Institutional Change

All human activities, including science, are essentially social in nature [14]. It is often an unpleasant shock to science students to learn that effective presentation of their results in talks and professional papers is as important (if not more so) to their careers as is the quality of the science they do. The information communicated through these peer activities is as much about the character and ability of the researchers as about the research findings. As Miller notes, social scientists studying the practice of science have found that science "turns out to look a lot like other social institutions, full of norms, beliefs, ideologies, practices, networks, and power and deeply engaged in the production and management of social order" [30].

When reformers attempt to introduce new institutional frameworks, they are faced with the task of deinstitutionalizing old rules and norms [31]. At a fundamental level, changing institutions translates into changing collective human behavior. This is an extremely difficult task [32,33]. Established patterns of behavior are habitual, and require little conscious thought. Uncertainty is relatively low because past experience provides good information about other people's reactions and responses, and about localized, short-term results. Any change to established behaviors and procedures introduces uncertainty about responses and outcomes, and thus is inherently risky for the responsible individuals and organizations. At organizational and societal levels, this resistance to change is referred to as institutional inertia. Three very common and powerful sources of institutional inertia are sunk costs, uncertainty, and political conflict. These reduce the attractiveness of institutional alternatives and act as a barrier to any switchover to new institutions [34]. Institutional inertia is not always a bad thing; it can be useful in "normal" situations, when past experience is a good predictor of future outcomes. Institutional inertia helps guard against changes that seem good over the short term, but which produce negative outcomes over longer time periods. However, this resistance to change also hinders our ability to recognize and implement necessary adaptations when dealing with novel events and circumstances.

Our science institutions are slowly beginning to change in response to changes in our understanding of complexity, as well as to related changes in the demands of both public and private sector funding sources. Transdisci-plinary, systems-based approaches to science research are beginning to gain a foothold in academia, through the boundary-spanning activities of programs located outside of individual departments and disciplines, such as research centers and institutes that are organized around particular societal problems and research questions. Such programs are not replacing the traditional departments and disciplines, but instead are augmenting them. The increasing importance of such programs is a promising sign that the institutional adaptations needed to deal with complexity are beginning to occur. However, the adaptations are progressing very slowly, as any junior faculty member who is attempting to conduct transdisciplinary research can attest. The reductionist paradigm still dominates in the organizational practices of universities, most notably in those related to hiring, promotion, and tenure decisions.

Changes in public sector management institutions are occurring even more slowly than in science institutions. The organizational structure of modern academic institutions has evolved to maintain stability of the organization as a whole, while supporting individual creativity and innovation. Although intellectual orthodoxies have an undeniable influence, academic institutions inherently value the concept of adaptation to new knowledge. In contrast, governmental management institutions tend to be cautious and risk-averse, and are strongly influenced by political and legal considerations. Any change in the status quo will produce "winners" and "losers," particularly in the short-term. Those who currently have wealth, power, and influence will resist changes that they believe will shift that wealth, power, and influence from them to others. Thus, special conditions are required for real change to occur.

Example: Science Impact Program (SI), United States Geological Survey (USGS).

Overcoming the considerable barriers to institutional change requires both dedicated champions and strong support from above. The SI of the USGS provides an example of the challenges facing institutional change efforts. Science Impact Program is described as a focused research effort to increase the use and value of USGS science in decision-making. The strategy for accomplishing this is for SI staff and collaborators to "develop methods, tools, and techniques to build explicit linkages between science and societal decision makers." A feature of SI that distinguishes it from USGS standard practice is active engagement of community decision makers and stakeholders in all phases of project planning and implementation. Another difference is an increased attention to integrating the social and economic aspects of the problem situation into the scientific analysis.

After receiving minimal funding for approximately three years through the Director's Venture Capital Fund, SI became a formal Program within the Survey's Geography Discipline in September of 2004, as part of a major reorganization of the USGS by outgoing Director Charles Groat. Acknowledging a lack of needed expertise in some areas, SI has established external partnerships with five academic institutions. Most relevant to this paper is the partnership with Massachusetts Institute of Technology (MIT). The MIT-USGS Science Impact Collaborative (MUSIC) is based in the Department of Urban Studies and Planning at the MIT. MIT-USGS Science Impact Collaborative is testing ways in which joint fact finding (JFF) can be used to promote collaborative planning in science-intensive policy disputes.

U.S. environmental agencies are supposed to maintain a separation between the technical and political (or policy) aspects of their decisions. Agencies have had to determine when they are doing science (which is permitted) and when they are doing policy (which is not) [35]. MIT-USGS Science Impact Collaborative has addressed this issue, arguing that:

By initiating the Science Impact program, the USGS becomes a champion for using science in decision making, without being an advocate for specific decisions. Although this boundary may appear to be less distinct as we attempt to cast our science more in a decision-making framework, the USGS must preserve its non-advocacy policy. We can act neither as advocates nor as consultants in this effort. The high degree of trust that our agency enjoys derives largely from our impartiality and our commitment to providing broad public benefits through our work. Admittedly, the boundary between supplying scientific information for decision making and recommending a course of action sometimes becomes somewhat difficult to delineate. Even when our scientific information seems to lead toward a certain solution or decision, however, we must present the scientific information in such a way as to refrain from making specific policy recommendations [36].

One way in which MUSIC is seeking to implement this vision is by providing training to USGS scientists in skills such as facilitation, mediation, and communication.

The SI champions and partners have encountered a number of barriers to institutional change, primarily linked to lack of funding coupled with limited opportunities for discourse among SI champions and the majority of the research staff. Science Impact Program has not been a line item in the USGS budget, therefore the very limited funding given to SI for program development was taken from established programs. Given the internal competition among programs and projects for limited funds, resentment of SI from within the organization developed before SI had a chance to introduce itself and its mission to potential partners and collaborators within the organization.

A related barrier involves cultural differences between researchers in the biophysical and the social sciences, as well as between researchers and practitioners. U.S. Geological Survey researchers, who overwhelmingly come from biophysical science backgrounds and institutions, have expressed difficulty in recognizing the differences between the SI approach and their past practice. My SI colleagues and I have frequently encountered statements along the lines of "we're already doing that." In the few instances where the SI champions have had the opportunity to explore the question with their colleagues through extended face-to-face interactions, the differences were made clearer, greater shared understanding was achieved, and resistance to SI concepts was reduced [37].

Another major barrier to success for SI is lack of an active champion within the highest echelons of the Survey. Interviews of USGS researchers that I have conducted suggest that SI has, in effect, received official permission to proceed from the USGS leadership, but with little concrete evidence of true commitment to the program. Few who were not directly involved with the program's inception knew that it exists. When known of at all, SI was lumped in with a number of organizational changes announced by former USGS Director Charles Groat soon before his departure from the Survey. Several of the people interviewed said that they did not understand the reasons for the whole suite of changes, or how they are to be implemented. Thus, SI has been buried within an internally controversial cluster of bureaucratic changes rather than being highlighted by the leadership as a positive addition to the methods and approaches available to Survey personnel. Unless active steps are taken to shift this situation, SI's long-term survival is in doubt.

Adaptive Management has been proposed as an integrated, multidisciplinary approach for confronting uncertainty in natural resource issues [38-40]. Adaptive management assumes that scientific certainty cannot be achieved prior to taking action, and thus treats every management plan as a set of testable hypotheses and every management action as an experiment. In any adaptive management effort, the role of science is primarily that of monitoring progress toward predefined goals. In other words, science steps in after the problem is well-defined and after desired goals are identified through political means [16].

Although appealing in theory, adaptive management has proven very difficult to implement, and has been repeatedly misapplied or misappropriated [41,42]. Many applications of adaptive management have stopped at the assessment phase and have failed to implement meaningful changes in management. The most promising adaptive management experiments in the United States mainly involve relatively simple institutional settings, with a single lead management agency and a few dedicated people who have organized and maintained the experimental initiative. Adaptive management has been more problematic in complex institutional settings such as those connected to the Florida Everglades, Columbia River, and Upper Mississippi River, where management, research, and policy change involve collaboration among several agencies with complicated, overlapping historical responsibilities and legal mandates [42]. Various reasons have been offered for low success rates in implementing adaptive management, most of which have to do with cost and institutional barriers [41,43-46].

It takes a long time to develop and implement an effective adaptive management effort, and even longer before positive outcomes are observed. In what Lee considers adaptive management's high-water mark, a project to deal with a declining ground fish fishery off northwest Australia, the adaptive learning program took about a decade to yield practical results [47]. Two factors contribute towards this slow progress. First, many of the critical processes that impact the natural system components and conditions of interest operate at timescales that are very long compared with those of management and decision making processes. Secondly, implementation of collaborative and adaptive processes requires a good working relationship among the responsible organizations and individuals. The necessary trust-based relationships are built through repeated interaction, and take time to develop. Lee argues that a collaborative structure should be in place before an adaptive exploration of the landscape gets underway. Too often, however, adaptive management efforts concentrate on developing models of biophysical processes and essentially ignore social dynamics.

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