Robert M Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton studies in complexity (Princeton, N.J: Princeton University Press, 1997).
This book builds upon Axelrod's earlier work on cooperation--specifically using the Prisoner's Dilemma--by adding complexity to the mix. "Adding complexity to that framework allows the exploration of many interesting and important features of competition and collaboration that are beyond the reach of the Prisoner's Dilemma paradigm" (3).
"Complexity theory involves the study of many actors and their interactions. The actors may be atoms, fish, people, organizations or nations...a primary tool of complexity theory is computer simulation" (3).
"Agent-based modeling is a third way of doing science. Like deduction, it starts with a set of explicitly assumptions. But unlike deduction, it does not prove theorems. Instead, an agent-based model generates simulated data that can be analyzed inductively. Unlike typical induction, however, the simulated data come from a rigorously specified set of rules rather than direct measurement of the real world. Whereas the purpose of induction is to find patterns in data and that of deduction to find consequences of assumptions, the purpose of agent-based modeling is to aid intuition" (3-4).
Showing posts with label Complexity Theory. Show all posts
Showing posts with label Complexity Theory. Show all posts
Tuesday, March 2, 2010
Wednesday, February 24, 2010
Clemens: Complexity Theory as a Tool for Understanding and Coping with Ethnic Conflict and Development Issues in Post-Soviet Eurasia
Clemens, WC. 2002. “Complexity Theory as a Tool for Understanding and Coping with Ethnic Conflict and Development Issues in Post-Soviet Eurasia.” International Journal of Peace Studies 7(2): 1-16.
“This paper contends that movement toward or away from resolution of ethnic problems in newly independent states can be more fully explained [than does Snyder, 2000 and the focus on democratization] by concepts derived from complexity theory. These concepts do not contradict explanations rooted in democratization but enrich them and offer linkages to other fields of knowledge. They start with a wider lens than democratization but include it. The concept of societal fitness, a major concern of complexity theory, subsumes political, economic, and cultural strengths. The precise role played by each strength in shaping societal fitness becomes an important but secondary question” (2).
“Generated by scholars from various disciplines, complexity theory integrates concepts from many fields to produce a new slant on evolution. Its exponents seek a general theory able to explain many different types of phenomena” (2).
“The analysis here suggests that complexity theory can enhance our ability to describe and explain the past and present. But the theory has much less utility for projecting alternative futures or prescribing policy. Still, complexity theory can enlarge our vision and complement other approaches to social science” (2).
“Complexity theory is anchored in nine basic concepts: fitness, coevolution, emergence, agent-based systems, self-organization, self-organized criticality, punctuated equilibrium and fitness landscapes” (3).
Fitness: how well does a system deal with complexity? All systems are on a range from highly unstable to highly stable, where fitness is located in the middle.
Coevolution: Everything evolves together, and the more connections that there are, the more difficult it is to understand what is happening.
Emergence: Macro complexity arising from micro complexity.
Agent-Based Systems: Systems where emergence comes from the behavior of individual units.
Self-Organization: The system organizes to create fitness.
Self-Organized Criticality: Claimed to not be essential to complexity theory, but posits a system that exists directly between order and chaos, where it can slip into chaos quicly.
Punctuated Equilibrium: Tipping points, where extinctions happen, mutations etc.
Fitness Landscapes: Fitness of different groups as they coevolve. Thus, the fitness of one group can be negatively or positively impacted by actions taken by another group.
The paper argues that certain countries demonstrate high levels of fitness, as can be seen in HDI scores. Other countries had lower levels of fitness. These countries had different histories from the countries experiencing high levels of fitness, and dealt with minority groups less well. Self-organization takes in democratic politics, market economies and the media. Coevolution explains that countries close to the West evolve with them. Emergence is seen in agreements and regional groups. Agent-Based systems in the fit groups, agents are free. Self-organized Criticality: some countries might not be as stable as they appear! Punctuated equilibrium: don’t expect steady progress. Fitness Landscapes: it might be possible to say something here, maybe not.
“The fundamental insight of complexity theory is its prediction that fitness will be found along the middle range of a spectrum ranging from rigid order to the other extreme—chaos. This insight helps explain why Central Asia is frozen in time, why the Caucasus explodes and why Russia resorts to an iron fist to overcome chaos” (10).
“This paper contends that movement toward or away from resolution of ethnic problems in newly independent states can be more fully explained [than does Snyder, 2000 and the focus on democratization] by concepts derived from complexity theory. These concepts do not contradict explanations rooted in democratization but enrich them and offer linkages to other fields of knowledge. They start with a wider lens than democratization but include it. The concept of societal fitness, a major concern of complexity theory, subsumes political, economic, and cultural strengths. The precise role played by each strength in shaping societal fitness becomes an important but secondary question” (2).
“Generated by scholars from various disciplines, complexity theory integrates concepts from many fields to produce a new slant on evolution. Its exponents seek a general theory able to explain many different types of phenomena” (2).
“The analysis here suggests that complexity theory can enhance our ability to describe and explain the past and present. But the theory has much less utility for projecting alternative futures or prescribing policy. Still, complexity theory can enlarge our vision and complement other approaches to social science” (2).
“Complexity theory is anchored in nine basic concepts: fitness, coevolution, emergence, agent-based systems, self-organization, self-organized criticality, punctuated equilibrium and fitness landscapes” (3).
Fitness: how well does a system deal with complexity? All systems are on a range from highly unstable to highly stable, where fitness is located in the middle.
Coevolution: Everything evolves together, and the more connections that there are, the more difficult it is to understand what is happening.
Emergence: Macro complexity arising from micro complexity.
Agent-Based Systems: Systems where emergence comes from the behavior of individual units.
Self-Organization: The system organizes to create fitness.
Self-Organized Criticality: Claimed to not be essential to complexity theory, but posits a system that exists directly between order and chaos, where it can slip into chaos quicly.
Punctuated Equilibrium: Tipping points, where extinctions happen, mutations etc.
Fitness Landscapes: Fitness of different groups as they coevolve. Thus, the fitness of one group can be negatively or positively impacted by actions taken by another group.
The paper argues that certain countries demonstrate high levels of fitness, as can be seen in HDI scores. Other countries had lower levels of fitness. These countries had different histories from the countries experiencing high levels of fitness, and dealt with minority groups less well. Self-organization takes in democratic politics, market economies and the media. Coevolution explains that countries close to the West evolve with them. Emergence is seen in agreements and regional groups. Agent-Based systems in the fit groups, agents are free. Self-organized Criticality: some countries might not be as stable as they appear! Punctuated equilibrium: don’t expect steady progress. Fitness Landscapes: it might be possible to say something here, maybe not.
“The fundamental insight of complexity theory is its prediction that fitness will be found along the middle range of a spectrum ranging from rigid order to the other extreme—chaos. This insight helps explain why Central Asia is frozen in time, why the Caucasus explodes and why Russia resorts to an iron fist to overcome chaos” (10).
Monday, June 29, 2009
Moeller: Luhmann Explained
Moeller, H. 2006. Luhmann Explained. Chicago: Open Court.
Excellent overview of Luhmann's Systems Theory. Outlines all key concepts, roots analysis in historical processes, focuses on functional differentiation, explores globalization and then analyzes mass media. I will not present an outline of this as most of this has been copied in other abstracts.
Excellent overview of Luhmann's Systems Theory. Outlines all key concepts, roots analysis in historical processes, focuses on functional differentiation, explores globalization and then analyzes mass media. I will not present an outline of this as most of this has been copied in other abstracts.
Friday, June 12, 2009
Phelan: What is Complexity Science, Really?
Phelan, SE. 2001. What is complexity science, really? Emergence 3, no. 1: 120-136.
"It is my contention that much of the work in complexity theory has indeed been pseudo-science, that is, many writers in this field have used the symbols and methods of complexity science...to give the illusion of science even though they lack supporting evidence and plausibility" (120).
"The purpose of this article is twofold: to provide a working definition of complexity science; and to use this definition to differentiate complexity science from complexity pseudo-science" (120).
The author then explores arguments in philosophy of science, moving from empiricism, to positivism, to historicism, to constructivism. The author argues that complexity theory offers a new way of studying regularities that is methodologically different from previous science. Science has always been about reducing complexity.
"Complexity science posits simple causes for complex effects. At the core of complexity science is the assumption that complexity in the world arises from simple rules. However, these rules...are unlike rules...of traditional science. Generative rules typically determine how a set of artificial agents will behave in their virtual environment over time, including their interaction with other agents. Unlike traditional science, generative rules do not predict an outcome for every state of the world. Instead, generative rules use feedback and learning algorithms to enable the agent to adapt to its environment over time" (130-1).
Three things that complexity science is not: general systems theory, a postmodern science and a set of "metaphores or analogies based on resemblance thinking" (132).
"Complexity science, defined earlier as the search for generative rules, does not embrace the radical holism of systems theory. Complexity scientists are seeking simple rules that underpin complexity...In contrast, systems theory almost seems to surrender to complexity because it is not particularly interested in the identification of regularities. Regularities do not exist in open systems, almost by definition.
"It is my contention that much of the work in complexity theory has indeed been pseudo-science, that is, many writers in this field have used the symbols and methods of complexity science...to give the illusion of science even though they lack supporting evidence and plausibility" (120).
"The purpose of this article is twofold: to provide a working definition of complexity science; and to use this definition to differentiate complexity science from complexity pseudo-science" (120).
The author then explores arguments in philosophy of science, moving from empiricism, to positivism, to historicism, to constructivism. The author argues that complexity theory offers a new way of studying regularities that is methodologically different from previous science. Science has always been about reducing complexity.
"Complexity science posits simple causes for complex effects. At the core of complexity science is the assumption that complexity in the world arises from simple rules. However, these rules...are unlike rules...of traditional science. Generative rules typically determine how a set of artificial agents will behave in their virtual environment over time, including their interaction with other agents. Unlike traditional science, generative rules do not predict an outcome for every state of the world. Instead, generative rules use feedback and learning algorithms to enable the agent to adapt to its environment over time" (130-1).
Three things that complexity science is not: general systems theory, a postmodern science and a set of "metaphores or analogies based on resemblance thinking" (132).
"Complexity science, defined earlier as the search for generative rules, does not embrace the radical holism of systems theory. Complexity scientists are seeking simple rules that underpin complexity...In contrast, systems theory almost seems to surrender to complexity because it is not particularly interested in the identification of regularities. Regularities do not exist in open systems, almost by definition.
Axelrod and Cohen: Harnessing Complexity
Axelrod, RM, and MD Cohen. 2001. Harnessing complexity: Organizational implications of a scientific frontier. Basic Books.
Complex Adaptive Systems are systems where there are a large number of participants interacting in various ways. There may be a wide variety of participants. Old patterns may continue forward, or they may change quickly.
The goal is not to try to overcome complexity, but to harness complexity.
The authors claim to make important contributions to the field of complexity: "These include the critical role of nonrandom interactions in adaptation, the contrast of biological with informational copying, the relationships between credit allocation and measures of performance" (xii-xiii).
"In our analysis there are there key processes in a Complex Adaptive System. These key processes provide the basis of our three central chapters: Variation, Interaction, and Selection. We see variation, interaction, and selection as interlocking sets of concepts that can generate productive actions in a world that cannot be fully understood. We show how the very complexity that makes the world hard to understand provides opportunities and resources for improvement over time" (xv).
In the first chapter of this pop book, the vocabulary of the framework is explicated: agents, strategy (how agents respond to environment), population, system, selection, adaptation, co-evolution (4-8).
Complex Adaptive Systems make prediction difficult if not impossible.
"Agents, of a variety of types, use their strategies, in patterned interaction, with each other and with artifacts. Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination, thus changing the frequencies of the types within the system" (154).
Complex Adaptive Systems are systems where there are a large number of participants interacting in various ways. There may be a wide variety of participants. Old patterns may continue forward, or they may change quickly.
The goal is not to try to overcome complexity, but to harness complexity.
The authors claim to make important contributions to the field of complexity: "These include the critical role of nonrandom interactions in adaptation, the contrast of biological with informational copying, the relationships between credit allocation and measures of performance" (xii-xiii).
"In our analysis there are there key processes in a Complex Adaptive System. These key processes provide the basis of our three central chapters: Variation, Interaction, and Selection. We see variation, interaction, and selection as interlocking sets of concepts that can generate productive actions in a world that cannot be fully understood. We show how the very complexity that makes the world hard to understand provides opportunities and resources for improvement over time" (xv).
In the first chapter of this pop book, the vocabulary of the framework is explicated: agents, strategy (how agents respond to environment), population, system, selection, adaptation, co-evolution (4-8).
Complex Adaptive Systems make prediction difficult if not impossible.
"Agents, of a variety of types, use their strategies, in patterned interaction, with each other and with artifacts. Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination, thus changing the frequencies of the types within the system" (154).
Thursday, June 11, 2009
Clemens: Complexity Theory as a Tool for Understanding and Coping with Ethnic Conflict and Development Issues in Post-Soviet Eurasia
Clemens, WC. 2002. Complexity Theory as a Tool for Understanding and Coping with Ethnic Conflict and Development Issues in Post-Soviet Eurasia. International Journal of Peace Studies 7, no. 2: 1-16.
This piece uses complexity theory to explore why some central Asian transitioned away from the USSR more peacefully than others. The focus of analysis is on the concept of "fitness".
"Complexity theory is anchored in nine basic concepts: fitness, coevolution, emergence, agent-based systems, self-organization, self-organized criticality, punctuated equilibrium, and fitness landscapes (Lewin, 1992; Kauffman, 1993, 1995, 2000; Axelrod, 1997; Axelrod and Cohen, 1999; Lewin and Regine, 2000; Richards, 2000)" (3).
Fitness, as a concept drawn from complexity theory, emphasizes how the organizational structure of a system determines its ability to cope with change. Organizational structures can vary from very chaotic to very hierarchically ordered. Fit systems are those that fall between chaos and rigidity.
This piece uses complexity theory to explore why some central Asian transitioned away from the USSR more peacefully than others. The focus of analysis is on the concept of "fitness".
"Complexity theory is anchored in nine basic concepts: fitness, coevolution, emergence, agent-based systems, self-organization, self-organized criticality, punctuated equilibrium, and fitness landscapes (Lewin, 1992; Kauffman, 1993, 1995, 2000; Axelrod, 1997; Axelrod and Cohen, 1999; Lewin and Regine, 2000; Richards, 2000)" (3).
Fitness, as a concept drawn from complexity theory, emphasizes how the organizational structure of a system determines its ability to cope with change. Organizational structures can vary from very chaotic to very hierarchically ordered. Fit systems are those that fall between chaos and rigidity.
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