Alternate Models of Decision Making

The four decision-making processes listed above, while not mutually exclusive, point to alternate models of decision making observed in organizations. We briefly discuss the merits and limitations of each of these here. In its most basic form, the “rational model of decision making” suggests that decision makers solve problems with very clearly defined objectives. These objectives are typically specified in terms of the direction and magnitude of change in one or more organizational metrics. For example, the objective tied to a decision regarding whether or not to invest in sales force training may be driven by an overall metric such as a reduction in customer churn. The selection of the metric might itself be driven by an assumed set of directional relationships such as the following:

sales training → improved sales force knowledge of products → higher sales force engagement → better customer experience → improvement in customer loyalty → reduction in customer churn

Equipped with such objectives, the decision makers then gather appropriate information and develop a set of alternate options. They eventually select an appropriate action option based on a logical and systematic comparison of the alternatives. This model of decision making, informed by classic economic theory, is rooted in the assumption that decision makers are completely rational in their approach. Of course, some of these assumptions, like those that follow, make the model somewhat unrealistic because they ignore the reality of time constraints, limits to human knowledge, and finite information-processing capabilities:

• The decision maker is aware of all alternatives.
• The decision maker can compute the odds of success for each alternative under consideration.
• The decision maker uses a very rational, evidence-based process to choose the best alternative.
• Political or other organizational considerations do not influence the outcome.

Now while every organization would like to follow the “rational model of decision making,” it cannot, because the approach requires limitless availability of resources, such as time, as well as complete knowledge of all available alternatives. It also assumes limitless capacity of the decision maker to gather and process information. Therefore, it is very unlikely that the rational model would be an applied model of decision making within most organizations. Think of a seemingly simple problem of menu optimization in a quick-serve restaurant (QSR) environment. A typical QSR menu can consist of over 60 items with many constraints existing between the prices of items on a menu as well as the presence or absence of individual menu items. For example, it would be very unlikely that the restaurant would offer a sandwich in a combo meal that it would not offer on the menu as a single sandwich. Also, the combo meal would have to be priced so that it is less than or equal to the sum of the individual menu items that compose the meal. In addition, QSRs have various size offerings for side items and drinks, requiring consumers to pay more for larger sized items but often not in proportion to the increase in size. Looking to optimize both the share of visits and the average ticket spent per visit by configuring which products should appear on the menu at what price requires running hundreds of millions of various menu configurations, all of which adhere to the traditional pricing and availability norms for a QSR menu. Computing the results for a single menu takes upward of 15 minutes, so time becomes an issue for running all possible menu configurations. The “rational model of decision making” is therefore best regarded as a theoretical limit, rather than a feasible and implementable option.

On the other end of the spectrum, the decision-making process in organizations can be viewed as extremely haphazard and unpredictable, where decisions appear completely random and unsystematic. The “garbage can model of decision making” views the organization in a chaotic state where problems, solutions, participants, and available opportunities float around randomly. If the four factors happen to connect, a decision is made. This model describes decision making as operating in highly ambiguous settings or “organized anarchies”—organizations that are beset by extreme ambiguity that surfaces in three principal ways described subsequently. The key assumptions under such a decision-making process are the following:

• Presence of inconsistent and ill-defined preferences of the decision makers
• Lack of a clear understanding of cause and effect and gain in organizational knowledge by participants’ trial and error
• Fluid entry and exit of decision-making participants from the decision process, with their involvement depending on their energy, interest, and other demands on their time

The garbage can model calls attention to the importance of chance, wherein what gets decided depends very strongly on timing and luck. While organizations can survive this chance approach during benevolent times, more trying times highlight the shortcoming of such an approach. Moreover, decisions have a fuzzy character—they lack clear start and end points and individuals are unsure of the objectives and change their minds often. One would hope that one’s own organization does not have a semblance of the “garbage can model of decision making” and that the decisions have some clarity of objectives, a somewhat systematic process of identifying cause and effects, and that decision makers do better than random trial and error.

The “political behavior” model of decision making is recognized as a little more real and valid reflection of organizational decision making. The two key ideas underlying the political dimension of decision making are the following:

• People in organizations have differences in incentives resulting from functional, hierarchical, professional, and personal interests.
• People in organizations try to influence the outcomes of decisions, so that their own interests are served, and they do so by using a variety of political techniques.

The presence of functional silos in organizations and friction among these silos is seen as a dominant cause of differences in interest among various stakeholders. Friction comes from competition among the silos for the common pool of organizational resources. Local groups of decision makers then engage in a zero-sum game, where the success of one silo is seen as the defeat of another. Rarely do they come together to seek cooperation and synergy toward achieving larger organizational goals. As might be obvious, such behavior reduces the effectiveness of strategic decision making, yet is accepted as a reality of corporate culture. From a comparison perspective, while the “garbage can model” ignores the cognitive capability of decision makers, the “political model” assumes that people are superheroes capable of calculating and implementing comprehensive political strategies to further their personal goals and interests.

Given the limitations of the various decision-making models, as well as a realization that objectives can be inconsistent across people and over time, the “bounded rationality model of decision making” seems to be a more realistic description of organizational decision making. This model, proposed by Herbert Simon, won him the Nobel Prize in 1978. The key premise of this model is that there are constraints that lead a decision maker to be less than completely rational while making decisions. Managers therefore often search for information and alternatives opportunistically. The model therefore realizes that managers satisfice—that is, select the first alternative that seems good enough, because the costs of seeking an optimal solution are impractical in terms of the required time and effort. Analysis of alternatives might thus be limited, and decisions may often not use a completely systematic analysis. The following are the four key assumptions of this model:

• Managers make decisions by rules of thumb or heuristics.
• Managers are comfortable making decisions without determining all the alternatives.
• Managers select the first alternative that is satisfactory.
• Managers realize that their conception of the world is satisfactory.

The model therefore assumes that managers develop shortcuts, called heuristics, to make decisions that are largely driven by what has worked in the past. Managers might thus develop multiple alternatives, even though cursorily, and then analyze them to some degree, relying on past experience to select the first seemingly acceptable alternative. Over a period of time, heuristics-based decision making can transform into intuition-based decision making, where such “intuition represents a collection of what we have learned about the world, without knowing that we have actually learned it.”² Managers then develop intuition-based mental models that they bring to bear across repeated problem solving opportunities. Consequently, in many cases, the primary reason for building strategy around the implementation of existing mental models is that they pass a face validity test. They sound and feel right and do not conflict with one’s intuition around how things should work.