Simon’s satisficing strategy is a decision-making technique where the individual considers various solutions until they find an acceptable option. Satisficing is a portmanteau combining sufficing and satisfying and was created by psychologist Herbert A. Simon. He argued that many individuals make decisions with a satisfactory (and not optimal) solution. Satisfactory decisions are preferred because they achieve an acceptable result and avoid the resource-intensive search for something more optimal.
- Understanding Simon’s satisficing strategy
- Satisficing versus maximizing
- Examples of Simon’s satisficing strategy
- Key takeaways
- What is a Heuristic? Beyond biases and the prevailing narrowed vision of the mind
- The central problem with a two-system thinking model
- Fast, frugal, yet accurate
- The two sides of Bounded rationality
- The importance of Ecological Rationality
- Redefining biases
- Building an adaptive toolbox for entrepreneurs
- Connected Visual Concepts
Understanding Simon’s satisficing strategy
Simon is also the father of bounded rationality.
Indeed, humans lack the cognitive resources to make optimal decisions. We have little understanding of outcome probabilities and can rarely evaluate relevant outcomes with sufficient precision. Furthermore, our memories tend to be unreliable.
Given these limitations, a more realistic approach involves logical and reasoned decision making. Simon called this process “bounded rationality”. Here, satisficing individuals make decisions that are based on certain, non-exhaustive criteria.
Satisficing versus maximizing
Satisficing is not exclusively driven by cognitive limitations. It also seeks to maximize utility, or the extent to which a task or choice is pleasant or desirable.
For many years, behavioral economists assumed that task desirability was linked to how much information the decision-maker had at their disposal.
But this is untrue. To prove this, consider the key differences between a satisficer and a maximizer:
- The satisficer is not attached to the very best outcome. As a result, they experience less regret and higher self-esteem than their maximizing counterparts – who tend to be outcome-dependent perfectionists.
- The satisficer can move on after deciding, while the maximizer needlessly expends more time and energy ruminating.
- The satisficer does not obsess over other options and is happier for it. Conversely, the maximizer makes decisions based on external comparisons and not on their own needs or pleasure. This tends to make them unhappier.
Examples of Simon’s satisficing strategy
Consider the consumer who has a leaking pipe in their basement on a weekend. The best solution to this problem is replacing the pipe, but this entails finding a suitable plumber and is an expensive fix. Instead, the consumer chooses to stem the leak with a temporary sealant. While the sealant is by no means a permanent fix, it is satisfactory enough to stem the leak and saves time, money, and energy.
Satisficing has implications for copywriting and web design too. Visitors will tend not to stay on a company site for long unless there are obvious and satisfactory solutions to their problems.
The strategy can also be seen in consumer psychology. When choosing a product such as a pipe sealant, the consumer is looking for the simplest, most readily available option. While more effective solutions exist, they do not come into consideration.
For example, an office worker might purchase a single piece of accounting software despite there being more benefit in buying the whole suite. A fitness fanatic may purchase a low-quality pair of earphones to use while running, despite several competitor products offering better sound rendition.
- Simon’s satisficing strategy is a form of decision making that advocates satisfactory and not optimal solutions.
- Simon’s satisficing strategy avoids cognitive overload in the often fruitless search for optimal outcomes. These outcomes result in needless expenditure of time, energy, or money.
- Simon’s satisficing strategy has applications in consumer psychology and user design. Consumers who adopt the strategy tend to be happier and have higher self-esteem than those who opt to maximize the outcomes of decision making.
As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.
What is a Heuristic? Beyond biases and the prevailing narrowed vision of the mind
In a 1996 paper entitled “Reasoning the Fast and Frugal Way: Models of Bounded Rationality” psychologists Gerd Gigerenzer and Daniel G. Goldstein highlighted:
Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might.
This is a very important concept to start with. Where modern psychologists and theorists of mind manufacture experiments in the lab, those experiments are tied to specific scenarios, that are hardly replicable in the real world.
Why is that? It all starts with a narrow theory of mind.
A narrow definition of rationality
Experiments are manufactured and often based on assumptions around how our minds work. For instance, if a psychologist will label rationality as the ability to optimize during a decision-making process (just like a machine would do) this requires the mind to gather all the possible information to come to a logical decision.
However, in the real world, decisions are made with incomplete information, a high degree of uncertainty and little to no understanding of what’s coming next. Therefore, when the psychologist mutters about the inability of the human brain to understand statistics or logic. In the real world, that means survival.
If surviving means losing some efficiency or avoiding optimization to prevent massive failure, our mind is working as it should.
Risk vs. Uncertainty
Another component that the conventional or prevailing school of thought is the lack of understanding of the domain in which the human mind is operating. That’s a key point to understand the difference between risk and uncertainty.
Risk is computable
Risk is a concept that analysts love. Why? It’s something that can be modeled. Thus, circumscribed to scenarios that have definite rules, like games. You often see in business books how game theory helped businessmen to be successful.
But that is a story crafted in hindsight. Game theory or your skills as a chess player might help you (in impressing others) in normal circumstances (assuming those exist) but they won’t help you much in the real world. Unless you have an alternative toolbox made of heuristics.
Uncertainty is not computable
When financial analysts evaluate risks they fall into the trap of thinking that we can understand the real world by modeling it. The modern approaches to entrepreneurship try to bring this same logic to the business world, with nefast consequences.
When there is a high variability of outcomes, it’s impossible to model the risk. If at all you need a simple set of rules of thumb to avoid the worst-case scenario because if that materializes that will be no risk-model that will help with that.
Indeed the consequences of an uncertain scenario might be too bad for you to actually even see its outcome because survival is at stake.
Unmodeling the real world
When psychological experiments are made in the lab, often times the psychologist starts with a preconceived idea of the human mind and she works her way back to prove it with an experiment.
When that happens experiments are “manufactured” (in many cases unconsciously) to produce a certain result (in short, biases are more a domain applicable to psychologists than of laypeople dealing with real-world uncertainty).
This has come up recently with what is called a Replication Crisis, which as highlighted on Wikipedia:
The replication crisis (or replicability crisis or reproducibility crisis) is, as of 2019, an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate or reproduce. The replication crisis affects the social sciences and medicine most severely.
Part of this trend is in the use of statistical tools that are not proper for real-world analyses, and the fact that research sometimes turns into an attention-driven activity. As pointed out by Noah Smith in Bloombergs’ “Why ‘Statistical Significance’ Is Often Insignificant:”
In psychology, in medicine, and in some fields of economics, large and systematic searches are discovering that many findings in the literature are spurious. John Ioannidis, professor of medicine and health research at Stanford University, goes so far as to say that “most published research findings are false,” including those in economics. The tendency research journals have of publishing anything with p-values lower than 5 percent — the arbitrary value referred to as “statistical significance” — is widely suspected as a culprit.
To be sure, this is not to say those experiments aren’t valid. Worse than that, in some instances, they carry from the beginning assumptions about the psyche of the subjects that are biased themselves.
In short, the biases that we all talk about nowadays, especially in the business world, in reality, might easily be explained with a theory of mind that goes beyond the conventional definition of rationality.
This definition starts by thinking of our mind as an easily tricked machine, that due to its survival mechanisms isn’t well-adapted anymore to modern times. Thus, it can easily fall prey to dozens if not hundreds of biases that affect our daily lives.
That is we see anywhere today in business publications massive lists of cognitive biases that make us more “aware.”
Heuristics: dirt and quick? Not really!
As highlighted in “Heuristic Decision Making:”
The goal of making judgments more accurately by ignoring information is new. It goes beyond the classical assumption that a heuristic trades off some accuracy for less effort.
The main perspective for which heuristics have been studied and communicated to a mass business audience is through the fact that by definition a heuristic is quick and dirty. In short, our error-prone mind generates biases because we use heuristics that made us sacrifice efficiency for speed in the face of a sort la lazy mechanism of the mind.
According to this view, the mind might ignore important information in an efficiency-driven way, almost like it was optimizing for computing power.
In reality, the mind might have learned that ignoring useless information is a more effective survival mechanism in that specific context. Therefore, focusing on one key data point is way more reliable than taking more information. This completely changes the paradigm.
Where a lazy-driven mind avoids too much information because it’s not computably able to process it (thus sacrificing efficiency for speed almost like it was a computer). In a new paradigm, where heuristics and rules of thumbs become central as a necessary filtering mechanism of the mind that learns ho to ignore useless and irrelevant information.
In short, what matters is the outcome of the action, not the process neither the motivation that drives the process.
Conflict of interests, marketing, and manipulation
New media have enabled companies to communicate at large scale. When this communication is done right we can call it marketing. When that’s done wrong we can call it a conflict of interest or at worst manipulation.
Thus, many of what we call biases are also the consequence of the way the message gets framed to us. In short, it’s like playing a game where one player has to trick the other. As the other player learns the tricks of the first player, new strategies need to be found.
One there is a gap between the trickster and the tricked a bias might emerge as a better ability of the trickster.
Blind faith in technology
While planning a trip back to the city I live in, I was thinking to postpone the trip due to bad weather. While consulting my GPS which optimizes for shorter routes (not certainly for the beauty of the landscape or chances of survival) I risked to get to the end of the trip underwater.
In short, the GPS was giving me the time to destination with a bit of delay but without necessarily mentioning that I was getting there by risking to be flooded!
This blind faith in technology isn’t due to our inability to deal with it. Rather with the way these technologies are framed. When technology is built to optimize, and when it is marketed so that you believe that optimization is what matters in any context (optimization works in narrow ordinary situations) you end up relying too much on it.
The central problem with a two-system thinking model
Yet the assumptions underlying these theories stand on a hypothetical optimization process humans should follow when making a decision. As highlighted in the paper “Heuristic Decision Making:”
As Kahneman (2003) explained in his Nobel Memorial Lecture: “Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models”
This view might start with a wrong definition and interpretation of bounded rationality formulated by Simon. Bounded rationality is not about systematic biases, it’s about decision-making in the real world, which is unpredictable.
Fast, frugal, yet accurate
Another key concept to internalize to deeply understand this alternative view of bounded rationality is the concept of ecological rationality. Ecological rationality looks for strategies that are better suited for a specific environment and context.
Therefore, the rules of thumb we might be able to use for each circumstance will help us take advantage of the structure of the environment we operate within.
Thus in this sort of decision-making process, it is like we do create a small world but highly adapted to context and circumstance, which is the opposite of what classic theories of rationality do, assuming that our mind works in a vacuum, or in a sort of free-context reality.
The two sides of Bounded rationality
Based on what we have said so far, let’s look again at the concept of bounded rationality. According to the definition given by his father, Simon, bounded rationality has two main sides:
- and cognitive
It’s ecological because “the mind is adapted for real-world environments.” Therefore, on the one side, the mind makes decisions based on the structure of the environment. And on the other side, there is the computational capability of the decision-maker (cognitive side).
As highlighted by Gerd Gigerenzer and Wolfgang Gaissmaier in “Heuristic Decision Making modern psychologists have focused their attention on the latter (the cognitive side).
More precisely, the focus on the cognitive side has produced the misunderstanding that as the human mind has limited ability to process information, it produces a set of irreparable biases.
Part of this misunderstanding might be given by the fact that those presumably simple heuristics that the mind uses to solve real-world problems are not sophisticated enough to look interesting to the norms of classical rationality.
The importance of Ecological Rationality
Once you understand the other side of rationality, not the cognitive, but the ecological, it changes everything.
In an ecological rationality sense, less-is-more becomes a powerful heuristic to rely on in many of the real-world scenarios.
Less-is-more is about ignoring cues that not only make us worse decision-makers. It also means that after a certain point more information leads to worse decisions, even when the costs of acquiring that information are zero.
In the conventional view, a bias is a cognitive error the mind makes, which is due to our lack of understanding of the real world driven by classic rationality. In the alternative way to look at bounded rationality in a decision-making process, it needs to balance out bias and flexibility to produce overall an inference which is more effective than a system that has no biases at all!
In that scenario, less information, ignoring a big chunk of noisy information and make “biased decisions” might lead to better decision-making.
Building an adaptive toolbox for entrepreneurs
Once you understand all the principles highlighted above, you start tinkering with simple algorithms, that we can call heuristics, extremely useful for the businessman who doesn’t want to fall trap of complex thinking for the sake of it.
The FourWeekMBA analysis and study into this adaptive toolbox has just started, and we’ll be looking more and more into a set of simple heuristics to use in different contexts, by starting from when it makes sense to use them in the first place.
There are a few contexts in the business world where gathering more information, data and complex models can indeed help build a successful company (like at an operational level). But there are many other places (strategy and vision) where those complex systems not only do not work but are harmful.
For the sake of having a better toolbox for directing your business in the right direction, we’ll continue our investigation!
- Reasoning the Fast and Frugal Way: Models of Bounded Rationality, Gerd Gigerenzer and Daniel G. Goldstein, Max Planck Institute for Psychological Research and University of Chicago, Psychological Review Copyright 1996 by the American Psychological Association, Inc. 1996, Vol. 103. No. 4, 650-669
- Heuristic Decision Making, Gerd Gigerenzer and Wolfgang Gaissmaier, Annu. Rev. Psychol. 2011. 62:451–82
- Simon, Herbert, 1983. “On the Behavioral and Rational Foundation of Economic Theory,” Working Paper Series 115, Research Institute of Industrial Economics.
- Simon, Herbert A., 1978. “Rational Decision-Making in Business Organizations,” Nobel Prize in Economics documents 1978-1, Nobel Prize Committee.
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