Theoretical and experimental research on the effects of complexity on reasoning and decision-making, and on the design of communication mechanisms to reduce complexity
Decision-making is about information processing: identifying the best course of action from those available. Decisions differ vastly in their information processing requirements. On the other hand, the brain can only process a limited amount of information at any point in time. Our decisions are shaped by the way we process the information available to us.
In our research, we use computational complexity theory to identify the computational resources required to make particular decisions. We are interested in the ways computational resource requirements affect decision-making, to what extent they can explain phenomena such as behavioural biases and how decision-making can be improved.
Our work is both theoretical and empirical. In our empirical work we study individual behaviour using behavioural, eye-tracking, neuro-imaging and pharmacological experiments. We also experiment with ways to reduce complexity for individual decision-makers through social interaction by purposely designing markets to facilitate the spread of knowledge and improve complex problem-solving (including of economic problems).
Peter Bossaerts, Elizabeth Bowman, Pablo Franco, Carsten Murawski, Nitin Yadav
- Approximation complexity and human decision-making
- How do humans solve the knapsack problem?
- Human performance on random instances of NP-complete problems
- Neural correlates of instance complexity
- Pharmaceutical enhancement of complex optimisation
- Phase transitions in the knapsack problem
- Use of pharmaceutical cognitive enhancers in the Australian financial services industry