There are numerous texts available in the human, managerial, or organizational decision-making areas that purport to depict how humans in general and managers in particular can and should make better decisions (e.g., Arkes & Hammond, 1986; Baron, 1994; Bazerman, 1998; Beach, 1993; Cooksey, 1996a; Goldstein & Hogarth, 1997; Goodwin & Wright, 1991; Harrison, 1995; Hogarth, 1987; Jennings & Wattam, 1998; Kahneman et al., 1982; Kleindorfer et al., 1993; Payne et al., 1993; Plous, 1993; Rowe & Boulgarides, 1992; Russo & Schoemaker, 1989; Shapira, 1997; Yates, 1990). However, a reasonable reading of any of these texts will tend to lead the typical reader to one or more of the following conclusions:
Unfortunately, these conclusions collectively fail to depict the true complexity and dynamic nature of decision making in organizational contexts. A further unfortunate outcome is that the literature summarized in many of these texts tends to be very paradigm bound (e.g., decision and utility analysis; heuristics and biases, attribution and information integration theory). This is further compounded by the rather uncritical reporting of the knowledge base on human decision making in texts covering other discipline areas in management education (e.g., organizational behavior, human resource management, management information systems, international business, general management, marketing management, operations research, strategy, and economics).
Of the texts listed earlier, Harrison (1995), Kleindorfer et al. (1993), Payne et al. (1993), Rowe & Boulgarides (1992), and Shapira (1997) appear to do some justice to dynamism and context effects in decision making. Cooksey (1996a, Chapter 8) highlights some of the dynamics that may be involved in the analysis of human judgment. However, these sources present only fragmentary and partial perspectives. That judgment and decision research remains a fragmented and divided discipline is highlighted by a recent symposium paper by Hammond (1997) and by earlier arguments presented by the same author (1990, 1996). Some hallmark characteristics of this division in human decision research include laboratory versus field or naturalistic focus; prescriptive/normative (axiomatic) versus descriptive/naturalistic focus; coherence (agreement of decision process with a normative set of rules, axioms, or procedures) versus correspondence (empirical accuracy of judgment or decision) focus; focus on errors versus focus on adaptive successes; and narrower focus on rationality versus wider focus on rationality and intuition.
This division translates into a selective importing of ideas into discussions of managerial decision making, often to the point of generating simplistic recommendations about the best or optimal method (usually linear in conception) for making “rational” managerial decisions. Acquainting managers with simplified and frequently tightly codified decision procedures creates giant blind spots to the contextual constraints and influences imposed in naturalistic decision contexts (such blind spots were highlighted in the management lexicon produced by Cooksey et al., 1998). Newer research in naturalistic decision making and systems thinking confirms that the study of decision making must be contextualized before serious theorizing and understanding can occur (Klein et al., 1993; Senge, 1990; Senge et al., 1994; Zsambok & Klein, 1997).
Thus, it seems clear that contextual factors must be explicitly embedded within any account of the managerial decision-making process. This is, in part, because current decision theories and approaches are context independent and have rather poor performance records as predictive devices for decision outcomes, and because their outcomes translate rather poorly from the frequently idealized laboratory conditions under which they are tested to the more naturalistic conditions where managerial decisions are made (see discussions in Klein et al., 1993). Cooksey and Gates (1995) highlighted the key roles that a myriad of contextual factors play in appropriately complexifying what had been somewhat simplistic and static theories and approaches in the discipline of human resource management. This perspective can be usefully extended to encompass managerial decision making (or any other type of decision making for that matter; for example, Cooksey (1998) applied a complexity perspective to the analysis of magistrates’ decision making in courtroom contexts). Cooksey (1996b, 1996c, 1996d) began the development of a complexity perspective for general and managerial decision making; it is this initial development work that is further modified and extended here. The discussion will culminate in the presentation of a decision audit tool to assist in implementing the complexity perspective.
Contrary to the persistent pressure of the law of parsimony, there are a number of reasons that it is important to pursue a complexifying perspective in the area of managerial decision making. Developing such a perspective can help avoid oversimplification and linear thinking; it can anchor decision making in its context, with all of its attendant complexity; it promotes method/data triangulation—complex systems approaches demand a variety of data types (quantitative and qualitative) and data sources (Churchman, 1971); it reinforces the inescapable ideas that both the human condition (Cooksey & Gates, 1995; Epstein, 1994; Johnson, 1995), as well as task considerations (Brunswik, 1952; Cooksey, 1996a; Hammond, 1996), must be factored into any account of decision making; and, finally, it signals the notion that high levels of predictability in decision outcomes cannot be expected or sustained, especially at the individual level (Guastello, 1995; Priesmeyer, 1992).
Irving Janis’s work (Janis, 1989, 1992; Janis & Mann, 1977) began to move toward a more complex textural perspective using a flowchart modeling approach to capture the processes involved with significant political and social decisions that have high emotive connotations and major societal implications. Loewenstein (1996) also began to move in this direction by incorporating the concept of visceral influences on decision behavior. However, these perspectives remained linear in conception, relatively static in focus, and, in Loewenstein’s case, highly axiomatic and anchored in a limited set of quantitative variables related in a series of simplistic equations. What is offered in the present article attempts to move beyond these limitations.
THE COMPLEX DYNAMIC DECISION PERSPECTIVE
General systems theory (Churchman, 1971; Flood & Carson, 1988), applied to managerial decision making, permits one to differentiate between positive and negative feedback. Negative feedback emerges from a comparison of the system’s goals to the system’s current position, and, if a discrepancy exists, actions are taken to close the gap. Negative system feedback thus seeks system equilibrium or stability by dampening variability (e.g., minimizing prediction errors). Thus, one form of adaptation in managerial decision making consists of correcting perceived performance gaps. Positive system feedback emerges from a realization that the focus of decision making may in fact not be the correct focus and seeks to create a discontinuous shift in system orientation to new forms of decision behavior. Positive system feedback thus seeks to create instability by encouraging variation in pursuit of new ways of attaining goals or of new goals themselves.
Gates and Cooksey (1998) argued that these concepts are closely related to Argyris’s (1990) concepts of single-loop learning (emphasis on negative system feedback) and double-loop learning (emphasis on both positive and negative system feedback). Consideration of both positive and negative system feedback necessarily invokes the importance of the time dimension in understanding the dynamic nature of complex decision processes.
Recently, nonlinear system dynamics has emerged as a way of conceptualizing systems that exhibit strong tendencies toward nonequilibrium. Such systems are characterized by sensitivity to initial conditions, problems in long-term system predictability, bounded instability, and periodic excursions into chaotic behavior (Cooksey & Gates, 1995). This chaotic state of behavior is often stimulated if the amount of negative dampening feedback in the system is exceeded by the amount of positive destabilizing feedback. For example, organizational culture tends to operate as a negative feedback mechanism, reinforcing conformity in decision-making expectations and discouraging deviance—thus resistance to change is built up. Changing market conditions tend to operate as a positive feedback mechanism that stimulates instability and change, especially in highly competitive markets (witness the increasing pressure on organizations to make strategic decisions that involve international considerations).
The result of the complex intermixing of positive and negative system feedback is fundamentally unpredictable behavior at the level of the individual manager (Gregersen & Sailer, 1993; Guastello, 1995), yet that is precisely what contributes to the overall texture of managerial decision making. Because dynamic systems are generally nonlinear, there are no simple pathways through them to some end state or outcome. Changes in input at one point in the decision process or context, even if minute, may cause huge changes in decision outcomes—a pattern characteristic of sensitivity to initial conditions. Decision system dynamics may be made even more complex if the character of the information on which the decision processes act is “fuzzy” rather than precise in nature (Kosko, 1993).
A reasonable understanding of the human condition, as it might be brought to bear on human decision making, has been achieved through a multiplicity of disciplines, including biology (evolutionary influences and motivational drives); physiology and neurophysiology (circadian, lunar, menstrual, and sleep cycles, emotional response, stress tolerance, and response mechanisms); cognitive psychology (memory, perception, and attention, and their intimate interconnectedness; cognitive complexity); personality psychology (decision styles, values, and beliefs, achievement motivation and self-efficacy, risk-taking propensity, introversion and extroversion, motivational needs, and locus of control); social psychology (conformity, interpersonal trust, attraction, power, interpersonal expectations, group dynamics, and conflict); organizational psychology (work roles, communication patterns, competencies); ergonomics (human limits and tolerances, information demands, fatigue, stress, and workload effects); and anthropology (culture, rituals, norms). Recent work by Epstein (1994), Hammond (1996), Janis (1989), Johnson (1995), Loewenstein (1996), and others has begun to reinforce the importance of tracking factors associated with the human condition. Thus any new perspective needs to incorporate the constraining impacts that the human condition has on decision processes unfolding in their natural context.
The Complex Dynamic Decision Perspective (CDDP, initially sketched in more general and rudimentary terms by Cooksey, 1996c, 1996d) described in the present article provides an appropriately complexified nonlinear dynamic perspective on managerial decision making.
The CDDP maps have been drawn using a few simple rules:all, and/or may add or subtract from the collectivity of contributors depending on circumstances).
The macro view of the CDDP highlights the first two layers of the perspective, the medial view highlights the first three layers of the perspective, and the micro view shows all four layers in detail. Note that the micro view is where the complex texture of managerial decision making emerges, in a generally unfolding pattern from the center outward, highlighting the intricate interconnectedness of contributors within and between different components, contextual influences, and the focal decision event.
The macro-systems representation, shown in
By way of illustrating the potential utility of the CDDP perspective, consider the following. One key problem highlighted in much judgment and decision making research is decision or judgment unreliability (see Cooksey, 1996a; Stewart, in press). Different decisions or judgments will be made by the same person under highly similar conditions and circumstances, even when faced with identical information. The macro and micro views of the CDDP show how and why such unreliability would be observed to occur—“highly similar conditions and circumstances” and “identical information” are conditions virtually unattainable at the micro level and are therefore illusory. In fact, Cooksey (1996a) argues that judgment unreliability could be explained not as unsystematic error, as is typically done in the literature (see Stewart, in press; Stewart & Lusk, 1994), but as a reflection of the sensitivity of the decision-making process to initial contextual conditions. Thus, slight variations in these initial conditions could produce major decision shifts, an outcome that typifies chaotic system behavior (see Priesmeyer, 1992).
An extension of this problem is seen in the area of expert judgment and expert disagreement. Here, Mumpower and Stewart (1996) observe that experts frequently disagree with each other about a specific judgment to be made, despite having access to identical profiles of information. Explanations for this phenomenon range from differing problem conceptualizations to simple judgmental unreliability. However, the CDDP perspective makes it clear that the contextual forces operating on any two experts’ decision processes would likely be very different, which would lead them to make different decisions—not necessarily because they are poor or unreliable decision makers (which, of course, they might be), but because of differences in the textures of their decisions (independently interviewing each expert might lead to the generation of rather different-looking CDDP maps, which would help explain their differences in judgment).
The CDDP diagrams themselves were assembled from a cognitive integration and elaboration of the implications and findings of several years of judgment and decision research, management research, and organizational behavior research, as well as from discussions with MBA students, colleagues and experts, and people in management roles. There is no sense in which any single decision perspective or theory could be mapped on to this perspective without resorting to excessive simplification and assumptions. This is the lesson of the diagrams—decontextualizing and reducing the texture of managerial decision making (as would be done in setting up decision trees, multi-attribute decision tables, analytic hierarchies, judgment analysis profiles, or simple spreadsheet OR models) forces one to ignore the very influences that may be giving dynamic shape to a decision. The CDDP forces one to complexify first, then simplify when and where it makes sense, using those theoretical and methodological tools that seem most sensible and defensible to apply.
The methodological way forward for the CDDP will require increased reliance on triangulated research methods that move across traditional philosophical boundaries. For example, human judgment may be better understood in context and under constraint if both normative and interpretive methods are employed. The necessity for statistical analysis and quantitative rigor, evident in many decision theories and approaches, would be relaxed in favor of employing a larger diversity of methods to target an individual manager’s decision task within the context in which it is embedded. The importance of using an idiographic approach to managerial decision making in implementing the CDDP cannot be overestimated. This reinforces the idea that an individual manager’s decision behavior must be understood in the context of representative environmental situations and tasks before generalizing inferences are drawn to other managers. The goal is to maintain close connectivity with the causal texture of the managerial decision-making context, even in circumstances where that texture is messy and difficult to disambiguate (Emery & Trist, 1965; Tolman & Brunswik, 1935).
Methodologically, then, the CDDP is probably best mapped, at least initially, by indepth qualitative interviews with managers focusing on specific decisions. In order to flesh out the CDDP for each decision maker using an idiographic approach, these interviews could, perhaps with assistance from appropriate computer software, incorporate a range of more specific methodologies such as storytelling and other forms of narrative collection (Kaye, 1996; Pennington & Hastie, 1993), observation and mental simulation (Taylor et al., 1998; Zsambok & Klein, 1997), process tracing and think-aloud protocols (Hussey & Hussey, 1997; Payne et al., 1993), mind mapping (Buzan, 1993), rich pictures (Checkland, 1981), fuzzy cognitive maps (Kosko, 1993), and concept mapping (Trochim, 1989). There is also scope, given appropriate quantification methods, for complex systems simulations to be conducted if warranted (see Morecroft & Sterman, 1994). However, even these simulations will often require simplifying assumptions or at least assumptions about parameter values and/or function forms.
The goal of the CDDP is to facilitate the understanding of a decision undertaken in context, rather than prediction or anticipation of how to achieve optimality or rationality. A rational decision theory, approach, or support system would, by definition, have to navigate a very simple path through the perspective—such a path emerging only when simplifying assumptions are made. The question to be asked in the CDDP is not so much what we gain by using rational, analytical or prescriptive decision theories or approaches, but what we lose by doing so. (Conversely, the CDDP permits one to see where a particular decision approach is forcing certain assumptions to be made, thus keeping awareness of the tradeoffs imposed by the assumptions of the approach active and explicit.) The roles of the intuitive (Hammond, 1996), the quasirational (Brunswik, 1952; Hammond, 1996), the bounded rational (March & Simon, 1958), and the experiential (Epstein, 1994), and their attendant sensitivities to context effects, therefore remain viable and necessary in the CDDP, although it adds further texture to these processes as well.
There are several important benefits to be realized from the CDDP. It embodies a reduction in the dominance of the principle of parsimony that creates pressure toward constructing simplistic models that often fail in practice. Multiple facets linked to the human condition are explicitly incorporated in a manner similar to what Janis (1989, 1992) attempted to achieve with his proposed Constraints Model. The CDDP engenders a closer correspondence to perceived reality with reduced interest in “objective” model coherence. It actively discourages linear solution seeking and over-reliance on cognitive shortcuts—tendencies that can lead to the short-term creation and adoption of fads and support systems that inevitably fall short of expectations (see Shapiro, 1995). The CDDP provides continual reinforcement of, and appreciation for, the dynamic, sometimes capricious and chaotic, nature of human judgment. Finally, it promotes the active seeking of individual perspectives (which, in terms of the work by Pennington & Hastie , leads to interest in subjective system coherence, i.e., “Does the story, from the perspective of the decision maker, hang together?”, rather than objective system coherence, i.e., “Is some specifiable set of axioms or guiding principles satisfied?”).
IMPLICATIONS OF THE CDDP FOR MANAGERS AND THEIR DECISION MAKING
Managers need exposure, early in their training, to the complexities of managerial decision making through exploration of a perspective like the CDDP, and this should be encountered as core training rather than left to experiential learning in the field. This will help managers achieve a healthy respect and caution for procedures promising optimal decisions if a specific procedure or software package is employed (a very real danger today when the decision support software business is virtually exploding), and avoid becoming overconfident about their own decision-making prowess. The utility of periodic decision audits (Russo & Schoemaker, 1989) is important to stress, as is learning from feedback and reflection (Gates & Cooksey, 1998), and the CDDP reinforces both of these imperatives. It highlights the necessity for a complex conceptual view, the utility of both quantitative and qualitative information, and the fact that much more is involved in managerial decision making than the decision maker him or herself. Any exposure of managers and students to specific decision theories and approaches, as well as to various types of decision support systems, is inescapable in any study of managerial decision making, but such exposure can be filtered through the lens of the CDDP in order appropriately to visualize the advantages and disadvantages of each approach.
Failure to do this kind of contextualization may tend to lead to blind adoption of those approaches that resonate most closely with the student or manager’s own value system, a particular organization’s culture and policies, and/or a particular instructor’s preferences—a trend that hampers flexibility and creativity. The traps lying down this road are many, as several authors have highlighted with respect to fads and fashions in business (e.g., Abrahamson, 1996; Cooksey et al., 1998; Dreilinger, 1994; Shapiro, 1995). Others (e.g., Levinson, 1994, 1996) have argued that many corporate failures can be traced back to the inability of CEOs to keep pace with the increasingly complex demands of business—a finding that has clear implications for complexifying the training of managers from the start.
A DECISION AUDIT TOOL FOR FLESHING OUT THE CDDP FOR A SPECIFIC MANAGERIAL DECISION
The CDDP, as depicted in
The template user is given the following typical set of instructions:
The purpose of this template is to assist you in either analyzing a decision you are about to make or auditing a decision you have already made. The level of analysis for this template is very flexible (as is the entire complexity perspective) in terms of whether or not an individual or group decision is analyzed. The template mirrors, in structure and intent, the CDDP macro and micro views you encountered earlier. In a sense, working through this template will require you to take a rather different approach to systems thinking from approaches such as Senge’s archetypes and causal loop diagrams. Such an analysis may seem rather difficult at first, but facility should come with practice. You should also find that, as you begin to put some “flesh on the bones” of the template, your recall of more and more relevant facets of the decision you are analyzing will be further stimulated by the notes you have already made. You may also find it useful to talk to other people involved in the decision in order to more fully round out your audit. In practice, the closer in time to making an important decision you are in your audit of that decision, the better you will be able to recall key factors, features, and influences with reduced memory distortions and hindsight bias. Take care to double-check your perceptions and recall.
To use the template:
The template can be worked through with or without the assistance of a discussion/reflection facilitator, at either an individual or group/team level. It may take students and managers a while to get used to the sort of thinking that the CDDP demands, in that contextual influences must be unpacked, identified, and analyzed (the five boxes), as well as identifying key aspects of interconnections between the various contextual influences (the ellipses anchoring each influence arrow). However, facility can be acquired through practice and through a realization that attending to a range of possibly influential factors can help one avoid the sorts of decision traps that await the unwary (see Russo & Schoemaker, 1989, for a discussion of common decision traps).
Note that, at one point, the template emphasizes the identification of blocking and facilitating factors—a process analogous to identifying driving and resisting forces in Lewin’s force-field model for organizational change (e.g., see French & Bell, 1995). This is useful in helping managers to visualize those factors that are seen to be rather more powerful or influential within the texture of their decisions.
To illustrate the utility of the auditing template,
The CDDP proposes a nonparsimonious, complex, nonlinear, and dynamic decision perspective that attempts to reflect the reality of managerial decision making in its natural contexts. The perspective incorporates general systems theory as well as the role of both positive (destabilizing) and negative (variability dampening) feedback operating through the myriad of linkages between various layers and facets of the CDDP. When positive system feedback outweighs negative system feedback, there is the potential for the CDDP system to be tipped into chaos, leading to inherently unpredictable (except in hindsight) decision outcomes.
The causal texture of decision tasks is messy; that is, constrained according to complex patterns, and causally ambiguous. The CDDP attempts to capture and mirror this complex texture. This immediately renders the perspective most potent at the level of the individual decision maker, although clearly group decision making can be understood within the same perspective (reconsider the interpersonal context influence aspect in
Finally, the CDDP provides an appropriate backdrop against which to gauge the advantages and disadvantages of any particular decision theory, approach, or support system, so that it remains clear what has been ruled in and what has been ruled out by the assumptions and mode of thinking required by that approach.
- Abrahamson, E. (1996) “Management fashion,” Academy of Management Review, 21: 254-85.
- Argyris, C. (1990) Overcoming Organizational Defenses: Facilitating Organizational Learning, Needham Heights, MA: Allyn & Bacon.
- Arkes, H.R. & Hammond, K.R. (1986) Human Judgment and Decision Making: An Interdisciplinary Reader, New York: Cambridge University Press.
- Baron, J. (1994) Thinking and Deciding (2nd edn), New York: Cambridge University Press.
- Bazerman, M.H. (1998) Judgment in Managerial Decision Making (4th edn), New York: Wiley.
- Beach, L.R. (1993) Making the Right Decision: Organizational Culture, Vision, and Planning, Englewood Cliffs, NJ: Prentice Hall.
- Brunswik, E. (1952) The Conceptual Framework of Psychology, Chicago: University of Chicago Press.
- Buzan, T. (1993) The Mind Map Book, London: BBC Books.
- Checkland, P. (1981) Systems Thinking, Systems Practice, Chichester, England: Wiley.
- Churchman, C.W. (1971) The Design of Inquiring Systems: Basic Concepts of Systems and Organizations, New York: Basic Books.
- Cooksey, R.W. (1994) “Chaos and human judgment: an evolving focus for policy capturing,” paper presented at the Annual Conference of the Society for Judgment and Decision Making, St. Louis, MO.
- Cooksey, R.W. (1996a) Judgment Analysis: Theory, Methods, and Applications, San Diego, CA: Academic.
- Cooksey, R.W. (1996b) “Beyond judgment analysis: Brunswik, complex systems, and the human dimension,” paper presented at the Twelfth Annual International Invitational Meeting of the Brunswik Society, Chicago, IL.
- Cooksey, R.W. (1996c) “Complexity, context, and constraints in human decision making,” paper presented at the Annual Conference of the Society for Judgment and Decision Making, Chicago, IL.
- Cooksey, R.W. (1996d) “Management systems and decision making: myths and realities,” paper presented at the 1996 Human Resource Management in Local Government (NSW) Conference, Armidale, NSW, Australia.
- Cooksey, R.W. (1998) “Decision making: an exploration of some important issues,” paper presented at the Local Courts Annual Conference, Sydney, NSW, Australia.
- Cooksey, R.W. & Gates, G.R. (1995) “HRM: a management science in need of discipline,” Asia Pacific Journal of Human Resources, 33 (3): 15-38.
- Cooksey, R.W., Gates, G.R., & Pollock, H. (1998) “'Unsafe' business acts and outcomes: a management lexicon,” Business Horizons, 41 (3): 41-9.
- Dreilinger, C. (1994) “Why management fads fizzle,” Business Horizons, 37: 11-15.
- Emery, EE. & Trist, E.L. (1965) “The causal texture of organizational environments,” Human Relations, 18: 21-32.
- Epstein, S. (1994) “Integration of the cognitive and the psychodynamic unconscious,” American Psychologist, 49 (8): 709-24.
- Elood, R.L. & Carson, E.R. (1988) Dealing with Complexity: An Introduction to the Theory and Application of Systems Science, New York: Plenum.
- Erench, W.L. & Bell, C.H. (1995) Organization Development: Behavioral Science Interventions for Organization Development (5th edn), Englewood Cliffs, NJ: Prentice Hall.
- Gates, G.R. & Cooksey, R.W. (1998) “Learning to manage and managing to learn,” Journal of Workplace Learning, 10 (1): 5-14.
- Goldstein, W.M. & Hogarth, R.M. (1997) Research on Judgment and Decision Making: Currents, Connections, and Controversies, New York: Cambridge University Press.
- Goodwin, P. & Wright, G. (1991) Decision Analysis for Management Judgment, Chichester, England: Wiley.
- Gregersen H. & Sailor L. (1993) “Chaos theory and its implications for social science research,” Human Relations, 46: 777-802.
- Guastello, S.J. (1995) Chaos, Catastrophe, and Human Affairs, Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
- Hammond, K.R. (1990) “Eunctionalism and illusionism: can integration be usefully achieved?,” in R.M. Hogarth (ed.), Insights in Decision Making: A Tribute to Hillel Einhorn, Chicago, IL: University of Chicago Press.
- Hammond, K.R. (1996) Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice, New York: Oxford University Press.
- Hammond, K.R. (1997) “One JSociety or two: drift, or judgment and decision?,” symposium paper presented at the Annual Conference of the Society for Judgment and
- Decision Making, Philadelphia, PA.
- Harrison, E.E (1995) The Managerial Decision-Making Process (4th edn), Boston: Houghton Mifflin.
- Hogarth, R.M. (1987) Judgement and Choice: The Psychology of Decision (2nd edn), New York: Wiley.
- Hussey, J. & Hussey, R. (1997) Business Research, London: Macmillan Business.
- Janis, I.L. (1989) Crucial Decisions: Leadership in Policymaking and Crisis Management, New York: Eree Press.
- Janis, I.L. (1992) “Causes and consequences of defective policy-making: a new theoretical analysis,” in EA. Keller (ed.), Decision Making and Leadership, London: Cambridge University Press.
- Janis, I.L. & Mann, L. (1977) Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment, New York: Eree Press.
- Jennings, D. & Wattam, S. (1998) Decision Making: An Integrated Approach (2nd edn), London: Pitman Publishing.
- Johnson, G. (1995) Monkey Business: Why the Way You Manage Is a Million Years out of Date, London: Gower.
- Kahneman, D., Slovic, P., & Tversky, A. (1982) Judgment under Uncertainty: Heuristics and Biases, New York: Cambridge University Press.
- Kaye, M. (1996) Myth-Makers and Story Tellers, Sydney, Australia: Business & Professional Publishing.
- Klein, G.A., Orasanu, J., Calderwood, R., & Zsambok, C.E. (eds) (1993) Decision Making in Action: Models and Methods, Norwood, NJ: Ablex.
- Kleindorfer, P.R., Kunreuther, H.C., & Schoemaker, P.J.H. (1993) Decision Sciences: An Integrated Approach, New York: Cambridge University Press.
- Kosko, B. (1993) Fuzzy Thinking, London: HarperCollins.
- Levinson, H. (1994) “Why the behemoths fell: psychological roots of corporate failure,” American Psychologist, 49: 428-36.
- Levinson, H. (1996) “When executives burn out,” Harvard Business Review, 74: 153-63.
- Loewenstein, G. (1996) “Out of control: visceral influences on behavior,” Organizational Behavior and Human Decision Processes, 65: 272-92.
- March, J.G. & Simon, H.A. (1958) Organizations, New York: Wiley.
- Morecroft, J.D.W. & Sterman, J.D. (eds) (1994) Modeling for Learning Organizations, Portland, OR: Productivity Press.
- Mumpower, J.L. & Stewart, T.R. (1996) “Expert judgment and expert disagreement,” Thinking and Reasoning, 2 (23): 191-212.
- Payne, J.W., Bettman, J.R., & Johnson, E.J. (1993) The Adaptive Decision Maker, New York: Cambridge University Press.
- Pennington, N. & Hastie, R. (1993) “A theory of explanation-based decision making,” in G. Klein, J. Orasanu, R. Calderwood, & C. Zsambok (eds), Decision Making in Action: Models and Methods, Norwood, NJ: Ablex.
- Plous, S. (1993) The Psychology of Judgment and Decision Making, New York: McGraw-Hill.
- Priesmeyer, H.R. (1992) Organizations and Chaos: Defining the Methods of Nonlinear Management, Westport, CT: Quorum Books.
- Rowe, A.J. & Boulgarides, J.D. (1992) Managerial Decision Making, New York: Macmillan.
- Russo, J.E. & Schoemaker, P.J.H. (1989) Confident Decision Making: How to Make the Right Decision Every Time, London: Piatkus.
- Senge, P.M. (1990) The Fifth Discipline: The Art and Practice of the Learning Organization, New York: Doubleday.
- Senge, P.M., Kleiner, A., Roberts, C., Ross, R.B., & Smith, B.J. (1994) The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization, London: Nicholas Brealey.
- Shapira, Z. (1997) Organizational Decision Making, New York: Cambridge University Press.
- Shapiro, E.C. (1995) Fad Surfing in the Boardroom: Reclaiming the Courage to Manage in the Age of Instant Answers, Sydney, Australia: HarperCollins.
- Stewart, T.R. (in press) “Improving the reliability ofjudgmental forecasts,” in J.S. Armstrong (ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners, Norwell, MA: Kluwer.
- Stewart, T.R. & Lusk, C.M. (1994) “Seven components of judgmental forecasting skill: implications for research and the improvement of forecasts,” Organizational Behavior and Human Decision Processes, 13: 579-99.
- Taylor, S.E., Pham, L.B., Rivkin, I.D., & Armor, D.A. (1998) “Harnessing the imagination: mental simulation, self-regulation, and coping,” American Psychologist, 53 (4): 429-39.
- Tolman, E. & Brunswik, E. (1935) “The organism and the causal texture of the environment,” Psychological Review, 42: 43-77.
- Trochim, W.K.M. (1989) “An introduction to concept mapping for planning and evaluation,” Evaluation and Program Planning, 12: 1-16.
- Yates, J.E (1990) Judgment and Decision Making, Englewood Cliffs, NJ: Prentice Hall.
- Zsambok, C.E. & Klein, G.A. (eds) (1997) Naturalistic Decision Making, Mahwah, NJ: Lawrence Erlbaum Associates, Inc.