Decision support experiments and evaluations using seasonal to ...

Organizational measures to hasten, encourage,
and sustain these knowledge-action systems
must include practices that empower people to
use information through providing adequate
training and outreach, as well as sufficient
professional reward and development opportunities.
Three measures are essential. First, organizations
must provide incentives to produce
boundary objects, such as decisions or products
that reflect the input of different perspectives.
Second, they must involve participation from
actors across boundaries. And finally, they
must have lines of accountability to the various
organizations spanned (Guston, 2001).
Introspective evaluations of the organizations’
ability to learn and adapt to the institutional and
knowledge-based changes around them should
be combined with mechanisms for feedback
and advice from clients, users, and community
leaders. However, it is important that a review
process not become an end in itself or be so
burdensome as to affect the ability of the organization
to function efficiently. This orientation
is characterized by a mutual recognition
on the part of scientists and decision makers
of the importance of social learning—that is,
learning by doing or by experiment, and refinement
of forecast products in light of real-world
experiences and previous mistakes or errors—
both in forecasts and in their application. This
learning environment also fosters an emphasis
on adaptation and diffusion of innovation (i.e.,
social learning, learning from past mistakes,
long-term funding).
4.3.4 The Value of User-
Driven Decision Support
Studies of what makes climate forecasts useful
have identified a number of common characteristics
in the process by which forecasts
are generated, developed, and taught to—and
disseminated among—users (Cash and Buizer,
2005). These characteristics (some previously
described) include:
• Ensuring that the problems forecasters address
are driven by forecast users;
• Making certain that knowledge-action
systems (the process of interaction between
scientists and users that produces forecasts)
are end-to-end inclusive;
• Employing “boundary organizations”
(groups or other entities that bridge the
Decision-Support Experiments and Evaluations using Seasonal to
Interannual Forecasts and Observational Data: A Focus on Water Resources
•
•
communication void between experts and
users) to perform translation and mediation
functions between the producers and
consumers of forecasts;
Fostering a social learning environment
between producers and users (i.e., emphasizing
adaptation); and
Providing stable funding and other support
to keep networks of users and scientists
working together.
As noted earlier, “users” encompass a broad
array of individuals and organizations, including
farmers, water managers, and government
agencies; while “producers” include scientists
and engineers and those “with relevant expertise
derived from practice” (Cash and Buizer,
2005). Complicating matters is that some “users”
may, over time, become “producers” as
they translate, repackage, or analyze climate
information for use by others.
In effective user-driven information environments,
the agendas of analysts, forecasters,
and scientists who generate forecast information
are at least partly set by the users of
the information. Moreover, the collaborative
process is grounded in appreciation for user
perspectives regarding the decision context in
which they work, the multiple stresses under
which they labor, and their goals so users can
integrate climate knowledge into risk management.
Most important, this user-driven outlook
is reinforced by a systematic effort to link the
generation of forecast information with needs
of users through soliciting advice and input
from the latter at every step in the generation
of information process.
Effective knowledge-action systems do not
allow particular research or technology capabilities
(e.g., ENSO forecasting) to drive the
dialogue. Instead, effective systems ground
the collaborative process of problem definition
in user perspectives regarding the decision
context, the multiple stresses bearing
on user decisions, and ultimate goals that the
knowledge-action system seeks to advance. For
climate change information, this means shifting
the focus toward “the promotion of broad, userdriven
risk-management objectives, rather than
advancing the uptake of particular forecasting
There is an emerging
consensus that the
utility of information
intended to make
possible sustainable
environmental
decisions depends
on the “dynamics of
the decision context
and its broader
social setting”.
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