The Saskatchewan farm sector is a dynamic system that is faced with the reality of farm consolidation and other structural adjustments. While structural adjustment may result in increased productivity at the farm-level, the declining farm population has a direct impact on rural regions. Given the economic difficulties now inherent in many rural regions, there has never been a more important time to improve our understanding of the structural dynamics of the farm sector. <p> By utilizing agent-based methods, competition that exists between farm households in land markets is modelled in a dynamic framework. By modeling land markets in this manner, structural adjustments that occur due to the re-allocation of land among farm household becomes endogenous to the model. The farming simulation was validated by evaluating its ability to replicate actual structural shifts that occurred during the period of 1960-2000. The results obtained from the simulation were found to mirror historic shifts, which gives the author confidence that the parsimonious assumptions made are robust, yet still characteristic of farm level behaviour in the region. Other scenarios were simulated in order to estimate a counterfactual structural evolution of the modelled region, in the absence of government stabilization and support programs. Significant deviations are observed between the base and zero transfer scenarios with regards to the consolidation of farm assets among a declining number of farm households. Most significantly, the decline in farm numbers accelerated significantly in the late 1980s in the zero transfer scenario compared to the base simulations. <p>The application of an agent-based framework allowed for the study of regional structure with an emphasis on the behaviour and actions of the primary decisions makers within the system. While structural change is driven by a number of factors, the ability of a farm household to fully employ their labour resource was an important factor in the simulations. This contrasts with the finding that productive efficiency, and purchasing and market power at the farm level is not a necessary condition for the observed consolidation of farm assets.

The epidemiology of malaria considers a complex set of local interactions amongst host, vector, and environment. A history of reemergence, epidemic transition, and ensuing endemic transmission in Iquitos, Peru reveals an interesting case used to model and explore such interactions. In this region of the Peruvian Amazon, climate change, development initiatives and landscape fragmentation are amongst a unique set of local spatial variables underlying the endemicity of malaria. Traditional population-based approaches lack the ability to resolve the spatial influences of these variables. Presented is a framework for spatially explicit, agent-based modeling of malaria transmission dynamics in Iquitos and surrounding areas. The use of an agent-based model presents a new opportunity to spatially define causal factors and influences of transmission between mosquito vectors and human hosts. In addition to spatial considerations, the ability to model individual decisions of humans can define socio-economic and human-environment interactions related to malaria transmission. Three interacting sub-models representing human decisions, vector dynamics, and environmental factors comprise the model. Feedbacks between the interacting sub-models define individual decisions and ultimately the flexibility that will allow the model to function in a diagnostic capacity. Sensitivity analysis and simulated interactions are used to discuss this diagnostic capability and to build understanding of the physical systems driving local transmission of malaria.

The epidemiology of malaria considers a complex set of local interactions amongst host, vector, and environment. A history of reemergence, epidemic transition, and ensuing endemic transmission in Iquitos, Peru reveals an interesting case used to model and explore such interactions. In this region of the Peruvian Amazon, climate change, development initiatives and landscape fragmentation are amongst a unique set of local spatial variables underlying the endemicity of malaria. Traditional population-based approaches lack the ability to resolve the spatial influences of these variables. Presented is a framework for spatially explicit, agent-based modeling of malaria transmission dynamics in Iquitos and surrounding areas. The use of an agent-based model presents a new opportunity to spatially define causal factors and influences of transmission between mosquito vectors and human hosts. In addition to spatial considerations, the ability to model individual decisions of humans can define socio-economic and human-environment interactions related to malaria transmission. Three interacting sub-models representing human decisions, vector dynamics, and environmental factors comprise the model. Feedbacks between the interacting sub-models define individual decisions and ultimately the flexibility that will allow the model to function in a diagnostic capacity. Sensitivity analysis and simulated interactions are used to discuss this diagnostic capability and to build understanding of the physical systems driving local transmission of malaria.

The Saskatchewan farm sector is a dynamic system that is faced with the reality of farm consolidation and other structural adjustments. While structural adjustment may result in increased productivity at the farm-level, the declining farm population has a direct impact on rural regions. Given the economic difficulties now inherent in many rural regions, there has never been a more important time to improve our understanding of the structural dynamics of the farm sector. <p> By utilizing agent-based methods, competition that exists between farm households in land markets is modelled in a dynamic framework. By modeling land markets in this manner, structural adjustments that occur due to the re-allocation of land among farm household becomes endogenous to the model. The farming simulation was validated by evaluating its ability to replicate actual structural shifts that occurred during the period of 1960-2000. The results obtained from the simulation were found to mirror historic shifts, which gives the author confidence that the parsimonious assumptions made are robust, yet still characteristic of farm level behaviour in the region. Other scenarios were simulated in order to estimate a counterfactual structural evolution of the modelled region, in the absence of government stabilization and support programs. Significant deviations are observed between the base and zero transfer scenarios with regards to the consolidation of farm assets among a declining number of farm households. Most significantly, the decline in farm numbers accelerated significantly in the late 1980s in the zero transfer scenario compared to the base simulations. <p>The application of an agent-based framework allowed for the study of regional structure with an emphasis on the behaviour and actions of the primary decisions makers within the system. While structural change is driven by a number of factors, the ability of a farm household to fully employ their labour resource was an important factor in the simulations. This contrasts with the finding that productive efficiency, and purchasing and market power at the farm level is not a necessary condition for the observed consolidation of farm assets.

Like many North American agricultural regions, Saskatchewan has experienced significant fundamental structural changes in farming. Structural change encompasses evolution in distribution of farm sizes, land tenure and financial characteristics, as well as variations in demographic and production characteristics. These issues are often a source of discontent among farm populations as it implies these populations are forced to adapt in a number of potentially unpleasant ways. These changes have profound and sometimes poorly understood effects on the rural economy for example, structural change affects rural population and therefore demand for rural infrastructure. <p>Traditional agricultural farm level analysis is often conducted using a representative farm or group, but this framework cannot capture the growing heterogeneity of modern farm operators or the current operating environment in agricultural regions. Farm profiles vary by demographic characteristics, such as age and education, and resource endowments. Agent-based simulation captures this heterogeneity through a farm by farm analysis, where after initialization, the regional economy evolves over time.<p>A synthetic population is created based on survey data and the land characteristics based on the actual land data in CAR 7B of Saskatchewan. A number of different price and yield time paths were created using a bootstrap procedure on historical data and the model evolved to potential agriculture structures that may occur in the model region, 30 years in the future.<p>Structural change occurs endogenously as farms interact in land markets, and make decisions on land use. Agents compete for available land in a purchase and lease market with land selling to the highest bidder. The dynamic nature of agent-based models allows individual farms to adjust land use in response to changing economic conditions and individual preferences. How individuals organize their resources will be critical to farm survival and growth.<p>The results indicate that many of the trends are the same under the different price and yield time paths, however the rate of change is significantly impacted by the price and yield time path that occurs. The model predicted the trend to fewer and larger farms will continue into the future. The forecasted distribution of smaller farms will decline and proportion of large farms will increase, while mid sized farms will remain relatively unchanged. The proportion of mixed farms, land use, and total livestock numbers depend significantly on the price and yield time path. The actual structure that will occur will be the result of the actual individual price and yield time path that occurs.

With the proliferation of new technological alternatives to the traditional classroom, it becomes increasingly important understand the role that innovative technologies play in learning. Computer environments for learning to teach have the potential to be innovative tools that improve the skill and effectiveness of pre-service and in-service teachers. There is a tacit sense in such environments that “realism” is best created through, and associated with, a kind of pictorial literalism. I designed a computer model (the Direct Instruction tool) that, though simple, appears realistic to many users and thus contradicts that sense of literalism. I also propose a theoretical classification of computer representations based on the relationship (or lack thereof) between perceived usefulness or relevance and realism. In this study, I investigate two questions: 1) What are the kinds of claims or insights that respondents generate in relation to using the DI tool to organize their experiences? 2) How do the functionalities of the DI tool fit with or support what respondents see as meaningful? Results indicate that a model can be seen as relevant and useful even if it is not internally consistent. Two major themes that were meaningful to study participants were the simultaneously positive and negative role of “difficulty” in the classroom, and the balance between past performance and future potential. The DI tool seems to promote a shared focus on these themes despite the diversity of past educational experiences among study participants. Responses to this model suggest that extremely abstracted representations of teaching are able to influence the claims and insights of users, affording a glimpse into the internal realities of pre-service teachers. This in turn creates an opportunity to articulate these alternative realities without judgment, describe them with respect, and make them an object of consideration rather than a hidden force. The results of this study contribute to a theory of computer environments for learning to teach that can shape the effective use of these tools in the present, as well as accommodate new models that may be developed as technologies change in the future. / text

The Hohokam Water Management Simulation (HWM) is a computer simulation for exploring the operation of the Hohokam irrigation systems in southern Arizona. The simulation takes a middle road between two common kinds of archaeological simulation: large-scale, detailed landscape and environmental reconstructions and highly abstract hypothesis-testing simulations. Given the apparent absence in the Hohokam context of a central authority, the specific aim of the HWM is approaching the Hohokam as a complex system, using principles such as resilience, robustness, and self-organization. The Hohokam case is reviewed, and general questions concerning how the irrigation systems operated are shown to subsume multiple crosscutting and unresolved issues. Existing proposals about the relevant aspects of Hohokam society and of its larger long-term trajectory are based on widely varying short- and long-term processes that invoke different elements, draw different boundaries, and operate at different spatial and temporal scales, and many rely on information that is only incompletely available. A framework for pproaching problems of this kind is put forward. A definition of modeling is offered that specifies its epistemological foundations, permissible patterns of inference, and its role in our larger scientific process. Invoking Logical Positivism, a syntactic rather than semantic view of modeling is proposed: modeling is the construction of sets of assertions about the world and deductions that can be drawn from them. This permits a general model structure to be offered that admits hypothetical or provisional assertions and the flexible interchange of model components of varying scope and resolution. Novel goals for archaeological inquiry fall from this flexible approach; these move from specific reconstruction to a search for more universal and general dynamics. A software toolkit that embodies these principles is introduced: the Assertion-Based Computer Modeling toolkit (ABCM), which integrates simulation with the logical architecture of a relational database, and further provides an easy means for linking models of natural and social processes (including agent-based modeling). The application of this to the Hohokam context is described, and an extended example is presented that demonstrates the flexibility, utility and challenges of the approach. An attached file provides sample output.

Utilizing game theory, learning automata and reinforcement learning concepts, thesis presents a computational model (simulation) based on general equilibrium theory and classical monetary model. Model is based on interacting Constructively Rational agents. Constructive Ratio- nality has been introduced in current literature as machine learning based concept that allows relaxing assumptions on modeled economic agents information and ex- pectations. Model experiences periodical endogenous crises (Fall in both production and con- sumption accompanied with rise in unemployment rate). Crises are caused by firms and households adopting to a change in price and wage levels. Price and wage level adjustments are necessary for the goods and labor market to clear in the presence of technological growth. Finally, model has good theoretical background and large potential for further de- velopment. Also, general properties of games of learning entities are examined, with special focus on sudden changes (shocks) in the game and behavior of game's play- ers, during recovery from which rigidities can emerge. JEL Classification D80, D83, C63, E32, C73, Keywords Learning, Information and Knowledge, Agent-based, Reinforcement learning, Business cycle, Stochastic and Dynamic Games, Simulation, Modeling Author's e-mail...

For over 40 years archaeologists have been using predictive modelling to locate archaeological sites. While great strides have been made in the theory and methods of site predictive modelling there are still unresolved issues like a lack of theory, poor data, biased datasets and poor accuracy and precision in the models. This thesis attempts to address the problems of poor model performance and lack of theory driven models through the development of a new method for predictive modelling, agent based modelling. Applying GIS and agent based modelling tools to a project area in southeaster New Mexico this new methodology explored possible behaviours that resulted in site formation such as access to water resources, travel routes and resource exploitation. The results in regards to improved accuracy over traditional methods were inconclusive as a data error was found in the previously created predictive models for the area that were to be used as a comparison. But, the project was more successful in providing explanatory reasons for site placement based on the models created. This work has the potential to open up predictive modelling to wider archaeology audiences, such as those based at universities. Additional findings also impacted other areas of archaeological investigation outside of predictive modelling, such as least cost path analyses and resource gathering analyses.

Three different multi-agent models are presented in this thesis, each with a different goal. The first model investigates the possible role migratory routes may have on song evolution and revolution. The second model investigates what social networks could theoretically facilitate song sharing in a population of whales. The third model implements a formal grammar algorithm in order to investigate how the hierarchal structure of the song may affect song evolution. Finally, the thesis attempts to reconnect the models with their origins and discusses how these models could potentially be adapted for composing music. Through the development of these different models, a number of findings are highlighted. The first model reveals that feeding ground sizes may be key locations where song learning from other population may be facilitated. The second model shows that small world social networks facilitate a high degree of agents converging on a single song, similar to what is observed in wild populations. The final model shows that the ability to recognise hierarchy in a sequence coupled with simple production errors, can lead to songs gradually changing over the course of time, while still retaining their hierarchal structure.