Limits to transparency exploring conceptual and operational aspects of the ICES framework for providing precautionary fisheries management advice

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1 ICES Journal of Marine Science Advance Access published May 15, 2007 Limits to transparency exploring conceptual and operational aspects of the ICES framework for providing precautionary fisheries management advice Kjellrun Hiis Hauge, Kåre Nolde Nielsen, and Knut Korsbrekke Page 1 of 6 Hauge, K. H., Nielsen, K. N., and Korsbrekke, K Limits to transparency exploring conceptual and operational aspects of the ICES framework for providing precautionary fisheries management advice ICES Journal of Marine Science, 64. ICES precautionary approach to fisheries management advice is based on limit reference points (LRPs) reflecting stock status and precautionary reference points (PRPs) reflecting risk levels. As LRPs are exclusively science-based, while PRPs are management-based, this framework is deployed towards satisfying the ideal of a clear division of science and management s responsibilities. We discuss the variety of technical definitions of reference points and their use in the advisory process. There are inconsistencies in the use of reference points and a tendency to downplay uncertainty. Although the framework can be improved, we argue that some dilemmas will remain. First, transparency of uncertainty presupposes a simple and understandable framework. However, translation of the complexity of natural and human interactions into simple concepts leads to problematic standardization. Second, a clear-cut division of responsibility between science and management is not feasible because LRPs cannot be purely science-based and PRPs cannot be purely management-based. Such dilemmas set fundamental limits to what can be expected from the framework in terms of handling and communicating uncertainty. We suggest that comprehensive dialogue between science and management and explicit reflection on their respective roles will prove more effective at enhancing precautionary and transparent advice on fisheries than adhering to the ideal of strict separation. Keywords: fisheries management, precautionary approach, reference points, transparency, uncertainty. Received 30 June 2006; accepted 5 March K. H. Hauge and K. Korsbrekke: Institute of Marine Research, PB 1870 Nordnes, N-5817 Bergen, Norway. K. N. Nielsen: Norwegian College of Fisheries Science, University of Tromsø, Tromsø, Norway. Correspondence to K. H. Hauge: tel:þ ; fax:þ ; kjellrun@imr.no Introduction Current fisheries management in the Northeast Atlantic is premised on accurate scientific assessments of the state of stocks and the ability to predict the effects specified in catch options, from which authorities subsequently can choose the total allowable catch (TAC). Given the enormous complexity of the interface between fisheries and the ecological systems in which they take place, however, the predictability on which such management systems hinge must be premised on a series of simplifications. In the ICES context, simplifications include the assumption that stock health can be defined and measured in terms of spawningstock biomass (SSB) and can be managed by adjusting fishing mortality rates (F) through decisions on TACs, in accordance with simulated catch forecasts. As fisheries science, for obvious reasons, remains a relatively uncertain science, the conceptualization and handling of uncertainty in the advice is basic to successful management. To address the issues of uncertainty in its advice, ICES (1996) developed a precautionary approach (PA) framework. This framework builds on establishing limit reference points (LRP) reflecting stock states that should be avoided, and precautionary reference points (PRP) reflecting the risk of crossing the LRPs. LRPs and PRPs are defined in terms of F and SSB, and the handling of uncertainty rests primarily on a simplification of the interface between fisheries and their environments, described above. Although the form of the advice is gradually changing to provide ecosystem-based advice and advice for mixed fisheries, the single-stock upper exploitation boundaries that are the fundamental building blocks of the ICES advice on fisheries management remain based on the PA biological reference points (ICES, 2005). Although designed to handle uncertainty, the PA framework has been instrumental in guiding the appropriate division of responsibilities between science and management: defining specific LRPs lies within the responsibility of ICES, whereas decisions on PRPs (defining acceptable risk levels) are for society to take (ICES, 2005). These two functions of reference points can be thought of as creating transparency in (i) the division of responsibility between science and management, and (ii) the communication of uncertainty in the advice. Although the central idea of the PA framework of handling uncertainty by way of LRPs and PRPs is simple, its development has met many challenges, as has communication on the framework with ICES customers. Specifically, the development of the technical aspects of the PRPs since 1996 has taken considerable effort (ICES, 1997, 1998a, 2001, 2002, 2003a, b, c). # 2007 International Council for the Exploration of the Sea. Published by Oxford Journals. 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2 Page 2 of 6 K. H. Hauge et al. A central issue has been the respective roles of science and management in deciding the reference points. For instance, the conclusion reached at a meeting between ICES and its clients was that reference levels and risk levels have to be decided by the managers based on scientific evidence (ICES, 1999). During a similar dialogue, a client recently complained that the precautionary approach has no science basis it is but a set of values.... How far can science go: where does science stop and the decision process begin?... There should be a clear border line and... the two issues should not be mixed (ICES, 2006b). Both examples reflect the ideal of science that Latour (1993) refers to as the modern constitution, which invents a separation between the scientific power charged with representing things and the political power charged with representing subjects. In science, this ideal reflects something that Merton (1942) described as one of the key constituents of the ethos of modern science: its institutional imperative of disinterestedness. Much of the authority of science rests on the assertion that it adheres to such Mertonian norms (Jasanoff, 1986), which in turn lends legitimacy to science-based management decisions. According to this ideal, science and politics benefit simultaneously by remaining distinct from each other. The PA framework is a central instrument in ICES attempts to maintain this separation. As indicated by the critical client cited above, however, ICES ability to achieve this separation is contested. We explore the contrast between the conceptual simplicity of the PA framework and the practical difficulties and complications it has met when put into operation. We examine the key concepts of reference points, their technical definitions, and their rationales. Further, we discuss to what extent one may expect the PA framework to deliver transparency in the two understandings introduced above. The ICES PA framework The value of the LRP for SSB (B lim ) for each stock is set (ICES, 2005)... on the basis of historical data, and chosen such that below it, there is a high risk that recruitment will be impaired (seriously decline) and on average be significantly lower than at higher SSB. The implicit premise is that the relationship between SSB and recruitment (R) is known. This, however, is not always the case: When information about the dependence of recruitment on SSB is absent or inconclusive, there will be a value of SSB, B loss, below which there is no historical record of recruitment. B lim is then set close to this value to minimize the risk of the stock entering an area where stock dynamics are unknown. Hence, B lim may be defined analytically as well as pragmatically. A second type of LRP is introduced as follows: the fishing mortality rate should not be higher than an upper limit F lim which is the fishing mortality that, if maintained will drive the stock to the biomass limit. However, because SSB and F can only be estimated with uncertainty,...ices applies a buffer zone by setting a higher spawning biomass reference point B pa (the biomass PRP). As long as the estimate of spawning biomass is at or above B pa, the true biomass should have a low probability of being below B lim (ICES, 2005). Similarly, to be certain that F will remain below F lim, F should be kept below some precautionary fishing mortality reference point (F pa ). The handling of uncertainty for giving advice on future management measures, therefore, is essentially operationalized by way of identifying buffer zones implied by the distance between the LRP and PRP for both SSB and F (Figure 1). The framework appears to be quite simple, providing an overview of possible stock states classified according to the reference points. This simplicity makes the presentation and handling of uncertainty appear transparent in the common meaning of the word: simple features are likely to be clearly communicated to, and readily understood by, the customers (management authorities and administrators), as well as by fishers and the public. A different but linked sense of transparency relates to the conception that the framework should allow for a clear division of responsibility between science and politics. Regarding the extent of the buffer zone, ICES (2005) notes that because B pa is a mechanism for managing the risk of the stock falling below B lim, the distance between these reference points is not fixed, but will vary with the uncertainty of the assessment and the amount of risk society is prepared to take. Therefore, while LRPs are biological and hence within the province of science, a decision on the acceptable risks defining the PRPs is for society to take (although ICES would assist in doing the necessary calculations). This seemingly transparent boundary between science and the public at large is an important feature of the PA framework. Reference points: concepts and practices To explore the two perspectives on transparency of the PA framework introduced above, we examine linguistic aspects of LRPs and Figure 1. An overview of the status of fish stocks in relation to reference points (the PA flag ), the basic idea being that, to have a high probability of avoiding a stock ending up in the red area (below B lim and above F lim ), decisions on management targets should be constrained within the green area (above B pa and below F pa ).

3 Limits to transparency in precautionary fisheries management Page 3 of 6 PRPs as used by ICES, their technical definitions, and their practical implementation for individual stocks. Limit reference points for SSB B lim delimits an unwanted situation in terms of reproductive potential. Operationally, this unwanted situation is interpreted as, and defined by, proxies for stock status identified by a specific SSB and a specific F. ICES (2005) uses several terms to describe characteristics of the unwanted situation identified by B lim : impaired recruitment, serious decline in recruitment, hampered recruitment, reproduction failure, stock depletion, and stock collapse (ICES, 2005). We have not been able to determine whether these different terms have (or are intended to have) equivalent meanings. Collapse is the only term associated with B lim that is explicitly defined as a situation when...the stock has reached a level where it suffers from severely reduced productivity. It is also stated that the collapse does not mean that a stock is at high risk of biological extinction. However, recovery to an improved status is likely to be slow, and will depend on effective conservation measures (ICES, 2005). The approaches used for defining LRPs have been diverse because of some theoretical and practical problems. The technical definitions of B lim for individual stocks can be divided into three distinct groupings: (i) statistical approaches or expert judgements based on empirical stock-recruitment (SSB/R) plots; (ii) setting B lim equal to the lowest historically observed SSB; and (iii) derivations from estimates of B pa. Whereas the first group of definitions is related directly to the concept of recruitment-overfishing (i.e. marking an unwanted situation of reduced productivity), the other two may be considered more pragmatic approaches to produce an appropriate, but rather arbitrary, value. Among the first grouping, the change-point regression analysis is the method recommended to calculate the appropriate B lim based on SSB/R paired observations (ICES, 2003c), a statistical method to identify a level of SSB at which recruitment starts to decline relative to some upper range where recruitment is assumed to be (on average) constant. Although representing a theoretically sound method for determining B lim, the changepoint regression analysis has been proven in practice to yield inconclusive results in several cases (ICES, 2003c). This is not surprising because the scatterplots of SSB/R often show no relationship whatsoever or indicate an underlying relationship that is very different from the assumed hockey-stick. We note here that there is a vast scientific literature discussing other important factors affecting recruitment (reviews by Cardinale and Hjelm, 2006; ICES, 2006c), and that the relationship between SSB and R may be in itself an unrealistic simplification. Expert judgement has been used, for instance, for the southern stock of European hake (Merluccius merluccius), for which B lim has been set as the level below which there are indications of impaired recruitment, based on visual inspection of the SSB/R plot (ICES, 2005). Within the second group of approaches, B lim is defined as the SSB level below which there is no historical record of recruitment (B loss ; ICES, 2005). This definition is used when information on the relationship between SSB and R is inconclusive, for instance when a stock appears not to have experienced impaired recruitment or when recruitment appears to vary independently of SSB. Inconclusive SSB/R relationships apply to most stocks assessed in the Northeast Atlantic. However, the use of B loss seems somewhat arbitrary in certain cases. For instance, the B lim for North Sea cod (Gadus morhua) equals B loss ¼ t, although ICES (1998b) claimed clear signs of impaired recruitment for SSB well above that level, and although ICES (2003a) has proposed a value of t based on a change-point regression. Reliance on B loss may be problematic because the environmental conditions at historically low SSB levels may have been favourable for recruitment by coincidence. Therefore, a stock may well show impaired recruitment if subjected to different environmental conditions, even when it is above B loss. This uncertainty illustrates a more general problem that the available timeseries of SSB/R data may be insufficient to identify periods during which a stock has been less productive, irrespective of SSB (Simmonds and Keltz, 2007). The third group of approaches, basing B lim on B pa, as exemplified by sole (Solea solea) in the Skagerrak and Kattegat (ICES, 2005), will be discussed below. Finally, we note that B lim has not been defined for several stocks because of lack of data (e.g. for oceanic redfish, Sebastes spp., in the Barents Sea; ICES, 2005). An important feature is that B lim is defined as a constant, resting on the implicit assumption that recruitment is more affected by variations in SSB than by environmental factors. There is an apparent tension between this simplification and the attention that is being paid to potential changes in productivity (Simmonds and Keltz, 2007) and regime shifts in ecosystems (Scheffer et al., 2001; Beaugrand, 2004; Choi et al., 2004; Drinkwater, 2006). Limit reference points for F F lim is defined as the F value that, on average, is expected to drive the stock to B lim (ICES, 2005). Its technical definition can be divided into two approaches: (i) the mathematical relationship with B lim and (ii) otherwise. We note that F lim has not been defined for most stocks (ICES, 2005). The first and most commonly used approach exactly matches the definition of F lim. F lim relates directly to B lim and may be estimated either from plots of SSB on R (F loss matching B loss ; e.g. northern hake), or from the estimated stock-recruitment function (e.g. Northeast Arctic cod). Defining F lim as being equal to F med (the F associated with the median SSB, which historically has been able to replace itself; e.g. sole in the Skagerrak and Kattegat) implies a definition different from that described in the PA framework. This is also the case if expert judgement is applied. For instance, the rationale for selecting a particular F lim for West of Scotland cod has been that higher Fs have historically led to stock decline (ICES, 2005). Hence, F lim is devised here to hinder a decline in the stock rather than to avoid crossing B lim (in this case defined as B loss ). F lim is not always considered a useful reference point for management, for example for stocks characterized by large fluctuations in biomass. Nevertheless, F lim may still have been defined, either to fit the PA framework or to allow scientific evaluations of harvest control rules (ICES, 2001). In such cases, F lim may be based on F pa (see below) and, therefore, not linked to B lim [e.g. Faroe Plateau cod and Faroe haddock (Melanogrammus aeglefinus); ICES, 2005]. The calculation of the true F lim (the F that drives the stock to B lim ) is associated with large uncertainty (ICES, 2003a), for instance because of the assumption of density-dependence

4 Page 4 of 6 K. H. Hauge et al. (Rochet, 2000) and its sensitivity to variations in the exploitation pattern (Cook, 1998). Precautionary reference points The purpose of defining PRPs is to provide management advice that ensures a low probability of crossing the LRPs. The rationale consists of: (i) an approach to reflect the uncertainty of the current stock estimate; (ii) an approach to reflect the uncertainty associated with stock forecasts; and (iii) pragmatic approaches not relating the PRP directly to the LRP. For many stocks, PRPs have not been defined because of lack of data. The debate over whether or not PRPs should reflect assessment uncertainty or prediction uncertainty does not seem to be settled. Although ICES (2003a) expressed a preference for basing B pa on assessment uncertainty, arguing that implementation error is unpredictable and thus cannot be quantified, ICES (2003c) considered approaches based on prediction uncertainty rather than assessment uncertainty, to be consistent with the framework. For most stocks, catch forecasts are based on a two-year extrapolation from the latest estimated stock size, involving assumptions on what is happening to the stock in the current year as well as in the prediction year. Therefore, prediction uncertainty can only be greater than the uncertainty in the estimated stock size entering the prediction. ICES (2003c) recommended a method for estimating PRPs based on retrospective analysis in combination with simulations to test their performance. Whereas a retrospective analysis can include some kind of empirical uncertainty irrespective of its cause, simulation tools are designed to test robustness regarding specific sources of uncertainty. The PRP for Northeast Arctic cod is an example for which these recommendations have been followed (ICES, 2003b, 2005). Notwithstanding, most PRPs have been calculated from a statistical formula based on general considerations of assessment uncertainty. This magic formula, in ICES vernacular, links PRPs to LRPs such that B pa ¼ B lim e 1.645s and F pa ¼ F lim e s.The value corresponds to a probability of 5% of the stock actually being below B lim when a stock is estimated to be at B pa. The value of s (the measure of the uncertainty in the estimates of SSB and F) is usually decided by expert judgment. However, the formula has been shown to underestimate the implied forecast uncertainty (Bertelsen and Sparholt, 2002). Within the rest of the group of pragmatic approaches for devising PRPs, three types can be distinguished. None of them correspond to the PA framework, because they are not based on evaluations of uncertainty of predicted stock status relative to B lim. First, the formula has been used inversely to calculate LRPs from PRPs (e.g. B lim for sole in the Skagerrak and F lim for Faroe Plateau cod; ICES, 2005). Second, B pa is sometimes based on a SSB/R relationship. The rationale for selecting the specific B pa for North Sea cod has been that SSB values below that level have been associated with signs of impaired recruitment (ICES, 2005), which actually appears to confound the definition of the two reference points. Third, if recruitment shows a decreasing trend with increasing SSB (e.g. plaice, Pleuronectes platessa, in the Skagerrak and Kattegat), B pa has been set equal to B loss. PRPs are usually presented as constants and the uncertainties accounted for are based on sorts of historical averages. An exception is the Barents Sea capelin (Mallotus villosus; ICES, 2005), for which a probability analysis is carried out annually, taking into account fluctuations in predation by cod. In this case, the advice allows a,5% probability of crossing B lim, and the uncertainty accounted for from different sources may vary between years. In practice, this is the same as changing B pa from year to year. Of course, reference points may also be changed when data or parameter estimates are revised or when an assessment model is replaced, because such changes can alter our perception of historical time-series. Because both B pa and F pa represent means to avoid B lim, choosing a higher B pa allows for a higher F pa, and vice versa. This interdependence, apart from increasing the complexity of the issue, indicates that there is some flexibility in selecting appropriate PRPs. It seems that the robustness of various reference points to sources of uncertainty is being tested more frequently and more comprehensively with simulation tools, as more harvest control rules are developed for more stocks (ICES, 2006a). However, the question whether all important sources of uncertainty are covered by the PRPs as currently defined remains open. Discussion The development and introduction of the ICES PA framework a decade ago offered considerable intellectual challenges to both assessment scientists and their customers. Although we acknowledge the importance of the ongoing work, which, in addition to developing ways to conceptualize and handle uncertainty, has rendered the issue an integral part of the management advice, we also find reasons to address the limits of what this framework can be expected to achieve. Our description of the various ways the reference points have been defined and implemented is an attempt to initiate an exploration of the conceptual and practical dimensions of the framework in its capacity to enhance transparency in the representation and communication of uncertainty, and in its division of tasks and responsibilities between science and management. The exploration examines how the framework is explained, how the reference points are technically defined, and where the terminology used conceals rather than clarifies important issues. From examining ICES (2005) closely, we conclude, perhaps somewhat surprisingly, that the advice for most stocks does not match the PA framework, as described. The framework largely comes out as a conceptual goal rather than something actually to be implemented. For many stocks, the available data are insufficient to decide on some or any of the reference points. Although the situation may be improved by way of enhanced databases and longer time-series, the practical question remains: how to provide precautionary advice transparently for stocks for which such improvements are unlikely in the near future. Should the PA framework be expanded to accommodate such situations? Generally, a wealth of different methods is used to define reference points. Specific methods may be required to deal with specific biological cases (such as the case of declining recruitment with increasing SSB), or because of data limitations. However, some methods do not seem compatible with the conceptual framework presented, such as F lim being defined without reference to B lim, PRPs that are not based on LRPs, and LRPs that are derived from PRPs. With respect to terminology, it is our opinion that the unwanted state that the PA framework is set up to avoid is defined loosely and inconsistently. The various terms used to describe such a state seem to be chosen rather arbitrarily. If their use is intended, at least the differences should be adequately

5 Limits to transparency in precautionary fisheries management Page 5 of 6 explained. Some terminological ambiguity relates to the risks and uncertainties that B lim and B pa, respectively, are supposed to reflect. As B lim represents a risk of impaired recruitment, B pa represents the risk of a risk of impaired recruitment, which presumably is somewhat unclear to both advisors and their customers. This ambiguity is amplified by the belief expressed by ICES (2005) that the phrases risk of reduced reproductive capacity and suffering reduced reproductive capacity are entirely equivalent. The PA framework does not seem to distinguish clearly between assessment uncertainty and prediction uncertainty. Although the descriptions in the stock status representation (e.g. harvested sustainably ; Figure 1) refer to the current situation, the PRPs are also used in the context of predictions when providing advice. In this case, they have no function in discriminating between stock states, but rather ensure that SSB has a low risk of actually being below B lim, or conversely, F has a low risk of actually being above F lim. Although a few PRPs are designed to reflect prediction uncertainty, the majority appear to reflect assessment uncertainty, or it is largely unclear which of the two (or maybe both) is implied. Whereas this conceptual problem represents an obvious shortcoming of the PA framework as currently applied, it also emphasizes a tendency to under-represent the uncertainty in the advice. This view is supported by Bertelsen and Sparholt (2002), who questioned whether the PRPs take the uncertainty in the advice sufficiently into account. After evaluating the ICES TAC advice for 33 stocks, they concluded that the uncertainty reflected in the PRPs for most stocks is considerably less than the implied uncertainty in the catch predictions on which the advice is based. There also is a tendency to confuse past and present uncertainty, for instance in providing a standard figure that supplies the historical trajectories of SSB and F relative to PRPs (ICES, 2005). Here, past and present uncertainty are mixed when a stock trajectory indicating that SSB has been below B pa (but above B lim ) is taken to imply non-precautionary management. The PA framework clearly needs general revision to clarify its interpretation regarding past, present, and future uncertainty. From the definition of collapse offered in ICES (2005), and because irreversible change and extinction are non-issues in the context of the PA framework, a lay reader of the advice may expect that recovery is always possible. The potential for (semi-) irreversible stock states or extinction, however, is discussed as a serious possibility in the scientific literature (Hutchings, 2000; Myers and Worm, 2003, 2005; Hutchings and Reynolds, 2004). Because extinction or irreversibility seem to involve more complexity than can be expressed within the current PA framework, different means for achieving a transparent communication of uncertainty should be developed (Hauge et al., 2005). Our findings suggest that the PA framework faces some challenges to enhance consistency and transparency, at both conceptual and operational levels. Nevertheless, there may be limits to the extent to which conceptual clarity can be matched with operational clarity. First, each stock is to some extent unique, and what we know about its SSB/R relationship refers to a specific period characterized by a specific set of environmental conditions. Our knowledge may not apply when conditions change. Second, data collections for different stocks are heterogeneous in form and quality. Third, there may be practical dilemmas regarding which uncertainties should be included in the advice, for instance those pertaining to illegal catches or discards. Taken together, these three issues imply that methods for defining reference points, because of ontological and/or epistemological stock-specific differences, may not always be simultaneously plausible and generally applicable. Consequently, the potential for handling uncertainty in a standardized way will not be without limits. Further, because the LRPs and PRPs are presented as a common class in terms of SSB or F and their values are derived from a heterogeneous set of methods and rationales, the PA framework is likely to produce a false expectation of transparency, especially to the customers. In this sense, we argue that its apparent transparency (owing to its conceptual simplicity) is compromised to some extent by ambiguities stemming from the contrast between the general introduction on reference points as a homogenous set of benchmarks and their heterogeneous derivation in practice. When science involves subjective judgement, and management decisions may change depending on this judgement, the roles of science and management become intricately interwoven. Decisions on reference points offer challenges to the classical (Mertonian) ideal of maintaining a distinct separation of science and management. The reason is that the LRPs cannot be purely science-based because there is no exact threshold in nature that helps to define a distinct unwanted state, and because the PRPs cannot be expected to be set based purely on a manager s decision because of the complexity of the uncertainty issues involved. When managers are invited to establish the probability level of crossing an LRP that is acceptable to them (or to society), the complexity of meeting this request should not be underestimated a statement that actually may go some way towards explaining why such invitations are rarely accepted. Understanding what risk levels actually represent in terms of future stock development, as well as the interpretation of different choices in terms of effects on the fishing industry, is extremely difficult, particularly if the design of reference points is associated with uncertainty. Although the framework gives the appearance of allowing for a tidy science politics boundary and a preformatted and objective communication of uncertainty, the actual advice is clouded by conceptual ambiguities and operational diversity in the methods used. Whereas a clear-cut boundary here is premised on our ability to quantify uncertainty and to deduce consequences of management under uncertainty, the actual role division between science and management is far more complex than suggested by the introduction to the ICES PA framework (ICES, 2005). We agree that the PA is value-based, but it does not follow that PRPs degenerate into nothing but a set of values (ICES, 2006b); they remain science-based, but in the sense that science has become politicized to some extent, and that scientific advisors have entered the realm of policy-makers (Jasanoff, 1990). Although a clear role division may remain suitable as an ideal, it may not be within reach, at least not in advisory science, and certainly not in such an uncertain domain as fisheries. If the advice cannot be purged of politics, just as the reverse is true, we should not be embarrassed and look away. Instead, the respective roles should be rendered an issue of explicit deliberation. The current focus on the ecosystem approach and harvest control rules may improve how uncertainty is handled in the ICES advice, but we expect that this will not make the division of tasks between science and management less complex. Nonetheless, a closer communication and interaction across traditional boundaries is likely to increase the potential for collective learning (Argylis and Schön, 1978) in relation to dealing with management under uncertainty (van der Sluijs, 2006).

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