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1. Empirical Evidence for Kahneman's System 1 and System 2 Thinking in Design

   Kannengiesser, U; Gero, John S (April 2019, Human Behavior in Design)

   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. 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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3. Nonlinear observer design for two‐time‐scale systems

   https://doi.org/10.1002/aic.16956  

   Duan, Zhaoyang; Kravaris, Costas (March 2020, AIChE Journal)

   Abstract A two‐time‐scale system involves both fast and slow dynamics. This article studies observer design for general nonlinear two‐time‐scale systems and presents two alternative nonlinear observer design approaches, one full‐order and one reduced‐order. The full‐order observer is designed by following a scheme to systematically select design parameters, so that the fast and slow observer dynamics are assigned to estimate the corresponding system modes. The reduced‐order observer is derived based on a lower dimensional model to reconstruct the slow states, along with the algebraic slow‐motion invariant manifold function to reconstruct the fast states. Through an error analysis, it is shown that the reduced‐order observer is capable of providing accurate estimation of the states for the detailed system with an exponentially decaying estimation error. In the last part of the article, the two proposed observers are designed for an anaerobic digestion process, as an illustrative example to evaluate their performance and convergence properties. 

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4. Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments

   Bergamaschi Ganapini, Marianna; Campbell, Murray; Fabiano, Francesco; Horesh, Lior; Lenchner, Jonathan; Loreggia, Andrea; Mattei, Nicholas; Rossi, Francesca; Srivastava, Biplav; Venable, Kristen Brent (January 2022, Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) 2022)

   Current AI systems lack several important human capabilities, such as adaptability, generalizability, selfcontrol, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities, which can be implemented by learning and reasoning components respectively, allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency. 

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5. Value-based Fast and Slow AI Nudging

   Ganapini, Marianna Bergamaschi; Fabiano, Francesco; Horesh, Lior; Loreggia Andrea; Mattei, Nicholas; Murugesan, Keerthiram; Pallagani, Vishal; Rossi, Francesca; Srivastava, Biplav; Venable, Kristen Brent (August 2023, Proceedings of the Workshop on Ethics and Trust in Human-AI Collaboration: Socio-Technical Approaches (ETHAICS 2023))

   Nudging is a behavioral strategy aimed at influencing people’s thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment. 

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