Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.
Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.
more » « less- Award ID(s):
- 1932035
- NSF-PAR ID:
- 10248967
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science
- Volume:
- 372
- Issue:
- 6547
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- p. 1209-1214
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The goal of this article is to introduce readers to theories, tools, and evidence from the field of neuroeconomics and to describe how health psychology and neuroeconomics can be mutually informative in the study of preventative health behaviors. Preventative health behavior here refers to both individual actions that impact one's health (e.g., exercise) and broader behavioral patterns, such as those captured in personality constructs. Although neuroeconomic researchers have begun to incorporate health‐relevant behaviors into their studies, the full potential of this research to inform preventative health models is as yet unrealized. What is needed to “translate up” is the unification of rich theoretical content from health psychology with investigations by neuroeconomic researchers of the decision‐making process during health‐relevant choices. We identify choice as a central, shared feature across models of preventative health behavior that can serve as an inroad for neuroeconomics to contribute to existing models and highlight commonalities that might not otherwise be apparent. A central premise of our argument is that, because health decisions are nearly always multiply determined, a more precise and mechanistic understanding of how choices are made is an important but understudied topic in health psychology. A partnership between health psychologists and neuroeconomic researchers can yield valuable insights into how preventative health choice is made and to identify targets and methods for intervention.
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Background Objective numeracy appears to support better medical decisions and health outcomes. The more numerate generally understand and use numbers more and make better medical decisions, including more informed medical choices. Numeric self-efficacy—an aspect of subjective numeracy that is also known as numeric confidence—also relates to decision making via emotional reactions to and inferences from experienced difficulty with numbers and via persistence linked with numeric comprehension and healthier behaviors over time. Furthermore, it moderates the effects of objective numeracy on medical outcomes.
Purpose We briefly review the numeracy and decision-making literature and then summarize more recent literature on 3 separable effects of numeric self-efficacy. Although dual-process theories can account for the generally superior decision making of the highly numerate, they have neglected effects of numeric self-efficacy. We discuss implications for medical decision-making (MDM) research and practice. Finally, we propose a modification to dual-process theories, adding a “motivational mind” to integrate the effects of numeric self-efficacy on decision-making processes (i.e., inferences from experienced difficulty with numbers, greater persistence, and greater use of objective-numeracy skills) important to high-quality MDM.
Conclusions The power of numeric self-efficacy (confidence) has been little considered in MDM, but many medical decisions and behaviors require persistence to be successful over time (e.g., comprehension, medical-recommendation adherence). Including numeric self-efficacy in research and theorizing will increase understanding of MDM and promote development of better decision interventions.
Highlights Research demonstrates that objective numeracy supports better medical decisions and health outcomes. The power of numeric self-efficacy (aka numeric confidence) has been little considered but appears critical to emotional reactions and inferences that patients and others make when encountering numeric information (e.g., in decision aids) and to greater persistence in medical decision-making tasks involving numbers. The present article proposes a novel modification to dual-process theory to account for newer findings and to describe how numeracy mechanisms can be better understood. Because being able to adapt interventions to improve medical decisions depends in part on having a good theory, future research should incorporate numeric self-efficacy into medical decision-making theories and interventions.
