Design for Market Systems

Recognizing the value in understanding the interplay among engineering, business, and policy decisions, the engineering design research community put forward the “design for market systems” (DMS) framework, an extension of “decision-based design” (Hazelrigg, 1999; Lewis et al., 2006; Michalek, 2008). DMS supports concurrent technical and non-technical decision making and optimization by companies, while accounting for consumer and government decisions. The figure below summarizes the key stakeholders in DMS along with their interactions, decision capacities, common modeling approaches, and overarching objectives. These studies primarily include engineering analysis models that govern product performance as a function of individual design decisions, along with the integration of a subset of theories, techniques, and models of product costs, consumer choice, economic game theory, and optimization.

Agent-based modeling in DMS

Some of our recent work in the Design SPACE Laboratory is to explore the use of agent-based modeling (ABM) to advance the capabilities of the DMS framework. ABM, in contrast to other complex systems modeling approaches such as system dynamics and discrete event simulation, is a generative approach to forecasting system behavior; it models individual-level behavior through “agents,” and the results of their decisions and interactions produces system-level responses (Bonabeau, 2002; Wellman, 2016). These responses can then be traced back to that individual-level behavior. For example, if an ABM simulation of a car market shows a system-level response that results in decreased carbon dioxide emissions, the modelers can investigate what design decisions by the automakers and what purchase decisions by the consumers led to this decrease; this can inform policy makers who may wish to encourage these behaviors through regulations or market signals.

We have generated a proof-of-concept ABM that simulates consumer-producer interactions in an automobile market system. The resulting simulation provides insights for producers on how to update their design decisions to improve their market performance, as well as how policy makers can use regulatory mechanisms to encourage a more fuel-efficient vehicle fleet. This work is presented in the following paper:

Zadbood, A. and Hoffenson, S. (2017), “An Agent-Based Model of Consumer and Producer Behavior in an Automobile Market System,” ASME International Design Engineering Technical Conferences, Cleveland, Ohio, 6-9 August

Social network word-of-mouth in DMS

Another study is looking into the incorporation of social network models within the DMS framework. When using an ABM modeling approach, individual agents can be connected to one another through social relationships. Through these connections, product recommendations are often shared, either in person or online, through word-of-mouth. This phenomenon can have substantial impacts on product success, and understanding this word-of-mouth spread can help producers in designing more successful products. This work is presented in the following paper:

Zadbood, A., Russo, N., and Hoffenson, S. (2019), "Word-of-mouth recommendations in an automobile market system," ASME International Design Engineering Technical Conferences, Anaheim, California, August 18-21.

Key references

Bonabeau, E. (2002). “Agent-based modeling: Methods and techniques for simulating human systems.” Proceedings of the National Academy of Sciences of the United States of America (PNAS), 99 (suppl 3) 7280-7287.

Hazelrigg, G. (1998). “A framework for decision-based engineering design.” Journal of Mechanical Design, 120(4):653-658.

Lewis, K., Chen, W., and Schmidt, L. (2006). Decision Making in Engineering Design, New York: ASME Press.

Michalek, J.J. (2008). “Design for market systems: Integrating social, economic, and physical sciences to engineer product success.” Mechanical Engineering: The Magazine of ASME, 130:32–36.

Wellman, M.P. (2016). “Putting the agent in agent-based modeling.” Autonomous Agents and Multi-Agent Systems, 1-15.