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1. Empirical evidence for Kahneman's system 1 and system 2 thinking in design

   Kannengiesser, Udo; Gero, John S. (January 2019, Human Behavior in Design89-100)

   This paper analyses design protocols of professional engineers and engineering students using the FBS schema, testing two hypotheses related to the use of system 1 and system 2 thinking. These two modes of thinking are characterised as: one that is fast and intuitive (system 1), and one that is slow and tedious (system 2). Their relevance for design thinking has already been shown conceptually. This paper provides empirical support for the existence of system 1 design thinking and system 2 design thinking. 

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2. Design thinking, fast and slow: A framework for Kahneman’s dual-system theory in design

   https://doi.org/10.1017/dsj.2019.9  

   Kannengiesser, Udo; Gero, John S. (January 2019, Design Science)

   In his book Thinking, Fast and Slow , Daniel Kahneman presented a model of human cognition based on two modes or ‘systems’ of thinking: system 1 thinking that is fast and intuitive and system 2 thinking that is slow and tedious. This paper proposes a framework for applying Kahneman’s model to designing based on the function–behaviour–structure ontology. It casts four instances of designing in this framework: design fixation, case-based design, pattern-language-based design and brainstorming. 

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3. Distributionally-Informed Recommender System Evaluation

   https://doi.org/10.1145/3613455  

   Ekstrand, Michael D.; Carterette, Ben; Diaz, Fernando (August 2023, ACM Transactions on Recommender Systems)

   Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty. In this paper, we argue for the need for researchers and practitioners to attend more closely to various distributions that arise from a recommender system (or other information access system) and the sources of uncertainty that lead to these distributions. One immediate implication of our argument is that both researchers and practitioners must report and examine more thoroughly the distribution of utility between and within different stakeholder groups. However, distributions of various forms arise in many more aspects of the recommender systems experimental process, and distributional thinking has substantial ramifications for how we design, evaluate, and present recommender systems evaluation and research results. Leveraging and emphasizing distributions in the evaluation of recommender systems is a necessary step to ensure that the systems provide appropriate and equitably-distributed benefit to the people they affect. 

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4. Design Strategies and Optimizations for Human-Data Interaction Systems in Museums

   https://doi.org/10.1109/ICALT49669.2020.00081  

   Alhakamy, A'aeshah; Cafaro, Francesco; Trajkova, Milka; Kankara, Sreekanth; Mallappa, Rashmi; Veda, Sanika (July 2020, 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT))

   Embodied interaction is particularly useful in museums because it allows to leverage findings from embodied cognition to support the learning of STEM concepts and thinking skills. In this paper, we focus on Human-Data Interaction (HDI), a class of embodied interactions that investigates the design of interactive data visualizations that users control with gestures and body movements. We describe an HDI system that we iteratively designed, implemented, and observed at a science museum, and that allows visitors to explore large sets of data on two 3D globe maps. We present and discuss design strategies and optimization that we implemented to mitigate two sets of design challenges: (1) Dealing with display, interaction, and affordance blindness; and, (2) Supporting multiple functionalities and collaboration. 

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5. Design thinking and computational thinking: a dual process model for addressing design problems

   https://doi.org/10.1017/dsj.2021.7  

   Kelly, Nick; Gero, John S. (January 2021, Design Science)

   null (Ed.)

   Abstract This paper proposes a relationship between design thinking and computational thinking. It describes design thinking and computational thinking as two prominent ways of understanding how people address design problems. It suggests that, currently, each of design thinking and computational thinking is defined and theorized in isolation from the other. A two-dimensional ontological space of the ways that people think in addressing problems is proposed, based on the orientation of the thinker towards problem and solution generality/specificity. Placement of design thinking and computational thinking within this space and discussion of their relationship leads to the suggestion of a dual process model for addressing design problems. It suggests that, in this model, design thinking and computational thinking are processes that are ontological mirror images of each other, and are the two processes by which thinkers address problems. Thinkers can move fluently between the two. The paper makes a contribution towards the theoretical foundations of design thinking and proposes questions about how design thinking and computational thinking might be both investigated and taught as constituent parts of a dual process. 

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