Process Models in Design and Development

Agent-based task network models

Finally, agent-based models (ABMs) have been developed that combine meso-level task relationships with micro-level models of agent behaviour. Such models offer the possibility to study factors impacting a process in a more realistic context than the other models described in this section. For instance, they can incorporate factors such as organisational structures and the many non-design activities that project participants must attend to - such as going to meetings, chasing colleagues for information, and other coordination activity that emerges as a project unfolds.

In one influential example, the virtual design team (VDT) developed by Cohen, Christiansen, and colleagues represents individual designers and managers in a project as information-processing agents. These agents interact by generating and responding to messages according to rules. Messages can involve passing design information between tasks and also the handling of exceptions, which occur when an agent must stop work and seek more information before they can complete their assigned task. In the model, message handling depends on factors such as the organisation structure and communication tools available. Later developments of the VDT accounted for additional influences such as incongruency between actors’ goals. Levitt et al. discuss a case study of satellite launch vehicle design, in which the VDT was used to evaluate the impact of proposed changes such as increasing individuals’ skill levels and improving alignment of their objectives. Other ABMs developed for the DDP context include the Agent Model for Planning and rEsearch of eaRly dEsign (AMPERE), which focuses on studying the impact of requirements changes during design, and the model of Crowder et al., which focuses on the factors involved in effective team working.

Some advantages of ABMs were discussed at the start of this subsection. In addition, it may be noted that ABMs can represent the decisions of situated actors and thus may be well suited to account for the responsive and emergent facets of the DDP. In terms of disadvantages, developing an ABM requires complex configuration or programming of a specialised tool and may be beyond the reach of many would-be modellers. Second, the models are each unique and do not lend themselves to graphical representation. As a result, their mechanics can be opaque except to their creator, which might lead to credibility concerns. Finally, although ABMs might be helpful to build understanding of the factors influencing DDP performance, they cannot easily be used to document or prescribe a process.