Collaborative decision making

building consensus group decisions for project success

This article is copyrighted material and has been reproduced with the permission of Project Management Institute, Inc. Unauthorized reproduction of this material is strictly prohibited.

© 2003 Mark A. Wilson

Introduction

Project teams must make decisions constantly. In fact, the ability to make decisions is a core project leadership skill. Some decisions can be made quickly by one person, while the project team may be convened to decide other issues. Sometimes decision-makers have the luxury of time to carefully analyze all the issues involved, while some decisions must be made with lightening speed, lest the business forfeit a market advantage or allow a deal to slip away.

The effective project leader systematically builds decisions upon a solid foundation of knowledge of project goals, objectives and relevant information. Those decisions may be made under conditions of tremendous stress and uncertainty, or they may be made in a very rigorous, controlled process with data. Some decisions are most appropriately made using “automatic” thinking (intuition), while others benefit from structured analytic or statistical techniques. Nevertheless, the way in which decision-makers think about the decision-making process itself should remain consistent.

This paper describes a collaborative method to build effective project decisions. This collaborative decision engineering method has been effective in both government and commercial arenas on issues ranging from deciding multi-million dollar procurements to allocating project resources. By thinking about the decision as a three step process of: (1) framing the decision to be made, (2) generating alternatives, and (3) deciding the course of action, decision makers can better understand how best to achieve the goal of creating and implementing decisions that endure.

Decision –Making Approaches

Decision Context

How should we define the concept of a “decision?” According to decision expert Kenneth Hammond, a decision is a response to a situation in which (a) there is more than one possible course of action; (b) the decision-maker can form expectations about the outcomes following each possible course of action; and (c) each outcome has an associated consequence that can be evaluated. (Hammond, pp. 25-26)

The collaborative decision engineering method illustrated in Exhibit1 was developed by the author to explain how to systematically approach project decisions. There are three common characteristics of tough project decisions. First, there is usually a degree of conflict or disagreement among stakeholders. Second, these decisions are usually made with incomplete or inaccurate information that leads to uncertainty about the outcome of the decision. Third, there may be some level of ambiguity in key decision elements (e.g., lack of a clear objective). Conflict, uncertainty, and ambiguity—why do some decision-makers appear to thrive under those conditions while others seem paralyzed by indecision?

Decision Engineering Method

Exhibit 1. Decision Engineering Method

Decision Theory

John von Neumann and Oskar Morgenstern are generally credited with developing modern decision theory in their book, Theory of Games and Economic Behavior, published in 1947. Their book was a mathematical treatment of utility theory, relative to optimal economic decision-making. Many subsequent works on decision theory tend to focus on this normative (how it should be) approach, which is rooted in probability theory that was developed in the sixteenth century. Actual human decision-making, however, is often far more complex, especially under real-life pressures of time constraints, dynamic conditions, uncertainty, and high stakes.

Humans employ both automatic and controlled thinking processes. (Hastie and Dawes, 2001, p. 4) Automatic thinking—intuition—is often defined in terms of pattern recognition without evident conscious thought. By contrast, controlled thinking processes employ obvious logic (e.g., if…then analysis). There is an extensive body of research to indicate that humans tend to alternate between automatic and controlled thinking when making decisions. Researchers have studied professionals from all walks of life to attempt to describe their decision-making processes. A key conclusion of these researchers is that the human mind has a remarkable ability to create patterns from facts and experiences. These patterns are then stored in long term memory and form the basis for subsequent decisions. Experienced professionals rapidly, and often unconsciously, assess developing situations on the basis of these stored memories to choose alternative options. This rapid pattern recognition and option selection based on stored memories is the essence of intuitive decision-making.

In contrast to intuitive decision-making, decision analysis can be defined as the use of probability theory to structure and quantify the process of making choices among alternatives. Decision analysts tend to approach a decision problem by structuring and decomposing issues into component parts that are presumably easier to decide, then aggregating those smaller decisions into a composite. Thus, the expectation is that “good” component decisions will aggregate into a “good” macro decision. Stated another way, decision analysis involves putting the facts in order and deciding based on the importance of each fact.

Intuition versus Analysis

Many decision analysts belittle intuition as a valid method for making decisions, but both analysis and intuition are both useful techniques. An intuitive approach is most useful in high speed, high risk, high uncertainty situations with experienced decision-makers, while an analytic approach is often better in non-time critical situations where the decision-maker must explain, defend, and/or justify a decision, or where the decision-maker may not have as much experience. In addition, decision analysis can help guard against the cognitive biases to which humans are susceptible when relying on intuition.

The United States Marine Corps has even codified the use of intuition as the preferred combat decision-making technique in its command and control doctrine.

The intuitive approach is based on the belief that war being ultimately an art rather than a science; there is no absolutely right answer to any problem. Intuitive decision making works on the further belief that, due to the judgement gained by experience, training, and reflection, the commander will generate a workable first solution…intuitive decision making is generally much faster than analytical decision making…the intuitive approach is more appropriate for the vast majority of typical tactical or operational decisions…(MCDP 6, Command and Control, Headquarters, United States Marine Corps, 4 Oct 96, Chapter 2, p. 13.)

Collaborative Decision Engineering Method

Why Collaboration?

The collaboration of the project team can be an effective insurance policy against the cognitive biases that often interfere with good judgment when relying on intuition. People are unique; each one of us perceives the world differently. Our brains filter incoming information based on what we are “ready” to see, thereby exposing us to the risk of overlooking key dangers and/or opportunities. Perhaps the overriding advantage of having more than one person involved in the decision process is the diversity of perspectives one gains, which provides additional insight into possible opportunities or risks. Moreover, people tend to support best that which they helped create. If you need the commitment of a group of people to execute a decision (remembering that an unimplemented decision is an academic exercise at best), get them involved in the decision process!

Nothing in life is free, and the price of additional perspectives and enhanced “buy-in” is time and effort. Collaborative decisions generally take longer than independent ones because you must allow for open dialogue and a free exchange of ideas. In addition, you must manage the group's dynamics for useful decisions to result. Most of us have had the unenviable experience of sitting through long, boring meetings (the weekly staff meeting?) where nothing substantive gets decided.

Collaborative decision meetings must be carefully planned and facilitated. A good way to think about the structure of a decision meeting is illustrated in Exhibit 2. Ideas must be allowed to diverge sufficiently to ensure consideration of fresh perspectives and to encourage creativity. The second part of the meeting is where the group begins to drive to closure to select an alternative. Another way to express this idea is divergent thinking (opening up the realm of the possible), followed by convergent thinking (making a choice).

Decision Meeting Structure

Exhibit 2. Decision Meeting Structure

A few cautions about collaborative decision-making are in order. First, individual self-interest can overcome the drive to make a choice for the common good. For example, in how many meetings have you participated where a department manager announced that they didn't need all of the budget allocated for their department that quarter and therefore intended to return it to the corporate level to reallocate for the good of the organization? Second, if the bullets are flying, it is probably not a good time to convene a decision meeting. Finally, if the decision makers are not directly affected by the outcome of their decision, there is a danger that they won't take the process seriously enough to really take a critical look at all of the ideas before making a selection.

The Proper Frame

The single most important step in the Decision Engineering method is establishing a proper frame for the decision. How one defines the problem or decision defines the available alternatives from which a selection can be made. The frame is the overall context for the decision. What is the ultimate objective of the decision? What is the root-cause of the issue/problem? Decision-makers sometimes lock onto a particular decision frame without critically examining the overall objectives of the decision itself.

Let's imagine that we are choosing among alternative houses built by a general contractor and we frame our decision problem as creating decision criteria that will yield a technical score for each alternative house. We could build a technical score by using decision factors such as: presence of a front door, has windows, etc., but how useful would that score be? However, if we frame our decision problem as coming up with selection discriminators, it changes the way we think about the decision problem and should result in more appropriate decision criteria.

Our recommended framing technique is rather simple—keep asking the question “why?” until it doesn't make sense anymore. Popular in the quality management profession for years, this technique is sometimes referred to as the Five Why's tool because you can generally ask the question “why?” five times or less before you get to the root cause of a problem or issue.

The proper decision frame opens up the spectrum of possible solutions, allowing truly creative possibilities to emerge. The typical decision-maker tends to focus on point solutions (…the way we've always done things) instead of taking the time to step back and really examine the underlying goal(s) of the decision. As illustrated in Exhibit 3, by asking the fundamental question “why,” a decision-maker can see alternatives and options that are not readily apparent if the chosen frame is constrained to answering the question “what's wrong.” Think of it this way, you must take the journey up the decision ladder from symptom to cause before you can travel back down to find a true remedy.

The Decision Hierarchy

Exhibit 3. The Decision Hierarchy

Generating Alternatives

The second step of the decision engineering method is to generate alternatives. This is where decision-makers can leverage creativity for good decision-making. The most important rule to remember at this step is size counts. The more options and alternatives generated by the decision team, the greater the likelihood the team will encounter an innovative solution or alternative. Decision expert Robin Hogarth sums it up succinctly:

“Imagination and creativity play key roles in judgement and choice.…predictive judgement requires the ability to imagine possible outcomes.…in many choice situations alternatives are not given but must be created.…Indeed, it can be said that a person who exhibits neither creativity nor imagination is incapable of expressing ‘free’ judgement or choice.” (Hogarth, 1987, p. 153)

The single most important rule to remember in the alternative generation phase is to keep idea creation separate and distinct from idea evaluation. In other words, do not criticize or judge ideas as they are being created. Numerous studies have shown that far more ideas are possible when the group defers judgement to a subsequent phase of the decision meeting. As we pointed out earlier, the group must open up the spectrum of the possible (divergent thinking) before coming to closure on a set of alternatives.

There are various creativity techniques available to the project team. Many of us are familiar with classic brainstorming where the meeting participants shout out ideas as a facilitator records them on large sheets of paper. There are also techniques that use the power of analogy as a way to stimulate creativity. A very powerful creativity technique that is easy to facilitate in a group setting is to leverage the power of wishes. Ask the participants to suspend disbelief for a few moments and think about what they would do if they omnipotent and all things were possible. The resulting ideas can be very creative and usually seem impossible to implement.

The project leader's job is to help the group find ways to overcome the obstacles of the “impossible” ideas in order to make them feasible options. Indeed, it can be said that nearly all truly innovative ideas look infeasible to many people when they are first created. For example, Allistair Pilkington was washing dishes one evening when he observed grease forming on the dishwater. He connected this to his work and the particularly difficult problem at the time of how to make plate glass smooth. Wishing he could make plate glass by pouring it on water so that it would be perfectly flat, he conceived the idea of pouring molten glass onto liquid tin. Pilkington's idea revolutionized the glass industry and the process is now used worldwide. (Synectics, Imagine, p 2-5)

A project leader encounters many different kinds of decision meetings, but perhaps nowhere is creativity more important than in developing a project portfolio. A creativity technique that we have used with excellent success is to ask the group to sit back for a moment and imagine what success would look like five years hence if all their wishes came true. Then we ask each person to write down each his/her description of that ideal future state. The next step is to share the scenarios with the entire group. Next, by facilitating the group through a process of dialogue and debate, we can help the group forge a shared vision of the future and the necessary project portfolio to help achieve that future. This technique is a wonderful way to leverage the power of intuition in the context of a defensible decision process.

Decide the Course of Action

Once the group has properly framed the decision problem and generated a sufficient number of alternatives, it is time to begin the process of converging to a solution. This third step of the decision engineering method is where decision analysis tools are generally the most useful. The decision facilitator's task at this stage is to help the group evaluate the alternatives and choose a solution. This can be achieved through a combination of intuitive and analytical approaches. In situations where speed and uncertainty are key factors, the intuitive approach requires that the decision-makers look at the options and choose a workable solution, then continually refine that solution as new information becomes available.

Simple group ranking tools such as multi-voting and nominal group technique directly tap into the intuition of the members of a decision team and quickly summarize group results. We even ask intuitive questions directly when examining the results of these activities. (Does this look right to you?) Intuitive decision-making is improved through experience, either actual or simulated, so a careful project leader doesn't rely on purely intuitive approaches with inexperienced decision teams.

A key challenge for the decision team is to find a way to systematically analyze how the alternatives stack up in order to choose the “best,” or most preferred alternative. This can generally be accomplished one of two ways: comparing the alternatives to one another or scoring the alternatives against some set of objective rating criteria. Comparing more than two alternatives can be a bit confusing, so it can be helpful to establish a simple 2 X 2 matrix with the alternatives arrayed across the top and along the vertical axis (Exhibit 4). The decision-maker can compare each option to every other option and derive a set of weights by which they can order the alternatives from most to least preferred. (The same technique can also be used to weight criteria for a ratings model, described in the next paragraph.)

Decision Matrix

Exhibit 4. Decision Matrix

The second basic method for ranking alternatives is to score each alternative against an objective set of ratings. This is a preferred technique when there are more than a handful of alternatives, because matrices can become unwieldy with large numbers of alternatives. To use ratings scales, the decision-maker must create and weight a set of decision criteria by which the alternatives are judged. These decision criteria should be few in number, reasonably independent of one another, and be clearly and unambiguously defined.

Groups often run into trouble when the decision criteria (and/or associated rating scales) are defined in the same way by all decision-makers. If the decision criteria do not have a common definition for all members of the decision team, the team results should be called into question, since various team members may have evaluated the alternatives differently. For example, let's assume we are evaluating the quality of the house a general contractor has built and our chosen rating scale is one through five. If we do not agree on the underlying meaning of a “one,” “two,” etc., a consensus score lacks precision at best and, at worst is no better than a random number.

During an analytical decision process, a good rule of thumb is to follow Albert Einstein's famous advice to keep things as simple as possible, but no simpler. The less complex the decision model, the more likely the group will actually understand the evaluation process, and the better the odds of the group coming to consensus on a result. There are two basic ways to simplify a decision model—eliminate decision criteria and/or eliminate alternatives. Starting the process with the critical few decision discriminators is key. In our construction example, would you bother verifying whether each alternative house had doors and windows? Local building codes demand that all houses have doors and windows, so unless you defined it in some unique way, such a criterion is not a decision discriminator.

Once the decision team has settled on the decision criteria that are truly needed and no more, the next place to look to simplify the decision problem is to eliminate alternatives. A word of caution is appropriate here. The decision team should only attempt to eliminate those alternatives that have no chance of being selected as the best. In other words, if an alternative is clearly inferior to at least one other alternative for every decision criteria, why consider it any further? Source selection teams refer to this as “narrowing the competitive range.” Decision analysts call this the “decision dominance principle.”

Decision Engineering in Project Management

We recently helped one customer team prioritize a project portfolio using our three-step method. The customer had an internally generated project list that exceeded the corporation's available resources. While people in different functional areas had generated the project list, many of the projects were cross-functional in nature. In addition, project resources needed to be allocated and de-conflicted from a corporate perspective. The leadership team assembled in a conference room for a two-day process of creating decision criteria and rating the projects against the criteria.

The customer team spent most of the first day choosing the correct frame for the overall corporate objective. While one might imagine that this would be obvious, getting people from different functional perspectives to agree on a succinct statement of the overall corporate goal was not a trivial task. Using successive waves of brainstorming and evaluation, the team established a frame to which they were all committed.

Once they had a consensus decision frame, the customer team next had to craft a set of decision criteria by which they could measure the value of the projects’ contribution toward the corporate goal. This also required significant discussion and successive waves of voting in order to ensure the team had a good set of well-defined criteria. Finally, the team evaluated the project list against the criteria and established a numerical score for each of the projects.

The fourth step of this process will be for the customer team to examine how the projects relate to one another and the available resources in a project management tool. Once they understand the project relationships and identify the resource conflicts, they will be able to use the project rating score to prioritize which projects will be implemented and which will be deferred or eliminated.

Summary

The decision engineering method is a structured approach to decision-making with wide applicability. Whether you are struggling with an important engineering decision or trying to decide a policy matter, you must be careful not to let conflict, uncertainty, and ambiguity slow you down. While certainly not a panacea, the decision engineering method can help project teams spend their time more effectively and efficiently while they seek solutions to difficult decision problems.

References

Bailey, A. (1998) Synectics Imagine, Cambridge, MA: The Synectics Corporation, The White Dove Press.

Dearlove, D. (1998). Key Management Decisions, London: Pearson Professional Limited.

De Geus, A. (1997) The Living Company, Boston, MA: Harvard Business School Press.

Hammond, J. S., Keeney, R. L. & Raiffa, H. (1999)Smart Choices, Boston, MA: Harvard Business School Press.

Hammond, K. R. (2000) Judgments Under Stress, New York: Oxford University Press.

Hastie, R. & Dawes, R. M. (2001)Rational Choice in an Uncertain World, Thousand Oaks, CA: Sage Publications, Inc.

Hogarth, R. (1987). Judgement and Choice, Second Edition, New York: John Wiley & Sons.

Kaner, S., Lenny Lind, Catherine Toldi, Sarah Fisk, Duane Berger. (1996) Facilitator's Guide to Participatory Decision-Making, Philadelphia: New Society Publishers.

Klein, G. (2003). Intuition at Work, New York: Doubleday.

Muoio, A. (1998, October) Decisions, Decisions - Unit of One, Fast Company, (issue 18) p. 93

Author Biography

Mark A. Wilson is President of Center for Systems Management (CSM), which serves clients worldwide in project management, systems engineering, and process improvement. CSM specializes in helping clients improve performance: better on-time-delivery, reduced life-cycle-costs, improved technical performance, increased velocity to market, enhanced revenue growth, and improved teamwork. He is a Certified Quality Manager (American Society for Quality) and holds a B.S. from the U.S. Naval Academy and M.S. from the U.S. Naval Postgraduate School.

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.

Proceedings of PMI® Global Congress 2003 – North America
Baltimore, Maryland, USA ● 20-23 September 2003

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