Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included

Complexity: Theoretical and methodological applications for sociology

This thesis examines the usefulness of Complexity as a new tool for sociology. Complexity, as its own branch of study, developed from the new science of Chaos. Recent paradigmatic disputes occurring in the scientific community have been the force of a growing sense of change in the way many different disciplines view complex systems. Since it is evident that social systems are typically highly complex, it makes sense that a scientific paradigm, which investigates the nature of complex systems, should also be applicable to social systems. Science now argues that the old Newtonian clockwork mentalities and classical experimental models cannot adequately describe highly complex systems. Instead anti-reductionist and nonlinear theories and methods may be much better suited for the task. The sociological relevance of Complexity---both its conceptual framework and its methodologies---is important and timely as we reach the limits of our current knowledge using standard reductionist thinking and methods

A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations

Data-driven & Theory-driven Science : Artificial Realities and Applications to Savings Groups

Paper I and Paper II is not published yet. They are excluded from the dissertation until they will be published.The scientific process is neither unique nor nomic. Two processes of scientific inquiry are theory-driven and data-driven science. This dissertation analyzes savings groups using theory-driven and data-driven methods. Simulated realities-based on data-driven theory-are used to understand the emerging dynamics of savings groups. Savings groups are grassroots, community-based organizations composed of 15 to 30 members. These organizations-usually supported by international development agencies-have weekly meetings during a cycle of operations that typically lasts a year. In the groups, savings are kept in two funds: a fund for loans and a social welfare fund that covers life-cycle events. The findings of Papers A to D in this dissertation provide new large-sample evidence about savings groups, their dynamics, and the factors affecting their financial performance. In practice, the results of Paper A to D shed light on the best policies to promote sustainable development with informal finance in a cost-effective way. A theory-driven approach indicates that the social fund in savings groups stimulates loan allocation among risk-sharing members, while implicitly covering idiosyncratic risks (Paper A). A data-driven approach based on Bayesian data-mining reveals that the macroeconomic environment and the facilitation model of development agencies have a strong influence on the profit-generating capacity of savings groups (Paper B). Machine-learning methods further show that business training is not the most frequent program implemented by development agencies, but it is in fact the most powerful intervention to encourage profits, particularly when a development agency stops working with a group and leaves a community (Paper C). Finally, the simulation of a village with artificial agents indicates that the businesses of savings groups can have higher profits due to the consolidation of social capital and the competitive advantage created through a process of homophily (Paper D). Metatheoretically, the theory-driven and data-driven approaches of this dissertation-and the complementarity between these approaches-contribute to the epistemology of data-intensive science. The dissertation concludes that the gelstaltic and quasi-teleological explanations of the data-driven approach help to the formulation of theories through inductive and abductive reasoning.publishedVersio

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Renewable Energy Transition: Dynamic Systems Analysis, Policy Scenarios, and Trade-offs for the State of Vermont

There is broad consensus that a transition to renewable energy and a low-carbon economy is crucial for future development and prosperity, yet there are differing perspectives on how such a transition should be achieved. The overarching goal of this dissertation, which is comprised of three interrelated studies, is to analyze and compare energy futures scenarios to achieve a renewable energy transition and low-carbon economy in the State of Vermont. In the first study, an analysis is presented of the role of energy pricing regimes and economic policy in the context of pursuing a renewable energy transition in the State of Vermont. Through the development and application of a system dynamics model, results address the limits to technological substitution due to path dependence on nonrenewable energy. The role of complementary economic policy is also highlighted to shift from a goal of quantitative growth to qualitative development in order to decouple economic welfare from energy consumption. In the second study, an analysis is presented of the impact of modeled energy transition scenarios to address energy development and land use trade-offs. Simulations with a spatio-temporal land cover change model find that Vermont could achieve a complete transition to renewable electricity using in-state resources through developing between 11,000 and 100,000 hectares of land for solar and wind, or up to four percent of state land area, including some environmentally sensitive land. This approach highlights the need for integration of energy policy and land use planning in order to mitigate potential energy-land use conflict. In the final study, trade-offs between energy, economic, environmental, and social dimensions of Vermont\u27s renewable energy transition are explored through the use of a multi-criteria decision analysis. Energy transition alternatives were designed to reveal trade-offs at the intersection of economic growth and carbon price policy. While there were no optimal pathways to achieving Vermont\u27s energy transition, some energy transition alternatives achieve a more socially desirable balance of benefits and consequences. Navigating the trade-offs inherent in the ongoing energy transition will require an adaptive approach to policymaking that incorporates iterative planning, experimentation, and learning