Improving human understanding and design of complex multi-level systems with animation and parametric relationship supports

Abstract

(2015). Improving human understanding and design of complex multi-level systems with animation and parametric relationship supports. Design Science, 1, e3 doi:10.1017/dsj. 2015 AbstractComplex systems are challenging to design, particularly when they contain multi-level organizations with non-obvious relationships among design components. Here, we investigate engineering students' capacity to search for optimal nanoscale biosystem designs with stochastic component and system behaviors. The study aims to characterize information types that facilitate human learning and improve their complex system understanding and design proficiency. It is hypothesized that learning parametric system relationships and/or inter-level causal mechanisms improves design proficiency; these relationships and mechanisms are teachable through software interfaces. Two contrasting learning/design interfaces were developed that presented differing information types: an interface with performance charts that emphasized parametric relationship learning and an interface with agent-based animations that emphasized inter-level causality learning. Users improved on pre-/post-learning design tasks with both interfaces; users who demonstrated inter-level causal relationship understanding, which occurred primarily with the animation interface, had greater improvement. All users were then presented contrasting animations of systems with opposing emergent behaviors, resulting in many more participants demonstrating an understanding of inter-level causal behaviors. These findings reveal the difficulties in understanding and designing multi-level systems and that interactive software tools may convey crucial information that supports engineering design, particularly with respect to the development of reasoning skills for how system components relate across levels.

The D3 Methodology: Bridging Science and Design for Bio-Based Product Development

“…In the simulation, each myosin's structure was designable and linked to behaviors that influence design performance [15]. Human designers interacting with the agent-based simulation gained understanding of the system that improved their ability to search a parametric design space [18]. These studies offer validation for using a parametric design representation for the human-in-the-loop approach.…”

“…Once computational biological models are validated, they may be utilized to evaluate new system configurations that are representative of designs. In our past work, agent-based myosin models were validated with empirical data [16] and provided a means for evaluation in engineering optimization tasks representative of myosin technologies [18]. Since these tasks represent a variety of technologies, an interesting design problem emerges when considering the best set of myosins for use in all technologies, such as selecting myosins from a design catalog.…”

“…In mechanical design domains, there is a great opportunity for leveraging biological components and design principles to develop technologies including nano-actuators, smart contractile materials, and shear-triggered medicines [14]. Recently, we have demonstrated the feasibility in utilizing computational approaches for designing myosin motor protein biosystems informed by scientific experiments [15,16] and have conducted empirical studies to understand and improve human bio-based design decision making [17,18]. In this work, findings and processes from these past studies are utilized to propose and implement a new general methodology that bridges science and design for bio-based product development.…”

Human and Computational Approaches for Design Problem-Solving

“…A human designer may then steer computational processes with a "human-in-the-loop" design approach by making highlevel decisions that guide the computational processes towards more beneficial solutions . In this framework, a human designer could potentially steer computational processes based on knowledge of multilevel parameter interactions that influence qualitatively distinct emergent system behaviours (Egan et al 2015c) and could potentially be difficult to formalize computationally. Empirical research studies can play a role in scientifically determining the most effective way to interface human and computational decision-making processes for solving such design problems.…”

“…One of our recent human participant experiments demonstrated that human understanding of qualitative behaviours across complex system scales improves human design decision-making performance (Egan et al 2015c). However, a precise cognitive mechanism for how designers translate such understanding towards better design decision-making was not identified.…”