OVERSIMPLIFYING DECISION MODELS

Losing your keys is rough, especially when you are in a hurry. Where are some of the first places you look? Probably places that readily come to your memory: the kitchen table, the desk, etc. What you do not take into account are other factors that can complicate the situation. For instance, when you came home, you put your keys on the nightstand next to the window, but the window was open and the wind blew your keys under your bed. Unfortunately, under the bed was the last place you looked.

This scenario illustrates how we often make decisions by absorbing the simplest model possible without taking into account other variables and alternatives. In Robert Jervis’ article, The Drunkard’s Search, he concluded, “the cost of gathering and processing information needs to be taken into account by any intelligent decision maker. But the pattern cannot be entirely explained by the rational search for and use of information…the use of simple benchmarks and analogies are manageable but inappropriate.” (p. 270).

Jervis discussed how people do not often consider possible alternatives before making a decision; that we may forget to account for changing conditions (the window) and how they might effect the decision. He also states that we use a linear spectrum when evaluating past decisions to compare to present ones. Decisions are either good or bad, which usually equates to a success or failure. “Use of this measure does not seem to be restricted to situations where it fits.” (p. 261). We cannot use this measure correctly because comparing past decisions with present ones does not help the present situation; this is due to the fact that the variables and alternatives will always be different.

When we only look at alternatives in front of us, we pick out important information but do not combine it with others. “To say the right hand does not pay attention to what the left hand is doing is not quite accurate: rather the right hand does not pay any attention to the implications of the left hand’s activities.” (p. 265).

Text Credit: Jervis, Robert, The Drunkard’s Search, Political Psychology, 2004. pp. 259-270.

Photo Credit: http://www.flickr.com/photo_zoom.gne?id=9255471&size=m

COMPARING SKILL AND PRODUCT SELECTION

You have just been accepted to a very prestigious graduate school, first of all, congratulations! Now you need some tools; your basic notebooks, pens, and of course a computer. Choosing a computer is a big decision. This is a top ten grad school, you want to make sure you look professional and have the best of the best technology right at your fingertips. Therefore, you want a sophisticated computer, which shouldn’t be a problem because you know your way around the internet, and you’re a fairly good typist. A week before school starts you find the perfect PC: it’s sleek, it’s hi-tech, and it’s all yours. Now it’s the first day of school and you’re setting up your fabulous new computer, but alas, you can’t figure out how to turn it on!

This is what happens when your skill perception does not match up with your purchase choice. In her recent study, Katherine Burson concluded, “that manipulations of perceived comparative skill change product choices.” (p. 106) Burson conducted two separate studies that measured the matching skills of a person’s ability to use a certain product, such as a golf club or a digital camera, and which type of product they actually purchased. Burson stated, “people rely heavily on their relative self-assessments in product choice, but these estimates are often inaccurate and thus lead to unintended and inconsistent choices.” (p. 104)

The following graph shows the discrepancies of skill matching, when subjects measured their camera using skills with the actual purchase of a high-end versus basic digital camera:

Burson concluded that a person’s perception of skill can be manipulated by tests, social perception, or other external factors.

Text Credit: Burson, Katherine, Consumer-Product Skill Matching, Jorunal of Consumer Research, 34, June 2007.

Photo Credit: http://www.flickr.com/photos/wesleychu/33323853/

EVALUATING CONTRIBUTIONS OF PAST RESEARCHERS AND THEORIES

In the past, several researchers in the judgment and decision making field have reviewed and updated past decision analysis theories; one of these revisions comes from a group of researchers who have titled their article: Risk-based Decision Analysis in Support of Precautionary Policies. In this article, researchers reviewed a specific decision making technique discussed by T. Page, a research scientist whose notable paper was published in 1978. His critique of the “expected-value approach” model has been praised by researchers for decades.

In this theory, Page enforces the statistical cautionary approach that we have been discussing, in which Page states that it is inadequate to “assume that new technologies and products are safe and beneficial until proven otherwise.” (p. 393). By presuming this, researchers think a decision maker will become naïve and complacent in their judgment making.

Page specifically suggested three other modifications to the expected-value approach: first, “he argued that consideration of social risk aversion implies greater restrictions on risky activities.” (p.393) secondly, “decision-makers should consider postponing taking actions that may result in irreversible negative outcomes…” (p. 393). And finally, Page argues that the original expected-value approach “ignores the distribution of costs and benefits, particularly when the costs and benefits occur at different times.” (p. 393).

Through his alterations of the theory, researchers agree with Page that, we must consider the social implications of our decisions as well as the benefits and costs presently and in the future.

Text Credit: DeKay, M., Small, M., Fischbeck, P., Farrow, R., Cullen, A., Kadane, J., Lave, L., Morgan, M., Takemura, K., Risk-based Decision Analysis in Support of Precautionary Policies, Journal of Risk Research 5 (4), 391-397, 2002.

Photo Credit: http://www.flickr.com/photos/obsessivephotography/455619787/

COMPONENTS OF RISKY DECISION MAKING

If all of your friends jumped off a bridge, would you jump too? That is quite a risky decision to make, but when you analyze your options before rushing to make your choice, the correct decision may surprise you.

You’ve got to weigh a few things: your own choice and decision making behaviors, the algebraic theories of outcomes, and cognitive computational theories. After looking at these routes of decision making, you should also look at your limitations: ‘what utilities or means are available to me? What’s the worst outcome possible, and am I able to handle that? Is anyone else going to be hurt by my choice? Will I be held accountable?’ An article by Reid Hastie titled: Problems for Judgment and Decision Making, analyzes the psychology behind making risky decisions.

For example, Hastie argues that a person’s desires, goals, and values, as well as his or her knowledge, means, and expectations are taken into account when making an uncertain decision. This can be illustrated in his diagram seen below:

The most influential part of risky decision making is the conclusion. Hastie divides this into two categories: outcomes, which are the public and well known, and consequences which are the public evaluation of your decision. Hastie states that we use the conclusions of our decision making to help our future judgment. “Decision making refers to the entire process of choosing a course of action. Judgment refers to the components of the larger decision-making process that are concerned with assessing, estimating, and inferring what events will occur and what the decision-maker’s evaluative reactions to those outcomes will be.” (p. 657).

In order to be more efficient and accurate when making uncertain decisions, Hastie argues that we must better evaluate our values and emotional beliefs in order to de-cloud the process and gain accurate information.

Text Credit: Hastie, R., Problems for Judgment and Decision Making, Annual Review of Psychology, Vol 52, p. 653-659, 2001.

Photo Credit: http://www.flickr.com/photos/peteretep/161884037/

FEAR OF FAILURE COSTING GOOD DECISION MAKING SKILLS

Failure. It’s a harsh and dominating word that is very quick to take over your conscience after a bad decision has been made. People usually blame failure on bad decision making, but isn’t there a difference between “making mistakes and being a failure”? (p. 203). In Harry Allen and Bonaro Overstreet’s book titled The Mind Alive, they discuss how we as decision makers can learn from mistakes rather than making excuses for our shortcomings and limitations.

Overstreet and Overstreet argue that when we don’t accept our bad decisions, then we limit the recycling of ideas, by this term we mean bad choices that can be learned from and translated into knowledge for future decisions.  When we limit or even ignore bad decisions it limits new decisions and therefore growth.

So what stops us from failing and making bad decisions? Knowledge. Information. Truth and facts. The more hard facts we absorb to base our decisions on, the easier and more successful the results will be. This doesn’t seem like such a difficult task, but sometimes, we fear asking questions of others to learn more information. Maybe it’s a bruise to our ego or fear of a looking ignorant (a bad decision maker), but somewhere along the way we lost our curiosity to ask, ‘why?’ or ‘how come?’ The poet Edwin Arlington Robinson said:

“What had truth done to us, that we must always be afraid of it, as a monster of a shape unknown?” (p. 207)

The fear of making a bad decision stems our desire to ask questions, which leads us right back around again to the lack of new information being generated. The solution? Ask. Question people’s decisions good or bad, learn from your own mistakes and from others, only this way will we better cope with making mistakes rather than labeling failures.

Text Credit: Overstreet, H.A. & Overstreet, B., The Mind Alive, Ch. 13, pp. 199-217, New York, NY, 1954.

Photo Credit: http://www.flickr.com/photos/26447726@N00/511200496

Imagine your mind as a vacuum, you can pick the color and brand later, this is important. Now, you only have one bag inside of you to get all of the dirt and crumbs and whatever else might be living on your kitchen floor up. Do you go for the giant stuff first? Or pace yourself to make sure you don’t overstuff the bag? You know that you don’t have that much space, so you end up leaving the tiny dust bunnies for the broom. Now that was a smart decision. You saved room for more important things to suck up.

Your mind is like a vacuum bag in that it only has so much room to retain information. In the book, Professional Judgment, edited by Jack Dowie and Arthur Elstein, there is an article titled, Psychology of Clinical Reasoning by Arthur Elstein and George Bordage. In this article, the researchers reviewed the steps that clinicians take when analyzing information, in hopes that they could narrow down a specific process map that is followed for every decision.

Researchers stated “limits exist to the human capacity for rational thought.” (p. 110) this means, the more processes we create to help make good decisions, the less “working memory” we have. They suggest using simple principles which can by broadly applied in order to cut down on the disadvantages of over-using the clinical approach, such as researcher bias, overemphasis on positives, and excessive data collection.

The basics of this principle are:

Cue acquisition- gathering information

Hypothesis generation- finding alternative solutions

Cue interpretation- data analyzed

Hypothesis evaluation- decision confirmed or recycled anew

In the article, the researchers concluded that this approach would be the most successful for uncertain decision making because it involves direct observation and can easily be translated into real life scenarios.

Text Credit: Dowie, Jack & Elstein, Arthur, Professional Judgment: A Reader in Decision Making, Psychology of Clinical Reasoning, Elstein, Arthur & Bordage, George, Cambridge, UK, 1988.

Photo Credit: http://www.flickr.com/photos/7133667@N04/404207546/

You have a cough. Don’t worry, you will be fine. Now, say you visit Doctor number one for your cough and he says you have a cold. You go to doctor number two and she says you have asthma, finally you visit doctor number three (just for good measure) and he says its allergies. Great. Now you have three prescriptions to fill for three different illnesses, and you’re still coughing.

When presented with identical information to analyze, people often come up with dozens of solutions and ideas to explain it. This is why we need accuracy in our lives. Knowing that the amount of information doesn’t always improve our decisions, but it does increase our confidence levels may lead us to make a bad decision a little faster. Accurate information can slim down options and offer clearer results.

In the book, Judgment and Decision Making, edited by Hal Arkes and Kenneth Hammond, there is an article titled: Simple Models or Simple Processes? By Lewis Goldberg. He states that accuracy and reliability go hand in hand such that inferential reliability- conclusive accuracy- can be broken down into three processes, stability (holding true over time), consensus (agreement among judges, you), and convergence (agreement with other data sources). The application of these processes in a clinical approach to decision making is as Goldberg says a model of “hidden cognitive processes.” (p. 338)

Goldberg concluded that the best way to increase accuracy in making uncertain decisions was enforcing immediate feedback; whether it is from patient to doctor, buyer to seller, or test results to student; through these forms of instant advice, a person can receive applause and the ability to learn from their mistakes.

Text Credit: Arks, Hal & Hammond, Kenneth; Judgment and Decision Making, Simple Models or Simple Processes?, Goldberg, Lewis; London, UK. 1988.

Photo Credit: http://www.flickr.com/photos/15495660@N00/298109955/

ANALYZING STRATEGIES OF UNCERTAIN DECISION MAKING

Today we have a 40% chance of Bob making the wrong decision on his big case today, and a partly cloudy prediction of what he’s going to have for dinner.

Wouldn’t it be nice if we could work out those statistics in our head before the big case, or the test, or our dinner selection?  Statistical approaches to uncertain decision making help us better understand when and where to make risky decisions and when to just walk away.  Wouldn’t it be nice to just put in some variables and come out with solid numbers, everyone likes numbers, right?

In Hillel J. Einhorn’s article titled, Accepting Error to Make Less Error, he concludes that by using a statistical approach such as our weather related percentage prediction above, numbers do prove to be better than experience. He states that “using a simple rule that is consistently applied” (p. 181) is the most efficient way to make good decisions. Einhorn explores this idea by separating decision making strategies into two main analysis categories: clinical and statistical.

Clinical decision making can be defined as a causal understanding of factors before a decision is made. This “prediction depends on backward inference” (p. 184). To use this strategy you must know all of your options and consider them based on previous experiences. However, these memories may be influenced by, mood, fatigue, anxiety, or hundreds of other factors just waiting to throw off your decision.

On the other hand, statistical analysis accepts error, but “trusts no one and takes little faith.” (p. 186). This approach is based on the uncertainty of the world and simplifying it into models such as a weighted linear regression model, among other options. Comparing both of these decision making strategies, can be difficult, but Einhorn has constructed a simple “decision matrix for comparing the clinical and statistical approaches” (p. 186):

Einhorn believes that the systematic choice and random state of nature is a more clinical than statistical approach, while the random choice and systematic state of nature is a more statistical than clinical approach. He concludes that clinical reasoning has a higher risk value than statistical reasoning, because statistical decision making has shown better outcomes on average.

Text Credit: Dowie, Jack & Elstein, Arthur, Professional Judgment: A Reader in Decision Making, Accepting Error to Make Less Error, pp 181-195. Cambridge, UK, 1988.

Photo Credit: http://www.flickr.com/photos/syrisstudios/436710585/

What makes up our behavior? How do you fit in with one group of people and not the other? In Robyn Dawes book Rational Choices in an Uncertain World, he points out an important study titled “Measuring Utility of Money through Decisions” by C.H. Coombs and S.S. Komorita. In this article, researchers state that behavior is largely looked upon as a series of decisions.

A person’s behavior can influence how much risk they are willing to take on any one decision. Coombs and Komorita concluded that a person would always try to maximize the value of their chance, especially if they have measured the “utility” of their bets. Betting is the most common way to test decisions having to do with uncertainty. After their own testing, researchers concluded “that an individual’s preferences among 50-50 bets can be explained and predicted in terms of his utility for money if an assumption regarding probability is satisfied.” (p. 388).

Conversely, Coombs and Komorita deduced that even in imaginary test situations, humans would rather not bet at all on an uncertain decision rather than taking the chance on uncertainty. It’s how we value what we have that influence our decision to take a risk, which eventually will turn into our behavior.

Photo Credit: http://www.flickr.com/photo_zoom.gne?id=201792653&size=m
Text Credit: C.H. Coombs; S.S. Komorita, The American Journal of Psychology, Vol. 71, No.2, (Jun., 1958), pp. 383-389.

Robyn Dawes’ book: Rational Choice in an Uncertain World explores how decisions concerning uncertain events usually test a human in both chance and skill. Humans, he says, fall to believe in superstitions when any shred of skill is involved. People may look for or even invent a pattern that will convince them that their risk is lessened.

Dawes analyzes how and why humans try so hard to grapple with chance, but ultimately he feels “An essential part of wisdom is the ability to determine what is uncertain; that is, to appreciate the limits of our knowledge and to understand its probabilistic nature in many contexts.” (p. 264). In other words, without uncertainty we would never know hope or face challenges. Dawes further explores the inevitability of chance by stating, “uncertainty is a necessary precondition for the existence of ethical choice.” (p. 267). Without chance, people would become all knowing and self-indulgent. Chance forces humans to second guess themselves and even hold back on risky decisions. Without those fears and constraints, ethics would be like a bad egg toss, gone.

To cope with uncertainty, humans make up rule-based systems (such as discussed in Subjective Probability) to increase the certainty in their decisions. These systems employ our values and ethics as well as our own feelings toward a subject. Dawes states, “Rationality does not dictate what to decide, only how.” (p. 272) In utilizing these formulas, we also make use of our hope, ethics, and freedom of choice.

Photo Credit:http: //www.flickr.com/photo_zoom.gne?id=144371101&size=m
Text Credit: Dawes, Robyn, Rational Choice in an Uncertain World, Harcourt Brace Jovanovich, Inc. Orlando, FL, 1988.