Four perspectives on human decision making
I have been rereading Gerd Gigerenzer’s collection of essays Rationality for Mortals: How People Cope with Uncertainty. It covers most of Gigerenzer’s typical turf - ecological rationality, heuristics that make us smart, understanding risk and so on.
In the first essay, Gigerenzer provides four categories of approaches to analysing decision making - unbounded rationality, optimisation under constraints, cognitive illusions (heuristics and biases) and ecological rationality. At the end of this post, I’ll propose a fifth.
1. Unbounded rationality
Unbounded rationality is the territory of neoclassical economics. Omniscient and omnipotent people optimise. They are omniscient in that they can see the future - or at least live in a world of risk where they can assign probabilities. They are omnipotent in that they have all the calculating power they need to make perfect decisions. And with that foresight and power, they make optimal decisions.
Possibly the most important point about this model is that it is not designed to describe precisely how people make decisions, but rather to predict behaviour. And in many dimensions, it does quite well.
2. Optimisation under constraints
In this approach, people are no longer omniscient. They need to search for information. As Gigerenzer points out, however, this attempt to inject realism creates another problem. Optimisation with constraints can be even harder to solve than optimisation with unbounded rationality. As a result, the cognitive power required is even greater.
Gigerenzer is adamant that optimisation under constraints is not bounded rationality - and if we use Herbert Simon’s definition of the term, I would agree - but analysis of this type commonly attracts the “boundedly rational” label. Gigerenzer’s does not want the unrealistic nature of optimisation under constraints to tar the concept of bounded rationality.
3. Cognitive illusions - logical irrationality
The next category is the approach in much of the behavioural sciences and behavioural economics. It is often labelled as the “heuristics and biases” program. This program looks to understand the processes under which people make judgements, and in many cases, seeks to show errors of judgment or cognitive illusions. This program has generated a long list of biases - just look at the Wikipedia page for a taste.
Gigerenzer picks two main shortcomings of this approach. First, although the program successfully shows failures of logic, it does not look at the underlying norms. Second, it tends not to produce testable theories of heuristics. As Gigerenzer states, “mere verbal labels for heuristics can be used post hoc to”explain” almost everything.”
An example is analysis of overconfidence bias. People are asked a question such as “Which city is farther north - New York or Rome?”, and asked to give their confidence that their answer is correct. When participants are 100 per cent certain of the answer, less than 100 per cent tend to be correct. That pattern of apparent overconfidence continues through lower probabilities.
There are several critiques of this analysis, but one of the common suggestions is that people are presented with questions that are unrepresentative of a typical sample. People typically use alternative cues to answer a question such as the above. In the case of latitude, temperature is a plausible cue. The overconfidence bias occurs because the selected cities are a biased sample where the cue fails more often than expected. If the cities are randomly sampled from the real world, the overconfidence disappears. The net result is that what appears to be a bias may be better explained by the nature of the environment in which the decision is made.
4. Ecological rationality
Ecological rationality departs from the heuristics and biases program by examining the relationship between mind and environment, rather than the mind and logic. Human behaviour is shaped by scissors with two blades - the cognitive capabilities of the actor, and the environment. You cannot understand human behaviour without understanding both the capabilities of the decision maker and the environment in which those capabilities are exercised. Gigerenzer would apply the bounded rationality label to this work.
On this basis, there are three goals to the ecological rationality program. The first is to understand the adaptive toolbox - the heuristics of the decision maker and their building blocks. The second is to understand the environmental structures in which different heuristics are successful. The third is to use this analysis to improve decision making through designing better heuristics or changing the environment. This can only be done once you understand the adaptive toolbox and the environments in which different tools are successful.
Gigerenzer provides a neat example of how the ecological rationality departs from the heuristics and biases program in its analysis of a problem - in this case, optimal asset allocation. Harry Markowitz, who received a Nobel Memorial Prize in Economics for his work on optimal asset allocation, did not use the results of his analysis in his own investing. Instead, he invested his money using the 1/N rule - spread your assets equally across N assets.
The heuristics and biases program might look at this behaviour and note Markowitz is not following the optimal behaviour determined by himself. He is making important decisions without using all the available information. Perhaps it is due to cognitive limitations?
As Gigerenzer notes, optimisation is not always the best solution. Where the problem is computationally intractable or the optimisation solution lacks robustness due to estimation errors, heuristics may outperform. In the case of asset allocation, Gigerenzer notes work showing that 500 years of data would have been required for Markowitz’s optimisation rule to outperform his practice of 1/N. In a world of uncertainty, it can be beneficial to leave information on the table. Markowitz was using a simple heuristic for an important decision, but rightfully so as it is superior for the environment in which he is making the decision.
5. Evolutionary rationality
Gigerenzer proposes four categories, but I’ll lay out a fifth (I’m not sure about the label I’ve just given it). Evolutionary rationality develops a deeper understanding of the cognitive capabilities of the decision maker through an analysis of the adaptive basis of traits. This perspective could inform all four of the above categories of decision making. It could be used to assess what is being optimised, what the constraints might be, how biases might be due to mismatch between past and present environments, and what the heuristics are.
Gigerenzer notes the possibility of going into this territory, but deliberately holds back. In the third chapter of the book, he writes:
[H]uman psychologists are not able to utilize many of the lines of evidence that biologists apply to justify that a trait is adaptive. We can make only informed guesses about the environment in which the novel features of human brains evolved, and because most of us grow up in an environment very different to this, the cognitive traits we exhibit might not even have been expressed when our brains were evolving. …
ABC avoids the difficult issue of demonstrating adaptation in humans by defining ecological rationality as the performance, in terms of a given currency, of a given heuristic in a given environment. We emphasize that currency and environment have to be specified before the ecological rationality of a heuristic can be determined; thus, take-the-best is more ecologically rational (both more accurate and frugal) than tallying in noncompensatory environments but not more accurate in compensatory ones. Unlike claiming that a heuristic is an adaptation, a claim that it is ecologically rational deliberately omits any implication that this is why the trait originally evolved, or has current value to the organism, or that either heuristic or environment occurs for real in the present or past. Ecological rationality might then be useful as a term indicating a more attainable intermediate step on the path to a demonstration of adaptation.
There is a lot more interesting material in Chapter 3 on the link between Gigerenzer’s program and the approach taken by biologists. That will be the subject of a later post.