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2.5 Conclusions
Clearly, the questions we have just listed are only the beginning of a set of research issues in the interdisciplinary study of coordination. However, we believe they illustrate how the notion of ''coordination''provides a set of abstractions that help unify questions previously considered separately in a variety of different disciplines and suggests avenues for further exploration.While much work remains to be done, it appears that this approach can build upon much previous work in these different disciplines to help solve a variety of immediate practical needs, including (1) designing computer and communication tools that enable people to work together more effectively, (2) harnessing the power of multiple computer processors working simultaneously on related problems, and (3) creating more flexible and more satisfying ways of organizing collective human activity.
Appendix A: Previous Definitions of Coordination
''The operation of complex systems made up of components.''(NSF-IRIS 1989)''The emergent behavior of collections of individuals whose actions are based on complex decision processes.''(NSF-IRIS 1989)''Information processing within a system of communicating entities with distinct information states.''(NSF-IRIS 1989)
''The joint efforts of independent communicating actors towards mutually defined goals.''(NSF-IRIS 1989)''Networks of human action and commitments that are enabled by computer and communications technologies.''(NSF-IRIS 1989)''Composing purposeful actions into larger purposeful wholes.''(A. Holt, personal communication, 1989)''Activities required to maintain consistency within a work product or to manage dependencies within the workflow.''(Curtis 1989)''The integration and harmonious adjustment of individual work efforts towards the accomplishment of a larger goal.''(Singh 1992)''The additional information processing performed when multiple, connected actors pursue goals that a single actor pursuing the same goals would not perform.''(Malone 1988)''The act of working together.''(Malone and Crowston 1991)
Appendix B: Results about Coordination from Selected Fields
Even though use of the term ''coordination theory''is quite recent, a great deal of previous work in various fields can contribute to the interdisciplinary understanding of coordination. In this appendix we briefly describe examples of such work from several different disciplines. These examples focus on cases where coordination has been analyzed in ways that appear to be generalizable beyond a single discipline or type of actor. We have not, of course, attempted to list all such cases; we have merely tried to pick illustrative examples from several disciplines.
Computer Science
Sharing Resources Much research in computer science focuses on how to manage activities that share resources, such as processors, memory, and access to input–output devices (e.g., Deitel 1983). Other mechanisms have been developed to enforce resource allocations. For example, semaphores, monitors, and critical regions for mutual exclusion are programming constructs that can be used to grant a process exclusive access to a resource (e.g., Hoare 1975; Dijkstra 1968). Researchers in database systems have developed numerous other mechanisms, such as locking or timestamping, to allow multiple processes to concurrently access shared data without interference (e.g., Bernstein and Goodman 1981).
Managing Unreliable Actors In addition protocols have been developed to ensure the reliability of transactions comprising multiple reads or writes on different processors (e.g., Kohler 1981). In particular, these protocols ensure that either all a transaction's operations are performed or none are, even if some of the processors fail.Segmenting and Assigning Tasks One of the important problems in allocating work to processors is how to divide up the tasks. For example, Gelernter and Carrerio (1989) discuss three alternative ways of dividing parallel programs into units: according to the type of work to be done, according to the subparts of the final output, or simply according to which processor is available.Managing Information Flows Another important set of issues involves managing the flow of information. For instance, researchers in artificial intelligence and particularly in distributed artificial intelligence (DAI; e.g., Bond and Gasser 1988; Huhns and Gasser 1989) have used ''blackboard architectures''to allow processes to share information without having to know precisely which other processes need it (Nii 1986; Erman et al. 1980), and ''partial global plans''to allow actors to recognize when they need to exchange more information (Durfee and Lesser 1987).
In a sense, almost all of economics involves the study of coordination, with a special focus on how incentives and information flows affect the allocation of resources among actors. For example, classical microeconomics analyzes how different sources of supply and demand can interact locally in a market in ways that result in a globally coherent allocation of resources. Among the major results of this theory are formal proofs that (under appropriate mathematical conditions) if consumers each maximize their individual ''utilities''and firms each maximize their individual profits, then the resulting allocation of resources will be globally ''optimal''in the sense that no one's utilities can be increased without decreasing someone else's (e.g., Debreu 1959).
Some more recent work in economics has focused on the limitations of markets and contracts for allocating resources. For instance, transaction cost theory analyzes the conditions under which a hierarchy is a better way of coordinating multiple actors than a market (e.g., Williamson 1975). Agency theory focuses on how to create incentives for some actors (''agents'') to act in a way that advances the interests of other actors (''principals'') even when the principals cannot observe everything their agents are doing (Ross 1973). One result of this theory is that there are some situations where no incentives can motivate an agent to perform optimally from the principal's point of view (Jensen and Meckling 1976).Finally, some parts of economics focus explicitly on information flows. For example, team theory and its descendants analyze how information should be exchanged when multiple actors need to make interdependent decisions but when all agents have the same ultimate goals (e.g., Marschak and Radner 1972; Hurwicz 1973; Reiter 1986). Mechanism design theory also analyzes how to provide incentives for actors to reveal information they possess, even when they have conflicting goals. For example, this theory has been applied to designing and analyzing various forms of auctions. In a ''second price auction,''for instance, each participant submits a sealed bid, and the highest bidder is only required to pay the amount of the second highest bid. It can be shown that this mechanism motivates the bidders to each reveal the true value they place on the item being sold, rather than trying to ''game the system''by bidding only enough to surpass what they expect to be the next highest bid (Myerson 1981).Operations research analyzes the properties of various coordination mechanisms, but operations research also includes a special focus on developing optimal techniques for coordination decisions. For instance, operations research includes analyses of various scheduling and queueing policies and techniques such as linear programming and dynamic programming for making resource allocation decisions optimally (e.g., Dantzig 1963).
Research in organization theory, drawing on disciplines such as sociology and psychology, focuses on how people coordinate their activities in formal organizations. A central theme in this work has involved analyzing general issues about coordination (e.g., Simon 1976; March and Simon 1958; Thompson 1967; Galbraith 1977; Lawrence and Lorsch 1967; summarized by Mintzberg 1979; and Malone 1990). We can loosely paraphrase the key ideas of this work as follows:
All activities that involve more than one actor require (1) some way of dividing activities among the different actors and (2) some way of managing the interdependencies between the different activities (March and Simon 1958; Lawrence and Lorsch 1967). Interdependencies between activities can be of (at least) three kinds: (a) pooled, where the activities share or produce common resources but are otherwise independent, (b) sequential, where some activities depend on the completion of others before beginning, and (c) reciprocal, where each activity requires inputs from the other (Thompson 1967). These different kinds of interdependencies can be managed by a variety of coordination mechanisms, such as standardization, where predetermined rules govern the performance of each activity; direct supervision, where one actor manages interdependencies on a case-by-case basis, and mutual adjustment, where each actor makes ongoing adjustments to manage the interdependencies (March and Simon 1958; Galbraith 1973; Mintzberg 1979).These coordination mechanisms can be used to manage interdependencies, not only between individual activities, but also between groups of activities. One criterion for grouping activities into units is to minimize the diffculties of managing these intergroup interdependencies. For example, activities with the strongest interdependencies are often grouped into the smallest units, then these units are grouped into larger units with other units with which they have weaker interdependencies. Various combinations of the coordination mechanisms, together with different kinds of grouping, give rise to the different organizational structures common in human organizations, including functional hierarchies, product hierarchies, and matrix organizations. For instance, sometimes all activities of the same type (e.g., manufacturing) might be grouped together in order to take advantage of economies of scale; at other times, all activities for the same product (e.g., marketing, manufacturing, and engineering) might be grouped together to simplify managing the interdependencies between the activities.
Many parts of biology involve studying how different parts of living entities interact. For instance, human physiology can be viewed as a study of how the activities of different parts of a human body are coordinated in order to keep a person alive and healthy. Other parts of biology involve studying how different living things interact with each other. For instance, ecology can be viewed as the study of how the activities of different plants and animals are coordinated to maintain a ''healthy''environment.Some of the most intriguing studies of biological coordination involve coordination between different animals in a group. For example, Mangel (1988) discusses the optimal hunting pack size for lions, who trade the benefit of an increased chance of catching something against the cost of having to share what they catch. Deneubourg (1989) point out that the interaction between simple rules—such as ''do what my neighbor is doing''—and the environment may lead to a variety of collective behaviors.
The most striking examples of such group behaviors are in social insects, such as honey bees or army ants, where the group displays often quite complex behavior, despite the simplicity of the individuals (e.g., Franks 1989; Seeley 1989). Using a variety of simple rules, these insects ''allocate''individual workers at economically effcient levels to a variety of tasks-including searching for new food sources, gathering nectar or pollen from particular sources (bees), carrying individual food items back to the bivouac (ants), guarding the hive (bees) and regulating the group temperature. For example, in honey bees, the interaction of two simple local rules controls the global allocation of food collectors to particular food sources. First, nectar storing bees unload nectar from foraging bees returning to the hive at a rate that depends on the richness of the nectar. Second, if bees are unloaded rapidly, they recruit other bees to their food source. The result of these two rules is that more bees collect food from better sources. Seeley (1989) speculates that this decentralized control may occur because it provides faster responses to local stresses (Miller 1978), or it may be simply because bees have not evolved any more global means of communication.
