In a recent perspective piece in published in Psychreg Journal of Psychology, Troy House from the University of Oregon has presented an intriguing perspective on how animals cope with uncertainty and the mechanisms that drive their decision-making process. House asserts that the use of heuristics is not a sign of irrationality as once presumed, but a strategic evolutionary adaptation for survival in an unpredictable environment.
Traditionally, it was believed that animal behaviour suggested irrationality due to their inability to make ‘optimal’ decisions. However, House explains that true optimality, a situation where every possible outcome is known, is unrealistic in real-world scenarios. Instead, what he refers to as “Platonically optimal” decisions are often an unachievable ideal due to the impossibility of computing all potential outcomes.
To illustrate his point, House refers to the ‘giving-up time rule’ (GUT) – a heuristic used by various species to optimise their foraging strategy. After spending a certain amount of time in an area without finding food, the animal decides to move on, approximating what’s known as a Lévy flight, an optimal random search pattern in space. This behaviour is observed in species ranging from sharks to bumblebees and even human hunter-gatherers.
Additionally, House points to a sampling strategy known as the ‘distance-dependent Chinese Restaurant Process’ (ddCRP), which forms clusters based on the similarity between past and current observations, guiding exploration based on previous successful outcomes. Entrepreneurs are cited as examples of humans employing heuristics to handle uncertainty and simplify decision-making processes.
Counterintuitively, individuals exhibiting more exploitative behavior, such as drug addicts and problem gamblers, have been found to use heuristics more frequently. House argues that this does not discredit the potential of heuristics as solutions to uncertainty, as these behaviours could indicate a heightened sensitivity to uncertainty and an inability to tolerate it.
House’s perspective on decision-making under uncertainty extends beyond animal behaviour, incorporating the fields of philosophy, psychology, and neuroscience. He integrates disparate literatures to provide a comprehensive conceptual framework for decision-making in the face of uncertainty.
He also explores the concept of similarity-based generalisation – the idea of transferring information from one experience to another based on overlapping features. Generalisation allows us to apply learned rules across different situations, optimising decision-making. Heuristic decision-making, according to House, can be viewed as a form of rule-based generalisation in a similarity-defined space.
House introduces a computational model that combines reinforcement learning and Gaussian Processes to integrate reward, rule- and similarity-based generalisation for decision-making in unpredictable environments. This model, he suggests, can offer a more ecologically valid framework for understanding decision-making processes.
House’s perspective piece argues that deviations from the Platonic ideal of optimality are not irrational, but are a rational response to the constraints imposed by an unpredictable environment. It underscores the concept that heuristics and generalisation are evolutionary adaptations, effectively enabling animals, including humans, to navigate uncertainty. This innovative take on the subject paves the way for further explorations into the role of heuristics in decision-making.