| dc.description.abstract | The purpose of this work is to improve understanding of existing and proposed decision systems, ideally to improve the design of future systems. A "decision system" is defined as a collection of
information-processing components -- often involving humans and automation (e.g., computers)
-- that interact towards a common set of objectives. Since a key issue in the design of decision
systems is the division of work between humans and machines (a task known as "function
allocation"), this report is primarily intended to help designers incorporate automation more
appropriately within these systems.
This report does not provide a design methodology, but introduces a way to qualitatively analyze
potential designs early in the system design process. A novel analytical framework is presented,
based on the concept of "semi-Structured" decision processes. It is believed that many decisions
involve both well-defined "Structured" parts (e.g., formal procedures, traditional algorithms) and
ill-defined "Unstructured" parts (e.g., intuition, judgement, neural networks) that interact in a
known manner. While Structured processes are often desired because they fully prescribe how a
future decision (during "operation") will be made, they are limited by what is explicitly
understood prior to operation. A system designer who incorporates Unstructured processes into
a decision system understands which parts are not understood sufficiently, and relinquishes
control by deferring decision-making from design to operation. Among other things, this design
choice tends to add flexibility and robustness. The value of the semi-Structured framework is
that it forces people to consider system design concepts as operational decision processes in
which both well-defined and ill-defined components are made explicit. This may provide more
insight into decision systems, and improve understanding of the implications of design choices.
The first part of this report defines the semi-Structured process and introduces a diagrammatic
notation for decision process models. In the second part, the semi-Structured framework is used
to understand and explain highly evolved decision system designs (these are assumed to be
representative of "good" designs) whose components include feedback controllers, alerts,
decision aids, and displays. Lastly, the semi-Structured framework is applied to a decision
system design for a mobile robot. | en |
