Is nature quantum non-local,complex holistic,or what?: II — Applications

In Part I of this paper [A. Sengupta, Is nature quantum non-local, complex holistic, or what? I–Theory & analysis. Nonlinear Anal.: RWA (2009) (in press)] to be referenced “I-”, we examined the linear–nonlinear divide of the natural world in an attempt to seek a rationale for the question “Is nature interactively nonlinear and holistic, or is it additively linear and reductionist?”: Is Nature governed by entanglements of linear superposition or does it represent the nonlinear holism of emergence, self-organization, and complexity? This second part carries the debate forward to propose that Quantum Mechanics is an effective linear representation of a fully chaotic, maximally illposed, multifunctional negworld that obviously is not just a mirror image of the functional real world we inhabit: in fact we argue that nonlinear complex holism represents a stronger form of entanglement than linear quantum non-locality. The bi-directionality of a self-organized, emergent, engine-pump system is analyzed with reference to the role of gravity as the compressive agent responsible for generation and maintenance of structures and life in Nature; we also explore the applicability of chanoxity to the metaphorical resolution of some of the long-standing paradoxes and puzzles in quantum measurement and non-locality, in Prigoginian intrinsic irreversibility, and in some core issues in cosmology and gravitational black holes.Holism is to be seen as complementing mainstream reductionism–linear science has after all stood the test of the last 400 years as quantum mechanics is acknowledgedly one of the most successful yet possibly one of the most mysterious of scientific theories: the success lies in its capacity to classify and predict the physical world, the mystery in what this physical world must be like to behave quantum mechanically–providing a unified picture of the dialectics of the evolutionary dynamics of Nature.     

A methodology for conducting interdisciplinary social research

The Vienna Circle idea that science is an interdisciplinary enterprise leads to the question of how the knowledge of the natural sciences can be used to further understanding in the social sciences. Analysis of the practice of social research shows there is no easy answer to this question. Ideologies colour the use of exact knowledge in social research methods; even in the natural sciences ideological misunderstandings seem inevitable in research practice. The concept of strangification is introduced to describe this situation and to give a framework for a methodology to handle this problem of scientific regression. It is applied to the problem how the mathematical theory of complexity can be used in the social sciences to understand complex social phenomena.     

A mathematical view of biological complexity

This essay is a trial on giving some mathematical ideas about the concept of biological complexity, trying to explore four different attributes considered to be essential to characterize a complex system in a biological context: decomposition, heterogeneous assembly, self-organization, and adequacy. It is a theoretical and speculative approach, opening some possibilities to further numerical and experimental work, illustrated by references to several researches that applied the concepts presented here.     

基于概念的数学系统及其结构

随着科学技术的发展，应用定量分析的数学方法已从自然科学发展到社会科学、思堆科学．为了处理这些问题的需要，许多学者建立了多种数学模型和数学方法，这些模型和方法都直接或间接地涉及到概念，因此归纳并研究基于概念的数学方法显得很有必要。本文应用系统的方法，尝试络出数学系境的概念，并建立了基于概念的数学系统及其结构的一般方法，期望更多的学者予以关注和研究。     

Dissonance Minimization as a Microfoundation of Social Influence in Models of Opinion Formation

Models of opinion formation are used to investigate many collective phenomena. While social influence often constitutes a basic mechanism, its implementation differs between the models. In this article, we provide a general framework of social influence based on dissonance minimization. We only premise that individuals strive to minimize dissonance resulting from different opinions compared to individuals in a given social network. Within a game theoretic context, we show that our concept of dissonance minimization resembles a coordination process when interactions are homogeneous. We further show that different models of opinion formation can be represented as best response dynamics within our framework. Thus, we offer a unifying perspective on these heterogeneous models and link them to rational choice theory.     

Social network topology: a Bayesian approach

Elucidating the pattern of links within social networks is a challenging problem. Of particular difficulty is determining the existence of links in those groups that take active measures to conceal their internal connections, such as terrorist or criminal organizations where conventional social network analysis data-gathering techniques cannot be applied. Network representations of such organizations are useful, because they often represent a useful point of departure in thinking both about the potential capabilities of organizations and how to conduct effective measures to counter them. Developing an effective process for constructing such network representations from incomplete and limited data of variable quality is a topic of much current interest. Here, a method based on Bayesian inference is presented that probabilistically infers the existence of links within a social network. It is tested on data from open source publications. Additionally, the method represents a possible approach to dynamically modelling networks, as it is feasible to calculate how a network will reconfigure following an intervention.     

Weakly Connected Domination in Graphs

In the last 50 years, Graph theory has seen an explosive growth due to interaction with areas like computer science, electrical and communication engineering, Operations Research etc. Perhaps the fastest growing area within graph theory is the study of domination, the reason being its many and varied applications in such fields as social sciences, communication networks, algorithm designs, computational complexity etc. Henda C. Swart has rightly commented that the theory of domination in graphs is like a ‘growth industry’. There are several types of domination depending upon the nature of domination and the nature of the dominating set. In the following, we present weakly connected domination in connected graphs.     

Structure and complexity in economic and social systems

An attempt is made to outline a theory of complexity for economics and social science purposes. A methodology is outlined that realizes complexity as an analytical category, and develops a comprehensive theory of structural forms, structural change and qualitative systems behavior based on these methods. Examples of different nature are given to support the need for such a theory of complexity.     

ISSUE INFORMATION

Modeling social‐ecological systems is difficult due to the complexity of ecosystems and of individual and collective human behavior. Key components of the social‐ecological system are often over‐simplified or omitted. Generalized modeling is a dynamical systems approach that can overcome some of these challenges. It can rigorously analyze qualitative system dynamics such as regime shifts despite incomplete knowledge of the model's constituent processes. Here, we review generalized modeling and use a recent study on the Baltic Sea cod fishery's boom and collapse to demonstrate its application to modeling the dynamics of empirical social‐ecological systems. These empirical applications demand new methods of analysis suited to larger, more complicated generalized models. Generalized modeling is a promising tool for rapidly developing mathematically rigorous, process‐based understanding of a social‐ecological system's dynamics despite limited knowledge of the system.     

Dependence Graphs: Dependence Within and Between Groups

This paper applies the two-party dependence theory (Castelfranchi, Cesta and Miceli, 1992, in Y. Demazeau and E. Werner (Eds.) Decentralized AI-3, Elsevier, North Holland) to modelling multiagent and group dependence. These have theoretical potentialities for the study of emerging groups and collective structures, and more generally for understanding social and organisational complexity, and practical utility for both social-organisational and agent systems purposes. In the paper, the dependence theory is extended to describe multiagent links, with a special reference to group and collective phenomena, and is proposed as a framework for the study of emerging social structures, such as groups and collectives. In order to do so, we propose to extend the notion of dependence networks (applied to a single agent) to dependence graphs (applied to an agency). In its present version, the dependence theory is argued to provide (a) a theoretical instrument for the study of social complexity, and (b) a computational system for managing the negotiation process in competitive contexts and for monitoring complexity in organisational and other cooperative contexts.     

基于信息量度量的企业管理复杂性评价方法——神东煤炭公司管理复杂性评价

应用物理学、数学、力学、信息论、统计学和管理学等理论方法 ,提出了管理力、管理功和管理复杂度的基本概念、定义、基本原理和研究范围 ;并基于管理熵理论的基本原理 ,建立了企业管理系统复杂性评价的新尺度和评价方法 ;构造了尺度的矢量空间、数学模型及量化模型 ;对神东煤炭集团公司的企业组织管理系统复杂性进行了实例分析与评价 .     

Justifying the methods of OR

Operational research practitioners use mathematical, statistical, scientific, and other methods to structure and analyse issues in order to advise and assist their clients. In doing so they apply values, follow rules and use methodologies. The paper examines the justification of these methods, values and methodologies. Starting with a conceptual model drawn from the philosophy of science, a justification framework is developed for operational research (OR). Making a distinction between OR academic research and OR practice helps to clarify the issues. OR research is similar to scientific, mathematical and social science research; OR practice, as technology, is closer to engineering. While OR academic researchers will seek justification in the academic discipline within which they choose to work, it is argued that the justification of OR practice lies in its usefulness. For academic OR, justification lies in the justification of mathematics, statistics, science and social science; for practice, it is practitioners who decide what usefulness means in their context.     

The evolution of convention: Conformity and innovation in task-oriented networks

The individual choice between conformity and innovation within task-oriented collectivities is presented as a social dilemma. Adaptive, network-embedded actors are seen to modify their propensities to conform or innovate retrospectively, based on performance differences between the individual and task group levels. A computational framework, based on simulation techniques and algorithmic complexity theory, is advanced to investigate the impact of antecedent structural conditions on innovative behavior and the effect that such behavior has on the evolution of patterns of interaction (conventions) and efficiency. Findings indicate that the dynamics of innovation differ dramatically based on the complexity of tasks faced by actors. When simple tasks are addressed, innovative behaviors have a destabilizing effect on social conventions and are clearly linked to contextual factors. When complex tasks are involved, commitment to innovation may actually help reinforce conventions and is not tied to structural antecedents.Department of Sociology, Stanford University     

Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”

The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis. We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective. We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels. We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles. Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication. The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure. Jana Diesner is a Research Associate and Linguistic Programmer at the Center for Computational Analysis of Social and Organizational Systems at the School of Computer Science (CASOS), Carnegie Mellon University (CMU). She received her Masters in Communications from Dresden University of Technology in 2003. She had been a research scholar at the Institute for Complex Engineered System at CMU in 2001 and 2002. Her research combines computational linguistics, social network analysis and computational organization theory. Terrill L. Frantz is a post-doc researcher at the Center for Computational Analysis of Social and Organizational Systems (CASOS) in the School of Computer Science at Carnegie Mellon University. His research involves studying the dynamics of organization social-networks and behavior via computer modeling and simulation. He is developing an expertise in workforce integration strategy and policy evaluation during organization mergers. He earned his doctorate (Ed.D. in Organization Change) from Pepperdine University, a MBA from New York University and a BS in Business Administration (Computer Systems Management) from Drexel University. Prior to entering academic research, for nearly 20 years he was a software applications development manager in the global financial services and industrial chemicals industries; most recently as a Vice President in Information Technology at Morgan Stanley in Hong Kong, New York and London. Kathleen M. Carley is a professor at the Institute for Software Research International in the School of Computer Science at Carnegie Mellon University. She is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) <http://www.casos.cs.cmu.edu/>, a university wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu) and has an associated NSF funded training program for Ph.D. students. She carries out research that combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are computational social and organization theory, group, organizational and social adaptation and evolution, social and dynamic network analysis, computational text analysis, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations.     

Cell state change dynamics in cellular automata

Cellular automata are discrete dynamical systems having the ability to generate highly complex behaviour starting from a simple initial configuration and set of update rules. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. This task is not easily achieved since coordinated global information processing must rise from the interactions of simple components with local information and communication. In this paper, a fast supporting heuristic of linear complexity is proposed to encourage the development of rules characterized by increased dynamics with regard to cell state changes. This heuristic is integrated in an evolutionary approach to the density classification task. Computational experiments emphasize the ability of the proposed approach to facilitate an efficient exploration of the search space leading to the discovery of complex rules situated beyond the simple block-expanding rules.     

Is nature quantum non-local,complex holistic,or what?, I: Theory & analysis

Is Quantum Mechanics a general theory that applies to everything from subatomic particles to galaxies; is Nature governed by entanglements of linear superposition in Hilbert space or is it an expression of the nonlinear holism of emergence, self-organization, and complexity? This paper explores these issues in the context of extensions of the recently formulated approach to Chaos-Nonlinearity-compleXity [A. Sengupta, Chaos, nonlinearity, complexity: A unified perspective, in: A. Sengupta (Ed.), Chaos, Nonlinearity, and Complexity: The Dynamical Paradigm of Nature, in: StudFuzz, vol. 206, Springer-Verlag, Berlin, 2006, pp. 270–352] and proposes that Nature manifests itself only through a bi-directional, contextually objective adaptation of the Second Law of Thermodynamics. The mathematics developed is that of convergence in topological spaces, multifunctions, exclusion topology, and discontinuities, jumps, and multiplicities induced by difference equations rather than the smoothness and continuity of the Newtonian world of differential equations.In the second Part of this paper [A. Sengupta, Is Nature Quantum Non-local, Complex Holistic, or What? II—Applications, Nonlinear Analysis: RWA (2009), in press (doi:10.1016/j.nonrwa.2008.09.002)] to be referenced “II-”, we examine the bi-directionality of a self-organized, emergent, engine-pump system with specific reference of the role of gravity as the compressive agent responsible for generation and sustenance of the delicate homeostatic balance in life, structures, and patterns appearing in this Participatory Universe. Holism of “systems and processes that interact with themselves and produce themselves from themselves” calls for radically different techniques and philosophy from that adapted in the mainstream reductionist Newtonian paradigm that we are accustomed to.     

Social theory and system dynamics practice

This paper explores the social theories implicit in system dynamics (SD) practice. Groupings of SD practice are observed in different parts of a framework for studying social theories. Most are seen to be located within `functionalist sociology'. To account for the remainder, two new forms of practice are discussed, each related to a different paradigm. Three competing conclusions are then offered:

• 1.The implicit assumption that SD is grounded in functionalist sociology is correct and should be made explicit.
• 2.Forrester's ideas operate at the level of method not social theory so SD, though not wedded to a particular social theoretic paradigm, can be re-crafted for use within different paradigms.
• 3.SD is consistent with social theories which dissolve the individual/society divide by taking a dialectical, or feedback, stance. It can therefore bring a formal modelling approach to the `agency/structure' debate within social theory and so bring SD into the heart of social science.

The last conclusion is strongly recommended.     

Using fuzzy integral for evaluating subjectively perceived travel costs in a traffic assignment model

A good traffic assignment model can be a powerful tool to describe the characteristics of traffic behavior in a road network. The traffic assignment results often play an important role in transportation planning, e.g., an optimal and economical network design. Many traditional traffic assignment models rely heavily on the travel cost function established by Wardrop’s principles; however, the Wardrop’s travel cost function has been proven to be weak for explaining the uncertainty and interactivity of traffic among links. This study tries to construct a traffic assignment model that is different from Wardrop’s in many aspects. First, it considers the cross-effect among the links. Second, a fuzzy travel cost function is established based on the possibility concept instead of precise calculation of traffic volumes. Third, the techniques of fuzzy measure and fuzzy integral are applied to calculate the subjectively perceived travel costs during traffic assignment. Furthermore, in order to validate our model, a detailed network with 22 nodes and 36 links is used to illustrate it. Study results show that our model explains more interactivity and uncertainty of traffic among links when compared with the traditional model of Wardrop’s.     

Structural heterogeneity mediates the effect of community structure on cooperation

Emergence of cooperation in evolutionary prisoner's dilemma game strongly depends on the topology of underlying interaction network. We explore this dependence using community networks with different levels of structural heterogeneity, which are generated by a tunable upper‐bound on the total number of links that any vertex can have. We study the effect of community structure on cooperation by analyzing a finite population analogue of the evolutionary replicator dynamics. We find that structural heterogeneity mediates the effect of community structure on cooperation. In the community networks with low level of structural heterogeneity, community structure has negative effect on cooperation. However, the positive effect of community structure on cooperation appears and enhances with increasing structural heterogeneity. Our work may be helpful for understanding the complexity of cooperative behaviors in social networks. © 2011 Wiley Periodicals, Inc. Complexity, 2012