Psychology Research Misses the Reasons for Decision Making Teams

I am reading “Straight Choices: The Psychology of Decision Making” by Benjamin Newell, David Lagnado and David Shanks (Psychology Press 2006). It is a very good summary of decision making from the viewpoint of cognitive psychology. It makes some muddy topics very clear. However, it totally fails in Chapter 14, Group Decision Making. This isn’t the authors’ fault – all work on team or group decision-making misses the main points. I will get to these in a moment.

First, some background. I attempted to address this topic in Chapter 5 of “Making Robust Decisions”, cleverly titled “Teams Don’t Make Decisions, But…” The title reflects the problem with the topic. In business and technology there is always one person who signs off on a project to move it forward. This manager is the ultimate “decision maker”. But, if this person is good at what they do, for complex problems they have a team that is studying the problem and advising them about what choice to make. On this team, some of the people know about some of the information, they all have different fields of expertise and knowledge. For complex problems, no one person can know all about all of the important features of all the alternatives. Further, they bring a range of viewpoints about what is important.

So, is the manager the decision maker or the team? Depends how you draw the line around the term “decision”. If it is an event, then the manager is the decision maker. If decision-making is a process, then it is the team. I see decision-making as a process. The cognitive psychologists seem to see a decision as the event.

In “Straight Choices”, the authors summarize research on group decision making. All of the studies they sight are for very simple problems, not for problems with distributed knowledge. In other words, the toy problems the psychologists have used to study teams do not reflect what happens in business and technology. They tend to focus on the "right" answer to simple problems with a known solution. This why Chapter 14 was such a let down.

From my reading, the two main reasons to use a team are:

1. Obtain the best information when no one person can know all that is needed to be known
2. Build stakeholder buy-in and accountability

These goals mean that you need to:

1. Build an environment and a team strategy that fosters communication of the right information and buy-in
2. Suppress the effect of differences in cognitive styles (“suppress” is not the right word, but you want to level the playing field so the alphas don’t dominate, the closers don’t stop things too soon, the wafflers don’t lead to team paralysis and so forth.)
3. Guard against group think
4. Help build a shared understanding

Of the six topics itemized above, Chapter 14 only addresses Group Think. It comes close to talking about a shared understanding when it discusses consensus, but consensus is not important for a decision buy-in. (Quoting Margaret Thatcher, "To me, consensus seems to be the process of abandoning all beliefs, principles, values and policies. So it is something in which no one believes and to which no one objects."). The two main goals are never discussed. It seems the cognitive research has focused on getting the “right” answers to toy problems. Most real decisions have no right answer, so the psychologists are asking the wrong questions.

In the authors’ defense, they end the chapter with “Research on group decision making is both appealing and frustrating”. In spite of this frustration, decisions are made by teams every day and I believe the key to robust team decisions is in the six items listed above. These are discussed at length in Making Robust Decisions. I just wish there was more good research on each of them so you don’t have to take my word for it.

Decision Anchoring

A nice summary of how results can be anchored just appeared on the web. This human behavior has great implications in business and technical decisions.

Anchoring sets a biased context for estimation. It is a cognitive limitation that affects the quality of our decisions. Anchoring occurs, for example, when a manager asks for an estimate with something like: "I don’t see how we could commit more than $10,000 to this." $10,000 now becomes the anchor point. This stated amount biases all the following estimates that are generated.

Anchoring can happen in subtle ways. Let’s say you are bidding on a project and you have been led to believe that the customer has a ceiling of, say, $10,000. You are now anchored to this value and will make decisions to try to force your project to fit it. This only seems logical, but it has interesting effects. First, the amount of work you propose will be descoped to fit the budget. But people always are optimistic, so, if you get the job, you will still have more work than money. Then begins the dance of working more for less money (overtime), further descoping or asking for more money. This dance is further discussed on pages 82-85 in Making Robust Decisions.

To demonstrate anchoring, I gave a group of people a simple estimation problem - asking them how long it would take them to wash a list of dishes. I described the dishes in detail, how dirty they were, and what "wash" meant. The mean estimate was 32 min with a standard deviation of 10 min. I then asked another group of subjects to estimate how long it would take to clean the dishes exactly as before, but this time I added "Your partner has told you that the kitchen needs to be clean in 15 minutes." This anchoring resulted in a new distribution with a mean of 18 minutes and a standard deviation of 6 min.

Think of the implications on decision making. All decisions are based on best estimates of past performance, assessments of the current situation, and visions of the future. Every one of these can be clouded by anchoring. You cant totally avoid it. However, you can be aware of how you word your need for information and consciously try not to anchor estimates on which decisions will be based.

Cognitive problems in making decisions

I have been reading "How Doctors Think" by Jerome Groopman. It is one of two books with the same title. Basically, reading this will make you scared to go to the doctor as doctors are human and make lousy decisions, just like all the rest of us. He has a good chapter titled "The Uncertainty of the Expert" which focuses on my favorite topic, decision making with uncertain information. All of Groopman's comments come from cognitive psychology and apply to business and technical decisions as well.

The key point in the chapter is captured in one sentence about half way in: "Physicians, like everyone else, display certain psychological characteristics when they act in the face of uncertainty". Uncertainty arises from 1) incomplete mastery of the available knowledge, 2) limitations of the knowledge, 3) difficulty distinguishing between the 1 and 2, and 4) (he leaves this one out) the variability of the nature of things.

In the face of uncertainty we all tend to:

1. Focus on the positive rather than the negative (except engineers who are pessimists by nature).
2. Ignore uncertainty. This is very evident in how we all talk about technical issue in terms of single values. Luckily we have terms like "about", "near to"
3. Go with what's been done before even if it is based on an unknown and unproven orthodoxy. Of course risk aversion is good and in medicine often leads to the correct diagnosis, whenever the problem is by-the-books. However, in business, technology this aversion can lead to being swamped by the competition.
4. Have a confirmation bias. This means that we look for support for our favorite alternative or hypothesis, at the expense of work on other possible options and discounting the negative (see item 1).

In the medical profession these characteristics are supported by the lack of time, risk aversion and an old-boys-club attitude. It is interesting watching the decision making on the TV program House. Here an arrogant, troubled doctor who hates the establishment diagnoses rare conditions and displays items 1, 2, and 3. He is really good at #3. But, most of us are.