Matthew Apps and Aaron Bornstein - Human Foraging
Insights into Decision Making

Rescheduled for May 9, 2023

Matthew Apps - From finances to fairness: How foraging principles shape human decision-making.

Everyday we constantly make decisions about when to “leave”. Should I sell this stock in favour of another? Should I leave the queue for my favourite restaurant and go find an open table at another place? Should I stay chatting with this person or go find someone else to talk to? Such questions shape our decisions about how much time to spend doing one activity over another. Yet, psychological and neuroscience research has provided few formalised accounts of the principles that guide how we allocate time to behaviours and make decisions to leave. However, decades of work examining animal foraging provides rich accounts of how features of an animals environment should shape decisions of when to leave one location in favour of another. Here, I will demonstrate that we can leverage such principles to design experiments to probe the psychological and brain mechanisms that guide human decision-making. In doing so I will show that manipulating dopamine levels in the brain influences how long people spend foraging for economic rewards, that humans may make decisions differently when foraging for economic rewards for themselves or others, and that the duration of our social interactions is shaped by how people forage for fairness.

Aaron Bornstein - Foraging in (Latent) Space: Rationalizing overharvesting.

Humans and animals are often maligned as being bad ("suboptimal") at making decisions, especially decisions under uncertainty. But is this allegation justified? In this talk, I will present recent findings in the domain of patch foraging. Foraging requires individuals to compare a local option to the distribution of alternatives across the environment. Foragers, across a range of species, have been observed to systematically deviate from exogenous notions of optimality by “overharvesting”—staying too long in a patch. We introduce a computational model that explains overharvesting as a by-product of two mechanisms: 1) statistically rational learning about the distribution of alternatives and 2) planning that adapts to uncertainty over this learned representation. We test the model using a variant of a serial stay–leave task and find that human foragers behave consistently with both mechanisms. Our findings suggest that overharvesting, rather than reflecting a deviation from optimal decision-making, is instead a consequence of optimal learning and adaptation.