CHAPTER 1

What Are Heuristics?

(Rules of Thumb)

A rule of thumb (termed “heuristic” hereafter)ⁱ is a description of an informal or a formal problem-solving process not necessarily 100 percent reliable.

Vignette: The Merger Fiasco

As the CEO of a technology company, I had to consider a number of merger or acquisition opportunities. These were often potential avenues for sources of growth capital. I found these opportunities challenging. On one hand, their appeal was high because they implied significant infusion of additional operating funds, something my growing company was always in need of; controversially, those potential ventures could involve loss of control, or a cultural change with the transformation that would most certainly follow a merger.

My board would support my recommendations when an opportunity of this kind arose. However, on one occasion, my partner and I disagreed on an acquisition opportunity by an American firm. My partner was 10 years older than me; he was more eager to sell the company and retire than I was.

I was convinced that it was not a good time for a merger; therefore, I advised against it.

I confess that my partner followed the standard due diligence process to reduce my doubts and to convince me of the value of the acquisition for senior management and shareholders alike. We visited the suitor’s premises, reviewed its operations, and met some of its key managers. This visit left me with a negative impression. Although the suitor’s company was profitable and efficient, its standards of operation were lower than ours and its quality control was questionable. Moreover, it had a dubious acquisitions history. I discovered that it had tried to acquire another company a few years before and failed. I remained unconvinced and argued against the acquisition at the board level. I was overruled, and I agreed to leave the company with a suitable arrangement. Nine months later, the company I left went bankrupt. I learned soon after that the suitor arranged for a large order that did not materialize. The company was undercapitalized to meet the demand. The bank foreclosed. The suitor bought the company in a fire sale at 20 cents on the dollar.

I was right to refuse the merger.

Hindsight is 20/20.

Formal or Informal Decision Making: What Works?

When not enough information is available to make a business decision, the executive needs to fill in the information void with a solution that may lead to a workable outcome.

Under these circumstances, qualitative rules of thumb (rules relying on incomplete, qualitative information) rather than quantitative rules of thumb (rules relying on numbers) can assist in making fast, frugal, and valid business decisions.¹

In most business schools, teaching formal decision-making processes is currently the norm. For example, some business management schools describe decision analysis courses as “decision-oriented courses that focus on the frameworks, concepts, theories, and principles needed to organize and use information to make informed business decisions.” A closer analysis of the courses’ content reveals that they cover mostly operations management and statistics. The formal decision-making process relies on quantitative data, hence limiting the decision-making process to the application of quantitative rules of thumb.

I am not advocating that these kinds of courses are not useful in business management. Managers need to apply various quantitative tools when they face quantifiable problems—like a financial opportunity that needs scrutiny or an operation gridlock that needs resolution. Many of these situations in large companies are usually delegated to professionals, such as statisticians, accountants, and operational or financial managers who have the time and required detailed analytical knowledge to study those types of problems and suggest appropriate solutions. Executives will then review the suggestions, consult with their managers, and ensure proper decisions are applied. These situations often do not require on-the-spot resolutions.

Even if the scientific research method takes for granted that one can arrive at valid conclusions based on formal logic and exhaustive testing, according to Daniel Kahneman,ⁱⁱ in daily business decision making, informal logic and the use of qualitative rules of thumb can also lead to satisfactory results.²

Clearly, understanding the importance of qualitative rules of thumb can be a helpful tool in decision making because business executives will need, at some point, to face their stakeholders (employees, customers, vendors, and shareholders) and explain or justify the decisions they have made.

Researchers like Herbert Simonⁱⁱⁱ and Gerd Gigerenzer have studied the importance of qualitative data as opposed to the use of quantitative data in decision making.

Simon introduced the term bounded rationality with useful application in economics. Simon states, “Boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information.”³

Bounded rationality theory maintains that human decision-making models should rely on what individuals know and not on assumptions using probability laws. Simon stressed, “Because of the limits of their [computers and the human brain included] computing speeds and power, intelligent systems must use approximate methods to handle most tasks. Their rationality is bounded.”³ These computing methods include recognizing elements of circumstances similar to those previously experienced, therefore reducing the need for additional information search. Simon further advocates the use of heuristics for information search and for needing to stop search. He suggests applying simple rules for deciding how to use newfound information, like rules of syllogism in formal logic. On the other hand, research by Gigerenzer^iv and his team at the German Max Planck Institute for Human Development reveals that applying rules of thumb for problem solving can lead to remarkably accurate results.

In addition, new research in judgment and decision making suggests that unquantifiable elements like emotion and feelings also have an important influence in decision making. Emotions and feelings are also often at the source of qualitative rules of thumb.⁴

In extreme conditions, when executives face major effects of faulty decision making based on incomplete information, the application of only formal logic and statistical probabilities can lead to disastrous consequences, as we will illustrate later on. Applying qualitative rules of thumb for business decisions comes with its caveats resulting from unsubstantiated assumptions, groupthink, prejudice, and personal bias.

As one increases the use of business rules of thumb in making business decisions, one also increases one’s experience, knowledge base, and comfort level of using fast and frugal heuristics. Ultimately, knowing what rules of thumb to apply does not imply that a decision will take place. The executive has the final say whether to apply the business heuristic or reject it.

In summary, business rules of thumb involve formal or informal application of rules, processes, and methods for problem solving a level of incompleteness or uncertainty. Rules of thumb can eventually lead to the discovery of a solution not necessarily 100 percent reliable, but a solution that can nevertheless result in positive business outcomes. A decision based on a rule of thumb does not need to follow formal logic to be acceptable.

What Is a Rule of Thumb?

Dr. Roger Martin, dean of Rotman School of Management (University of Toronto), proposes the following definition for heuristics: “Heuristics are rules of thumb or sets of guidelines for solving a mystery by organized exploration of the possibilities.” He continues:

Heuristics do not guarantee success. They simply increase the probability of getting to a successful outcome. They represent an incomplete understanding of a heretofore mystery. Business people will have to become more like designers—more “masters of heuristics” than “managers of algorithms.”⁵

Charles Hinkle, an emeritus professor at the College of Business, University of Colorado, argues:

Value creation in the 20th century was largely defined by the conversion of heuristics to algorithms. It was about taking a fundamental understanding of a “mystery”—a heuristic [or a rule of thumb]—and driving it to a formula, an algorithm—so that it could be driven to huge scale and scope.⁶

The aforementioned two citations illustrate different views of the wide range of rules of thumb interpretations in academia.

The most common definitions of heuristics contain the words invention or discovery. Additional interpretations of heuristics include trial-and-error handling, problem solving, unstructured proof, incremental exploration, learning from experience, comparison to previously recognized patterns, intelligent guesswork, speculative formulation, investigative discovery, conducive discovery, rules of thumb, algorithmic search, and even common sense.

Sometimes rules of thumb do not contain clear information about what to apply and how. They presume that the person to whom we convey those rules possesses the missing or omitted information required to make a decision.

For example, the rule of thumb that states to “apply a meaningful and prompt response” (to an irate customer) is not clear because the understanding of meaningful and prompt response can vary with each person. One interpretation of meaningful and prompt response could be to “call the client right away, and confirm your call with an email or letter to ensure the issue was resolved,” whereas, for another person, the same rule of thumb could mean to “call the client—within the week” and “write a letter, as soon as you find some time for it.”

Early applications of rules of thumb in business took the form of statistical or quantitative analysis. Certainly, when facing quantifiable financial or operational problems, executives have at their disposal a plethora of mathematical models, economic laws, statistical formulas, algorithms (as specific computational procedures for numerical manipulations), and various risk analysis tools to assist them with decision making. Nevertheless one cannot always quantify business risk.⁷ What are executives supposed to do when the outcomes are not quantifiable?

Numerous decision-making theories, such as Daniel Bernoulli’s^v Expected Utility Theory,⁸ Daniel Kahneman’s Rank Dependent Expected Theory,⁹ and Prospect Theory,¹⁰ use risk analysis reasoning requiring some form of quantitative estimates of the outcome. The decision maker is asked to put forward a probability percentage of what may happen.

For example, “There is a 45 percent probability that this will be a winning bid,” or “I think that I have a 70 percent chance in meeting the deadline.”

What is missing is the process that leads to that probability number. How do we compute this probability? We may as well pick up a number, any number.

Despite their informational constraints, some concrete applications of these theories have been useful in psychology, economics, and finance; however, they strike me as meaningless for most daily business decisions.

Often the daily challenges an executive faces originate from dilemmas that need immediate response, leaving little time for detailed analysis.

Most often, small business executives do not have the time, the expertise, or the experts available when they might require it.

These conditions establish a need for the use of heuristics-based decision making commonly known as rules of thumb.

I am not advocating here that quantitative analysis relying on quantifiable data is to be disregarded. I argue that one should not use exclusively quantitative data when making business decisions.

What, then, are those rules of thumb that may provide solutions for the pressing daily business problems executives face?

I will illustrate rules-of-thumb decision making using examples from my extensive business experience of more than 40 years combined with the results of my 15-year academic research.

ⁱA heuristic technique (/hjʊəˈrɪstɪk/; Ancient Greek: εὑρίσκω, “find” or “discover”), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. (Source: https://en.wikipedia.org/wiki/Heuristic.)

ⁱⁱDaniel Kahneman (born March 5, 1934) is an Israeli American psychologist notable for his work on the psychology of judgment and decision making, as well as behavioral economics, for which he was awarded the 2002 Nobel Memorial Prize in Economic Sciences (shared with Vernon L. Smith). His empirical findings challenge the assumption of human rationality prevailing in modern economic theory (Source: Wikipedia).

ⁱⁱⁱHerbert Alexander Simon (June 15, 1916, to February 9, 2001) was an American political scientist, economist, sociologist, psychologist, and professor—most notably at Carnegie Mellon University—whose research ranged across the fields of cognitive psychology, cognitive science, computer science, public administration, economics, management, philosophy of science, sociology, and political science. With almost a thousand highly cited publications, he was one of the most influential social scientists of the twentieth century. (Source: Wikipedia.)

^ivGerd Gigerenzer (born September 3, 1947, Wallersdorf, Germany) is a German psychologist who has studied the use of bounded rationality and heuristics in decision making. Gigerenzer is currently director of the Center for Adaptive Behavior and Cognition (ABC) at the Max Planck Institute for Human Development [1] and director of the Harding Center for Risk Literacy, [2] both in Berlin, Germany. (Source: Wikipedia.)

^vDaniel Bernoulli (1700 to 1782) was a Swiss mathematician and physicist and was one of the many prominent mathematicians in the Bernoulli family. He is particularly remembered for his applications of mathematics to mechanics, especially fluid mechanics, and for his pioneering work in probability and statistics. His name is commemorated in the Bernoulli principle, a particular example of the conservation of energy, which describes the mathematics of the mechanism underlying the operation of two important technologies of the twentieth century: the carburetor and the airplane wing. (Source: Wikipedia.)