Explaining human decision-making by combining choice and process data

Introduction

Decision-making is a core element of human activity, and the decisions we make can have significant consequences for ourselves, others, and the world we live in. Thus, the ability to explain and predict decision-making is vital for improving individual and societal outcomes, especially when addressing global challenges like climate change, sustainable energy, ageing populations, and public health. Mathematical models of decision-making can help us to understand and predict human behaviour.

Challenges in Decision-Making Models

Despite significant progress, several scientific challenges persist in the applicability of advanced behavioural models to real-world problems. Many models focus on predicting decision outcomes rather than explaining the decision process itself.

Data Limitations

Information contained in neurophysiological process data that emerge during decision-making is often ignored. Additionally, methods for collecting behavioural data fail to create realistic impressions of scenarios representing the future.

Objectives of IMMERSION

IMMERSION aims to advance the study of human decision-making by developing new innovative methods for combining choice and process data. This includes:

1. New models for integrating choice and process data.
2. New statistical inference procedures tailored to such models.
3. New methods for collecting rich behavioural data in immersive experiments.

The proposed research will create a paradigm shift in behavioural research with impacts on many application domains. IMMERSION will equip researchers with a new powerful toolkit for extracting deep behavioural insights from rich data using advanced models.

Empirical Work and Applications

The proposed research includes substantial empirical work applying IMMERSION’s methodological innovations to real-world problems with implications for the human-centric design of future transport systems.

Case Studies

This work includes case studies to explain and predict human decision-making in the contexts of:

• Transportation infrastructure development.
• Pedestrian-autonomous vehicle interactions.
• Pedestrian wayfinding.