Project Management Institute

Complex project management

towards a theory of cognition for ill-structured tasks

Michael Kilpatrick


The enhancement of intellectual performance has been one of the more enduring and yet elusive goals of mankind. It has been the source of numerous heated debates over the nature of human intelligence, such as whether our intelligence is amenable to improvement. Many but not all of these models of intellectual performance are illustrated in Table 1. Most of the research has focused on selective aspects of cognition under highly structured empirical conditions. Despite this extensive body of research, however, there exists no cohesive framework addressing the multifaceted nature of human cognition relating to the complex, ambiguous, and ill-structured problems that are endemic to the modern work world. In rebuttal, this paper seeks to expand the study of intelligent performance to include the ill-structured environments common to the project managers’ everyday experience. This goal is no longer a luxury but a necessity if we are to address the high rate of project failure, which is made worse in recent years by the increasing volume and complexity of the tasks demanded of businesses.

In a world in which the optimization of task was once sufficient, organizations must now supplement optimization with innovation. In a world in which the rationalization of cost and profit were once dominant, corporate considerations have expanded to include a number of value-laden influences involving stakeholder, regulatory, and broader community issues. In addition, many businesses have experienced the challenges beset by downsizing, re-engineering, process change, or trying to meet ISO quality standards or just-in-time production requirements. Where companies could once rely on a product life cycle to extend over a decade, turnaround times have been reduced to months.

Just as organizations are faced with increasingly difficult demands, the analysts and project managers working within them are under greater pressure to live up to their organization's heightened expectations to do more with less, at higher quality, with better service, more efficiently, more effectively, in a coordinated and integrated manner, while also trying to be innovative and creative, and anticipate future consequences, opportunities, and trends--and always within tight time frames. Consequently, the type of task assigned project managers today can be characterized by both its growing complexity and the increasingly interdisciplinary nature of the technical and interpersonal skills required for completion. As a case in point, the classic conundrum of the project manager is how to manage what is euphemistically referred to as the triple constraint. Perhaps reflecting the growing complexity of projects, the triple constraint that once referred only to managing time, scope, and cost of projects, must now consider quality, customer satisfaction, safety, security, operational performance, consistency with organization's strategic goals, and compliance with government regulation, to name just a few.

Table 1. Models of Intellectual Performance (Partial list) images

Compounding these trends is the accelerated pace of change. In such an environment, both individual projects and the skill sets that accompany them can quickly become redundant. The tasks of the future may necessitate not only continual retraining, but also cognitive retooling in the way we think about and approach complex, novel, and innovation-demanding tasks. However, while mastery over the tools and techniques of the trade is often identified as a primary competency for project managers, it addresses only the latter half of the “80/20” rule. The vast majority of the project manager's tasks involve value-laden judgments under conditions of dynamic uncertainty, made worse by severe consequences for failure. Under this definition, more accomplished project managers would be wise to master themselves before attempting to master the task, especially as the task becomes increasingly dimensional and difficult.

Given the changing nature of the task environment, we should not continue to expect that 20th century modes of thinking and the methods derived from them are sufficient to address the problems we face today. Expertise that once required a decade to develop is a stillborn entity in the new century. The faster turnaround times demanded of increasingly complex and novel tasks require a more cognitively and behaviorally adaptive response to their execution. As one researcher noted, if we are to address the ingenuity gap (Homer-Dixon, 2000) between our expectations and our abilities, new approaches to enhancing our intellectual competencies may be required.

This paper introduces the idea that, given the relatively poor project performance to date, with failure rates exceeding success by a margin of two to one (Flyvberg, Bruzilius, & Rothengatter, 2003; Pavlak, 2004; Standish Group, 2001), organizations must better understand the human side of the performance equation if individual and organizational performances are to be enhanced. This paper explores the work of researchers in three sub-domains of the field of psychology – error theorists, expert theorists, and learning theorists – in an attempt to apply some of the lessons learned from these disciplines to the new meta-discipline of project management.

The Mind of a Project Manager

In contrast to academic environments, in which the problems are generally well defined and usually have one correct response, the problems encountered by the analyst-project manager tend to be ill-defined and open to many interpretations and approaches. One of the features that make the emerging discipline of project management so powerful is the concordance between the techniques of the trade and the cognitive mindset required to traverse complex, ill-structured problem space. However, the same cognitive attributes that make project managers effective in managing these ill-defined, multi-factorial and highly interrelated tasks also makes them vulnerable to certain innate error tendencies.

For example, planning and scheduling techniques help structure our thoughts into manageable schema (Bartlett, 1932), thus reducing cognitive overload and freeing up cognitive resources for other higher-order evaluative tasks. More generally, the planning tasks provide a means of simulating future possibilities, thus helping prime our memory for a more adaptive and proactive response to contingencies. But our tendency to simplify complex issues into easy-to-understand concepts may also give us the illusion of control (Langer, 1975), as was recently highlighted by the Columbia Accident Investigation Board Report (Columbia Accident Investigation Board, 2003), which noted the limitations in using PowerPoint presentations to simplify complex issues into simple, easy-to-understand language.

The reductive processes involved in the decomposition of the work breakdown structure bears striking similarity to the Gestalt thought processes that allow for the systematic exploration of a problem's constituent elements and their interrelationships to the larger project. However, as one of its originators, Max Wertheimer, reminds us, the blind decomposition of complex systems into disaggregated parts can result in analysis unsuited to its context or unintended purpose (Wertheimer, 1959). During any decomposition, the analyst must retain an understanding of the internal dynamics among constituent parts, as well as their contextual relationship to the larger entity.

Similarly, environmental scanning methodologies used in constraint identification and risk management parallel our perceptual system's ability to perceive threats and envision future possibilities. However, our perceptual systems are also prone to emphasizing the salient aspects of an issue at the expense of the logical or relevant. In addition, estimation techniques that rely on intuition are also subject to judgmental biases that predispose individuals to ignore mundane but relevant information, giving rise to idiosyncratic correlations that do not take into account base rate effects. This weakness is as common among experts as it is in novices (Camerer & Johnson, 1991).

The iterative development process, referred to as progressive elaboration, is fundamental to traversing complex problem space in that it allows conscious sequential chains of cognition to interact with subconscious, reflective, non-judgmental forms of cognition. However, time pressure can quickly undermine the synergy required between these two quintessential thought processes. Similarly, the meta-cognitive and evaluative skills that are fundamental to balancing the triple constraint and other integration management tasks—such as verification and change control—are subject to stress-induced lapses in judgment, or analysis paralysis.

In addition, the interpersonal aspects of projects that are fundamental to effective interaction and communication with team members, stakeholders, or senior management would do well to be informed by the research into tacit knowledge acquisition (Wagner & Sternberg, 1986) and emotional intelligence (Salovey & Mayer, 1997). Suffice it to say that human cognition is fundamental to performance, and that intelligent performance is fundamental to successful performance.

Subsequent sections elaborate on these themes by exploring the work of human error theorists, expert theorists, and learning theorists. First and fundamental, when enhancing human performance in complex domains, is coming to terms with the systemic error biases common to most human endeavor. Thus, an introduction to the study of human error is offered. Second, the work of expert theorists will highlight both the changing nature of expertise and some of the attributes necessary for exceptional performance. Finally, performance enhancement is not achievable without a learning theory and instructional methodologies designed to enhance our aptitude for learning in a hurried and complex environment.

Error Theorists

Despite constant reminders of mankind's failures, we still cling to the myth of human infallibility. While this may seem at odds with our efforts to reduce failure, it is found in our belief that human error can be treated in relatively simplistic causal terms, such as relying on hindsight to attribute causality, or our tendency to externalize error to organizational, technical, or situational causes. It is also in our naive belief that, with more knowledge, more training, more tools, techniques and technology, we will eventually achieve success in our efforts. Why is it, then, that failure continues to occur not just infrequently, but at an alarmingly high rate? These trends become even more prevalent as system complexity increases.

Into the gap between our desire and ability, a number of researchers have offered a refreshingly candid discussion about stupidity. The earliest incursions into this discussion date back to Francis Bacon's “Idols of the Tribe” (Bacon, 1620/2001), in which he noted our bias towards affirming generalizations rather than critiquing them. James Sully's treatise on Illusions (Sully, 1881) attributed them to the error-laden attributes of the dual character of our mind, between perception-introspection, the active-passive, and the conscious-unconscious states of mind. In 1904, Sigmund Freud's book on The Psychopathology of Everyday Life introduced the idea of parapraxis, or what is more commonly referred to as the Freudian slip. While these early researchers represent only a few of the many involved in creating a composite model of fallible man, they give life to the idea that performance is not a simple matter of teaching correct behavior or informing people of their mistakes. Human error is more deeply rooted in our conscious and unconscious cognitive processes.

Among more modern theorists, Tversky and Kahneman challenged the Bayesian notion that people are rational optimizers. They found, instead, that individuals tend to engage a limited number of subjective judgmental heuristics and biases in order to “reduce the complex task of assessing probabilities and predicting values to simpler judgmental operations” (Tversky & Kahneman, 1974, p. 1124). These heuristic forms of behavior become more prevalent under conditions of uncertainty and pressures of time. Tversky and Kahneman highlighted two judgmental processes in particular, referring to the representative heuristic, which makes judgments on the basis of superficial similarities, and the availability heuristic, which bases its judgmental validity on the first thought that comes to mind, giving prominence to illusory correlations and anchoring biases. Another theorist, Herbert Simon, introduced the idea of bounded rationality (Simon, 1983) as an explanation for the inherent limitation of conscious cognition to comprehend complex situations. It is this inherent limitation, he argues, that gives rise to satisficing behavior in lieu of rational optimizing behavior. Sternberg highlighted error tendencies related to syntactic induction and semantic induction (Sternberg, 2000), instances when individuals tend to over-generalize or over-weigh aspects of an issue, based on either superficial similarities or simple analogies, in order to draw conclusions.

Within the modern work place, these and other heuristics are endemic to our decision-making processes. For example, satisficing behavior is seen in risk-averse behavior that emphasizes incrementalism, conservatism, and extreme cases of scope minimization. Our reliance on heuristics is seen in our belief biases and framing preferences. For example, people tend to be risk-seeking if an issue is framed in negative terms, and risk-averse if framed in positive terms (Sternberg, 2000). Decision-making in complex situations often gives rise to a desire for immediate results, leading to data-driven decisions rather than the formation of a predictive model based on the underlying drivers (Brehmen, 1987). Wagner catalogued a wide range of managerial biases (Wagner, 2002), such as: professional biases when managers have difficultly conceptualizing problems in ways that transcend their professional knowledge; first-in biases when greater weight is given to first impressions than to subsequent information; or group-think errors stemming from excessive confidence or the repression of adverse indications (Janis, 1972). In other cases, researchers have noted a tendency for reflective rationalizations and strong but wrong rule selection (Reason, 1990). As is the case with most of these examples, while heuristics are fundamental to cognition, they can also lead to human error.

A number of researchers have helped advance the state of knowledge about cognitive failure, but the work of one theorist stands out. James Reason is noteworthy for his articulation of the severe consequences of human failure (Reason, 1990). Reason's empirical study of man-made disasters, such as Chernobyl, Three Mile Island, Bhopal, and the space shuttle Challenger, demonstrates the cognitive interplay between the situational context and the innate error tendencies of individuals. The fact that these disasters occurred with highly intelligent individuals whose expertise was directly related to the domain in which their failings occurred should be cause enough for a more thorough study of human error in project management.

Reason's study of original fallibility presents a picture of human error as an integral element of the powerful adaptive character of human cognition.

Far from being rooted in irrational or maladaptive tendencies, these recurrent error forms have their origins in fundamentally useful psychological processes. . . .Correct performance and systematic error are two sides of the same coin. Or, perhaps more aptly, they are two sides of the same cognitive ‘balance sheet’. Each entry on the asset side carries a corresponding debit. Thus, automaticity (the delegation of control to low-level specialists) makes slips, or actions-not-as-planned, inevitable. The resource limitations of the conscious ‘workspace’, while essential for focusing computationally powerful operators upon particular aspects of the world, contribute to informational overload and data loss. A knowledge base that contains specialized ‘theories’ rather than isolated facts preserves meaningfulness, but renders us liable to confirmation bias. An extraordinarily rapid retrieval system, capable of locating relevant items within a virtually unlimited knowledge base, leads our interpretations of the present and anticipations of the future to be shaped too much by the matching regularities of the past. (Reason, 1990, pp. 1-2)

Consistent with this model of human error is the fact that it is not always a simple matter of knowing the unknown or relying on hindsight in a naive attempt to root out causality. Human induced error is a highly intertwined and illusive phenomenon that is often embedded in our belief that we are doing the right thing at the time. Moreover, knowing the outcome (i.e., availability heuristic) and working backward from it predisposes a structure to the analysis of antecedent actions that gives the illusion of control. If we are to learn from error theorists, we should conclude that project failures rarely result from the single failing of an individual or solely from situationtal causes. They occur through the interaction of a coincidental network of pre-existing contextual influences and human biases that are difficult to predict through hindsight.

A means must be found to reconcile the paradox between our presumption of human infallibility and the error theorist's study of original fallibility. Given the project management discipline's elaborate articulation of the processes and techniques involved in managing complex tasks, the research of error theorists readily lends itself to the development of a predictive model of human error. Naturally, predictive models require a higher standard of care that includes a well-founded theoretical basis with the scope of applicability empirically vetted. While an elaboration of this predictive model is beyond the scope of this introductory paper, a brief outline of the methodological direction of the research is in order. The development of this predictive model will require cataloguing the multifaceted task characteristics commonly encountered in complex environments, such as its structured-unstructured nature, its rational/objective versus subjective/value-laden characteristics, and organizational-environmental influences. These characteristics need to be mapped to the cognitive and behavioral attributes involved in task completion, which are, in turn, mapped to known human error tendencies. Once this mapping stage is complete and empirical validation underway, we can begin to offer some task-specific advice as well as some general insight into our cognitive-behavioral tendencies when confronted with complex, ill-structured tasks.

The study of error management is prevalent in many high-risk industries, such as medicine, aeronautics, and the nuclear industry, in which the severity of consequence underpins the absolute necessity of a broader study of the human mind's involvement in task performance. Given that project managers perform one of the more challenging roles in industry, it makes sense that a study of error theory be undertaken. Addressing the problems associated with product design and implementation failure at its root conception has significant potential in leveraging a higher success rate from complex projects.

Expert Theorists

The study of genius and expert performance provides another useful lens through which we can view the anatomy of intelligent performance. There are a number of schools of thought that concern themselves with the nature of human expertise and its development. While there are significant differences, if not opposing perspectives, among the various schools, each paradigm contributes to a broader understanding of expert performance in complex environments.

One of the earliest schools of thought emphasized the genetically inherited nature of intelligence. In response to its assumption that intelligence is fixed, this school focused its study on the external influences on the character and motivation of exceptional people. For example, extraordinary minds, argues Howard Gardner, go beyond drill and practice to incorporate other differentiating characteristics such as obsessive master within a domain, innovative creation of new domains, alienation and introspection, and productive-destructive challenges to the status quo (Gardner, 1997). While a strong opponent of the notion of fixed intelligence, Carol Dweck, nevertheless offers a similar perspective on the character of the exceptional individual,

Creative geniuses were often ordinarily smart or talented people who went for it – who became enraptured or obsessed with something and devoted themselves to it – be it music, science, poetry, or philosophy. They were not people who shrank from challenge or held back their effort for fear of revealing ignorance or low ability. Nor were they people who were daunted by the inevitable obstacles that arise in the pursuit of anything difficult. Instead their extraordinary commitment converted their talent into genius. (Dweck, 2002, p. 36)

A second school of thought focuses on the cognitive processes involved in expert performance. While most research in this paradigm focuses on the conscious information processing theorists, this paper broadens the paradigm to encompass the cognitive unconscious. Wertheimer offers an example of the rational conscious at work through his analysis of Einstein's thought processes. Starting with a lingering question, Einstein broke the problem down into its constituent parts, systematically exploring all possible alternatives as well as any ambiguous or incongruent results in relation to all other elements until he reached the point of proving the apparent contradiction between conventional wisdom and a broader understanding of the problem space. As Wertheimer states,

When we proceed with an analysis in the sense of traditional logic, we easily forget that actually all operations were parts of a unitary and beautifully consistent picture, that they developed as parts within one line of thinking; that they arose, functioned, and had their meaning within the whole process as the situation, its structure, its needs and demands were faced. (Wertheimer, 1959, p. 228)

Other theorists suggest that the creative insight of genius extends well beyond rational and logical conceptions of cognition. Supported by the testimony of notable scientists, such as Max Planck, Ernst Kris, Einstein, and others, Simonton highlights some of the creative adventures of the cognitive unconscious. The origins of productive and creative thought, he suggests, may come in many forms, including janusian analysis, blind variation and associative plurality, insight and imagery, and fantasy and sensory images (Simonton, 1994). As suggested by Max Planck (Nobel laureate in quantum physics), scientific discovery requires “vivid intuitive imagination, for new ideas are not generated by deduction, but by an artistically creative imagination.” (Planck, 1949, p. 109)

While subconscious thought remains hidden from conscious thought, by definition, it nevertheless plays an important role in expert cognition, especially as it relates to the evaluative decision processes that are fundamental to project management. For example, Poincaré challenged rational theorists who believed that mathematics was about logically following the rules of inference, where one contention must exist in sequence with its antecedent and its logical consequences (Papert, 1993). While the first and last stages of our creative thought processes flow from a conscious analysis of the problem space, the intermediary subconscious stage is guided by an aesthetic sense where the unconscious mind ceaselessly plays with chaining variations to form new sequences, networks, and images, culminating with an idea coming into consciousness. As much as we might think project management is dependent on conscious, planned forms of cognitive activity, the critical thinking skills involved in comparison, evaluation, estimation, decision-making and judgment under conditions of uncertainty, all engage the cognitive unconscious.

A third school of expert performance bases its paradigm on the acquisition of learned knowledge. While there are different forms of this paradigm, such as the structural knowledge and knowledge competency models, all are founded on the need for a structured approach to learning domain-specific expertise. Some of this school's earliest proponents argued that expertise could only be acquired under optimal learning conditions that involved intense, prolonged and structured effort, resulting in the 10-year rule (Chase & Simon, 1973). However, more recent research has challenged the ubiquity of the 10-year rule (Ericsson, Krampe, & Tesch-Römer, 1993), arguing that the knowledge-based paradigm is unsuited to ill-structured tasks in which the ability to manage a dynamic and ambiguous environment is more important than creating solutions based on previously stored knowledge.

The knowledge competency paradigm (Glaser & Chi, 1988) comes closest to approximating the characteristics of a model of expertise suited to complex tasks. It emphasizes the importance of cognitive processes acting in tandem with conceptual and procedural knowledge. The model introduces the idea that expertise requires a broader understanding of a problem's attributes, including its task characteristics, cognitive processes, and learning strategies, as well as a need to mesh the internalized cognitive knowledge structures with the structure inherent in the domain.

These investigations into knowledge-rich domains show strong interactions between structures of knowledge and processes of reasoning and problem solving. The results force us to think about high levels of competence in terms of the interplay between knowledge structure and processing abilities... Now research needs to go beyond this stage of analysis. We must better understand the properties of the domain structure and integrated knowledge; the mechanisms of problem-space definition with minimal search through rapid pattern-recognition; and the processes involved in redefining the space of ill-structured and difficult problems. (Glaser & Chi, 1988, p. xxi)

To a large extent, the earlier models of expert performance were typical of an industrial era in which hierarchal control of increasingly specialized functions was considered necessary for successful project performance. As such, the study of expert performance tended to focus on domain-specific forms of expertise (e.g., science, music, and math prodigies) that emphasized the conscious acquisition of content knowledge within specific domains. In a business climate requiring accelerated performance in an interdisciplinary world, a more adaptive form of expertise is suggested.

Paralleling the evolution in the nature of the task are newer conceptions of the human mind. Newer theories, such as those put forward by the dual processing theorists, suggest that we may have grossly underestimated the potential of the human mind. Citing our uncanny ability to rapidly extract structure from noisy environments through, for example, pattern matching, associative forms of logic, comparative probability estimates, and abduction of purpose (Levinson, 1995), studies have shown our ability to acquire an implicit understanding of complex situations at a far greater rate than could be acquired through conscious modes of processing (Sternberg, 2000). As Levinson describes, this interactional intelligence is a far more complex and productive cognitive process than any other: “Deduction and induction are relatively trivial human skills, of no great computational complexity; It is abduction or theory construction which is the outstanding characteristic of human intelligence” (Levinson, 1995, p. 254).

Although a thorough discussion of the implications and opportunities stemming from these newer models of intelligent performance is beyond the scope of this paper, a brief introduction to their practical implications is offered. In working toward a model of expert performance suited to conflict-ridden and ambiguous environments, we must first re-evaluate our dependence on traditional knowledge-intensive ways of approaching problems. Rather than presuming that expert performance requires domain-specific solutions, consideration should be given to determining the characteristics of expertise suited to the domain. This slight reversal in emphasis is intended to shift our attention away from a one-approach-fits-all type of expertise, towards developing a model of expertise based on discovering the latent structure intrinsic to ill-structured domains. Only then can we expect to reveal the underlying themes and characteristics required to develop principles of engagement suited to ill-structured environments.

Second, a broader theoretical understanding is required of our cognitive behavior in cognitively challenging environments, such as is being developed by the dual processing theorists (Epstein, 1994; Reber, 1993; Sloman, 1996). Where there is lack of external structure to guide the exploration of complex problems, greater care must be taken to manage our internal thought processes, especially given our tendency to err. Just as psychologists and cognitive therapists try to highlight the origins of behavioral issues, the discipline of project management would benefit from developing a cognitive therapy for the rational mind. This task-driven form of therapy would encourage greater self-awareness and self-regulation of our innate error tendencies. In addition, it would further explore methods of directing the conscious and subconscious mind's involvement in problem resolution, such as the use of mental simulations and associative priming, feedback loops and attentional checks, qualitative categorization and reconceptualization, gestalt extrapolations and janusian juxtapositions, schema development and tacit knowledge acquisition. At a higher level, these cognitive techniques would extend beyond task-specific reasoning to include more generic forms of problem negotiation through, for example, a better matching of task characteristics with the cognitive approach to task completion. Learning to master ambiguous, dynamic environments will require not only mastery over the domain content, but also better management of our critical thinking competencies.

Learning Theorists

New ideas and new vernacular are entering the workplace, highlighting the shift from tangible, resource-intensive products to intangible, knowledge-intensive products. In response, several authors have stressed the importance of enhancing the productivity of knowledge workers as a survival strategy for the 21st century (Kyte, 2002; Logan, Austin, & Morello, 2004). In a similar vein, the Organization for Economic Cooperation and Development cites an urgency to understand the impact that the knowledge economy is having on the education sector, given that knowledge-based industries account for half of Organisation for Economic Cooperation and Development (OECD) countries’ Gross Domestic Product (GDP) (Organisation for Economic Cooperation and Development, 2001).

The changing nature of the project manager's task parallels the trends in education towards the deepening specialization of skill sets required to address complex systems as well as growing pressure for multidisciplinary and collaborative expertise. Yet, classical notions of education that emphasize passive knowledge acquisition, reductive analysis, reproductive performance, and curriculum-dominant instructional methods cannot adequately address the need to manage complex knowledge systems nor create new knowledge (Koper, 2000). As such, traditional forms of instruction have been criticized for failing to support higher-order thinking and problem solving, as well as the cultivation of compliant and superficial understanding of complex issues (Jonassen & Land, 2000).

A review of learning theory shows that there are at least 50 different theories, some of which claim general applicability while most have been developed for use within specific fields of practice. As suggested by Gagne, the methodologies of instruction are dependent on the type of learning outcome desired (Gagne, 1985). For example, if the task is traditional in nature, such as troubleshooting, mathematics, or mnemonics, the work of Cronbach and Snow's aptitude treatment interaction (Cronbach & Snow, 1977) or Paivio's dual coding theory (Paivio, 1986) may find relevance. If the task is novel and exploratory, then the work of Bruner's constructivist approach (Bruner, 1966) or DeBono's lateral thinking (DeBono, 1970) may be relevant. If the focus is problem-solving, then Spiro's cognitive flexibility theory (Spiro, Feltovich, Jacobson, & Coulson, 1992), Jonassen's problem-solving methodologies (Jonassen, 2000), or Mangieri's approach to power thinking (Mangieri & Block, 2004) may help to achieve task mastery.

In contrast to traditional forms of education, which address well-defined problems, learning how to navigate complex problem space requires a more adaptive response. Among the newer methods of instruction that show promise in addressing ill-structured systems are constructivist theories that emphasize contextualized knowledge creation, emergent performance, and experiential forms of comprehension. In contrast to the curriculum-dominant models of traditional education, constructivist models emphasize the centrality of the learner (Land & Hannafin, 2000; Merrill, 2002), a phrase that refers to the need to create a learning environment conducive to exploring the problem space, as opposed to the learner being told how to think through the problem. However, these newer models may require actively unlearning old mental habits and biases. As one author noted, “by focusing on whether we can do the old things just as well in different ways, we are blind to the possibilities of doing new and different things” (McDonald, 2002, p. 3).

Unfortunately, professional domains are notoriously complex, to such an extent that rarely is there sufficient time to master the domain before being asked to apply partially learned concepts. Most real-world experience takes place without an ongoing commitment to learning. Yet, over the span of a project manager's career, and even within a project, project managers will require mastery over a variety of analytical, organizational, interpersonal, and even intrapersonal skills.

What is required for complex domains is a well-designed instructional methodology that engages and stages the learner through a continuum of learning experience. For example, without reiterating the competencies required of project managers, the typical multi-attribute task environment may require a staged learning experience starting with instruction in the basic concepts and vocabulary of the discipline, moving toward a broader exploration of the problem space, then to a deeper meta-cognitive understanding of self when faced with ambiguous tasks, and finally to the work-learn environment that includes in-situ feedback mechanisms.

In the early stages of learning, the novice must first develop some basic vocabulary and a reference body of knowledge around which his or her domain space can be understood and communicated. The PMBOK® Guide provides an organized body of conceptual and procedural knowledge that lays the foundation for expert performance in managing complex tasks. As noted earlier, it provides tools and techniques that closely align with many of our natural thought processes. But success in most projects extends beyond a talent for the tools and techniques to include better self-management of our cognitive behavior.

Once a foundation level of knowledge is obtained, mastery would then require instructional methods suited to the characteristics of the task environment. In ambiguous environments that involve creating something from nothing, the approach espoused by constructivist theorists comes closest to fulfilling this mandate (i.e., bridging the content knowledge – tacit knowledge gap). In this second stage, greater attention must be given to the learning environment and instructional design to allow the learner to take ownership of the problem, preferably within work-relevant contexts. This exploration is not intended to occur by happenstance. Instead, it requires sequencing the learner through a series of progressively more elaborate and complex tasks that permit a deeper, broader understanding of the discipline. Worked examples, meta-analysis, and discovering alternative perspectives are essential in managing ill-defined tasks, especially when these involve non-recurring skills.

So far, the emphasis has been on addressing the content portion of expert performance. A third stage of learning is suggested, one that concerns itself with gaining greater awareness and control over our cognitive behavior when confronted with complex tasks. This stage would have individuals address their innate error tendencies, as well as help embed an aptitude for drawing meaning from ill-structured circumstance. Along with an understanding of our learning and thinking styles, this stage would focus on allowing a more adaptive approach to handling the challenges common to dynamic systems. For example, people who have been trained in the applied sciences, such as engineering or economics, tend to favor rational decision-making methods. Those trained in the business professions tend to favor a more interactive, people-oriented approach to problem negotiation. Because project managers require both skill sets, they need a more responsive approach to managing projects.

The ultimate venue for learning is the everyday work environment. However, under pressure to get things done, there is often little time to sharpen our skill set. Nevertheless, the workplace can be one of the more powerful teaching environments. It is also the place where the limitations of human endeavor manifest themselves with unintended consequence. Without attention to our everyday performance, individuals tend to revert to old mental habits. If newly learned knowledge is to become a new mental habit, there must be ongoing mechanisms to entrench new learning into our everyday work habits. While a variety of techniques have been tried, such as knowledge management or lessons learned, these tend to address the issue either after the fact or out of context. Alternatively, methods must be found that facilitate the in-situ assessment of performance, such as mentorship, interactive feedback from peers, the provision of a normative framework of expectations, as well as time for simple reflection. Moreover, given the time constraints in most workplaces, greater consideration must be given to student-centered instructional designs that accelerate the learning of the immediate task requirements along with its contextual constraints and contingencies.

In addition to the changes taking place in educational theory, technology is becoming a major enabler of new educational possibilities. There are several examples: the development of expert systems and personal tutors that mimic human expertise in narrow fields of practice; intelligent agents that do task-specific jobs, such as searching online databases; computer-assisted instruction that is tailored to each student's learning profile; and systems that help develop problem-solving, critical thinking, and comprehension skills. All have potential in enhancing the cognitive performance of individuals. Supplementing these new learning technologies is a soft infrastructure that may allow the meta-analytic consolidation of information into more useable knowledge. For example, researchers at the Open University of the Netherlands are developing an educational modeling language (EML) in an attempt to find a flexible platform upon which technology could support a variety of educational methods requiring different learning outcomes (Koper, 2000). For example, whereas our libraries currently codify knowledge in terms of books and authors, the new technologies will enable the codification of individual concepts and paragraphs within books. While these electronic learning technologies are still experimental, these offer enormous potential in expanding the cognitive performance of individuals. However, educational technology that does not have a firm foundation in learning theory is in danger of becoming nothing more than an expensive fad. If technology is to serve its educational master, it needs to be designed to support educational goals, not market objectives (Roblyer & Schwier, 2003).


Embedded in this hurried overview of the history of intelligent performance are a number of conceptual sub-themes that have been the subject of heated debate. Fundamental among these debates has been the issue of whether human intelligence, and thus performance, is a fixed or malleable entity. Despite Francis Galton's proclamation that intelligence is hereditary and that any attempt to improve it should be considered “hopeless efforts by fallacious promptings of overweening vanity” (Galton, 1869/1952, p. 14), the past century of research has seen a broadening of the conceptions of human intelligence. While the innate-versus-malleable debate still lingers, there has been a slow convergence towards a view of the mind as a highly adaptive system in which the interplay among biological, developmental, motivational, procedural, contextual, and task characteristics determine our capacity for intelligent performance.

If there is a limiting feature to our intellectual performance, it is not so much our biological intelligence, but our belief biases that assume that intelligence is untrainable. Despite our many professional achievements, there still lingers a fatalistic acceptance of our intellectual competencies. While there are certainly constraints on exceptional performance, these should not be a result of self-imposed assumptions that predispose us to mediocrity. Success is not expected to be easy, nor is striving for exceptional performance for everyone.

Project management is in the unique position of being able to both enhance its practitioners’ competencies and help advance the state of knowledge about the psychology of exceptional performance in ill-structured domains. The foundation for this eventual outcome has been laid in the work that project managers currently do. The question is whether we are ready to supplement our current approach to project management with new ways of thinking about project performance. Given the accelerated pace of economic and technological change, rethinking old paradigms about intelligent performance seems appropriate. A much broader definition is required when addressing complex tasks in ill-structured environments, a definition that must be informed, but not constrained, by conventional thought.

This paper argues that our intellectual performance on complex tasks can be enhanced if three conditions are addressed. First, we need to understand our error modalities in complex environments. Second, the enhancement of our meta-cognitive skills and cognitive flexibility is fundamental to managing complex dynamic systems. And third, learning theories and instructional design methodologies must be developed that better match task requirements with the cognitive attributes required for complex task completion. All three criteria must be founded on a cognitive model of human performance suited to ill-structured environments.

For this to happen, a multidisciplinary effort is required, one that engages researchers from the three sub-domains mentioned earlier, as well as dual cognition theorists, human systems engineers, and project managers. As Ochsner and Kosslyn (1999) suggest, theories of cognitive psychology would be better served if their empiricism were based on converging evidence that crosses disciplinary boundaries. Moreover, without a theoretical foundation well-grounded in empiricism, any model of human cognition runs the risk of implausibility. At the same time, any model that ignores the realities of the ill-structured task environment runs the risk of irrelevance.

As a leading-edge solution to some of industries’ more challenging problems, the discipline of project management, more than any other, is in the unique position of taking a leadership role in this new field of study.


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