Dynamic Decision-Making, Learning and Mental Models

摘要

Bounded by limited cognitive capabilities, decision-makers resort to using mental models (reduced versions of real world dynamics) for decision-making and interventions in complex tasks. Such mental models are constantly updated with new experience and knowledge acquired, facilitating a learning process. Through this learning process, mental models can be refined to better represent real world dynamics.Systems theory suggests that updates of mental models happen in continuous cycles involving conceptualisation, experimentation, and reflection (C-E-R), which closely resembles a dynamic decision-making process (DDM).This study investigates the learning process of decision-makers in DDM tasks. Participants involved in simulated environments (Management Flight Simulators and Microworlds) are observed, with proceedings of their DDM tasks recorded and analysed to trace and identify any patterns of learning. Updates of mental models are recognized in changes of their performance, and their perceptions towards performance indicators and systems behaviour, before and after the decision tasks.Findings of this study show significant changes in mental models after participation in DDM tasks. However, the level of learning is questionable.