Over the last half century multinational enterprises (MNEs) have essentially "swarmed" the globe with regional and local offices in an attempt to benefit from expanded production or market opportunities or to meet international social or health needs. The number of such offices is enormous and rapidly expanding. For example, in 2005 there were over six thousand local and regional offices of global organizations located in Hong Kong alone [1] while large individual MNEs may have country or affiliate offices in over 80 nations with scores of local or branch offices in each (e.g., Sony, Microsoft, BP). The theme of the present paper is that this phenomenon can usefully be viewed as significantly self-organized swarms searching a fitness landscape for optimal solutions to challenges presented by new and rapidly changing organizational ecologies across the globe--ecologies typically characterized by cultural diversity, but also by a broad range of other sociocultural, physical/technological and biological factors. These challenges are associated with finding effective strategies for marketing, leadership, communication, staffing, screening and self-selection, training, succession planning, management style, organizational design, community or government relations, and so forth.

The agents within these swarms can be defined at different levels of analysis ranging from the MNEs, themselves, to their regional and local offices, to the individual departments within these offices, to the typically more transitory, more diverse teams completing the tasks necessary to get things done, to the myriad of personnel shunted about between these teams, departments, branch offices and often back to the home office. These levels of analysis differ in the total numbers of swarms as well as their organizational complexity and lifespan. This paper focuses most particularly on the latter levels both because they represent those at which most of my previous works has been done and because it may be at these more micro levels that the process most clearly matches a swarm intelligence or particle swarm optimization model in which optimization occurs as agents compare their best previous practices and those of their neighbors.

Fish, termites, and often people can be conceptualized as behaving as "swarms." A particle swarm is "a loosely structured collection of interacting agents. ... An ant colony can be thought of as a swarm whose individual agents are ants, a flock of birds, traffic is a swarm of cars, a crowd is a swarm of people, an immune system is a swarm of cells and molecules, and an economy is a swarm of economic agents" [2]. In recent decades work in evolution computation theories, genetic algorithms, evolutionary programming, evolution strategies, and genetic programming has all viewed agents in a population (e.g., people or teams) as potential problem solutions and has identified strategies to optimize the solutions to challenges presented by the ecology. Increasingly this approach is being utilized in business as well [3]. All utilize some kind of process based on survival of the fittest and include properties such as mutation or migration. Thus evolution is seen as a kind of general problem solver and people--or agents--are, if you like, walking potential solutions!

The process of particle swarm optimization uses a population of potential solutions to evolve--or search for--optimal solutions to problems in a "fitness landscape" (a graphic representation of a range of solutions varying in effectiveness or "fitness"). In each iteration of a computer simulation these potential solutions--or particles or agents--are "flown" through a problem hyperspace with different stochastically assigned velocities accelerated toward each agent's best previous fitness value and the best value of an agent in its neighborhood. The process continues until some specified degree of "fitness" is--or isn't--reached in some specified number of trials. Particle swarm optimization converges on solutions that meet fitness criteria without any global evaluation of the progress made. It is a "bottom-up" process working with cues entirely at the local level determined by examination of an agent's best previous solution and that of it's best neighbor. As Kennedy and Eberhart [4] say, in real life "no ant knows how well the swarm is doing."

Particle swarm optimization is basically a social problem-solving algorithm and is consistent with the theories of social learning or impact as applied to populations of individuals. As individuals progress through the ecology by trial and error they are carried nearer to success. As they interact, communicate and work to solve mutual problems, they converge. This convergence or clustering of individuals leads to cultures at the micro, organizational and, over time, macro levels. Retrospectively, in attributing meaning to what happened people may attribute a certain logic or rationality or plan to what occurred. But, the process is not necessarily a rationale one: decisions are made to do things that have worked best before or worked best for one's neighbor. And some of those generate improvements and some do not. The improvements spread--stochastically. A culture--or culture change--emerges.

This particle swarm perspective on evolution of species and cultures is particularly valuable at times of ecological change, transition, and novelty--in what I have earlier called "strange lands" [5] or what Langton [6] describes as "the edge of chaos." And this is just the sort of situation faced by today's organizations as they try to perform optimally--or at least survive--in a rapidly changing global ecology. I suggest viewing this as a form of global swarming has useful implications for meeting the challenges faced.

I and others have for decades made the case that the optimal strategy for completing tasks in culturally diverse, changing or novel situations--again, "strange lands"--is the development of something like International Microcultures (IMCs). Doing such requires a generic search process in which the specific strategy selected is that best accommodated to the international or intercultural ecology of that task [7, 5, 8, 9]. They represent one meaning of the term "third cultures" in the intercultural/international literature [10]. An IMC is associated with a particular occurrence of a task and will differ from those associated with other occurrences of that type of task as the task ecology varies. For example, the IMC used to negotiate a particular "petroleum rights" treaty will nearly always differ from that used with other "petroleum rights" treaties negotiated at other times with other participants under different economic, social, political, environmental conditions, etc. Because an IMC is accommodated to a particular task ecology, participant perceptions and behaviors within it are not necessarily consistent with those across this and other types of tasks at the level of national or even organizational culture.

IMCs are largely self-organized by the task participants--often the expats and the host country nationals with whom they are working in regional and local offices. It is they who must both negotiate an appropriate IMC and then work within it. It is most typically a "local" process in that participants interact face-to-face (f2f), although recent expansion of computer mediated communication in geographically dispersed teams suggests a need to redefine "local" more in terms of accessibility than geography.

Viewing the development of IMCs from the perspective of significantly self-organizing swarms, they can be seen as a the emergent products of search strategies with fitness to the problem and ecology as the primary objective. IMCs are more commonly emergent properties of local interactions in organizations than the result of "top-down" analysis, planning or leadership. The development of IMCs is essentially a search process modeled well by the particle swarm optimization model. One could argue that from this perspective a significant function of expatriation is--or should be--to place people in the social context that allows searching for optimal or near optimal solutions based on the person's previous best solutions (perhaps in other contexts or tasks) and the best of the local "neighbors." As emergent products they are neither led nor planned--and they are very difficult to predict.

Research in swarm intelligence and the development of "cultures" demonstrates that after many iterations participants will often cluster around one or more optimal solutions or IMCs. Through a process like particle swarm optimization clusters will draw adjacent neighbors towards themselves and the more optimal they are the more they will draw their neighbors, and so forth. If another subset of the population, say in another team or local office, achieves clustering around another optimal solution, then a separate culture 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 strategy that is "optimal") or different optima for somewhat different ecologies. In some cases, the optimal solution may absorb a less optimal one, while in others more mediocre solutions between the optima may buffer the process (by inhibiting communication and social comparison). In the latter, polarization may then support continuation of separate, distinct cultures.

In many ways local offices of MNEs and the microcultures evolving in them are "free to swarm" or at least much more free to swarm than their home office, home country counterparts. That is so because constraints of distance, time and communication allow (or necessitate) their being somewhat more independent of the home office and because 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: Distance matters [15]! This relative independence of the local from the global is a key condition for both self-organization and swarming. Further, in terms of communication the role of adjacent local agents often has more impact because of their physical immediacy compared with the home office and because the typically high sense of presence [16, 17] of those abroad gives them a heightened awareness of stimuli (such as people or agents) in their immediate surroundings.

Expat and host personnel at the local level often communicate more with their local neighbors--in and outside their company--and are otherwise more "visible" than among them than those in the home office/country that might be outside of sight distance, a concept frequently used in swarm simulation models to refer to the distance from which agents can influence one another. The localized sight distance is another key condition for self-organization. Thus behavioral settings that support communication between these local agents can play a key role in information exchange and strategy optimization. These settings can range from AMCHAMs (e.g., American or other chambers of commerce) to social and recreational facilities to locally focused web support forums to "after hours" bars. Much of the transfer of tacit knowledge that occurs in these settings may be through chance encounters with people who are for a variety of reasons not in an expat's or host's normal communication networks but nevertheless have knowledge key to getting things done [18]

Interestingly, the scope of the sight distance for agents in swarms and the relative degree to which agents are shuttled around between different teams, departments and offices as a part of assignment or expatriation policies can also impact the extent to which the search strategy for optimization is more one of exploitation or exploration [19]. Exploitation involves a focused search within a promising region of the landscape for a solution that is the best available within that region, "good enough"--a local optimum. A common exploitation strategy is "hill climbing"--once a promising region is found small trial and error steps are taken to find the best available solution in that region, the top of that hill. In the case of globalization, the strategy could be manifested by a modest expatriation policy in which personnel are shuttled around relatively little in order to take advantage of their growing knowledge and skills from prolonged experience in a local region. On the other hand, exploration involves a broader sampling of alternative solutions across a variety of regions looking for the overall global optimum. A common exploration strategy is a "random walk" throughout the solution landscape hoping to hit upon the highest point, the best strategy, the Everest within the landscape. In our case here, that would imply a relatively robust expatriation policy in which personnel are frequently, and broadly, shuffled between teams, departments, offices and regions. Interestingly, Tsang [20] found that successful MNEs "were companies that assigned their expatriate managers to China on a full-time basis, had very frequent contacts with their China operations, and rotated the expatriate managers systematically."

One of the difficulties with hill climbing is that agents 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 choice 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), diversity of those individuals, immigration (bringing in new individuals), creativity, and chance encounters (as mentioned above, with others outside the normal network).

While much attention has been paid to the role of leadership in the globalization process [23], there are way too few leaders, experienced "global managers," or old China hands" to fill the needs for rapid globalization. Nor is there the reasonable likelihood that internationally focused MBA programs or more advanced executive training programs will--or could--ever provide a pool of expert leaders that can "show the way" in a rapidly changing, global world in which there is no stable way that can be taught. Likewise, though there is no shortage of blueprints and recipes for designing business abroad (e.g., how to do business in … books; organizations like the Japan America Institute of Management Science; Thunderbird and other international management programs, executive "globalization" workshops, etc.) there is no reason to think they have any more immediate or defining influence on behavior in organizations abroad than similar ones do at home.

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 be allowed 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 simulation. But the six thousand regional and local offices in Hong Kong multiplied by the hundreds, if not thousands, of bigger or smaller "Hong Kongs" in the world provide lots of iterations. The game is on!

1. Hong Kong Census & Statistics Department (www.investhk.gov.hk/content1q.aspx?id=864&code=IHK2-RESULTS-RL&lang=1).
2. Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann, p. 102.
3. Bonabeau, E. & Meyer, C. (2001). Swarm Intelligence: A Whole New Way to Think About Business. Harvard Business Review, May, 107-114.
4. Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann, p. 108.
5. Fontaine, G. (2000) Skills for successful international assignments to, from, and within Asia and the Pacific: Implications for preparation, support, and training. In U. C. V. Haley (Ed.), Strategic management in the Asia Pacific: harnessing regional and organization change for competitive advantage.
6. Langton, C. G. (1991). Computation at the edge of chaos: Phase transitions and emergent computation. In S. Forrest (Ed.), Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks. Cambridge, MA: The MIT Press.
7. Fontaine, G. (1989). Managing international assignments: the strategy for success. Englewood Cliffs, NJ: Prentice Hall.
8. Hofner-Saphiere, D. M. (1996). Productive behaviors of global business teams. International Journal of Intercultural Relations, 20(2), 227-259.
9. Kimmel, P. R. (1989). International negotiation an intercultural exploration: Toward cultural understanding. Washington, D.C.: U. S. Institute of Peace.
10. Casmir, F. L. (1999). Foundations for the study of intercultural communication based on a third-culture building model. International Journal of Intercultural Relations, 23(1), 91-116.
11. Argyris, C. & Schon, D. A. (1978). Organizational learning: A theory of action perspective. Reading, Massachusetts: Addison-Wesley.
12. Nonaka, I. & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press.
13. Mohrman, S. A., Klein, J. A. & Finegold, D. (2001). Managing the global knowledge-creation network: A sense-making perspective. Massachusetts Institute of Technology Engineering System Division. Working Paper Series ESDWP2001-05.
14. Gupta, A. K. & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21, 473-496.
15. Latane, B., Liu, J. H., Nowak, A., Bonevento, M. & Zheng, L. (1995). Distance matters: Physical space and social impact. Personality and Social Psychology Bulletin, 21, 795-805.
16. Fontaine, G. (1993). The experience of a sense of presence in intercultural and international encounters. Presence: Teleoperators and Virtual Environments, 1(4), 1-9.
17. Fontaine, G. (2004). A sense of presence and self-reported performance in international teams. Psychological Reports, 95, 154-158.
18. Fontaine, G. (2002) Teams in Teleland: working effectively in geographically dispersed teams "in" the Asia Pacific. Team Performance Management, 8(5/6), 122-133.
19. Kennedy, J. & Eberhart, R. C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann.
20. Tsang, W.W. K. (1999). Internationalization as a learning process: Singapore MNCs in China. The Academy of Management Executive, 13(1), p. 7.
21. Pollard, D. & Hong, J. F. L. (2001). Cross-cultural Learning Issues in International Joint Ventures. Euro Asia Journal of Management, 22(11/2), 53-78.
22. Bird, A. (2001). International assignments and careers as repositories of knowledge. In Mendenhall, M. E., Kuhlmann, T. M. & Stahl, G. K. (Eds.), Developing global business leaders: Policies, processes, and innovations. Westport, Connecticut: Quorum Books.
23. Gregersen, H. B., Morrison, A. J. & Black, J. S. (1998). Developing leaders for the global frontier. Sloan Management Review, 40, 21-32.