon laskun kansantaloudessa. Vaikutus oli jopa suurempi kuin puuteollisuuden viennin romahtamisen. Tulokset osoittavat toisaalta, että yksityisen kulutuksen merkitys kansantaloudelle oli erittäin vahva. Esimerkiksi puuteollisuuden viennin romahtaminen aiheutti yli 4 % tuotannon vähenemisen mutta huomioitaessa mallissa myös yksityisen kulutuksen väheneminen, oli kokonaisvaikutus yli 10 %. Yksityisen kulutuksen huomioiminen mallissa siis yli kaksinkertaisti toimialojen vaikutukset kansantalouteen. Tulokset vahvistivat aiemmissa tutkimuksissa esitettyjä johtopäätöksiä tullipolitiikasta ja osoittivat maatalouteen läheisesti liittyvän elintarviketeollisuuden olleen eniten suojeltu toimiala kansantaloudessa. Muut kotimarkkinoiden toimialat eivät kuitenkaan hyötyneet tullipolitiikasta lamakauden aikana. Panos-tuotoshintamallilla osoitettiin, ettei tullipolitiikka ollut niin onnistunutta kuin aikalaistutkimuksissa väitettiin, vaan tullit korkeintaan pystyivät hidastamaan hintojen alenemista. Tutkimuksen liitteenä esitetään kaikki keskeiset Suomen kansantaloutta vuonna 1928 kuvaavat tilastolliset taulukot, mukaan lukien käyttö- ja tarjontataulukot, panos-tuotostaulukot, panoskertoimet, Leontiefin käänteismatriisi sekä työ- ja pääomapanoskertoimet.

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This paper examines how volatility in financial markets can preferable be modeled. The examination investigates how good the models for the volatility, both linear and nonlinear, are in absorbing skewness and kurtosis. The examination is done on the Nordic stock markets, including Finland, Sweden, Norway and Denmark. Different linear and nonlinear models are applied, and the results indicates that a linear model can almost always be used for modeling the series under investigation, even though nonlinear models performs slightly better in some cases. These results indicate that the markets under study are exposed to asymmetric patterns only to a certain degree. Negative shocks generally have a more prominent effect on the markets, but these effects are not really strong. However, in terms of absorbing skewness and kurtosis, nonlinear models outperform linear ones.

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Human activities extract and displace different substances and materials from the earth s crust, thus causing various environmental problems, such as climate change, acidification and eutrophication. As problems have become more complicated, more holistic measures that consider the origins and sources of pollutants have been called for. Industrial ecology is a field of science that forms a comprehensive framework for studying the interactions between the modern technological society and the environment. Industrial ecology considers humans and their technologies to be part of the natural environment, not separate from it. Industrial operations form natural systems that must also function as such within the constraints set by the biosphere. Industrial symbiosis (IS) is a central concept of industrial ecology. Industrial symbiosis studies look at the physical flows of materials and energy in local industrial systems. In an ideal IS, waste material and energy are exchanged by the actors of the system, thereby reducing the consumption of virgin material and energy inputs and the generation of waste and emissions. Companies are seen as part of the chains of suppliers and consumers that resemble those of natural ecosystems. The aim of this study was to analyse the environmental performance of an industrial symbiosis based on pulp and paper production, taking into account life cycle impacts as well. Life Cycle Assessment (LCA) is a tool for quantitatively and systematically evaluating the environmental aspects of a product, technology or service throughout its whole life cycle. Moreover, the Natural Step Sustainability Principles formed a conceptual framework for assessing the environmental performance of the case study symbiosis (Paper I). The environmental performance of the case study symbiosis was compared to four counterfactual reference scenarios in which the actors of the symbiosis operated on their own. The research methods used were process-based life cycle assessment (LCA) (Papers II and III) and hybrid LCA, which combines both process and input-output LCA (Paper IV). The results showed that the environmental impacts caused by the extraction and processing of the materials and the energy used by the symbiosis were considerable. If only the direct emissions and resource use of the symbiosis had been considered, less than half of the total environmental impacts of the system would have been taken into account. When the results were compared with the counterfactual reference scenarios, the net environmental impacts of the symbiosis were smaller than those of the reference scenarios. The reduction in environmental impacts was mainly due to changes in the way energy was produced. However, the results are sensitive to the way the reference scenarios are defined. LCA is a useful tool for assessing the overall environmental performance of industrial symbioses. It is recommended that in addition to the direct effects, the upstream impacts should be taken into account as well when assessing the environmental performance of industrial symbioses. Industrial symbiosis should be seen as part of the process of improving the environmental performance of a system. In some cases, it may be more efficient, from an environmental point of view, to focus on supply chain management instead.  

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This thesis presents an interdisciplinary analysis of how models and simulations function in the production of scientific knowledge. The work is informed by three scholarly traditions: studies on models and simulations in philosophy of science, so-called micro-sociological laboratory studies within science and technology studies, and cultural-historical activity theory. Methodologically, I adopt a naturalist epistemology and combine philosophical analysis with a qualitative, empirical case study of infectious-disease modelling. This study has a dual perspective throughout the analysis: it specifies the modelling practices and examines the models as objects of research. The research questions addressed in this study are: 1) How are models constructed and what functions do they have in the production of scientific knowledge? 2) What is interdisciplinarity in model construction? 3) How do models become a general research tool and why is this process problematic? The core argument is that the mediating models as investigative instruments (cf. Morgan and Morrison 1999) take questions as a starting point, and hence their construction is intentionally guided. This argument applies the interrogative model of inquiry (e.g., Sintonen 2005; Hintikka 1981), which conceives of all knowledge acquisition as process of seeking answers to questions. The first question addresses simulation models as Artificial Nature, which is manipulated in order to answer questions that initiated the model building. This account develops further the "epistemology of simulation" (cf. Winsberg 2003) by showing the interrelatedness of researchers and their objects in the process of modelling. The second question clarifies why interdisciplinary research collaboration is demanding and difficult to maintain. The nature of the impediments to disciplinary interaction are examined by introducing the idea of object-oriented interdisciplinarity, which provides an analytical framework to study the changes in the degree of interdisciplinarity, the tools and research practices developed to support the collaboration, and the mode of collaboration in relation to the historically mutable object of research. As my interest is in the models as interdisciplinary objects, the third research problem seeks to answer my question of how we might characterise these objects, what is typical for them, and what kind of changes happen in the process of modelling. Here I examine the tension between specified, question-oriented models and more general models, and suggest that the specified models form a group of their own. I call these Tailor-made models, in opposition to the process of building a simulation platform that aims at generalisability and utility for health-policy. This tension also underlines the challenge of applying research results (or methods and tools) to discuss and solve problems in decision-making processes.

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Various reasons, such as ethical issues in maintaining blood resources, growing costs, and strict requirements for safe blood, have increased the pressure for efficient use of resources in blood banking. The competence of blood establishments can be characterized by their ability to predict the volume of blood collection to be able to provide cellular blood components in a timely manner as dictated by hospital demand. The stochastically varying clinical need for platelets (PLTs) sets a specific challenge for balancing supply with requests. Labour has been proven a primary cost-driver and should be managed efficiently. International comparisons of blood banking could recognize inefficiencies and allow reallocation of resources. Seventeen blood centres from 10 countries in continental Europe, Great Britain, and Scandinavia participated in this study. The centres were national institutes (5), parts of the local Red Cross organisation (5), or integrated into university hospitals (7). This study focused on the departments of blood component preparation of the centres. The data were obtained retrospectively by computerized questionnaires completed via Internet for the years 2000-2002. The data were used in four original articles (numbered I through IV) that form the basis of this thesis. Non-parametric data envelopment analysis (DEA, II-IV) was applied to evaluate and compare the relative efficiency of blood component preparation. Several models were created using different input and output combinations. The focus of comparisons was on the technical efficiency (II-III) and the labour efficiency (I, IV). An empirical cost model was tested to evaluate the cost efficiency (IV). Purchasing power parities (PPP, IV) were used to adjust the costs of the working hours and to make the costs comparable among countries. The total annual number of whole blood (WB) collections varied from 8,880 to 290,352 in the centres (I). Significant variation was also observed in the annual volume of produced red blood cells (RBCs) and PLTs. The annual number of PLTs produced by any method varied from 2,788 to 104,622 units. In 2002, 73% of all PLTs were produced by the buffy coat (BC) method, 23% by aphaeresis and 4% by the platelet-rich plasma (PRP) method. The annual discard rate of PLTs varied from 3.9% to 31%. The mean discard rate (13%) remained in the same range throughout the study period and demonstrated similar levels and variation in 2003-2004 according to a specific follow-up question (14%, range 3.8%-24%). The annual PLT discard rates were, to some extent, associated with production volumes. The mean RBC discard rate was 4.5% (range 0.2%-7.7%). Technical efficiency showed marked variation (median 60%, range 41%-100%) among the centres (II). Compared to the efficient departments, the inefficient departments used excess labour resources (and probably) production equipment to produce RBCs and PLTs. Technical efficiency tended to be higher when the (theoretical) proportion of lost WB collections (total RBC+PLT loss) from all collections was low (III). The labour efficiency varied remarkably, from 25% to 100% (median 47%) when working hours were the only input (IV). Using the estimated total costs as the input (cost efficiency) revealed an even greater variation (13%-100%) and overall lower efficiency level compared to labour only as the input. In cost efficiency only, the savings potential (observed inefficiency) was more than 50% in 10 departments, whereas labour and cost savings potentials were both more than 50% in six departments. The association between department size and efficiency (scale efficiency) could not be verified statistically in the small sample. In conclusion, international evaluation of the technical efficiency in component preparation departments revealed remarkable variation. A suboptimal combination of manpower and production output levels was the major cause of inefficiency, and the efficiency did not directly relate to production volume. Evaluation of the reasons for discarding components may offer a novel approach to study efficiency. DEA was proven applicable in analyses including various factors as inputs and outputs. This study suggests that analytical models can be developed to serve as indicators of technical efficiency and promote improvements in the management of limited resources. The work also demonstrates the importance of integrating efficiency analysis into international comparisons of blood banking.

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This work develops methods to account for shoot structure in models of coniferous canopy radiative transfer. Shoot structure, as it varies along the light gradient inside canopy, affects the efficiency of light interception per unit needle area, foliage biomass, or foliage nitrogen. The clumping of needles in the shoot volume also causes a notable amount of multiple scattering of light within coniferous shoots. The effect of shoot structure on light interception is treated in the context of canopy level photosynthesis and resource use models, and the phenomenon of within-shoot multiple scattering in the context of physical canopy reflectance models for remote sensing purposes. Light interception. A method for estimating the amount of PAR (Photosynthetically Active Radiation) intercepted by a conifer shoot is presented. The method combines modelling of the directional distribution of radiation above canopy, fish-eye photographs taken at shoot locations to measure canopy gap fraction, and geometrical measurements of shoot orientation and structure. Data on light availability, shoot and needle structure and nitrogen content has been collected from canopies of Pacific silver fir (Abies amabilis (Dougl.) Forbes) and Norway spruce (Picea abies (L.) Karst.). Shoot structure acclimated to light gradient inside canopy so that more shaded shoots have better light interception efficiency. Light interception efficiency of shoots varied about two-fold per needle area, about four-fold per needle dry mass, and about five-fold per nitrogen content. Comparison of fertilized and control stands of Norway spruce indicated that light interception efficiency is not greatly affected by fertilization. Light scattering. Structure of coniferous shoots gives rise to multiple scattering of light between the needles of the shoot. Using geometric models of shoots, multiple scattering was studied by photon tracing simulations. Based on simulation results, the dependence of the scattering coefficient of shoot from the scattering coefficient of needles is shown to follow a simple one-parameter model. The single parameter, termed the recollision probability, describes the level of clumping of the needles in the shoot, is wavelength independent, and can be connected to previously used clumping indices. By using the recollision probability to correct for the within-shoot multiple scattering, canopy radiative transfer models which have used leaves as basic elements can use shoots as basic elements, and thus be applied for coniferous forests. Preliminary testing of this approach seems to explain, at least partially, why coniferous forests appear darker than broadleaved forests in satellite data.

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To a large extent, lakes can be described with a one-dimensional approach, as their main features can be characterized by the vertical temperature profile of the water. The development of the profiles during the year follows the seasonal climate variations. Depending on conditions, lakes become stratified during the warm summer. After cooling, overturn occurs, water cools and an ice cover forms. Typically, water is inversely stratified under the ice, and another overturn occurs in spring after the ice has melted. Features of this circulation have been used in studies to distinguish between lakes in different areas, as basis for observation systems and even as climate indicators. Numerical models can be used to calculate temperature in the lake, on the basis of the meteorological input at the surface. The simple form is to solve the surface temperature. The depth of the lake affects heat transfer, together with other morphological features, the shape and size of the lake. Also the surrounding landscape affects the formation of the meteorological fields over the lake and the energy input. For small lakes the shading by the shores affects both over the lake and inside the water body bringing limitations for the one-dimensional approach. A two-layer model gives an approximation for the basic stratification in the lake. A turbulence model can simulate vertical temperature profile in a more detailed way. If the shape of the temperature profile is very abrupt, vertical transfer is hindered, having many important consequences for lake biology. One-dimensional modelling approach was successfully studied comparing a one-layer model, a two-layer model and a turbulence model. The turbulence model was applied to lakes with different sizes, shapes and locations. Lake models need data from the lakes for model adjustment. The use of the meteorological input data on different scales was analysed, ranging from momentary turbulent changes over the lake to the use of the synoptical data with three hour intervals. Data over about 100 past years were used on the mesoscale at the range of about 100 km and climate change scenarios for future changes. Increasing air temperature typically increases water temperature in epilimnion and decreases ice cover. Lake ice data were used for modelling different kinds of lakes. They were also analyzed statistically in global context. The results were also compared with results of a hydrological watershed model and data from very small lakes for seasonal development.

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This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

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Agriculture is an economic activity that heavily relies on the availability of natural resources. Through its role in food production agriculture is a major factor affecting public welfare and health, and its indirect contribution to gross domestic product and employment is significant. Agriculture also contributes to numerous ecosystem services through management of rural areas. However, the environmental impact of agriculture is considerable and reaches far beyond the agroecosystems. The questions related to farming for food production are, thus, manifold and of great public concern. Improving environmental performance of agriculture and sustainability of food production, sustainabilizing food production, calls for application of wide range of expertise knowledge. This study falls within the field of agro-ecology, with interphases to food systems and sustainability research and exploits the methods typical of industrial ecology. The research in these fields extends from multidisciplinary to interdisciplinary and transdisciplinary, a holistic approach being the key tenet. The methods of industrial ecology have been applied extensively to explore the interaction between human economic activity and resource use. Specifically, the material flow approach (MFA) has established its position through application of systematic environmental and economic accounting statistics. However, very few studies have applied MFA specifically to agriculture. The MFA approach was used in this thesis in such a context in Finland. The focus of this study is the ecological sustainability of primary production. The aim was to explore the possibilities of assessing ecological sustainability of agriculture by using two different approaches. In the first approach the MFA-methods from industrial ecology were applied to agriculture, whereas the other is based on the food consumption scenarios. The two approaches were used in order to capture some of the impacts of dietary changes and of changes in production mode on the environment. The methods were applied at levels ranging from national to sector and local levels. Through the supply-demand approach, the viewpoint changed between that of food production to that of food consumption. The main data sources were official statistics complemented with published research results and expertise appraisals. MFA approach was used to define the system boundaries, to quantify the material flows and to construct eco-efficiency indicators for agriculture. The results were further elaborated for an input-output model that was used to analyse the food flux in Finland and to determine its relationship to the economy-wide physical and monetary flows. The methods based on food consumption scenarios were applied at regional and local level for assessing feasibility and environmental impacts of relocalising food production. The approach was also used for quantification and source allocation of greenhouse gas (GHG) emissions of primary production. GHG assessment provided, thus, a means of crosschecking the results obtained by using the two different approaches. MFA data as such or expressed as eco-efficiency indicators, are useful in describing the overall development. However, the data are not sufficiently detailed for identifying the hot spots of environmental sustainability. Eco-efficiency indicators should not be bluntly used in environmental assessment: the carrying capacity of the nature, the potential exhaustion of non-renewable natural resources and the possible rebound effect need also to be accounted for when striving towards improved eco-efficiency. The input-output model is suitable for nationwide economy analyses and it shows the distribution of monetary and material flows among the various sectors. Environmental impact can be captured only at a very general level in terms of total material requirement, gaseous emissions, energy consumption and agricultural land use. Improving environmental performance of food production requires more detailed and more local information. The approach based on food consumption scenarios can be applied at regional or local scales. Based on various diet options the method accounts for the feasibility of re-localising food production and environmental impacts of such re-localisation in terms of nutrient balances, gaseous emissions, agricultural energy consumption, agricultural land use and diversity of crop cultivation. The approach is applicable anywhere, but the calculation parameters need to be adjusted so as to comply with the specific circumstances. The food consumption scenario approach, thus, pays attention to the variability of production circumstances, and may provide some environmental information that is locally relevant. The approaches based on the input-output model and on food consumption scenarios represent small steps towards more holistic systemic thinking. However, neither one alone nor the two together provide sufficient information for sustainabilizing food production. Environmental performance of food production should be assessed together with the other criteria of sustainable food provisioning. This requires evaluation and integration of research results from many different disciplines in the context of a specified geographic area. Foodshed area that comprises both the rural hinterlands of food production and the population centres of food consumption is suggested to represent a suitable areal extent for such research. Finding a balance between the various aspects of sustainability is a matter of optimal trade-off. The balance cannot be universally determined, but the assessment methods and the actual measures depend on what the bottlenecks of sustainability are in the area concerned. These have to be agreed upon among the actors of the area

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The question at issue in this dissertation is the epistemic role played by ecological generalizations and models. I investigate and analyze such properties of generalizations as lawlikeness, invariance, and stability, and I ask which of these properties are relevant in the context of scientific explanations. I will claim that there are generalizable and reliable causal explanations in ecology by generalizations, which are invariant and stable. An invariant generalization continues to hold or be valid under a special change called an intervention that changes the value of its variables. Whether a generalization remains invariant during its interventions is the criterion that determines whether it is explanatory. A generalization can be invariant and explanatory regardless of its lawlike status. Stability deals with a generality that has to do with holding of a generalization in possible background conditions. The more stable a generalization, the less dependent it is on background conditions to remain true. Although it is invariance rather than stability of generalizations that furnishes us with explanatory generalizations, there is an important function that stability has in this context of explanations, namely, stability furnishes us with extrapolability and reliability of scientific explanations. I also discuss non-empirical investigations of models that I call robustness and sensitivity analyses. I call sensitivity analyses investigations in which one model is studied with regard to its stability conditions by making changes and variations to the values of the model s parameters. As a general definition of robustness analyses I propose investigations of variations in modeling assumptions of different models of the same phenomenon in which the focus is on whether they produce similar or convergent results or not. Robustness and sensitivity analyses are powerful tools for studying the conditions and assumptions where models break down and they are especially powerful in pointing out reasons as to why they do this. They show which conditions or assumptions the results of models depend on. Key words: ecology, generalizations, invariance, lawlikeness, philosophy of science, robustness, explanation, models, stability

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Ecology and evolutionary biology is the study of life on this planet. One of the many methods applied to answering the great diversity of questions regarding the lives and characteristics of individual organisms, is the utilization of mathematical models. Such models are used in a wide variety of ways. Some help us to reason, functioning as aids to, or substitutes for, our own fallible logic, thus making argumentation and thinking clearer. Models which help our reasoning can lead to conceptual clarification; by expressing ideas in algebraic terms, the relationship between different concepts become clearer. Other mathematical models are used to better understand yet more complicated models, or to develop mathematical tools for their analysis. Though helping us to reason and being used as tools in the craftmanship of science, many models do not tell us much about the real biological phenomena we are, at least initially, interested in. The main reason for this is that any mathematical model is a simplification of the real world, reducing the complexity and variety of interactions and idiosynchracies of individual organisms. What such models can tell us, however, both is and has been very valuable throughout the history of ecology and evolution. Minimally, a model simplifying the complex world can tell us that in principle, the patterns produced in a model could also be produced in the real world. We can never know how different a simplified mathematical representation is from the real world, but the similarity models do strive for, gives us confidence that their results could apply. This thesis deals with a variety of different models, used for different purposes. One model deals with how one can measure and analyse invasions; the expanding phase of invasive species. Earlier analyses claims to have shown that such invasions can be a regulated phenomena, that higher invasion speeds at a given point in time will lead to a reduction in speed. Two simple mathematical models show that analysis on this particular measure of invasion speed need not be evidence of regulation. In the context of dispersal evolution, two models acting as proof-of-principle are presented. Parent-offspring conflict emerges when there are different evolutionary optima for adaptive behavior for parents and offspring. We show that the evolution of dispersal distances can entail such a conflict, and that under parental control of dispersal (as, for example, in higher plants) wider dispersal kernels are optimal. We also show that dispersal homeostasis can be optimal; in a setting where dispersal decisions (to leave or stay in a natal patch) are made, strategies that divide their seeds or eggs into fractions that disperse or not, as opposed to randomized for each seed, can prevail. We also present a model of the evolution of bet-hedging strategies; evolutionary adaptations that occur despite their fitness, on average, being lower than a competing strategy. Such strategies can win in the long run because they have a reduced variance in fitness coupled with a reduction in mean fitness, and fitness is of a multiplicative nature across generations, and therefore sensitive to variability. This model is used for conceptual clarification; by developing a population genetical model with uncertain fitness and expressing genotypic variance in fitness as a product between individual level variance and correlations between individuals of a genotype. We arrive at expressions that intuitively reflect two of the main categorizations of bet-hedging strategies; conservative vs diversifying and within- vs between-generation bet hedging. In addition, this model shows that these divisions in fact are false dichotomies.