In high–risk domains like aviation, medicine and nuclear power plant control, automation has enabled new capabilities, increased the economy of operation and has greatly contributed to safety. However, automation increases the number of couplings in a system, which can inadvertently lead to more complexity from the perspective of the operator. The automation of a system transforms the work domain of the human operator, and his role changes from controlling the core processes to managing the automated processes. The complexity of the automation and the lack of proper support can make the control task’s overall difficulty larger than it needs to be, restricting safety, productivity, and efficiency. To address and limit the automation introduced complexity in the operator’s work domain, and to find representations to support him, the ecological approach to automation design was taken. The ecological approach focuses on the relationship between the human operator and his work domain including the system he is controlling. The main research goals were to find how the ecological approach could be used to help limit the automation introduced complexity, and how the ecological approach could be used to support the human operator in controlling automated processes. The formulation of Ecological Automation Design (EAD) was based on the Ecological Interface Design (EID) paradigm. One of the main underlying questions asked about the interface between the work domain and the human operator is: “how to represent work domain complexity?". The inter face design paradigm was transformed into an automation design paradigm by first separating the automation component from the work domain and asking the same underlying question about the interfaces between the work domain, the human operator, and the automation. Then, the conceptual shared domain representation was defined to visualize that the apparent complexity of the system could be reduced when both the human operator and the automation view the same representation of constraints that the work domain imposes on control. As part of the ecological approach, Work Domain Analysis (WDA) was used to analyze and represent the constraints in a work domain. However, WDA is not yet fully developed and suffers from some methodological and conceptual issues. The research therefore, focused on the further development and extension of WDA to include the representation of automated processes. Four case studies were conducted, and each case study generated new insights into the application of and extension of WDA. In the first case study, EID was applied to the design of the Energy Augmented Tunnel In the Sky display. This display was designed to aid a pilot to fly the approach to landing by presenting energy management information. The WDA revealed the significance of the energy coupling between vertical flight path and speed control as an intermediate control goal. Based on the analysis, a creative design process resulted in a novel display that has the energy representations fully, and graphically integrated in the tunnel in the sky display. A preliminary evaluation indicated that the additional energy management information shown in relation to the control actions and control goals helped pilots to fly the approaches. The display is not expected to give a performance increase but to change the way in which pilots control the throttle and elevator to fly approaches. The second case study was the analysis of the already existing Total Energy Control System (TECS). TECS is an unconventional automated flight control system that was based on the same energy management constraints as that were represented in the energy augmented display of the first case study. The design of TECS was mapped onto the abstraction hierarchy to represent the energy management principles as part of the whole automated system. The analysis and useful representation of TECS using the abstraction hierarchy was not straightforward. It involved a search for the interpretation of the levels of the abstraction hierarchy and the use of the means–ends relationship in conjunction with the aggregation relationship. The resulting WDA showed that the abstraction hierarchy could be used to map out the reasons for TECS’s design features. Many constraints were represented in the same space, which cluttered the energy management principles. The focus was put on the energy management principles through selective aggregation of the represented functions, but other design principles were omitted. To provide a complete representation of the system but without the clutter, the levels of control sophistication were introduced to represented nested control problems separately. At each level of control sophistication the abstraction hierarchy was applied, resulting in the Abstraction–Sophistication Analysis (ASA). In the third case study, the ASA framework was used to guide the design of SmartUAV. SmartUAV is a newly designed mini–UAV system that is capable of controlling multiple small UAVs from a laptop computer. By designing and developing SmartUAV we gained hands–on experience with how WDA, and especially the ASA, helped to keep track of and deal with the automation introduced constraints in the design phase. The levels of control sophistication were used from the beginning to separate the different control problems in the domain. They ranged from flying the platform to the achievement of missions. Starting at the lowest level of control sophistication, each higher level allowed the designer to include a larger part of the complete work domain incrementally, and to focus on more sophisticated control of the UAV. Furthermore, the ASA supported the visualization of how automation transformed the work domain, thus how automated functionalities that were created at lower levels of control sophistication affected the (automated) functions at higher levels of control sophistication. This study showed that the ASA could span a much larger problem space than the original WDA through the nesting of abstraction hierarchies. The ASA provided a systematic way to address the abstraction of the control problems (levels of control sophistication) and the abstraction of functions per control problem (abstraction hierarchy). The fourth case study dealt with the analysis of a subset of a well structured domain that lacks automation; sailboat racing. This study generated a clearer view on the nested structure that is inherent in a work domain, as apposed to the nested structure of the automation as found in TECS and SmartUAV. The nested structure inherent to this work domain was found to be the result of how sailboat racing has evolved over time, based on the capabilities of equipment, human performance and the racing rules. Due to the lack of automation, it became clear that human performance is in fact part of the work domain, in contrast to the original formulations of WDA. The crew’s performance formed the basis for achieving the more sophisticated control of boat speed, tactics and strategy, thus was essential in the analysis. It was shown that the performance of the human crew could be represented in the ASA at a level of control sophistication, while this could not be supported in a non–nested WDA based on a single abstraction hierarchy. The four case studies exemplified WDA and led to its extension with a structure to explicitly nest abstraction hierarchies that map out different control problems: the ASA. Through generating the analyses, extensive modeling experience with the abstraction hierarchy was obtained, reducing its ambiguity and potential methodological and conceptual problems. We found that the abstraction hierarchy could be used to model the structure of the knowledge about a work domain but could not model the knowledge itself. Therefore, the abstraction hierarchy is a framework for structuring knowledge, linking different representations of a control problem, and explaining the reasons for design features of a system. The abstraction hierarchy addressed the abstraction of elements belonging to a control problem, and the levels of control sophistication addressed the abstraction of the control problem itself. Representations in the ASA framework ranged from physical at the lower levels of control sophistication to non–physical at the higher levels of control sophistication. It allowed the structuring of, for example: the sailboat racing rules at the higher levels, and the law of conservation of energy at the lower levels. Although the application of the ASA did not inherently reduce the complexity of the design of SmartUAV, it enabled us to better understand the elements of the work domain that contribute to complexity of the system prior to and during its design. The extension of work domain analysis with the levels of control sophistication has led to a richer representation of the studied work domains than a single abstraction hierarchy or the abstraction–decomposition space.