Most strategic decisions occur under considerable uncertainty. For example, when investing in the stock market, a trader may use purely probabilistic models to estimate risk in the market’s fluctuations. In contrast, when negotiating a deal in person, the trader’s risk assessment may rely instead on how trustworthy the other party appears. In standard utility models, the rules governing such decisions are the same, regardless of the source of uncertainty (e.g. human vs on-line platform). However, recent advances in social neuroscience suggest that separate brain networks might distinctly process probabilistic and social information, possibly leading to different outcomes.
To date, there is no unified framework for integrating social and non-social sources of decision uncertainty as previous studies looked at these factors in isolation. This shortcoming is mainly due to the interdisciplinary nature of the endeavour, which requires major methodological developments in experimental design and brain analytics.
During this studentship, we will combine two popular brain imaging techniques (EEG-fMRI), with novel experimental design and computational modelling to obtain information on when, where and how the brain processes social and non-social information during decision-making. The marriage of social and non-social forms of uncertainty into a comprehensive theory of decision-making promises to significantly improve our understanding of important real-life events, ranging from policy making and risk management to informing individual decisions on health behaviours and savings strategies.
For more details on essential requirements, funding details and a link to the online application click here.
