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1. Undergraduate Student Learning of Market-Driven Design Topics in a Third-Year Design Course

   Hoffenson, Steven; Pitterson, Nicole (July 2021, 2021 ASEE Virtual Annual Conference)

   Generally, the focus of undergraduate engineering programs is on the development of technical skills and how they can be applied to design and problem solving. However, research has shown that there is also a need to expose students to business and society factors that influence design in context. This technical bias is reinforced by the available tools for use in engineering education, which are highly focused on ensuring technical feasibility, and a corresponding lack of tools for engineers to explore other design needs. One important contextual area is market systems, where design decisions are made while considering factors such as consumer choice, competitor behavior, and pricing. This study examines student learning throughout a third-year design course that emphasizes market-driven design through project-based activities and assignments, including a custom-made, interactive market simulation tool. To bridge the gap between market-driven design and engineering education research, this paper explores how students think about and internally organize design concepts before and after learning and practicing market-driven design approaches and tools in the context of an engineering design course. The central research question is: In what ways do student conceptions of product design change after introducing a market-driven design curriculum? In line with the constructivism framework of learning, it is expected that student conceptions of design should evolve to include more market considerations as they learn about and apply market-driven design concepts and techniques to their term projects. Four different types of data instruments are included in the analysis: Concept maps generated by the students before and after the course, open-ended written reflection assignments at various points in the semester, surveys administered after learning the market simulation tool and at the end of the course, and final project reports in which student teams listed their top 3-5 lessons learned in the course. Using the changes between the pre- and post-course concept maps as the primary metric to represent evolving design conceptions, data from the reflections, surveys, and reports are evaluated to assess their influence on such learning. Because market-driven design is a multi-faceted topic, a market-driven design is hierarchically decomposed into specific sub-topics for these evaluations. These include profitability (which itself encompasses pricing and costs), modeling and simulation, and market research (which encompasses consumers and competition). For each topic, correlation analyses are performed and regression models are fit to assess the significance of different factors on learning. The findings provide evidence regarding the effectiveness of the course’s market-driven design curriculum and activities on influencing student conceptions of design. 

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2. Undergraduate Student Learning of Market-Driven Design Topics in a Third-Year Design Course

   Hoffenson, Steven Pitterson (July 2021, American Society of Engineering Education)

   Generally, the focus of undergraduate engineering programs is on the development of technical skills and how they can be applied to design and problem solving. However, research has shown that there is also a need to expose students to business and society factors that influence design in context. This technical bias is reinforced by the available tools for use in engineering education, which are highly focused on ensuring technical feasibility, and a corresponding lack of tools for engineers to explore other design needs. One important contextual area is market systems, where design decisions are made while considering factors such as consumer choice, competitor behavior, and pricing. This study examines student learning throughout a third-year design course that emphasizes market-driven design through project-based activities and assignments, including a custom-made, interactive market simulation tool. To bridge the gap between market-driven design and engineering education research, this paper explores how students think about and internally organize design concepts before and after learning and practicing market-driven design approaches and tools in the context of an engineering design course. The central research question is: In what ways do student conceptions of product design change after introducing a market-driven design curriculum? In line with the constructivism framework of learning, it is expected that student conceptions of design should evolve to include more market considerations as they learn about and apply market-driven design concepts and techniques to their term projects. Four different types of data instruments are included in the analysis: Concept maps generated by the students before and after the course, open-ended written reflection assignments at various points in the semester, surveys administered after learning the market simulation tool and at the end of the course, and final project reports in which student teams listed their top 3-5 lessons learned in the course. Using the changes between the pre- and post-course concept maps as the primary metric to represent evolving design conceptions, data from the reflections, surveys, and reports are evaluated to assess their influence on such learning. Because market-driven design is a multi-faceted topic, a market-driven design is hierarchically decomposed into specific sub-topics for these evaluations. These include profitability (which itself encompasses pricing and costs), modeling and simulation, and market research (which encompasses consumers and competition). For each topic, correlation analyses are performed and regression models are fit to assess the significance of different factors on learning. The findings provide evidence regarding the effectiveness of the course’s market-driven design curriculum and activities on influencing student conceptions of design. 

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3. Optimal Taxation and R&D Policies

   https://doi.org/10.3982/ECTA15445  

   Akcigit, Ufuk; Hanley, Douglas; Stantcheva, Stefanie (January 2022, Econometrica)

   We study the optimal design of corporate taxation and R&D policies as a dynamic mechanism design problem with spillovers. Firms have heterogeneous research productivity, and that research productivity is private information. There are non‐internalized technological spillovers across firms, but the asymmetric information prevents the government from correcting them in the first best way. We highlight that key parameters for the optimal policies are (i) the relative complementarities between observable R&D investments, unobservable R&D inputs, and firm research productivity, (ii) the dispersion and persistence of firms' research productivities, and (iii) the magnitude of technological spillovers across firms. We estimate our model using firm‐level data matched to patent data and quantify the optimal policies. In the data, high research productivity firms get disproportionately higher returns to R&D investments than lower productivity firms. Very simple innovation policies, such as linear corporate taxes combined with a nonlinear R&D subsidy—which provides lower marginal subsidies at higher R&D levels—can do almost as well as the unrestricted optimal policies. Our formulas and theoretical and numerical methods are more broadly applicable to the provision of firm incentives in dynamic settings with asymmetric information and spillovers, and to firm taxation more generally. 

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4. Decentralized, communication-and coordination-free learning in structured matching markets

   Maheshwari C; Sastry S; Mazumdar E (December 2022, Advances in neural information processing systems)

   We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other side, the firms, through repeated interaction while competing with other agents for successful matches. We propose a class of decentralized, communication- and coordination-free algorithms that agents can use to reach to their stable match in structured matching markets. In contrast to prior works, the proposed algorithms make decisions based solely on an agent’s own history of play and requires no foreknowledge of the firms’ preferences.Our algorithms are constructed by splitting up the statistical problem of learning one’s preferences, from noisy observations, from the problem of competing for firms. We show that under realistic structural assumptions on the underlying preferences of the agents and firms, the proposed algorithms incur a regret which grows at most logarithmically in the time horizon. However, we note that in the worst case, it may grow exponentially in the size of the market. 

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5. Coordination of verification activities with incentives: a two-firm model

   https://doi.org/10.1007/s00163-020-00352-7  

   Kulkarni, Aditya U.; Wernz, Christian; Salado, Alejandro (January 2020, Research in Engineering Design)

   null (Ed.)

   In systems engineering, verification activities evaluate the extent to which a system under development satisfies its requirements. In large systems engineering projects, multiple firms are involved in the system development, and hence verification activities must be coordinated. Self-interest impedes the implementation of verification strategies that are beneficial for all firms while encouraging each firm to choose a verification strategy beneficial to itself. Incentives for verification activities can motivate a single firm to adopt verification strategies beneficial to all firms in the project, but these incentives must be offered judiciously to minimize unnecessary expenditures and prevent the abuse of goodwill. In this paper, we use game theory to model a contractor-subcontractor scenario, in which the subcontractor provides a component to the contractor, who further integrates it into their system. Our model uses belief distributions to capture each firm’s epistemic uncertainty in their component’s state prior to verification, and we use multiscale decision theory to model interdependencies between the contractor and subcontractor’s design. We propose an incentive mechanism that aligns the verification strategies of the two firms and using our game-theoretic model, we identify those scenarios where the contractor benefits from incentivizing the subcontractor’s verification activities. 

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