Harnessing Complexity: Organizational Implications of a Scientific FrontierSimon and Schuster, 12/05/2000 - 208 من الصفحات Recent advances in the study of complexity have given scientists profound new insights into how natural innovation occurs and how its power can be exploited. Now two pioneers in the field, Robert Axelrod and Michael D. Cohen, provide leaders in business and government with a guide to complexity that will help them make effective decisions in a world of rapid change. Building on evolutionary biology, computer science, and social design, Axelrod and Cohen have constructed a unique framework for improving the way people work together. Their approach to management is based on the concept of the Complex Adaptive System, which can describe everything from rain forests to the human gene pool, and from automated software agents to multinational companies. The authors' framework reveals three qualities that all kinds of managers must cultivate in their organization:
This simple, paradigm-shifting analysis of how people work together will transform the way we think about getting things done in a group. Harnessing Complexity is the essential guide to creating wealth, power, and knowledge in the 21st century. |
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الصفحة ix
... Types 58 III . Interaction 62 The Importance of Interaction 62 Example : Social Capital How Interaction Works 64 68 Proximity and Activation 68 Spaces : Physical and Conceptual Example : Combating the AIDS Virus , Part 1 External ...
... Types 58 III . Interaction 62 The Importance of Interaction 62 Example : Social Capital How Interaction Works 64 68 Proximity and Activation 68 Spaces : Physical and Conceptual Example : Combating the AIDS Virus , Part 1 External ...
الصفحة 4
... type . ) An agent has the ability to interact with its environment , in- cluding other agents . An agent can respond to what happens around it and can do things more or less purposefully . Most commonly , we think of an agent as a ...
... type . ) An agent has the ability to interact with its environment , in- cluding other agents . An agent can respond to what happens around it and can do things more or less purposefully . Most commonly , we think of an agent as a ...
الصفحة 5
... type become more ( or less ) common in a population . For example , “ aggressive " and " low- key " might be types of sales strategies that a particular firm distin- guishes . Another firm might distinguish " recurring ” from “ onetime ...
... type become more ( or less ) common in a population . For example , “ aggressive " and " low- key " might be types of sales strategies that a particular firm distin- guishes . Another firm might distinguish " recurring ” from “ onetime ...
الصفحة 6
... types by grade levels . For other purposes , genders might be the relevant types . Our rough criterion for the boundaries of a population will be that two agents are in the same population if one agent could employ a strategy used by ...
... types by grade levels . For other purposes , genders might be the relevant types . Our rough criterion for the boundaries of a population will be that two agents are in the same population if one agent could employ a strategy used by ...
الصفحة 9
... type of strategy . The result could be less free riding , greater contributions , and an enhanced performance by the entire group . The team member harnessed the complexity of the sys- tem by taking advantage of the fact that visible ...
... type of strategy . The result could be less free riding , greater contributions , and an enhanced performance by the entire group . The team member harnessed the complexity of the sys- tem by taking advantage of the fact that visible ...
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actions activation agents or strategies approach artifacts attributing credit attribution of credit barriers biological Carl Simon chapter Cohen Complex Adaptive Systems complex systems complexity research conceptual space consequences context cooperation copying costs create criteria dynamics economic effective elements example exploitation exploration factors failures forkball framework genetic genetic algorithm Grameen banking HARNESSING COMPLEXITY ideas imitation important improvement increase Information Revolution interaction patterns interventions kind learning Linux measures of success mechanisms ment military natural selection neighborhood networks open source operating system organizational organizations patterns of interaction performance measures physical space policy makers populations of agents possible prediction problem processes proximity random recombining result Riolo risk Robert Axelrod selection self-organized criticality signal simulation situations social capital social systems spread structure tags teraction tion Tit for Tat tive trade-off types University Press users variation variety virus
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الصفحة 65 - These communities did not become civic simply because they were rich. The historical record strongly suggests precisely the opposite: they have become rich because they were civic. The social capital embodied in norms and networks of civic engagement seems to be a
الصفحة 65 - First, networks of civic engagement foster sturdy norms of generalized reciprocity: I'll do this for you now in the expectation that down the road you or someone else will return the favor.
الصفحة 64 - social capital," the features of social organization, such as networks, norms, and trust, that facilitate coordination and cooperation.
الصفحة 64 - are engaged by public issues, not by patronage. They trust one another to act fairly and obey the law. Social and political networks are organized horizontally, not hierarchically. These "civic communities" value solidarity, civic participation, and integrity.
الصفحة 26 - among their elements. This, of course, is exactly what the Information Revolution is doing: reducing the barriers to interaction among processes that were previously isolated from each other in time or space. Information can be understood as a mediator of interaction. Decreasing the costs of its propagation and storage inherently increases possibilities for interaction effects. An Information Revolution is therefore likely to beget a complexity revolution.
الصفحة 7 - a system is complex when there are strong interactions among its elements, so that current events heavily influence the probabilities of many kinds of later events.
الصفحة 15 - complexity" does not simply denote "many moving parts." Instead, complexity indicates that the system consists of parts which interact in ways that heavily influence the probabilities of later events.
الصفحة 26 - If complexity is often rooted in patterns of interaction among agents, then we might expect systems to exhibit increasingly complex dynamics when changes occur that intensify
الصفحة 65 - levels of economic and institutional performance generally much higher than in the South, where social and political
الصفحة 14 - What makes prediction especially difficult in these settings is that the forces shaping the future do not add up in a simple, systemwide manner. Instead, their effects include nonlinear interactions among the components of the