Heuristics

What Are Heuristics?

A heuristic, or heuristic technique, is any approach to problem-solving that uses a practical method or various shortcuts in order to produce solutions that may not be optimal but are sufficient given a limited timeframe or deadline.

Heuristics methods are intended to be flexible and are used for quick decisions, especially when finding an optimal solution is either impossible or impractical and when working with complex data. These cognitive shortcuts feature prominently in behavioral economics.

Key Takeaways

  • Heuristics are methods for solving problems in a quick way that delivers a result that is sufficient enough to be useful given time constraints.
  • Investors and financial professionals use a heuristic approach to speed up analysis and investment decisions.
  • Heuristics can lead to poor decision-making based on a limited data set, but the speed of decisions can sometimes make up for the disadvantages.
  • Behavioral economics has focused on heuristics as one limitation of human beings to behave like rational actors.
  • Availability, anchoring, confirmation bias, and the hot hand fallacy are some examples of heuristics people use to in their economic lives.
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Understanding Heuristics

The various advents and innovations of digital technology have disrupted aspects of many different industries, including finance, retail, media, and transportation. Some daily activities have become obsolete; for example, checks are deposited to bank accounts without visiting a local branch, products and services are purchased online, and take-out food is delivered by food-service delivery apps.

All of this new technology creates data, which is increasingly shared across multiple industries and sectors. A professional in any industry may find themselves working with mounds of complex data to solve a problem. Heuristic methods can be employed to help with data complexity, given limited time and resources.

Advantages and Disadvantages of Using Heuristics

Heuristics facilitate timely decisions. Analysts in every industry use rules of thumb such as intelligent guesswork, trial and error, the process of elimination, past formulas, and the analysis of historical data to solve a problem. Heuristic methods make decision-making simpler and faster through shortcuts and good-enough calculations.

There are trade-offs with the use of heuristics that render the approach prone to bias and errors in judgment. The user’s final decision may not be the optimal or best solution. Or the decision made may be inaccurate, and the data selected might be insufficient (thus leading to an imprecise solution to a problem). For example, copycat investors often imitate the investment pattern of successful investment managers to avoid researching securities and the associated quantitative and qualitative information on their own.

Copycat investors hope that the formulas used by these managers will continually earn them profits, but this is not always the case. For example, the tech-heavy Ark Innovation ETF (ARKK) was the paradigm of investment prowess through 2020, but the fund, which was widely copied, failed to deliver in 2021. While the S&P 500 returned more than 25%, ARKK lost more than 20%.

Example of Heuristics

Representativeness

A popular shortcut method in problem-solving identified in behavioral economics is called representativeness heuristics. Representativeness uses mental shortcuts to make decisions based on past events or traits that are representative of or similar to the current situation. Say, for example, Fast Food ABC expanded its operations to India and its stock price soared. An analyst noted that India is a profitable venture for all fast-food chains. Therefore, when Fast Food XYZ announced its plan to explore the Indian market the following year, the analyst wasted no time in giving XYZ a “buy” recommendation.

Although his shortcut approach saved reviewing data for both companies, it may not have been the best decision. Fast Food XYZ may have food that is not appealing to Indian consumers, which research would have revealed.

Anchoring and Adjustment

Anchoring and adjustment is another prevalent heuristic approach. With anchoring and adjustment, a person begins with a specific target number or value—called the anchor—and subsequently adjusts that number until an acceptable value is reached over time. The major problem with this method is that if the value of the initial anchor is not the true value, then all subsequent adjustments will be systematically biased toward the anchor and away from the true value.

An example of anchoring and adjustment is a salesman begins negotiations with a very high price (that is arguably well above the fair value). Because the high price is an anchor, the final price will tend to be higher than if the car salesman had offered a fair or low price to start.

Heuristics and Psychology

Heuristics were first identified and taken seriously by scholars in the middle of the 20th century with the work of Herbert Simon, who asked why individuals and firms don't act like rational actors in the real world, even with market pressures punishing irrational decisions. Simon found that corporate managers do not usually optimize, but instead rely on a set of heuristics to "satisfice" (a combination of the words satisfy and suffice); that is, they use a set of shortcuts to get the job done in a way that is good enough.

For Simon, people cannot consistently compute and process all the information at their disposal because of the biological limitations of the human mind. Thus, people may want to behave rationally but are bound by these limitations—what he called bounded rationality.

Later, in the 1970s and '80s Amos Tversky and Daniel Kahneman working at the Hebrew University in Jerusalem, building off of Herbert Simon's work, developed what is known as Prospect Theory. A cornerstone of behavioral economics, Prospect Theory catalogues several heuristics used subconsciously by people as they make financial evaluations. One major finding is that people are loss-averse—that losses loom larger than gains (i.e., the pain of losing $50 is far more than the pleasure of receiving $50). Here, people adopt a heuristic to avoid realizing losses, sometimes spurring them to take excessive risks in order to do so—but often leading to even larger losses.

More recently, behavioral economists have tried to develop policy measures or "nudges" to help correct for people's irrational use of heuristics, in order to help them achieve more optimal outcomes. For instance, by having people opt out of a retirement savings plan by default, instead o having to opt in.

What Are the Types of Heuristics?

To date, several heuristics have been identified by behavioral economics—or else developed to aid people in making otherwise complex decisions. In behavioral economics, representativeness, anchoring and adjustment, and availability (recency) are among the most widely cited. Heuristics may be categorized in many ways, such as cognitive vs. emotional biases or errors in judgment vs. errors in calculation.

What Is Heuristic Thinking?

Heuristic thinking uses mental shortcuts—often unconsciously—to quickly and efficiently make otherwise complex decisions or judgments. These can be in the forms of a "rule of thumb" (e.g., save 5% of your income in order to have a comfortable retirement) or cognitive processes that we are largely unaware of like the availability bias.

What Are Computer Heuristics?

In computer science, a heuristic refers to a method of solving a problem that proves to be quicker or more efficient than traditional methods. This may involve using approximations rather than precise calculations or with techniques that circumvent otherwise computationally-intensive routines.

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