Learning and choosing in a complex world. How do people make choices in a complex world? Making good choices requires expertise, but people must often forego rewards in order to acquire this knowledge. This is the essence of an "explore-exploit dilemma": to maximise rewards across a long time frame, people must take the time to explore and learn now. Empirically, this project aims to unify much of the existing psychological literature and extend it to cover richer, more complex problems. Theoret ....Learning and choosing in a complex world. How do people make choices in a complex world? Making good choices requires expertise, but people must often forego rewards in order to acquire this knowledge. This is the essence of an "explore-exploit dilemma": to maximise rewards across a long time frame, people must take the time to explore and learn now. Empirically, this project aims to unify much of the existing psychological literature and extend it to cover richer, more complex problems. Theoretically, the project aims to use tools from machine learning to compare human decision making to optimal planning models.Read moreRead less
Choice foundations: Diagnostic tools for individual-level model development. This project aims to improve policy making in areas such as transport, environment and health, by better representation of how people make decisions. An interdisciplinary team of economists and psychologists will build on new mathematical and statistical tools to test for adherence to choice axioms that underlie observed choice behaviour. The project will produce a set of computerized statistical tools to implement the ....Choice foundations: Diagnostic tools for individual-level model development. This project aims to improve policy making in areas such as transport, environment and health, by better representation of how people make decisions. An interdisciplinary team of economists and psychologists will build on new mathematical and statistical tools to test for adherence to choice axioms that underlie observed choice behaviour. The project will produce a set of computerized statistical tools to implement the testing of choice axioms using Bayesian methods with the capacity to improve a wide array of applied economics work at the national and international levels.Read moreRead less
Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diag ....Uncovering the processes underlying human reasoning: A state-trace approach. This project aims to answer the most important unresolved question in the psychology of reasoning; how many distinct cognitive processes underlie human reasoning? To answer this question, this project aims to conduct an extensive experimental investigation of the factors that selectively impact inductive and deductive inferences and the application of high-dimensional state-trace analysis; a powerful new method for diagnosing underlying processes from behavioural data. The project is expected also to develop a new computational model that accounts for both inductive and deductive forms of reasoning.Read moreRead less
Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits ....Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits such as understanding how people operate so effectively in real environments, when even the most powerful computers struggle to handle the complexities of everyday learning problems.Read moreRead less