Finding effective ways to improve subjective probability predictions through model learning

Abstract

Predicting probabilities of marketing events and choices is a primary activity in marketing.Prediction can be from the outcome of a formal model or one's knowledge but the two sources often conflict.Although overwhelming evidence has demonstrated that models often outperform people in terms of accuracy, there is little doubt that decisions are made mostly by people based on their own knowledge.Past research suggests that models and intuition should work together for better outcomes but it is unclear how this may be accomplished other than by using "plain vanilla" style Decision Support Systems (DSSs).This researcher adopted an alternative approach and believes that the key to solving this problem is to improve people's own knowledge.To do so, people need to gain a substantive understanding of a reliable model to improve predictions.Four generalisable model-learning approaches based on concepts from learning theories in psychology and cognitive science were developed and tested in an experiment to ascertain which approach was more effective in helping learners develop an understanding of the model's parameters and to improve their consequent predictions.The experiment was supported by an online Intelligent Support System (ITS) with both learning approaches and a target model built in.This target learning model is a consumer choice model of airline flights.The system evaluates predictions, estimates learner models, and classifies answers.Moreover, it provides real-time feedback matching the design of each learning approach.According to the results, the most effective approach for both model learning and prediction improvement is a learning approach generating outcome feedback with correct answers after each experimental design controlled training task.This finding disagrees with a common view of multiple cue probability learning (MCPL).Having regard for effectiveness, the above learning approach is followed by an approach showing feedback with a comparison of estimated learner During this long journey, I received encouragement, friendship, support and help from so many people around me and this is an opportunity for me to thank them formally.First and foremost I would like to thank my principal supervisor Professor Jordan Louviere.Jordan started this journey with me, guided me along the whole way, discussed with me a range of big ideas to minor details, and always encouraged me at the right time when he saw I was frustrated.Jordan, it is such an honour to be your PhD student to learn not only knowledge from you but also the ways in which you do things.I am sure the experience with you in the past seven years will greatly benefit me in my work and life.I would like express my sincere gratitude to my two other supervisors, Professor Mary-Anne Williams and Dr Tiago Ribeiro.Mary-Anne, you have opened the door for me to fields such as cognitive science, machine learning and intelligent systems, and suggested new directions for this research.I will keep improving my understanding of these fields because I truly believe that understanding the process of how people learn is the key to future research in decision support.Tiago, even though you came onto my committee at late stage, your contribution to this research is so significant.You helped me understand the nature of the data I collected and showed me new and different analysis methods with which I was unfamiliar.Without your help, the analysis would not have been conducted so smoothly.I thank

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