Agent‐based computational models and generative social science

This article argues that the agent‐based computational model permits a distinctive approach to social science for which the term “generative” is suitable. In defending this terminology, features distinguishing the approach from both “inductive” and “deductive” science are given. Then, the following specific contributions to social science are discussed: The agent‐based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent‐based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent‐based (“bottom up”) models. The agent‐based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent‐based modeling offers powerful new forms of hybrid theoretical‐computational work; these are particularly relevant to the study of non‐equilibrium systems. The agent‐based approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent‐based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. © 1999 John Wiley & Sons, Inc.     

Self-organization and social science

Complexity science and its methodological applications have increased in popularity in social science during the last two decades. One key concept within complexity science is that of self-organization. Self-organization is used to refer to the emergence of stable patterns through autonomous and self-reinforcing dynamics at the micro-level. In spite of its potential relevance for the study of social dynamics, the articulation and use of the concept of self-organization has been kept within the boundaries of complexity science and links to and from mainstream social science are scarce. These links can be difficult to establish, even for researchers working in social complexity with a background in social science, because of the theoretical and conceptual diversity and fragmentation in traditional social science. This article is meant to serve as a first step in the process of overcoming this lack of cross-fertilization between complexity and mainstream social science. A systematic review of the concept of self-organization and a critical discussion of similar notions in mainstream social science is presented, in an effort to help practitioners within subareas of complexity science to identify literature from traditional social science that could potentially inform their research.     

Functions: a modelling tool in mathematics and science

It is difficult for the students to transfer concepts, ideas and procedures learned in mathematics to a new and unanticipated situation in science. An alternative to this traditional transfer method stresses the importance of modelling activities in an interdisciplinary context between mathematics and science. In the paper we introduce a modelling approach to the concept of function in upper secondary school is introduced. We discuss pedagogical and didactical issues concerning the interplay between mathematics and science. The discussion is crystallized into a didactical model for interdisciplinary instruction in mathematics and science. The model is considered as an iterative movement with two phases: (1) the horizontal linking of the subjects: Situations from science are embedded in contexts which are mathematized by the students, (2) the vertical structuring in the subjects: The conceptual anchoring of the students' constructs from the horizontal linking in the systematic and framework of mathematics and science respectively.     

Structuring mathematical knowledge and skills by means of knowledge graphs

The aim of the study reported on in this paper was to develop, test and improve a cognitive tool which could help students structure their mathematical knowledge and skills. Mathematics teaching as an auxiliary subject in the context of secondary or tertiary education courses in other disciplines pays too little attention to the structure of the mathematical concepts presented. For the students, therefore, the network of relationships between these concepts does not become a part of their mathematical knowledge and skills, and is consequently not fully available for purposes of reasoning, proving, mathematicizing and solving problems. Knowledge graphs (KGs) can be used by students as a tool to visualize this structure of the concepts and the relations between them. The learning activity of structuring one's mathematical knowledge and skills can be supported by a model, the Mathematical Knowledge Graph Model (MKGM), which serves as a pre-structured heuristic framework. The elements of this model include a central concept, special cases of this concept, operations or actions on the concept, areas of application and properties of the concepts and operations. The present paper reports on a trial among five students of the Open University of the Netherlands (OUNL), who constructed a KG in accordance with the MKGM model. The paper focuses on the graphs produced by the students, their appreciation of the structuring activity and the relation between their graphs and test results.     

Changing canons of mathematical and physical intelligibility in the later 17th century

Learning to use the new calculus in the late 17th century meant looking at quantities and configurations, and the relationships among them, in fundamentally new ways. In part, as Leibniz argued implicitly in his articles, the new concepts lay along lines established by Viète, Fermat, Descartes, and other “analysts” in their development of algebraic geometry and the theory of equations. But in part too, those concepts drew intuitive support from the new mechanics that they were being used to explicate and that was rapidly becoming the primary area of their application. So it was that the world machine that emerged from the Scientific Revolution could be both mechanically intelligible and mathematically transcendental.     

集值映射的函数单调性和余强制性

本文介绍几个新的集值映射的函数单调性和余强制性概念，并讨论它们与已有概念间各种可能的蕴含关系．     

Learning to teach science in urban schools: Context as content

The purpose of this phenomenological study was to explore how science teachers who persisted in urban schools interpreted and responded to the unique features of urban educational contexts. With 17 alumni who taught in metropolitan areas across seven states, the Science Educators for Urban Schools (SEUS) program provided a research setting that offered a unique view of science teachers’ development of knowledge of urban education contexts. Data sources included narratives of teaching experiences from interviews and open‐ended survey items. Findings were interpreted in light of context knowledge for urban educational settings. Findings indicated that science teaching in urban contexts was impacted by the education policy context, notably through accountability policies that narrowed and marginalized science instruction; community context, evident in teacher efforts to make science more relevant to students; and school contexts, notability their ability to creatively adjust for resource deficiencies and continue their own professional growth. Participants utilized this context knowledge to transform student opportunities to learn science. The study suggests that future science education research and teacher preparation efforts would benefit from further attention to the unique elements of urban contexts, specifically the out of classroom contexts that shape science teaching and learning.     

Re‐creating a Recipe for Science Instructional Programs: Adding Learning Progressions,Scaffolding, and a Dash of Reading Variety

The purpose of this article is to focus on the development and refinement of a science instructional design program arguing for the feasibility and usability of integrated reading and science instruction as implemented in third‐ and fourth‐grade science classrooms to meet the learning needs of diverse learners. These instructional components are easily inserted into existing programs that build students' science background knowledge and abilities to apply learning through scaffolded activities focused on (1) providing structured opportunities for students to engage in hands‐on activities; (2) increasing vocabulary knowledge and understanding of concept‐laden terms, and (3) reading paired narrative and informational science texts. Extensive research shows that as students transition from third to fourth grade and beyond, they are often challenged in science by new vocabulary coupled with new concepts. Active ingredients of our reconceptualized science instructional design program are narrative informational texts, hands‐on science activities, and science textbook(s).     

关于拟概率测度的收敛理论

本文研究了拟概率空间上收敛概念之间的关系这一问题.利用类比的方法,在拟概率空间上提出了一些新的关于拟-随机变量的收敛概念并讨论了这些收敛概念之间的关系,获得了模糊测度下的收敛理论,推广了关于经典测度的收敛概念.     

On special mathematical and computer science methods in medical sciences

In the paper, we review some special methods of our research: survival analysis for ancylopoetica spondylitis, the gravity of the disease, and malignant processes in the human organism. Proceedings of the Seminar on Stability Problems for Stochastic Models, Hajdúszoboszló, Hungary, 1997, Part I.     

Vector Topical Function,Abstract Convexity and Image Space Analysis

In this paper, we introduce a new type of vector topical function. It contains some other categories of topical functions as special cases and can be interpreted as weak separation functions in image space analysis. We establish its envelope result and investigate its properties in the frame of abstract convexity. Then, we present the corresponding conjugation and subdifferential, and observe the relationships among these concepts. Finally, as applications, we obtain some dual results for some vector optimization, where the object is expressed as the difference of vector topical functions.     

Computational and mathematical organization theory: Perspective and directions

Computational and mathematical organization theory is an interdisciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization's, position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally. The second most developed area is organizational learning. This research, however, is more tied to the work in psychology, cognitive science, and artificial intelligence than to general organization theory. Currently there is increased activity in the subareas of organizations and information technology and organizational evolution and change. Advances in these areas may be made possible by combining network analysis techniques with an information processing approach to organizations. Formal approaches are particularly valuable to all of these areas given the complex adaptive nature of the organizational agents and the complex dynamic nature of the environment faced by these agents and the organizations.This paper was previously presented at the 1995 Informs meeting in Los Angeles, CA.     

Collaborative distributed decision making for large scale disaster relief operations: Drawing analogies from robust natural systems

One of the most ignored, but urgent and vital challenges confronting society today is the vulnerability of urban areas to extreme events. Current organization of response systems, predominantly based on a command and control model, limits their effectiveness and efficiency. Particularly, in decision‐making processes where a large number of actors may be involved. In this article, a new distributed collaborative decision‐making model is proposed to overcome command and control limitations encountered in stressful, hostile, chaotic, and large‐scale settings. This model was derived by borrowing concepts from the collective decision making of honeybees foraging, a successful process in solving complex tasks within complex settings. The model introduced in this article was evaluated through differential equations, i.e., continuous analysis, and difference equations, i.e., discrete analysis. The most important result found is that the best available option in any large‐scale decision‐making problem can be configured as an attractor, in a distributed and timely manner. We suggest that the proposed model has the potential to facilitate decision‐making processes in large‐scale settings. © 2005 Wiley Periodicals, Inc. Complexity 11:28–38, 2005     

Multiplex Multi-Core Pattern of Network Organizations: An Exploratory Study

Recent research on small-world theory has been expanded upon organizations behavior. However, most individual studies have taken a simplified view of individuals and relationships among members by focusing on a single type of links mainly within dyadic relationships. In reality, members of organizations are interacting with each other and often connected via many types of links within more complex relationships. To explore this complex interaction, this study models organization as network and proposes a multiplex approach that captures the complexity of relationships among members in organizations. This approach accounts for the multiple types of links among members and the multiple roles of members within network organizations. It is illustrated via a case study of a network organization. The case study demonstrates how this approach could capture the many types of relationship among members as well as the various roles that members play within the network organization. Such an approach can yield new insights on how to better manage network organizations.     

The mathematical theory of endosymbiosis I

This work presents a new model of the evolutionary process formulated by the Serial Endosymbiosis Theory represented by a succession of stages involving different metabolic and ecological interactions among populations of bacteria considering both the population dynamics and production processes of these populations. In such an approach we make use of systems of differential equations known as Volterra–Hamilton systems as well as some geometric concepts involving KCC Theory and the Projective Geometry of Berwald Spaces and also correct a statement of M. Matsumoto in the literature on this topic. We also recount in some detail previous work comparing production stability of Endosymbiosis Theory with that of Ancestral Commune Theory.     

Preparing New Science Teachers for Urban Classrooms : Consensus Within an Expert Community

Preparing future science teachers for U.S. city classrooms is an important yet poorly understood process. The purpose of this study was to determine the philosophies and practices of university ‐based science educators associated with programs supplying teachers for metropolitan school systems. Through an iterative process of mailed questionnaires, 20 participants rated their views on issues pertinent to science teacher education. The responses to questionnaires were used in the creation of items for each subsequent round. The three rounds of questionnaires contained Likert ‐scale and open‐ended questions. For many issues, there was consistently high consensus among the expert panelists, including the presence of students for whom English is a second or new language, the importance of science education professors remaining connected to urban school issues, and practices often affiliated with reform (e.g., alternative assessment, nature of science). Several issues emerged as having low regard by the participants, including the role of student ethnicity on teaching strategies, providing instruction about reading strategies within science teacher preparation, and the value of professors having themselves taught science in urban settings.     

Management science in the era of smart consumer products: challenges and research perspectives

Smart products not only provide novel functionalities, but also may establish new business models, markets, or distribution channels, strengthen relationships with consumers, and/or add smart remote services. While many technical obstacles of such products have already been overcome, the broad market dissemination of smart products still poses some vital managerial challenges for decision makers. In this paper, we outline the technical potential and future trends of smart consumer products, discuss economic challenges in four scopes, namely, preference-based new product development, market analysis, supply chain design, and industry development, and, in particular, we highlight research perspectives for management science in this promising field.     

Recursive estimation: A unified approach to the identification estimation,and forecasting of hydrological systems

Mathematical models of hydrological and water-resource systems have been formulated in many different ways and with various levels of complexity. There are advantages to be gained, therefore, by trying to unify some of the more common models within a statistical framework which will allow for more objective methods of model calibration. In this paper, we consider the general class of linear, dynamic models, as applied to the characterisation of flow and dispersion behavior in rivers, and show how these can be unified within the context of recursive time-series analysis and estimation. This allows not only for more objective, data-based approaches to stochastic model structure identification, but also for improved statistical estimation and the development of both constant parameter and self-adaptive, Kalman-filter-based forecasting procedures. The unified approach presented in the paper is being applied successfully in other environmental areas, such as soil science, climatic data analysis, meterological forecasting, and plant physiology.