Since the Enlightenment, the main model of rational judgment in an uncertain world has been probability theory. The laws of probability, however, do not deal with the constraints in time, information, memory, and other resources that are characteristic of the decision making of actual humans (and machines). As a consequence, the underlying vision of rationality has been termed “unbounded rationality”—an omniscient and omnipotent fiction with little or no regard for the limitations in time, knowledge, and computational capacities that humans face. To make rationality more human than God-like, the concept of “bounded rationality” has been proposed. The key difference between unbounded and bounded rationality is the concept of limited search, to be defined by a stopping rule. The vision of bounded rationality, however, is not of one kind.

Rationality comes in many forms. The first split in Figure 2 separates models that assume the human mind has essentially unlimited demonic or supernatural reasoning power from those that assume we operate with only bounded rationality. There are two species of demons: those that exhibit unbounded rationality, and those that optimize under constraints. Optimization under constraints means optimization given various constraints, that is, limited resources such as attention, time, money, or information. The vision of constrained optimization is that minds would calculate the optimal trade-off between the benefits and costs of further search at regular time intervals, and stop search when the costs would outweigh the benefits. The rule “stop search when costs > benefits” sounds plausible at first glance, but a closer look reveals that this… can demand even more knowledge and computation than unbounded rationality.

There are also two main forms of bounded rationality: satisficing heuristics for searching through a sequence of available alternatives, and fast and frugal heuristics that use little information and computation to make a variety of kinds of decisions.

An example of an ignorance heuristic in action:

Let me illustrate the way this heuristic works with one example: Which US City Has More Inhabitants: San Diego or San Antonio? We posed this question to students at the University of Munich and the University of Chicago. The latter, who have a reputation for being among the most knowledgeable in the US, were correct 62% of the time. Yet 100% of the Germans got the correct answer 100% of the time. How did the Germans infer that San Diego was larger? All of the Germans had heard of San Diego, but many of them did not recognize San Antonio. They were thus able to apply the recognition heuristic and make a correct inference. The American students were not ignorant enough to be able to apply the recognition heuristic.

Read more: Fast and Frugal Heuristics: Simple, Adaptive Decision-Making Strategies Leverage Ignorance To Make Us Smart

Treating Investors Like Customers

Seen from the perspective of the financial markets, a company’s ultimate product is its equity. So companies need to start applying to their shareholders the same kind of strategic disciplines they typically apply to customers. Treating investors more like customers does not mean employing misguided, and increasingly discredited, techniques for “managing earnings.” Nor does it mean that corporate executives should let investors determine business strategy any more than they should let customers determine product strategy. What it does mean: developing a detailed process for ensuring that a company’s strategy is informed by the perspectives and requirements of its investor base, and then working over time to create alignment between strategy and shareholders.

In marketing practice, customer segmentation is perhaps the cornerstone to creating any successful campaign. Segmentation is the process of surveying a pool of potential customers, segmenting them into manageable groups based on shared traits, analyzing those traits to understand what product attributes are important to each segment (e.g. style, convenience, price), and finally aligning your products and strategies with one or more of those segments. An extension of customer segmentation is that not all customers are equally valuable to a company. It is not uncommon for a minority of a company’s customers to generate the majority of their profits. What would it mean to apply the process of customer segmentation to one’s shareholders?

Just as some customers are more profitable than others, some investors are more attractive than others–whether because of their timeframe (long horizon, low churn), investment objectives (more in tune with future direction than past portfolio), or interdependence (insiders, employees, and alliance partners). Cultivating these aligned investors will help the company migrate toward an owner base that supports the long-term strategy and will reduce unnecessary volatility as short-term investors move into and out of the stock.

The presence of this type of misalignment between shareholders and corporate strategy raises some troubling questions related to market efficiency. How would such a misalignment arise? We could guess that investors have a poor understanding of a company’s strategic direction, but this is unlikely in the case of institutional investors. A more likely explanation is what I’ll call “strategy drift”, where investors and corporate executives had the same objectives at the time of purchase, but over time the positioning of the company has changed. Various factors, behavioral and otherwise, could cause institutional shareholders to maintain their holdings, at least in the short run, despite the misalignment.

In the end, BCG argues that shareholder value can be created directly by resolving these misalignments where they occur. Companies need to understand who their shareholders are, what attributes of the company’s stock they value, and if these values conflict with corporate strategy, either the strategy needs to shift, the shareholder base needs to be “migrated” towards a better fit, or a combination of both. This is an intriguing approach to value creation, which seems to run counter to orthodox financial theory.

Read more: BCG Perspectives: Treating Investors Like Customers

Read more: Mauboussin on Strategy: Common Errors in DCF Models

Previously:

• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

He distills his analysis into four key points:

• Edge is key: you should be investing money in areas of the market where you have an analytical edge over the market at large
• Greater opportunity suggests a larger bet: the greater the edge you have over the market, the more you should invest
• Mean/variance is not the best way to think about maximizing long-term wealth if you are reinvesting your investment proceeds: the standard type of mean/variance portfolio optimization proposed by Markowitz is relevant for one-time financial decisions, but in situations where profits are reinvested, optimizing your geometric mean of returns is better
• Applying the Kelly Criterion is hard psychologically: adhering to the Kelly Criterion means accepting volatile returns — as investors we are risk averse and frequent losses are stressful psychologically, even if they provide the path to maximizing returns

Read the paper here: Mauboussin on Strategy: Size Matters