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Global Swarming

Global Swarming

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Global Swarming

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  1. Global Swarming Gary Fontaine School of Communications, University of Hawaii Honolulu HI USA 96822 fontaine@hawaii.edu Over the last half century multinational enterprises have essentially "swarmed" the globe with regional and local offices in an attempt to benefit from expanded opportunities. The theme of the present paper is that this phenomenon can be usefully viewed as significantly self-organized swarms searching a fitness landscape for optimal solutions to challenges presented by new and rapidly changing organizational ecologies. The paper applies a particle swarm optimization perspective, relates it specifically to current models of knowledge building and exchange in these enterprises, and discusses the implications for the globalization process. Paper presented at the “Third International Workshop on Swarm Intelligence and Patters” at the “Sixth International Conference on Intelligent System Design and Applications,” Jinan, Shandong, China, 2006.

  2. The Theme Over the last half century multinational enterprises (MNEs) have essentially "swarmed" the globe with regional & local offices to benefit from expanded production/market opportunities or to meet international social/health needs. The number of offices is enormous & rapidly expanding. • Large MNEs may have country/affiliate offices in over 80 nations with scores of local or branch offices in each. • In 2005 over 6,000 local & regional offices of MNEs in Hong Kong alone. • Total of 500,000 expatriates working in major cities like Shanghai and Beijing. Theme --this phenomenon can usefully be viewed as significantly self-organized swarms searching a solution landscape for optimal solutions to challenges presented by new & rapidly changing organizational ecologies across the globe These ecologies are typically characterized by cultural diversity & a broad range of other sociocultural, physical/technological and biologicalfactors. These challenges are associated with marketing, leadership, communication, staffing, screening, training, succession planning, management style, organizational design, community or government relations, & so forth.

  3. Swarms within Swarms Agents in these swarms can be defined at different levels of analysis— • the MNEs, themselves • their regional and local offices • individual departments within these offices • the typically more transitory, diverse teams completing the tasks necessary to get things done • the myriad of personnel shunted about between these them. I focus on the latter levels because— • {That’s where most of my previous work has been done} • It’s at these more micro levels that the process most clearly matches a particle swarm optimization model in which optimization occurs as agents compare their best previous practices & those of their neighbors.

  4. Swarms and Optimization A particle swarm is "a loosely structured collection of interacting agents" (Kennedy & Eberhart, 2001, p.102). The process of particle swarm optimizationuses a population of potential solutions to evolve optimal solutions to problems in a "fitness landscape." It’s basically a social problem-solving strategy. As agents interact and communicate to solve mutual problems decisions are made to do things that have worked best before or worked best for one's neighbor, they converge, improvements spread--stochastically. A culture -- or culture change -- emerges. Retrospectively, people may attribute a certain rationality to what has happened, but the process is not necessarily a rationale one. This optimization process is particularly valuable at times of ecological novelty or change -- "Strange Lands" (Fontaine, 2000) or the “edge of chaos" (Langton, 1991). And this isjust the situation faced by today's organizations as they try to perform optimally -- or at least survive -- in a rapidly changing global ecology.

  5. International Microcultures The optimal strategy for completing tasks in culturally diverse, changing or novel ecologies is the development of International Microcultures (IMCs). Doing such requires a generic search processin which the specificstrategy selected is that best accommodated to the international ecology of that task. An IMC is associated with a particular occurrence of a task & will tend to differ from those associated with other occurrences of that task as the ecology varies.

  6. Org B Org A MacroCulture & Subcultures Rel C Org D Rel E Org F Emergent Ecology Immergent Organizationalor RelationshipCulture Sit a Task c MicroCulture Task b Task c Levels of Culture

  7. IMCs & the Ecology Sociocultural Environment Physical/ technological Environment Biological Environment Parameters of appropriateness of perceptions, strategies & cultures Ecology • Perception-ecology link • Strategy-ecology link • Culture-ecology link

  8. IMCs as Self-Organizing Swarms a IMCs are local, significantly self-organized, with activities spatially & temporally removed from global leadership, "top-down" analysis, mission statements, mandated policies/procedures, even “best practices.” The development of IMCs is essentially a search processin whichIMCs will draw adjacent neighbors towards them & the more optimal they are the more they will draw. If another part of the MNE achieves clustering around another optimal solution, then a separate IMC will emerge through this same process. These different IMCs may either represent different optimal strategies for completing the same task in the same ecology (there may be more than one optimum) or different optima for somewhat different ecologies. This process is well modelled by Swarm Intelligence. The relative independence of the local from the global is a key condition for both self-organization & swarming. In many ways local offices of MNEs & the IMCs evolving in them are relatively "free to swarm" because— • constraints of distance, time & communication allow independence from the home office. • they are often given somewhat more "latitude" by that home office because of the recognized need to be doing things differently in an attempt to accommodate to the local ecology. Expat & host personnel at the local level often communicate more with their local neighbors -- in & outside their company. They are more "visible"than those back in the home office/country outside of sight distance. Thus a key role in information exchange & IMC optimization for behavior settings that support local interaction. Local agents often have more impact also because the typically high sense of presence.

  9. IMCs as Self-Organizing Swarmsb Localized sight distance (the distance from which agents can influence one another) is another key condition in self-organization and swarm models, although computer mediated communication in gdts suggests a need to redefine "local" more in terms of accessibility than geography. Sight distance and expatriation policies (how agents are shuttled around between, departments, offices – a form of “migration.”) can impact the search strategy for optimization-- Exploitation.Focusedsearch within a promising region of the solution landscape for a local optimum (e.g., "hill climbing"-- small trial & error steps to find the best solution in that region). Exploration. Broad sampling of alternative solutions searching for the global optimum (e.g. -- a "random walk“) (Kennedy & Eberhart, 2001). One of the difficulties with hill climbing is that we can get stuck on the hill -- or a specific region of solutions -- and not get off it to search other regions for solutions that might be better. The trade-off between exploration and exploitation is central to optimization and the difference is the size of steps through the search space or the ability to "jump" from one solution region to others. That ability can be affected by -- mutation(random changes in the individuals searching) diversityof those individuals creativity (coming up with new ideas) chance encounters(with others outside the normal network). immigration(bringing in new individuals)

  10. Conclusion One of the requirements for an "evolutionary"--as oppose to "rational," leadership, blueprint, recipe--approach to optimization through swarm intelligence is lot's of iterations. Natural selection must to "run it's course" such that the more fit solutions emerge. That's why, of course, so much research in the area must rely on computer simulations. But the 6000 in Hong Kong multiplied by the hundreds, if not thousands, of bigger or smaller "Hong Kongs" in the world provide lots of iterations.

  11. The game is on!

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