Choice modellers aim to build mathematical models that predict and forecast which alternative(s) an individual will choose in a given scenario. The key aim of these models is to understand the key factors that lead to different individuals making different choices. Increased globalization and migration have however led to marked diversity among the decision makers who not only have different sensitivity to the factors influencing the choices (e.g. time and cost in the context of travel mode selection), but also decision-rule heterogeneity, where the way in which individuals think and make decisions may differ. For example, there have been psychological theories put forward to explain how and why Asians and Westerners think differently and can make substantially different choices (e.g. Nisbett 2004). These differences are particularly prominent in the case for ‘difficult’ decisions where the alternatives cannot be categorized as ‘right’ or ‘wrong’ and/or there is inherent uncertainty in the outcomes of a choice. For example, during the COVID-19 pandemic, significant heterogeneity has been observed around the world and among different ethnic groups in terms risk taking propensity which in turn affected the shift in activity and travel patterns throughout this year (Dryhurst et al. 2020); country-level variations have been observed regarding what is the ‘right’ decision in the so called ‘moral machine’ choice setting, where an individual must choose who to save if an autonomous vehicle (AVs) hypothetically were to crash (Awad et al. 2020).
To understand individual preferences, choice modellers have historically relied on stated preference (SP) surveys and responses to questionnaires on psychometric scales. But these are subject to hypothetical biases and measurement errors. These biases become more influential in the case of difficult/risky/moral decision-making, as decision-makers likely do not face the consequentiality of their choices and additionally may not wish to appear immoral. In our early research in this area, we have demonstrated how physiological data can be used to help understand decision-making. For example, Paschalidis et al., 2018, demonstrate integrating physiological response data with driving choice data can better capture the effect of stress on driving decisions; Bogacz et al. (2019) demonstrate using electroencephalogram (EEG) data to understand risk perception when cycling in a virtual reality (VR) setting. A key advantage of physiological data is that it can sidestep hypothetical biases by providing outputs that cannot be controlled by the decision-maker (i.e. heart-rate).
The aim of this project is to advance this further, both methodologically and empirically, by using physiological data combined with choice data in a wide range of VR representation of real-world scenarios to obtain more reliable forecasts of how individuals respond to risky or moral choice scenarios. This will allow us to better understand human reactions to risky situations with uncertain outcomes which can be useful for artificial intelligence based policy planning. They can also help to better predict choices involving moral aspects, for example, policies to promote the uptake of electric vehicles or to encourage carbon neutrality through carbon offsetting schemes.
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