1. Farmers who: 1) want to retain full control over their land (private property rights issue); 2) want to maximize farm revenue; and 3) have concern for erosion control.
2. Conservationist (public and private) who want to conserve the ecological vitality of the wetland community.
3. Regional economists who are looking for ways to diversify and bolster the weak regional economy of this area through: 1) agricultural; 2) industrial; and 3) recreational opportunities.
To help address issues of natural resource management The Nature Conservancy (TNC) and the Natural Resource Conservation Service established the Resource Planning Committee (RPC). This committee, comprised wholly of individuals who reside within the watershed, is charged with the responsibility of prioritizing natural resource problems and identifying feasible solutions. A support committee has also been established to provide technical advise to these individuals. My role on this committee is to provide information on how GIS and SDSS technologies can be used to help understand and solve natural resource related problems in the Cache River watershed. In addition, I am participating in a TNC-funded research project that will investigate the impact of alternative resource policy and management scenarios on the economy, hydrology, and ecology of Cache River watershed. Collectively, these planning and research activities directly address many of the issues relevant to ecosystem management in multiple- ownership landscapes and indirectly address many of the challenges associated with the development of CSDM for ecological problem solving.
To be successful, an ecosystem management plan must represent a consensus of all decision making classes. Before we can design a CSDM system that can support this kind of consensus building we must understand how policy and management initiatives effect interrelated human, biological and physical processes through time and space. To gain this understanding we must identify and model key system processes and inter-system flows. Key processes, as defined here, determine how human and bio-physical systems behave and inter-system flows determine how these systems interact. To completely support the decision making classes identified above and help individuals evaluate how well alternative management scenarios meet their objectives it is necessary to construct models that predict how land management practices will change given alternative policy scenarios and then to trace the effect of these decisions through economic, sociologic, hydrologic, and biologic systems.
These systems, however, operate over different spatio-temporal scales (see Table 1). For example, the decision making process of individual farmers is driven more by economics, public policies, and peer pressure than, the rate at which soil moves across a field, the rate at which species disperse, or even the spatial pattern of soil productivity. Decisions based on these socioeconomic factors influence landscape structure at a particular point in time and for a particular tract of land; i.e., they are spatially and temporally discrete. Furthermore, these decisions are made relatively frequently (e.g., seasonally or annually) and, as such, changes in the socioeconomic system can quickly effect the form and function of an agricultural landscape. Bio-physical subsystems are, on the other hand, driven by continuous processes that govern the storage, transport, and use of energy and matter, and often are formed by events and processes that set the ecological stage for millennia. Because human and bio-physical processes operate over multiple spatio-temporal scales the links between these systems are often indirect. Yet, an understanding of how these linkages operate through space and time is imperative to the success of ecosystem management.
TABLE 1. Characteristics of Human and Bio-physical Systems Process Characteristic Human System Bio-physical System Temporal pattern More Discrete More Continuous Spatial pattern More Discrete More Continuous Spatial Model More Object Based More Field Based Response time 3-10 years 100-1000 years Response type Active/ Passive/Reactive Proactive Interaction Weakly linked Strongly linked Behavioral pattern More Probabilistic and probabilistic deterministic Model of behavior More More math-based knowledge-base Flows between systems More aspatial More spatial Flow "Currency" $, cultural, Matter/energy policy restrictions
This notion of a society of mind seems well suited to our CSDM problem. It is easy to conceptualize decision makers as a society of complex interacting agents. Classes of decision makers (e.g. farmers, environmentalist) form agencies that works toward a common goal. Classes that possess similar goals could even form super-agencies through compromise and collaboration. These human agents will need access to models and data to determine how effective alternative management scenarios are in meeting their goals. A CSDM system, therefore, needs automated agents that:
1. store, manage, access, analyze, and display data,
2. store, manage, access, execute, analyze, and display models,
3. evaluate, compare, and display differences between competing scenarios; and
4. coordinate, facilitate, and document the decision making process.
By structuring the CSDM as a "society of decision making" we can develop conceptual models for specific CSDM systems by defining a network of interacting human and automated agents. Furthermore, these models would provide a relatively straight forward path to implementation through the design and construction of automated intelligent agents (Edmonds et al., 1994). Note that the rudiments of this approach exists in the CSDM literature. Armstrong (1994) discusses the need for agency in the design of CSDM user interfaces and user communication technologies and Armstrong's (1993) trace function is analogous to Minski's k-line.
Consider, for example, a ring of workstations (distributed processing), one workstation for each decision making agency, and a central workstation the provides a forum for debate and compromise. Each agency submits potential solutions to this forum, or takes out contributions from other agencies that it believes can be modified to help further their goals. This "debate" machine could store and manage commonly held resources such as data and models. Agencies would work in parallel, somewhat in isolation but collaborating with other agencies through the central "debate" machine and through inter-agency communication utilities (implemented as automated intelligent agents that coordinate and facilitate the use of white board and message posting technologies). Each agency would have an agenda (i.e. a set of objectives) that can, in part, be evaluated as a set of metrics that they are trying to maximize or minimize (e.g. multiple objective functions). This agenda could be modified as new information is provided or alliances emerge between collaborating agencies. An agency's agenda would be considered met when all metrics fall within a predefined range.
The consensus building process would begin when agencies select initial solutions. Each agency would then request automated agents to: 1) calculate all metrics relevant to all groups and then contribute this knowledge to a collective pool (cooperative system); or 2) calculate just those metrics important to itself (uncooperative system). Some of these metrics would be derived from the simulation of spatial processes. Differences in spatial and temporal scale that exist among these processes must be reconciled by "coordinating agents" that are part of a larger agency of modelbase managers. Note that the way in which this coordination occurs is an important topic for future research. Through an iterative process initial solutions evolve to more acceptable states. This process could proceed through a generate and test approach or, conceivably, feasible solutions could evolve through the application of genetic algorithms (see e.g. Dibble and Densham, 1993). Scientific visualization could enhance the users ability to perform quick qualitative assessments of the impacts that management scenarios have on the ecosystem and it could improve inter-agency communication At an even higher level of agent complexity we could envision broker-agents that analyze potential solutions and recommend options, data, and models to agencies that may be able to make use of them. This would require machine learning to develop associations between specific modifications and concomitant outcomes (i.e. a broker-agent learns inductively about what works for what situations and acts in an unbiased manner to provide that knowledge to individual groups).
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