Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures

Laboratory experiments have recently become popular methods for understanding decision-making behaviour of humans in evacuations. When designed for individual-level data extraction, these experiments can be analysed by econometric models. These models can subsequently be implemented as part of broader computational tools that simulate evacuation processes. In taking this approach, a modeller will face several major questions at both experiment design and estimation/implementation phases: (I) Can the behaviour be instead inferred, with adequate accuracy, from stated choice surveys (i.e. hypothetical bias)? (II) Given that these laboratory experiments are performed in specific geometric layouts, are their modelling outcomes transferable to geometric layouts other than that of their origin (i.e. external validity)? (III) At the modelling phase, how critical is to determine whether to set the decision rule as random regret minimisation as opposed to random utility maximisation (i.e. decision rule)? This study investigates how each of these three problems impact on prediction outcomes when these models are employed to simulate an evacuation system.