China 
Africa 
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Inclusive Decision-Making
Published in Rod D. Roscoe, Erin K. Chiou, Abigail R. Wooldridge, Advancing Diversity, Inclusion, and Social Justice Through Human Systems Engineering, 2019
Jacklin Stonewall, Michael C. Dorneich, Linda Shenk, Caroline C. Krejci, Ulrike Passe
Human factors, psychology, and the social sciences share many concepts and methods—these disciplines are concerned with understanding how users think, make decisions, and are influenced by internal and external factors (Ingram, Shove, & Watson, 2007). Human factors applies cognitive psychology principles to understand the role of attitudes, beliefs, and emotions in decision-making (Isen, Daubman, & Nowicki, 1987; Klein, Moon, & Picard, 2002; Graesser, Chipman, Haynes, & Olney, 2005). Similarly, user-centered design (UCD) processes borrow concepts and methods from fields as diverse as ethnography, computer science, social science, and psychology (Rogers, Sharp, & Preece, 2007). For instance, sociological theories of consumption and practice can inform the design of consumer products (Ingram et al., 2007).
Emotion-involved human decision-making model
Published in Mathematical and Computer Modelling of Dynamical Systems, 2021
Furthermore, as stated in [27], the fundamentally dynamic nature of emotions is increasingly being taken into consideration in the development of human-mimicking decision-making models. Because our daily lives are dynamic, our emotional responses to our environment are also dynamic. That is, our environment changes every moment, and therefore every time we make a decision, the resulting response may differ from the past ones. The authors of [27] organized the core principles of emotion dynamics in terms of contingency, inertia, regulation, and interaction, after conducting research to investigate how emotions evolve. To the best of the authors’ knowledge, there are no computational frameworks to describe the dynamics of emotions during decision-making processes. However, if there was such a computational framework, human-mimicking decision-making and the prediction of human behaviour could be realized and applied for various purposes, such as the realization of an affective artificially intelligent (AI) character, a humanoid robot, or a persuasive dialogue system, or the enhancement of human-computer interaction. The study of [28] formulated dynamic emotions in belief-desire-intention agents using difference equations, in which the authors simulated bushfires evacuation in Australia as an example. An AI model was also developed and reported in [29]; through the implementation of Newton’s laws of motion, the researchers demonstrated that it could imitate the affective character of humans and exhibit the dynamical characteristics of emotions. In [30,31], the authors reported on an OCC theory-based model that implemented a mass-spring model and cyclical appraising and reappraising stimuli as the basis for emotion dynamics. Consequently, they developed architectures to reproduce an affective character. Additionally, the authors of [32] developed differential equations to express known romantic feelings. The feelings, love and hate of Romeo and Juliet, are quantified and the time evolution of the feelings is expressed in a coupled ordinary differential equation.