Decision Making and the Limits of Rationality

Brenda Barker Scott, Queen's IRC Facilitator
Human Resources

Decision making is a central activity in organizational life. Independent of one’s role or profession, the ability to make effective decisions is a core competence that must be practiced daily. Despite its importance, evidence suggests that we’re not particularly skilled at making decisions, especially the complex, strategic ones. According to Professor Paul Nutt (1999, 2010), approximately half of the decisions made in organizations fail. In his exploration of hundreds of strategic decisions made in North American and European organizations, Nutt found that upwards of fifty percent of decisions are abandoned, judged by those charged with implementing them as unworthy. Why the high failure rate? Perhaps the answer lies in our understanding of what decision making actually is. In other words, perhaps the assumptions we hold around decision making, how it is best facilitated, and our cognitive abilities have a strong influence on how we practice it. Perhaps those assumptions are guiding us to faulty practices.

If the way that we practice decision making is based on our assumptions around effective decision making, how might our preferred models and modes shift as we adopt alternative perspectives? Decision making has been explored from many points of view and those views have evolved, in part, with the dominant worldviews of the day. Below I explore a sampling of those perspectives with the purpose of uncovering the contributions and limitations of each. Based on the insights gained, I offer suggestions for how we might tip the balance toward more fruitful decision making in our fast-paced, contemporary workplaces.

The Rational Systems Perspective

Early perspectives viewed decision making as a rational choice, based on a logical and sequential cause and effect analysis. With the assumption that environments were knowable and predictable, the “economic man” as decision maker was presumed to identify quantifiable problems, search for plausible options, prioritize those options according to predefined criteria, and select the optimized choice with certainty (Miller & Wilson, 2006).

Based on these rational assumptions, classicists such as Taylor (1911) and Weber (1947, in Dessler, 1980) were preoccupied with designing jobs and organizational forms that enabled rational and efficient decision making. The assumption was that codified rules, predictable relationships, set jobs, and clear lines of authority would enable organizations to make rational decisions with machine-like proficiency. In turn, employees were viewed as mechanical parts that could be expected to perform as directed.

Weber’s (1947, in Dessler, 1980) bureaucracy granted decision rights to individuals on the basis of their position within the hierarchy. In turn, employees were bound by predefined decision rules, which were assumed to produce accurate responses. Taylor (1911) rigorously analyzed work patterns to determine the one best way for structuring tasks. To ensure consistency, he carefully selected employees and trained them to perform tasks, as directed. For both Weber and Taylor, compliance was sought through the use of directives, tightly defined jobs, explicit training, and financial incentives (Scott & Davis, 2007).

To summarize, rational managers sought stable and predictable attitudes, skills, and behaviours. Decision making was assumed to be a relatively straightforward process, bound by the parameters of one’s job. Decisions were shaped, controlled, and coordinated through a system of carefully constructed job rules, decision rights, and norms of conduct.

The Natural Systems Perspective

In the 1920s and 1930s pioneering theorists Elton Mayo (1933) and Kurt Lewin (1947) saw that, in addition to the formal organization, an informal and unpredictable organization exists. In this informal organization, human aims and needs have a significant impact on attitudes and effort. Indeed Mayo’s Hawthorne studies, originally based on the rational hypothesis that lighting could be optimally adjusted to support worker productivity, illuminated quite another reality. As worker productivity increased independent of the lighting levels, the researchers discerned that social conditions—group norms, supervision, and relationships—were critical to employee effort. Theorists also began to note that rules and procedures could produce unintended, and in some cases, deleterious consequences. Such was the case with the incentives designed to pressure slower workers to increase their pace at Hawthorne Electric. Ironically, the incentives had the opposite effect; it was the slower workers who pressured the faster employees to slow down through the process of binging (Dessler, 1980; Scott & Davis, 2007).

The discovery that employee effort is a function of more than directives, rules, and rewards exposed the limits to the rationalist paradigm. Recognizing that people are limited by both human and organizational factors, March and Simon (1958) offered an alternative view of the organizational decision maker; one where employees identify satisfactory rather than optimal solutions. Burdened by incomplete information, unclear decision criteria, limited time, and partial perspective, decisions may be rational, but only from the unique perspective of each decision maker’s aims, role, and reach (Miller & Wilson, 2006; Scott & Davis, 2007).

A central question for theorists therefore became: How to ensure compliance when humans are motivated by a wide array of personal and work related factors? While March and Simon (1958) agreed that decision making boundaries were important to distributed decision making, they reasoned that decision rules could not be pre-programmed for every eventuality. Rather, novel and complex challenges require rigorous and thoughtful analysis of ambiguous data. Here, they reasoned, decision makers require the requisite “attitudes, habits and state of mind” (Simon, 1976, in Dessler, 1980, p. 41) that can only be developed through enculturation and education (Miller & Wilson, 2006).

To summarize, as the rational paradigm gave way, theorists recognized that decision making could not necessarily be bound by formal job-related rules and protocols. Not only were social factors at play, but rules and incentives often produced unintended consequences. Adding to the complexity, pre-programmed responses were impossible to predict for novel, hard-to-define challenges. With the aim of creating organizational predictability and control, theorists prescribed a well designed system of decision rights for programmable decisions, supplemented with efforts to develop perspective and judgment amongst those charged with more complex tasks.

The Open Systems Perspective

With the emergence of the open systems perspective following World War II, many theorists began studying the interrelationships between subsystems, as well as the relationship of systems to their environments. Studies by Lawrence and Lorsch (1967) in the United States and Burns and Stalker (1961) in the United Kingdom, set out to explore a number of open system tenants. Both sets of authors, working independently, found that organizations operating in stable environments tend to develop simple, formal structures, with centralized and disciplined information sharing and decision making. On the other hand, organizations operating in more complex environments developed more organic structures with informal communication patterns and decentralized decision making. Contingency theory was thus born, providing theorists with a wider lens from which to explore how environmental complexity (or perceptions thereof) shape organization form, decision rights and information processing patterns (Scott & Davis, 2007).

Building from contingency theory, Daft and Weick (1984) developed a theory of organizations as interpretive systems to model how managerial assumptions about the complexity of their environments shape decision making practices. Those adhering to a rationalist perspective are more likely to assume that the environment is knowable and predictable. These managers collect predefined data and assess it against specific metrics to find the “right” answers. On the other hand, managers who assume that the environment is not knowable or analyzable, enact their environments by using trial and error approaches. Independent of whether managers see the environment as knowable or chaotic, those who assume the environment is competitive will be more active and apply greater rigour in their scanning, interpreting, and deciding processes than those who assume the environment is benevolent. In addition to highlighting the important role that managers play in capturing, translating, and responding to environmental cues, Daft and Weick’s model calls attention to trial and error processes for deciding that might be more applicable to complex, ambiguous environments.

Applying contingency thinking to decision making orientations, Mintzberg and Westley (2001) identified three orientations to decision making. Thinking first approaches work best when the issue is clear, the data are reliable and equivocal, and the context is unchanging and known. Here, rational cause-and-effect reasoning works well. Seeing first approaches involve envisioning and prototyping solutions so that they can be tested, analyzed, and improved. Doing first approaches involve acting and experimenting in order to learn before deciding. The authors suggest that seeing first and doing first approaches are essential when the context is ambiguous, many elements need to be combined into creative solutions, tacit knowledge needs to be shared, and the only real way of knowing is by doing and reflecting.

In summary, a key assumption underpinning open systems theory is that systems evolve in direct relationship to the complexity of their environments. Complex environments are less predictable, highly competitive, and in constant flux. In turn, complex organizational systems need to be more fluid, capable of taking in, sharing and processing information, and more responsive to their environments. Decision making processes therefore need to be more iterative with prototyping and trial and error replacing, or perhaps supplementing, pre-programmed methodologies.

The Chaos Perspective

Turning the rationalist perspective of decision making on its head, Cohen, March, and Olsen (1972) describe organizations as anarchies. Anarchies, as in the case of the universities they studied, are defined as chaotic systems rife with unclear goals, authority clashes, and stakeholders with competing interests. The authors likened the decision making arena to a garbage can. Goals, issues, players and their solutions are tossed into the can. Depending on the contents of the can, some issues, solutions, and people stick and surface, while others get lost. Cohen and colleagues suggest that some organizations, or even all organizations part of the time, are a messy collection of “choices looking for problems, issues and feelings looking for decision situations in which they might be aired, solutions looking for issues to which they might be the answer, and decision makers looking for work” (p. 2). Recognizing the absurd view that the garbage can metaphor offers, the authors point out that while the garbage can process does not produce optimal solutions, ” it does enable choices to be made and problems resolved, even when the organization is plagued with goal ambiguity and conflict, with poorly understood problems that wander in and out of the system… and with decision makers who may have other things on their minds” (p. 16).

Employing yet another analogy, this time a trading zone, Kellogg, Orlikowski, and Yates (2006) suggest that in dynamic and volatile environments, such as the internet marketing firm they studied, decisions are best made through an emergent, ongoing exchange of ideas that get adopted and altered over time. Just like traders interacting to exchange goods, so too do organizational members need a space and process for exchanging ideas, work progress, and solutions. The notion of trade or exchange does not require shared goals or meaning amongst the parties and does not presume permanent outcomes. Rather, in conditions of uncertainty and change, the authors suggest that collaborators are best served by a trading zone, whereby they can prototype and display their ideas for others to build on, adapt, or even omit, as the work progresses in “dynamic alignment” (p. 36). The authors claim that the trading zone analogy becomes important as traditional bureaucratic structures give way to more dynamic, fluid forms, which are better suited to today’s complex, fast moving environments.

In summary, the chaos perspective assumes the decision environment to be complex and the players to be guided by their unique needs, abilities, and perspectives. Instead of trying to reduce ambiguity to simplify the data set, chaos approaches embrace it. While the garbage can model favours perseverance, the trading zone analogy provides decision makers with joint space and common tools to display and combine ideas from which decisions emerge. Given the volatility of the context, permanence is not sought or valued. Rather, the decisions themselves are assumed to evolve and shift, in dynamic alignment as needs and conditions shift.

The Power Perspective

While the concepts of decision making, authority, and power are closely linked, some theorists have chosen to focus on power in decision making in its own right. Here, theorists have studied decision making from the lens of power brokers competing for control of scarce resources. Decision making is viewed as being “far removed from the coolly logical appraisal and selection of alternatives. Rather, it is at the center of a web of political machinations and dynamic power exchanges, the true nature of which is not fully recognized, even by those involved” (Miller & Wilson, 2006, p. 474).

From the power perspective, stakeholder groups engage in bargaining behaviors that often lead to sub-optimized compromises with winners and losers. In an early, yet influential study, Hickson et al. (1971) found that the complexity and politically of decisions do indeed shape the decision process and outcomes. When decisions were relatively straightforward and politically benign, a fluid, streamlined process was more apt to occur. On the other hand, politically tumultuous decisions were more apt to follow a sporadic process, with delays, disruptions, and uneven use of information. The researchers found that in approximately one third of the cases, the decisions had been made before the process was completed, or in some cases even started, due in part to the covert maneuvering of powerful interest groups.

In summary, the power perspective assumes the environment to be competitive, with parties vying for their interests to be met, often at the expense of the others. Decision making is therefore a strategic process of building allies and winning small victories, all in pursuit of one’s ultimate aim.


While each and every day employees make decisions vested by the authority of their position, decision making in organizations is not a straightforward endeavor. Returning to Paul Nutt’s (2010) analysis of managerial decisions, he concludes that, all in all, decision makers do not employ good process. Typically, decisions are made in isolation, with decision makers over estimating the clarity of their challenges, the reliability of their data, the effectiveness of their solutions, and the commitment of the implementers.

What can we make of this? With each turn of the viewfinder from the rational perspective, through systems, chaos, and power perspectives, we come to understand that decision making is not a rational, choice based process. Daft and Weick’s (1984) interpretive systems, Cohen, March, and Olsen’s (1972) garbage can metaphor, and Hickson and colleagues’ (1971) power dynamics all contribute to bursting the bubble of the rational paradigm; first logical reasoning, followed by the optimal choice. Rather, decision makers are influenced, sometimes blinded, by perspective, need, and social/political forces. At times, and in some situations, interpretation follows choice; actions are taken and supportive reasoning follows. Alternatively, solutions may emerge, as in the case of the trading zone, following trial and error prototyping.

The good news is that organizational theorists and practitioners will continue to wrestle with the many sides of decision making from fresh perspectives. The gift of the newer interpretive approaches is that they embrace ambiguity and invite decision makers to experiment, act, and learn. If, as open systems theory predicts, systems evolve in direct relationship to the complexity of their environments, then any approach that enables decision makers to wrestle with the complexity of their challenges will serve them well. Interestingly, this is essentially what Paul Nutt found. While practices enabling participation and learning were the least used in his study sample, they were the most effective in producing workable solutions with staying power. Given our less than stellar track record with the rational perspective of decision making, perhaps it is time to make a turn of the viewfinder, and in so doing, expand our decision making repertoire.


About the Author

Brenda Barker Scott

Brenda Barker Scott has extensive experience in all aspects of organizational development acquired over a twenty-year career in teaching and consulting. When working with leadership teams she combines strong theoretical knowledge with practical methodologies to ensure that the right people are engaged in the right conversations to design robust and workable solutions. Brenda is an instructor on a number of the Queen’s IRC programs including Building Smart Teams, Organization Development Foundations, Organizational Design and HR Decision Making. A frequent presenter, Brenda has been a keynote speaker for the Public Health Agency of Canada, the Conference Board of Canada, the Human Resources Planners Association of Ontario and the Canadian Institute for Health Research. Brenda is co-author of Building Smart Teams: A Roadmap to High Performance. She is a graduate of Queen’s University and lives in Kingston with her husband and two sons.



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