Biologists are usually among the first to tell me that economists rely on unrealistic assumptions about human decision making. They laugh at the idea that people are rational optimisers who care only about maximising consumption.
Some of the points are undoubtedly correct. Humans do not care primarily about consumption. They seek mates or other objectives related to their fitness. And of course, humans do not solve complex optimisation problems with constraints in their heads.
But, as most economists will tell you, the assumptions of rationality and consumption maximisation are mechanisms to derive general predictions about behaviour. And the funny thing is, biologists often do the same. Biologists tend to treat their subjects as optimisers.
That places biologists in a similar position to economists. Biologists may be able to predict or explain behaviour, but often they have not actually explained how their subjects make decisions. If they were to attempt to predict how their subjects would behave in a changed environment – which is the type of predictive task many economists attempt to do – they would likely fail as their understanding of the decision making process is limited.
In Rationality for Mortals: How People Cope with Uncertainty, Gerd Gigerenzer has a great chapter considering how biologists treat decision making, and in particular, to what extent biologists consider that animals use simple decision-making tools such as heuristics. Gigerenzer provides a few examples where biologists have examined heuristics, but much of the chapter asks whether biologists are missing something with their typical approach.
As a start, Gigerenzer notes that biologists are seeking to make predictions rather than accurate descriptions of decision making. However, Gigerenzer questions whether this “gambit” is successful.
Behavioral ecologists do believe that animals are using simple rules of thumb that achieve only an approximation of the optimal policy, but most often rules of thumb are not their interest. Nevertheless, it could be that the limitations of such rules of thumb would often constrain behavior enough to interfere with the fit with predictions. The optimality modeler’s gambit is that evolved rules of thumb can mimic optimal behavior well enough not to disrupt the fit by much, so that they can be left as a black box. It turns out that the power of natural selection is such that the gambit usually works to the level of accuracy that satisfies behavioral ecologists. Given that their models are often deliberately schematic, behavioral ecologists are usually satisfied that they understand the selective value of a behavior if they successfully predict merely the rough qualitative form of the policy or of the resultant patterns of behavior.
You could write the same paragraph about economists, minus the statement about natural selection. That said, if you were to give the people in an economic model objectives shaped by evolution, even that statement might hold.
But Gigerenzer has another issue with the optimisation approach in biology. As from most analysis of human decision making, “missing from biology is the idea that simple heuristics may be superior to more complex methods, not just a necessary evil because of the simplicity of animal nervous systems.” Gigerenzer writes:
There are a number of situations where the optimal solution to a real-world problem cannot be determined. One problem is computational intractability, such as the notorious traveling salesman problem (Lawler et al., 1985). Another problem is if there are multiple criteria to optimize and we do not know the appropriate way to convert them into a common currency (such as fitness). Thirdly, in many real-world problems it is impossible to put probabilities on the various possible outcomes or even to recognize what all those outcomes might be. Think about optimizing the choice of a partner who will bear you many children; it is uncertain what partners are available, whether each one would be faithful, how long each will live, etc. This is true about many animal decisions too, of course, and biologists do not imagine their animals even attempting such optimality calculations.
Instead the behavioral ecologist’s solution is to find optima in deliberately simplified model environments. We note that this introduces much scope for misunderstanding, inconsistency, and loose thinking over whether “optimal policy” refers to a claim of optimality in the real world or just in a model. Calculating the optima even in the simplified model environments may still be beyond the capabilities of an animal, but the hope is that the optimal policy that emerges from the calculations may be generated instead, to a lesser level of accuracy, by a rule that is simple enough for an animal to follow. The animal might be hardwired with such a rule following its evolution through natural selection, or the animal might learn it through trial and error. There remains an interesting logical gap in the procedure: There is no guarantee that optimal solutions to simplified model environments will be good solutions to the original complex environments. The biologist might reply that often this does turn out to be the case; otherwise natural selection would not have allowed the good fit between the predictions and observations. Success with this approach undoubtedly depends on the modeler’s skill in simplifying the environment in a way that fairly represents the information available to the animal.
Again, Gigerenzer could equally be writing about economics. I think we should be thankful, however, that biologists don’t take their results and develop policy prescriptions on how to get the animals to behave in ways we believe they should.
One interesting question Gigerenzer asks is whether humans and animals use similar heuristics. Consideration of this question might uncover evidence of the parallel evolution of heuristics in other lineages facing similar environmental structures, or even indicate a common evolutionary history. This could form part of the evidence as to whether these human heuristics are evolved adaptations.
But are animals more likely to use heuristics than humans? Gigerenzer suggests the answer is not clear:
It is tempting to propose that since other animals have simpler brains than humans they are more likely to use simple heuristics. But a contrary argument is that humans are much more generalist than most animals and that animals may be able to devote more cognitive resources to tasks of particular importance. For instance, the memory capabilities of small food-storing birds seem astounding by the standards of how we expect ourselves to perform at the same task. Some better-examined biological examples suggest unexpected complexity. For instance, pigeons seem able to use a surprising diversity of methods to navigate, especially considering that they are not long-distance migrants. The greater specialism of other animals may also mean that the environments they deal with are more predictable and thus that the robustness of simple heuristics may not be such as advantage.
Another interesting question is whether animals are also predisposed to the “biases” of humans. Is it possible that “animals in their natural environments do not commit various fallacies because they do not need to generalize their rules of thumb to novel circumstances.” The equivalent for humans is mismatch theory, which proposes that a lot of modern behaviour (and likely the “biases” we exhibit) is due to a mismatch between the environment in which our decision making tools evolved and the environments we exercise them in today.
Finally, last year I wrote about why economics is not more “evolutionary”. Part of the answer there reflects a similar pattern to the above - biologists aren’t that evolutionary either.