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The order of information in the presence of uncertainty plays a fundamental role in decision making. Yet, modelling such processes by classical Bayesian inference is difficult. Using judgement errors …
The order of information in the presence of uncertainty plays a fundamental role in decision making. Yet, modelling such processes by classical Bayesian inference is difficult. Using judgement errors and optimal foraging as examples, this talk describes quantum probability theory to model decision problems. Subsequent observations change the decision maker's context, imposing a restricted space for decisions. If consecutive observations are incompatible  they relate to different aspects of a system  then the order of the observations will matter. Departing from Heisenberg's uncertainty principle, risk and ambiguity cannot be simultaneously minimised in this framework, hence putting a formal limit on rationality in sequential decision making. This pattern is universal and helps explaining similar phenomena in a wide range of decision problems, and it also aids our understanding why simultaneous decision making evolved.
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