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Scientific Uncertainty, Complex Systems, and the Design of Common-Pool Institutions

James Wilson

This paper addresses the question of how we cope with scientific uncertainty in exploited, complex natural systems such as marine fisheries. Ocean ecosystems are complex and have been very difficult to manage, as evidenced by the collapses of many large-scale fisheries (Boreman et al. 1999; Ludwig et al., 1993; National Research Council, 1999). A large part of the problem arises from scientific uncertainty and our understanding of the nature of that uncertainty. The difficulty of the scientific problem in a complex, quickly changing, and highly adaptive environment such as the ocean should not be underestimated. It has created pervasive uncertainty that has been magnified by the strategic behavior of the various human interests who play in the game of fisheries management.

This paper argues that scientific uncertainty in complex systems creates a more difficult conservation problem than necessary because (1) we have built into our governing institutions a very particular and inappropriate scientific conception of the ocean that assumes much more control over natural processes than we might hope to have (i.e., we assume we are dealing with an analog of simple physical systems), and (2) the individual incentives that result from this fiction, even in the best of circumstances, are not aligned with social goals of sustain-

I would like to thank the many people who have commented on various drafts of this chapter. Spencer Apollonio, Jefferson White, Gisli Palsson, Teresa Johnson, Deirdre Gilbert, Yong Chen, Robin Alden, Ted Ames, Elinor Ostrom, William Brennan, Jennifer Brewer, and Carolyn Skinder have all made helpful comments and often have caused me to rethink and rework many of the ideas in the chapter.



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The Drama of the Commons 10 Scientific Uncertainty, Complex Systems, and the Design of Common-Pool Institutions James Wilson This paper addresses the question of how we cope with scientific uncertainty in exploited, complex natural systems such as marine fisheries. Ocean ecosystems are complex and have been very difficult to manage, as evidenced by the collapses of many large-scale fisheries (Boreman et al. 1999; Ludwig et al., 1993; National Research Council, 1999). A large part of the problem arises from scientific uncertainty and our understanding of the nature of that uncertainty. The difficulty of the scientific problem in a complex, quickly changing, and highly adaptive environment such as the ocean should not be underestimated. It has created pervasive uncertainty that has been magnified by the strategic behavior of the various human interests who play in the game of fisheries management. This paper argues that scientific uncertainty in complex systems creates a more difficult conservation problem than necessary because (1) we have built into our governing institutions a very particular and inappropriate scientific conception of the ocean that assumes much more control over natural processes than we might hope to have (i.e., we assume we are dealing with an analog of simple physical systems), and (2) the individual incentives that result from this fiction, even in the best of circumstances, are not aligned with social goals of sustain- I would like to thank the many people who have commented on various drafts of this chapter. Spencer Apollonio, Jefferson White, Gisli Palsson, Teresa Johnson, Deirdre Gilbert, Yong Chen, Robin Alden, Ted Ames, Elinor Ostrom, William Brennan, Jennifer Brewer, and Carolyn Skinder have all made helpful comments and often have caused me to rethink and rework many of the ideas in the chapter.

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The Drama of the Commons ability. As a result, I believe we have slowed significantly the process of learning about the ocean, defined scientific uncertainty and precautionary acts in a way that may turn out to be highly risky, and created dysfunctional management institutions. This chapter suggests we are more likely to find ways to align individual incentives with ecosystem sustainability if we begin to view these systems as complex adaptive systems. This perspective alters especially our sense of the extent and kind of control we might exercise in these systems and, as a result, has strong implications for the kinds of individual rights and collective governance structures that might work. AN EXAMPLE FROM THE NEW ENGLAND FISHERIES When ocean fisheries management began after World War II, practical scientific and political concerns dictated a large-scale, single-species approach to management. International fisheries management institutions were given very large geographical jurisdictions, few resources, and little real governance authority. Yet they were asked to develop regimes for the conservation of ocean resources. The scientific problem these institutions and the scientists working for them confronted was extraordinarily difficult, especially given the problems and costs of observation and the relatively undeveloped state of ecological theory at that time. Consider how one might have started, at that time, to conceptualize a complex system that can be perceived only in the most indirect, costly, and occasional way. The fisheries scientists of that time chose a reductionist approach that emphasized sophisticated mathematical modeling of individual populations. It was consistent with scientific understanding of natural systems, with their (hoped-for) ability to measure and quantify, and with the authority given to the agencies for which they were working.1 In particular, the conception was to concentrate on area- and species-specific populations (stocks) located within broadly identified fishing areas or ecosystems. The International Commission for the Northwest Atlantic Fisheries (ICNAF), for example, broke its enormous jurisdiction into numerous smaller, but still very large, statistical areas that were thought to correspond with major ecological or fishing areas, such as, Georges Bank, the Gulf of Maine, the Grand Banks of Newfoundland, the Scotia Shelf, and so on. Its scientific efforts concentrated almost exclusively on the commercial species of interest to the parties of ICNAF (Halliday and Pinhorn, 1990). From both a scientific and institutional perspective, it is difficult to argue that these early approaches were “wrong,” given the constraints and the complexity of ocean ecosystems. Nevertheless, a scientific pattern was established—a kind of intellectual path dependency that persists today.2 With the advent of extended national fisheries jurisdiction in 1977, both the United States and Canada adopted with almost no changes the single-species scientific perspective and scale of application that had developed under ICNAF.3 In

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The Drama of the Commons both countries, initial fisheries management plans were simply a continuation of a course that had been set by ICNAF. Even today the United States and Canada use the same statistical areas and definitions that were defined in the early 1950s. Except for refinements in statistical procedures, longer data series, the attention to some new species, and much more complete recording of fishing mortality, essentially the same methodology—certainly the same fundamental theory—is still used to assess the status of each stock and reach recommendations about acceptable levels of catch. The most significant inheritance from the international era, however, was and is the scientific approach that simplifies the reality of complex ocean systems by treating each individual species as if it were an independent or isolated entity. The core of single-species theory is the belief that the future size of individual stocks is strongly related to spawning stock biomass, which, in turn, is strongly determined by how much fishing occurs. The relationship between fishing and spawning stock size is clear and easy to measure. But the theorized relationship between the spawning stock and recruitment is generally unknown and only claimed to exist for a few stocks, and then only at very low population sizes (Hall, 1988; Myers et al., 1995).4 In spite of the absence of confirming evidence, fisheries scientists are firmly convinced that the sustainability of each population depends on the maintenance of an adequate spawning stock biomass. Consequently, in the day-to-day management of fisheries, there is no attempt to predict recruitment. It is simply hoped, or assumed, that recruitment will proceed at a rate that is close to the average for some recent time period—one or two decades. Fisheries scientists advise managers about desirable catch rates, or amounts, in terms of what they estimate will produce the best yield from the year classes already in the water while maintaining a reasonable level of spawning stock biomass. There is an implicit but strong assumption that ecological interactions are minimal and not disturbed in any fundamental way by simultaneously fishing all or many species at moderate or even high rates. In addition, there are very difficult measurement and estimation problems. Errors of measurement on the order of 30 to 50 percent are common (Hilborn and Walters, 1992; Walters, 1998). As William Fox, science director of the National Marine Fisheries Service (NMFS), puts it, “there’s a bit of experience involved, not something that can be repeated by another scientist. It’s not really science; it’s like an artist doing it—so a large part of your scientific advice comes from art” (Appell, 2001). Most fisheries scientists are reasonably well aware of the shortcomings of the theory and uncertainties regarding measurements and estimates of population size. THE RESPONSE TO UNCERTAINTY When these uncertainties became apparent in the early years of extended jurisdiction, they were met by a few interested parties in the fishing industry with honest expressions of skepticism and, more commonly, with gaming strategies

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The Drama of the Commons that reflected the interests and circumstances of various individuals and groups. The nonstrategic industry response came in the form of a rather inarticulate skepticism about the underlying theory concerning the relationship between the spawning stock and subsequent recruitment and about how best to conserve, or sustain, the resource (Smith, 1990). I do not believe this argument ever was recognized by government scientists simply because it was not contained within a formally stated doctrine (or maybe it was that “paradigmatic” talking past one another, or incomprehension, that Kuhn, 1962, discusses). Nevertheless, this argument was inextricably bound up with the industry’s highly critical and strategic response to scientists’ uncertainty about estimates of (changes in) stock sizes. These estimates are especially important to industry because they are the basis for short-term policy setting regarding allowable catches and other rules restraining fishing. Furthermore, because the New England industry at that time was essentially an open-access industry, it had the usual tendency toward a strongly myopic perspective. Industry arguments tended to be supported by a large amount of anecdotal evidence. Almost without exception this evidence was marshaled to show economic hardship and to argue against biological estimates of scarcity and, of course, the need for reduced fishing efforts. Given the patchy nature of the resource and fishermen’s finely honed skills at locating those patches, statements about localized abundance did not impress NMFS scientists, who were doing their best to carry out surveys based on stratified random sampling of the resource. Economic hardship arguments were simply interpreted as exaggerated claims that reflected the expected zero-profit state of the industry given open access. However, members of the management council,5 who were nearly all nonscientists, were influenced by both the biological and economic hardship arguments. They shared the values of those users or, at least, gave them credence and, as a result, did tend to discount or modify scientific advice in the direction of higher harvests or fewer restrictions. The results of council deliberations were almost always less restrictive, or at least different, regulations than those recommended by NMFS scientists. From the perspective of NMFS scientists, it was as if the council, when given a confidence limit around a recommended catch level, would always choose the higher end of that range rather than the average or an even more conservative level. According to those scientists, the council lacked the political will to act in a way that would conserve the stocks (Rosenberg et al., 1993). NMFS and the environmental community became very frustrated at the council’s unwillingness to act (or, at least, to act in the way they wanted).6 They viewed the council’s response to this uncertainty as a sure way to gradually, if not quickly, erode the stocks. NMFS officials, in either explicit or tacit agreement (with one another), appear to have decided that the relatively democratic pro-

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The Drama of the Commons cesses of the council could not be relied on to achieve the greater good of conservation. Especially problematic was the council’s perceived tendency to sacrifice biological restraint in order to solve politically important economic problems. NMFS mounted a campaign to require the use of only quantitative data in council decision making, began to provide only point estimates of stock size and changes, and did its best to separate biological decisions from what were called allocative decisions (e.g. NOAA, 1986 [also known as the Calio report]; 1989 [602 guidelines and overfishing definitions]; Sustainable Fisheries Act [Public Law No. 104-297, 110 Stat. 355, 1996]. At the same time, the regulatory process increasingly became the object of court complaints in which NMFS was forced to defend its decisions (really its decisions to accept the advice of the councils). These challenges frequently questioned NMFS science (that is, estimates of changes in population size, not the basic theory) and were most easily met in court by thorough quantification of the basis for the decision. As a result, a strong bias seemed to enter into the choice of regulatory tools. Rules that were easily quantified were strongly preferred. Rules that were more difficult to quantify or that could not be analyzed easily within the context of the standard set of management models were not. For example, industry often proposed spawning area closures. Just as often NMFS opposed these suggestions with statements that no benefit could be shown or that “it doesn’t matter when you kill the fish.” In short, every effort was made to insulate the regulatory process from the problems posed by scientific uncertainty. The preferred approach of NMFS and a number of environmental groups was to give experts (i.e., NMFS) control over biological objectives and the councils control over who got what—the allocation problem (NOAA, 1986). They hoped that through this approach, biological objectives would not be sacrificed even though it would leave the public (i.e., the councils) to engage in a dogfight over who got what. This response to the political problems raised by scientific uncertainty is not uncommon; one has to assume that this policy approach was adopted in a good-faith attempt to promote the conservation and sustainability of our fisheries. After all, even if it was realized that current theory was inadequate, it was still the only theory—the only guidance—available, and given the perceived threats to the stocks and a perceived need to act, avoidance of a discussion of scientific uncertainty might have seemed justified. However, given the inability to verify the core relationship in the theory, this kind of approach to the uncertainty problem carries unusual risks. Precautionary management steps taken on the assumption that the single-species “spawning stock/recruitment” line of causation is the operative long-term determinant of sustainability may turn out to be highly risky if other ecological factors (e.g., habitat, spatial distributions of local stocks, population behavior, trophic hierarchy, and so on, which tend to be ignored in the single-species scientific agenda) are determinative of species abundance. Under these circumstances, the usual

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The Drama of the Commons prescription of single-species management—to fish moderately—still could lead to overfishing through the piece-by-piece loss of local stock spawning groups (Ames, 1998; Hutchings, 1996; Rose et al. 2000; Stephenson, 1998; Wroblewski, 1998; Wilson et al., 1999), through the destruction of essential habitat (Watling and Norse, 1996), through a gradual reduction in average trophic level (Pauly et al., 1998), and/or through the reduction or destruction of other ecological factors important to sustainability. In short, restraints appropriate to a single-species approach might simply perpetuate the problem. Taking uncertainty out of the public discussion may deprive us of the only defense we have against the even greater and more catastrophic uncertainty arising from an incomplete or incorrect understanding of the system. Removing uncertainty from the public discussion can be expected to retard our ability to learn, risk the credibility of science and the governance process on unproven theory, and most of all, diminish our long-term ability to conserve the resource (Rosa, 1998a). The New England experience has been repeated in one form or another all around the globe. It is a problem that afflicts the advisory processes of the New England Council, but it has been just as difficult for the consultative processes of Canada and other countries (e.g., Finlayson, 1994). The problem this history raises is whether a democratic process or any collective process that gives serious weight to user input is capable of dealing with environmental uncertainty in a way that conserves resources. Or is it the case that the strategic response to uncertainty of the various individuals and groups and the resulting difficulty of building trust effectively forecloses successful negotiation of agreements concerning mutual restraint? The argument of this chapter is that we can probably deal with uncertainty in an open democratic fashion, but that we have to be clear about the kind of uncertainty we face and the design of the institutions we build for dealing with that uncertainty. We can create institutions nicely tailored to a particular scientific theory and preconception of the nature of the uncertainty (we believe) we face, or we can design institutions on an alternative basis, one that assumes as little as possible about the nature of causal relationships and emphasizes the role of collective learning and institutional evolution. The appropriateness of one or the other approach would appear to depend on the state of our scientific knowledge or, alternatively, our ability to test and validate. The next sections of the chapter turn to a brief discussion of the view of uncertainty in a normal, reductionist scientific environment and how one’s view of uncertainty changes in the context of a complex adaptive system. CONVENTIONAL VIEW OF UNCERTAINTY As Pahl-Wostl (1995:196) writes, “Judged from a traditional point of view, uncertainty and the lack of predictive capabilities equal ignorance. Such thinking

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The Drama of the Commons still pervades most scientific practice. It determines how knowledge is valued, what type of knowledge is required for decision making. It has shaped both scientific and political institutions. Such a view is inadequate to deal with the complexity of the environmental problems facing us today.” Generally we think of three types of uncertainty in the study of natural systems (Walters, 1986:162). There is the uncertainty that arises from exogenous disturbances—noise. There is uncertainty about the values of system parameters, and, finally, there is uncertainty about system structure—sometimes called model uncertainty. A quantitative measure of the first two kinds of uncertainty, according to the American Heritage Dictionary, is simply “the estimated amount or percentage by which an observed or calculated value may differ from the true value.” Implicit in this definition is the assumption that we know or believe we know the basic cause-and-effect relationships—the system structure—in the fishery or whatever we are studying. In these circumstances, what stands for good science is the ability to detect relationships in what might otherwise appear to be noise and/or to narrow the uncertainty about our knowledge of the value of the parameters of the system. Normally, the smallest confidence interval around parameter estimates is generally believed to be the best science. It is through a continuing scientific process that we reduce or resolve parametric uncertainty. The instance of model uncertainty is also best addressed through a scientific process, but in this case one that consists of the discovery of causal relationships. Once that discovery occurs, the problem of uncertainty melds almost indistinguishably into the statistical process associated with parametric uncertainty.7 From the social point of view, uncertainty is not a desirable state of affairs but it is not especially problematic when science is in a position to learn rapidly. Repeated, consistently good predictions tend to validate the theory and to create trust and a willingness to invest in still more precise knowledge. Eventually, issues that previously might have been subject to strategic, self-interested argument (e.g., whether my steel or yours is better for use in a bridge) instead can be referred to experts for a disinterested (or public interested) decision. Normal peer review for quality control is generally a sufficient safeguard. In these circumstances, relatively insular expert-driven institutions operating under an umbrella of legislative objectives and standards are efficient and consistent with public interest. These are the kind of arrangements we generally make for building, bridge, auto, and pharmaceutical safety, among other things.8 The history of technological advance over the past 200 years illustrates the power of this method. But unlike civil engineering and the many other fields that have flourished using a reductionist approach, the sciences dealing with complex natural and human systems such as marine fisheries have not been able to develop a track record that generates broad social trust. Walters was (at least in 1986:162-163) very pessimistic about our ability to deal with these kinds of systems: “I

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The Drama of the Commons doubt that there can, in principle, be any consensus about how to plan for the inevitable structural uncertainties that haunt us, any more than we can expect all human beings to agree on matters of risk taking in general.” UNCERTAINTY IN COMPLEX ADAPTIVE SYSTEMS The growth of understanding of complex adaptive systems in the past two decades suggests we may be dealing with ecological and human systems whose structure and dynamic behavior bear little resemblance to the equilibrium, single-species environment characterized by conventional resource theory. If we conceptualize fishery systems from the complex systems perspective, we are likely to approach the uncertainty (and the institutional design) problem in a way very different from the conventional. In a Newtonian world, the stability of cause-and-effect relationships makes it possible to pursue reductionist science. This stability makes the observation and measurement of system relationships reliable and, more importantly, allows us to accumulate useful knowledge and to intervene in the system with predictable outcomes at whatever scale we find appropriate to our needs. As mentioned earlier, there is no doubt that many parts of our world fit this paradigm well. What is problematical about complex systems in this regard are their pervasive nonlinear, causal relationships (Holling, 1987). At any time a large number of factors may influence the outcome of a particular event, each one to a greater or lesser extent; at another time, the strength of those same causative factors on the same event may be very different. The result is a decline in predictability and/or often a shift in the scale or dimension of predictability (e.g., Levin, 1992; Costanza and Maxwell, 1994; Pahl-Wostl, 1995; Ulanowicz, 1997). This happens simply because the relative intensity of causal relations in the system changes from time to time. Extreme examples are the regime shifts such as have occurred in response to fishing and/or environmental changes in many places around the world (e.g., Dickie and Valdivia, 1981 [Peru]; Boreman et al., 1999 [Grand Banks and Georges Bank]). Under these circumstances similar species may be present, but in such radically altered proportions that predictions based on extrapolations of past relationships would be far off the mark. Certainly, if one were in a position to compare the entirety of the two systems (before and after the shift) as if they were stable systems, one probably would find strong dissimilarities in the intensity and relative importance of the interactions among components. Examples less extreme than regime shifts take place as the normal course of events in complex systems. Components in the system are continually adapting and evolving (not simply changing magnitude) in response to developments within the system itself (e.g., fishermen’s response to a change in regulations, changes in the species distribution, or the driving forces in an economic system). Not only are we faced with ignorance about the strength of any particular caus-

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The Drama of the Commons ative relationship because of the pervasive nonlinearities of the system, but we can no longer be sure that a particular causative agent still enters the equation. These characteristics of complex adaptive systems clearly limit our ability to extrapolate on the basis of past system states and, consequently, the feasibility of prediction as usually defined from a reductionist scientific perspective (Pahl-Wostl, 1995). Recognition of the instability of the parameters of complex adaptive systems expands our understanding of the possible scope of our ignorance (Ulanowicz, 1997). Nevertheless, there is perceptible order in these systems. This order can be understood and that understanding allows for the formation of a vision (a fuzzy prediction) of the future. Over time the order is exhibited in what many authors refer to as dynamic, or characteristic, patterns (Pahl-Wostl, 1995; Levin, 1999). I would describe this order as recurring similar patterns, never quite the same, sometimes startlingly novel because of the changing and adapting elements of the system, but also usually distinguishable from patterns in other systems (Holling, 1987). Recognition of the patterns of change in a particular complex system can lead to an understanding of that system. That is, we can view patterns as historical events and understand the mechanisms that led to a particular outcome. But this understanding may provide us with the ability to predict in only the most qualitative ways—especially when we get beyond the immediate (inertial) term. This characteristic of complex systems raises fundamental and difficult questions: How can we cope with or successfully intervene in ways that sustain the resources of these systems over the long run if we cannot predict the long-term consequences of our own actions? More importantly, how can we hope to make collective decisions in these circumstances? Won’t honesty about our lack of knowledge lead to a situation in which groups or individuals can honestly question and oppose restraint because it is costly in the short run and with unproven benefits in the long run? In short, if we are in a world of complex systems, does the absence of predictability mean that we have no rational basis for making conservation decisions? LEARNING IN COMPLEX ADAPTIVE SYSTEMS In complex ocean systems, learning the appropriate kind and extent of restraint required for sustainability is definitely a more difficult problem than one might be led to believe from a single-species theoretical perspective. Conventional resource management theory and practice is founded on the presumption that it is possible to simplify and predict fisheries systems at the scale of individual stocks using the same methods that have been applied so successfully to physical systems. If managers could predict in this way, even with wide confidence limits, they would be in a position to manipulate outcomes in the system. They would be able to create meaningful property rights and enter into implicit,

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The Drama of the Commons or explicit, contracts with fishers (e.g., “If you harvest only x amount today, then in the following year[s] there will be y amount [plus or minus] available to harvest”). These contracts would tend to be enforceable because individual incentives would be aligned with social goals and, as a result, would tend to lead to sustainable resources (Scott, 1992). Unfortunately, this kind of straightforward quid pro quo, top-down, contractual methodology is likely to be effective only when we can quickly learn, predict, and control outcomes. The lack of predictive ability in complex systems clearly impairs this kind of straightforward contractual methodology; nevertheless, because these systems can be understood in some sense, the basic economic idea of a valuable return to restraint remains viable. The key to understanding the appropriate kinds of restraint lies in the recognition of patterns. Imagine a world of many possible system states that change from one to another in recognizable, but generally novel, patterns and each with different causative relationships. The system’s propensity for one or another state, then, depends on the probability that a particular set of causative relationships with a particular set of values will appear at any point in time (Ulanowicz, 1997). In circumstances that are close in time or space, one might expect similarity of system states simply because of inertia. As time accumulates (or separating distance becomes greater), there is more scope for change in the circumstances of the system and less predictability. This does not mean the system in a particular place continues to diverge forever from its earlier state; it simply means that the set of possible system states changes. Part of the reason for recurring patterns may be found in the differing response times (i.e., fast and slow) of the variables in the system (e.g. Simon, 1969; Allen and Starr, 1982; O’Neill et al., 1986; Holling, 1987).9 For example, the highly fecund fish of the ocean can change their numbers dramatically over the course of a single spawning cycle. Other organisms in the system—sponges, corals—may exhibit changes of similar magnitude, but only over a much longer period of time. Generally aspects of the system that are slow to grow or develop or evolve—population age structures that include older animals, physical structures such as corals, tube worm colonies, learned and genetic behavioral aspects of populations such as migration routes and spawning sites—can be expected to constrain the faster elements in the system.10 Put differently, the timing and flows of energy among the population components of the system are constrained by the attributes or structure of the slow or relatively constant components of the system. If the values of these slow, longer term variables change, the set of possible system configurations changes as well; if the longer term variables remain relatively constant or nearly so, the short term is characterized by recurring configurations derived from a limited set of system states. Thus, one would expect a system in which long-term variables such as habitat and abiotic factors remained

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The Drama of the Commons unchanged, to generate an always-changing set of similar system states (Pahl-Wostl, 1995). (Seasonal patterns, for example, are an obvious and easy pattern to discern.) It follows that destruction or erosion of long-term constraining variables, such as, habitat, trophic structure, and behavioral factors such as a learned migration pattern, would be expected to change the set of possible system states so that it includes states unlike those experienced previously and, consequently, reduces the ability to perceive patterns and learn.11 In his book, Emergence, Holland (1998) describes the learning process a computer12 (and presumably humans) must go through to learn the game of checkers. He describes checkers as a very simple example of a complex adaptive system. Checkers has a limited number of pieces subject to a very few rules of movement, and its slow variables (the rules of the game, the size of the board, the kinds of pieces) are comfortably constant. Yet checkers is very difficult to predict and yields an immense number of possible board states. After only the first few moves of a game, it is unlikely that even an experienced player will encounter board configurations identical to those he’s seen before. The state of the “system”—the configuration of the board—is nearly always novel, but patterns of configurations more or less similar to those experienced previously are likely. The train of causation in the system is not stable, varying with each configuration of the board. Feedback about one’s interventions in the system is rarely clear. A “good” move can only be interpreted as such after the game has ended; it is entirely possible that a “double jump” might have led to the loss of a game or that a “poor” move might have set up a winning sequence. Looking ahead to try to predict the outcome of one among a set of alternative moves is an exercise that can yield only an ambiguous answer. So how do we learn to play checkers? Or in our case, how do we learn about the impact of human actions in the ecosystem? As mentioned earlier, the fundamental basis for learning and prediction in this kind of environment is the recognition of patterns. Because of the multiplicity and novelty of board configurations, and especially because of the adaptive behavior of one’s opponent, outcomes from any given decision cannot be expected to be the mean of outcomes of past similar situations. The adaptive behavior of the player’s opponent introduces a strong tendency for surprise and unintended results, especially for a player with a naïve statistical strategy. Holland (1998) describes a number of measures that help the player assess and evaluate the current configuration of the board (for example, simple measures such as “pieces ahead,” “kings ahead,” and “net penetration beyond center line”). The same set of measures can be used to assess the likely outcome of alternative moves the player faces. In other words, the player can think through the possible board configurations—two, three, or more moves ahead—that might arise from each alternative move. Conservative and generally more successful assessments assume the other player knows at least as much about the game as the player making the assessment. A kind of worst case precautionary principle

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The Drama of the Commons individuals inside a network that is capable of generating appropriate feedback. For all the reasons discussed to this point, hierarchical and, by necessity, representative governance structures are most likely to be able to convey to individuals the collective experience at all scales in the system—that is, most likely to provide feedback about system patterns. These same governance organizations also provide the mechanisms for attaching meaning to observations, for deliberation, and for taking ameliorative action. These capabilities are essential to the understanding of the ecosystem. They are capabilities that can be partitioned into their nearly decomposable tasks, but cannot be isolated from the system as a whole. In other words, given the mobility of resources in the system, the rights and the incentives of a person operating at a low level in the hierarchy are dependent on information generated at the same and at higher levels. Patterns at all scales and the efficacy of rules also at all scales are of interest to the individual. In short, in a complex system, the creation of individual incentives that might lead to collective restraint involves the identification of system patterns, the formation of a broad, not narrowly specified, vision of the future, and the ability to adapt to that future. Given all the difficulties of learning discussed thus far a rights system that relies on only individual learning is likely to be untenable. The collective learning process has to be an enterprise whose organization parallels the structure of feedback in the system. The tight local coupling on the ecosystem side that Levin (1999) refers to has to be captured by tight local coupling on the social side. There have to be broad, relatively stable networks that link multiple localities. A collective deliberation facilitates and converts those deliberations into meaningful restraint or a process that can lead to meaningful restraint (Dietz, 1994; Dietz and Stern, 1998). Thus, a rights system that relies on only individual learning is likely to be untenable. The individual’s perception of the environment and the formation of his incentives are intimately dependent on this governance process. Inclusion within a stable network of discussion, being a part of the experience and analysis of a broad array of individuals, learning the likely response of others to changes in rules, and having a vote or substantial role in the decision process all contribute to the alignment of individual incentives. However, if this process is not organized so that it can capture feedback about the effect of human interventions, the incentives and the actual behavior of individuals and groups is not likely to lead to conservation. Externalities will persist. On the other hand, to the extent that individual incentives can be aligned with the social goal of conservation (or sustainability), the state is relieved, by and large, of the need to rely on its police powers and threats of force in order to ensure individual behavior. Administrative and enforcement costs are reduced and the scope of feasible rules is expanded.19 Most important, however, is the change in the kinds of information strategies individuals (and groups) find it in their interest to pursue. In the typical top-down administrative approach to man-

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The Drama of the Commons agement, individuals (or groups) rarely find it in their interests to be forthcoming with information. All sorts of exaggerations, games, lies, dissembling, and other behavior is encouraged because there is generally only a limited and costly ability for others to verify such (mis-)information and generally no penalty—and often a reward—for its introduction into the public process. This kind of behavior always will be difficult to constrain in a complex environment; however, when management organization and resource rights are designed with the problem of learning in mind and actually lead to “tight local coupling” in the form of social networks, problems of information verification can be reduced and the costs of dissembling increased. Individual and collective learning can be encouraged. This increases the feasibility of conducting a constructive “analytical deliberation,” arriving at a shared vision of the future and aligning individual incentives. This kind of institutional arrangement, which I believe is principally consistent with decentralized, democratic governance, does not resolve scientific uncertainty but it does create a constructive environment in which the collective pursuit of useful knowledge can take place. This may appear to be a woefully complicated process, but it is nothing more than what we accomplish in our everyday governance. Society and the economy are extremely complex, multi-scale, rapidly changing systems in which we’ve learned to govern ourselves. SUMMARY Finding ways to effectively restrain human activity in complex ecosystems has been very difficult. A large part of the problem arises from scientific uncertainty, which is often used as a pretext for not making hard political decisions for conservation. This chapter suggests we have wrongly characterized our knowledge of the natural environment and, consequently, have viewed the uncertainty and learning problem as if it were a typical engineering problem. As a result, we have created institutions and administrative procedures ill adapted to a solution of the conservation problem. Usually we assume we are dealing with a classical Newtonian system in which cause-and-effect relationships are stable, or at least can be treated as if they were. In systems that truly conform to this assumption, the normal procedures of science can lead to understanding and reliable prediction. From the social point of view, repeated successful prediction generates trust even when there may be a lack of understanding among affected nonscientists. It also creates the circumstances for effective accountability and provides the rationale for reliance on expert-staffed institutions for the resolution of science-related problems. Complex adaptive systems do not lend themselves to long-term prediction consistent with the needs of sustainability because of their changing, complex, and usually nonlinear causal relationships. We may be able to understand the structure and dynamics of these systems without being able to predict anything

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The Drama of the Commons but broad patterns, or propensities, to use Ulanowicz’ (1997) terminology. This is a fundamentally different and important characteristic when compared with Newtonian systems; it raises two closely related social problems: (1) How do we collectively learn what kinds of restraint will work when the time-honored reductionist process of “predict → test → learn → revise → and predict again” by which we hone our understanding cannot be followed; and (2) in this kind of environment, what kinds of institutions are necessary to best facilitate learning, accountability, and incentive alignment? Holland (1998) suggests that learning in this kind of environment is based on the identification of recurring system patterns. The checker board game that he uses as an example of pattern learning is a relatively simple example of a complex adaptive system. It presents a limited and stable set of possible system states and patterns; the criteria for successful intervention in the system are fairly clear and the time and resource costs of learning are relatively low. When this same learning problem is applied to ecosystems, especially those in which humans play an active or dominant role, such as fisheries, the complexity and extent of the environment transforms the learning problem. Patterns in this kind of system, I suggest, are best understood in terms of the differing time steps of variables in the system. The relative stability of slower changing variables, such as habitat, constrains and limits the range of patterns that appear in the more quickly changing aspects of the system, such as the size of populations. It may be possible to ameliorate, or minimize, the learning problem through policies meant to affect the range of patterns we encounter. However, we will always be faced with a multiscale system in which observation is costly, analysis is difficult, and prediction about specific results of our intervention in the environment is not possible. This is not the kind of environment in which it is easy to build an atmosphere of credibility and trust. For all these reasons, learning in this kind of environment is very much a collective enterprise that has to be mediated by institutions. The design of those institutions is important. An institution’s success in minimizing the cost and difficulty of observation and analysis depends principally on its ability to capture the feedback in the system it governs. To do this well, the organization of institutions must take on a hierarchical structure that reflects the patchy, multiscale hierarchical structure of the natural system. At each level in the hierarchy, institutions must be “positioned” so that their boundaries correspond as much as possible in terms of scale and location to the boundaries of strong interactions in the biological system. There must be connections (information flows) between locations at the same scale and between higher and lower scales as in the ecosystem. The purpose of this parallelism is to align the “receptors” of the institution as much as possible with the spatial patterns of feedback in the system. In a situation with a crazy quilt of social boundaries that bear no resemblance to ecological boundaries, it might be possible to disaggregate and reaggregate observations in a way that made ecological sense, if analysis and observation were costless. How-

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The Drama of the Commons ever, noncongruent boundaries are much more likely to simply compound, or even confound, the learning process. A parallel structure, on the other hand, minimizes observational and analytical problems and, if across-scale and between-scale connections exist, provides for a flow of information that can be used to generate an understanding of processes at various scales and locations. A very important—the dominant—aspect of the collective learning problem is the need to extend the process of learning down to the individual level. Individual incentives—and, importantly, the willingness to enter into restraining agreements—have to be based on a perception of a beneficial connection between restrained current actions and future states of the natural system. In a complex system, in which it is difficult to predict the future state of system components (e.g., species abundance), this would appear difficult to achieve. Nevertheless, so long as individuals are in a position to adapt to changes in system states, the connection between current and (expected) future states does not have to be mechanically precise. It is sufficient that the resulting (expected) future state(s) are positioned within the set of patterns that characterize the typical system and that individuals are in a position, technologically and legally, to adapt to those new states when they appear (i.e., not tied to the fate of particular species). Under these circumstances the probability of a positive economic outcome for the individual is very high and, as a result, so also is the rationality of entering into restraining agreements. NOTES 1   I have in mind here people like Schaefer, Gulland, Ricker, Cushing, Berverton, and Holt— scientists whose work during the 1950s and 1960s formulated the still-extant structure of fisheries population dynamics. 2   By path dependency I mean the tendency to become locked into a particular (in this case) theoretical approach (Waldrop, 1992). In this instance I would hypothesize that the inability to depart from a particular path stems from the great difficulty that attends any attempt to validate or invalidate theory in this area. Over time programs, data collection, equipment, careers, and legal authority all become more and more tailored to the approach; change becomes more difficult and the inability to validate obscures all but the most compelling reasons to change. 3   This is not too surprising when one realizes that the Canadian and U.S. scientists were the same people who had worked for ICNAF. 4   Just as this paper was being sent to the editor, I became aware of an article by Brodziak et al. (2001) that claims a stock relationship is discernable in 14 Georges Banks stocks. 5   The Fisheries Conservation and Management Act of 1977 established eight regional fisheries management councils that act as advisory bodies to NMFS. NMFS is located within the National Oceanic and Atmospheric Administration (NOAA) within the Department of Commerce. Council members are appointed by the Secretary of Commerce from a set of nominees supplied by governors of relevant states. Generally, there appears to be an attempt to appoint representatives of the major stakeholders. The regional councils appear to have more weight than the usual federal advisory committee. So long as their advice conforms reasonably with a set of national standards, NMFS/NOAA/ Commerce is more or less constrained to follow. 6   This interpretation is the result of my observations as a member, and sometimes chair, of the Scientific and Statistical Advisory Committee of the New England Fisheries Management Council.

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The Drama of the Commons 7   See Rosa, 1998a for a thorough review of the study of risk. 8   However, I should note that two reviewers of this paper believe I am overly confident about these areas. 9   An earlier version of this perspective appears in Wilson et al. (1994) and Wilson et al. (1991). Also see Fogarty (1995) and Hilborn and Gunderson (1996) for disagreements with that perspective. 10   A problem I have with this terminology concerns the often asymmetric or episodic rates of change in many environmental variables. Biotic habitat may take a long time to build up and, once built, may persist for a long time (as might fit the definition of a slow variable), but it is also possible that that habitat could be destroyed by humans or a storm or internal dynamics in a very short time (Holling, 1987). 11   But Holling (1973, 1987) and Gunderson et al. (1995) argue that the accumulation of energy in older age structures (e.g., old-growth forests, woody scrub lands) can set the stage for dramatic system shifts through processes such as fire, suggesting the familiar is not easy to attain for long. 12   See also Samuel (1959). 13   The discussion that follows puts the emphasis on the social organization problem rather than the scientific problem. This does not mean the scientific problem is not important; it is simply not my first interest here. 14   It may be appropriate to attribute these ideas solely to Simon. See Pattee (1973) and O’Neill et al. (1986) and even the Federalist Papers (V. Ostrom, 1991). Interestingly, much of the work in corporate learning also traces back to Simon and Barnard, as does work on bounded rationality (see Williamson, 1995). Perhaps these questions are inevitable once one starts looking at the world as if it was a complex, adapting system rather than a stable clockwork mechanism. 15   They do not, however, conform to the aggregation from species to system implicit in species-centered population approaches such as used conventionally in fisheries management. I believe it is generally recognized that aggregation to the system from a species base presents intractable measurement and modeling problems. 16   Significantly, these arguments have little weight in circumstances where production is completely routine. The lack of change in local situations means it is possible for central authorities to acquire the knowledge necessary to direct and control such operations (Williamson, 1986). 17   For approximately 25 years, the legislature of the state of Maine tried unsuccessfully to adopt statewide trap (or effort) limits for the lobster fishery. Then the legislature created seven local jurisdictions, giving each limited local powers. Within a year of their creation, each jurisdiction adopted a trap (effort) limit. 18   This assumes a system in which there is considerable niche overlap, compensation among species, a relatively stable system energy input, and broad acceptance of species in the market (Wilson, 1990). 19   When willing compliance is low, only those rules that are enforceable with low cost to the state are feasible. These rules are not by any means likely to be the best rules for achieving conservation. REFERENCES Acheson, J.M. 1988 Patterns of gear changes in the Maine fishing industry. Maritime Anthropological Studies 1:49-65 Ahl, V., and T.F.H. Allen 1996 Hierarchy Theory: A Vision, Vocabulary, and Epistemology. New York: Columbia University Press. Allen, T.F.H., and T.B. Starr 1982 Hierarchy. Chicago: University of Chicago Press.

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