Thinking & Reasoning

Buying an airline ticket is a familiar optimal stopping problem. The goal is to minimize the cost of the ticket, but this is made difficult by changes in the price over time. Part of the change in ticket prices is unpredictable fluctuation, but part is a predictable change in the price distribution, which notoriously increases rapidly as the day of travel approaches. Managing this uncertainty is the key to good decisions, since if a cheap ticket is not purchased it is not possible to go back in time, but once a ticket is purchased future prices are not available. We study how people solve this problem in a controlled experiment, using changing price distributions based on airline industry analysis. Over a set of problems, people are given 12 opportunities to buy a ticket ranging from 6 months before travel to 1 day before. We characterize their behavior in terms of threshold models, and compare their performance to optimal purchasing behavior.

This is an in-person presentation on July 20, 2023 (15:20 ~ 15:40 UTC).

Thus far, models for large-scale multi-attribute, multi-alternative preferential choice data typically include sociodemographic parameters to capture deterministic heterogeneity in preferences and use random parameters or latent class constructs to capture stochastic heterogeneity. Integrated choice and latent variable (ICLV) models are also often used to explain both attitudinal responses and preferential choice. Recently, several ideas from psychology have been incorporated into large-scale choice modelling including the use of psychological choice models (decision field theory) for travel route and mode choice behaviour. However, thus far, neither econometric or psychological choice models have incorporated or modelled different possible thinking styles (e.g. `actively open-minded' vs `closed-minded'; `intuitive thinker' vs `effortful thinker') that may vary more substantially across individuals from different parts of the world. We collect attitudinal data, responses to questions on thinking style, and choice responses to stated preference tasks on travel mode from 1,100 respondents from the East (China, Singapore), Middle East (UAE) and the West (Sweden, UK, USA). In particular, we use the data to develop an integrated choice and latent variable decision field theory model that disentangles the effects of socio-demographic characteristics, attitudes towards the environment/technology adoption and thinking styles on the travel choices. The results reveal that there are significant differences in environmental awareness, technology adoption and thinking styles among the respondents. In particular, an individual’s geographical location has a larger impact on their choices, attitudes and thinking styles than sociodemographic variables such as age, gender and income.

This is an in-person presentation on July 20, 2023 (15:40 ~ 16:00 UTC).

Firefighters, emergency paramedics, and airplane pilots are able to make correct judgments and choices in challenging situations of scarce information and time pressure. Experts often attribute such successes to intuition and report that they avoid analysis. Similarly, laypeople can effortlessly perform tasks that confuse machine algorithms. We utilise research on human intuitive decision making to build a model of mixing intuition and analysis over a set of interrelated tasks, where the choice of intuition or analysis in one task affects the choice in other tasks. In this model, people may use any analytical method, such as multi-attribute utility, or a single-cue heuristic, such as availability or recognition. We make two contributions. First, we study the model and derive a necessary and sufficient condition for the optimality of using a positive proportion of intuition (i.e., for some tasks): Intuition is more frequently accurate than analysis to a larger extent than analysis is more frequently accurate than guessing. Second, we apply the model to synthetic data and also natural data from a forecasting competition for a Wimbledon tennis tournament and a King’s Fund study on how patients choose a London hospital: The optimal proportion of intuition is estimated to range from 25% to 53%. The accuracy benefit of using the optimal mix over analysis alone is estimated between 3% and 27%. Such improvements would be impactful over large numbers of choices as in public health.

This is an in-person presentation on July 20, 2023 (16:00 ~ 16:20 UTC).

Autonomous vehicles (AVs) are no longer fictional. The success of AVs will depend on how they handle ethical issues in a socially acceptable manner. For example, the decisions that AV should make when there is no way to save everyone (i.e., the trolley problem). A famous cross-cultural Moral Machine experiment evaluated societal expectations about the ethical principles that should guide AV behavior in scenarios based on the trolley problem paradigm and showed that the subjective beliefs of the population play a critical role in the valuation of the ethical aspects of AV behavior. This study aims to expand our knowledge related to the effect of subjective beliefs on the valuation of the ethical aspects of AV behavior. We hypothesize that the brain activity of respondents may provide additional information about the subjective aspects of the valuation process. To test the hypothesis, we carry out video-based discrete choice experiments in which participants choose between two victims/pedestrians of road accidents involving an AV. The experiment is designed such that the AV has no choice but to hit one of two pedestrians approaching from both ends of the road. We vary the socio-demographics, such as the age and gender of the pedestrians, across trials to reveal their impact on the participant’s decisions. We fit a discrete choice model (DCM) to experimental data to uncover the impacts of the considered socio-demographics on choices. Finally, we test if the different neural mechanisms (biomarkers) could explain the valuation of socio-demographics and incorporate them into DCM to better account for the subjective beliefs of the respondents. Our study contributes to multiple fields, including AV research, choice modeling, and psychology. For AV research, we enhance our understanding of societal expectations about ethical aspects of AV behavior. For choice modeling, we advance traditional choice models by including brain signals. For psychology, we reveal mechanisms underlying the perception and valuation of ethical problems.

This is an in-person presentation on July 20, 2023 (16:20 ~ 16:40 UTC).

A central question in reasoning research is what computational level principles, if any, people follow when drawing inferences and when making judgments about how strong or weak a particular inference is. Any measure of inference quality depends on the meaning people ascribe to the statements that make up the inference. The statement types with the most contentiously debated meaning in the literature are conditionals. For example, whether the inference “There is beer or wine. Therefore if there is not beer then there is wine” is deductive or not depends on how the conditional that makes up its conclusion is interpreted. Distinguishing between different interpretations of conditionals requires finding situations in which they lead to non-overlapping behavioral predictions. We present a Bayesian latent-mixture model to distinguish between a material conditional, a probabilistic conditional, and a probabilistic biconditional interpretation of conditionals along with a fourth response to capture guessing. The model correctly classifies the responses expected under each interpretation given premise and conclusion probability judgments for six inference types. We simulate data to illustrate the behavior of the model and discuss characteristics of experiments that would be required to distinguish between interpretations.

This is an in-person presentation on July 20, 2023 (16:40 ~ 17:00 UTC).