Judgment and Decision- Making

Opening Activity I have a rule in mind that applies to the sequencing of three numbers. You have to try to guess the rule using the following table. I will give you one sequence that fits my rule. Also, as you generate sequences of your own to try to figure out my rule, I will tell you whether your guess is consistent with my rule or inconsistent with my rule . When you get to 100% sure, you are done. Sequence Or  Your guess about the ruleHow sure are you? 2, 4, 6

Why Study Judgment? Principles based standards Movement toward fair-value accounting Accounting for leases Convergence toward a single set of high quality standards—greater judgment, fewer prescriptive rules. Greater emphasis on inspections—greater scrutiny Auditors subject to PCAOB inspections have to justify decisions made in the conduct of the audit. In order to provide such justification, auditors need to have clarity on how to reach important decisions.

The KPMG Professional Judgment Framework 4

McGladrey Professional Judgment Framework

Center for Audit Quality: Judgment Tools

Factors affecting judgment Bounded Rationality Incomplete information Time and cost constraints Cognitive constraints Memory Intelligence Perception Attention

Dealing with Bounded Rationality Satisficing Accepting an available alternative Heuristics “Rules of thumb” or simplifying judgment strategies Mostly unconscious or intuitive Work well in many situations Can lead to logic errors, inconsistency, or incorrect decisions. The errors that result from heuristics are called biases.

Heuristics and Biases Availability—Use information that is readily available for making decisions. Resulting bias—Information that is more readily available is usually given greater weight in making decisions than information that requires greater effort to acquire or use. Representativeness—When faced with a new thing, place it in a category of things we already understand based on its similarity to a prototype of the category. Resulting biases: Insensitivity to base rates Insensitivity to sample size Confirmation bias Anchoring—Usually numeric—When determining an amount or likelihood, start somewhere and adjust up or down. Resulting bias—Overconfidence

The Problem with Biases-- Overconfidence Examples: “They couldn’t hit an elephant at this dist…” (last words of Union General John B. Sedgwick) Two phrases that signal overconfidence bias: “Watch this…” and “Hold my beer…” Overconfidence can lead to: Taking on too many projects Over promising on deadlines Considering only one alternative Truncating information search or even skipping evidence gathering Snap judgments Avoidance or poor execution of judgment model What to do about it: Challenge “experts”. Challenge your own opinions. Discover underlying assumptions and question them

The Problem with Biases--Confirmation Examples Confirmation bias can lead to Overconfidence (see previous) Loss of skepticism An environment in which critical thinkers feel marginalized Acceptance of simplistic explanations and reliance on anecdotes rather than rigorous inquiry Polarization of beliefs What to do about it: Decision models Embrace surprises—when you encounter something you didn’t expect, don’t just dismiss it. Try to figure out what might explain it. Open your mind—Think of improbable explanations and look for confirmation of those. Seek diversity Actively seek falsifying information, “If I were to observe this, I would know that I am wrong.” Ask questions with concrete answers rather than answers that you have to interpret (but be careful—sometimes these aren’t the right questions) Don’t ask leading questions. People will tell you what you want to hear.

The Problem with Biases--Anchoring Examples: Polleverywhere results Anchoring biases can lead to Narrow confidence intervals Skewed negotiations What to do about it: Get a fresh perspective from somebody who has not seen the information. Generate your own expectations before you see the client’s amount. Question why the “same as last year” approach is appropriate.

The Problem with Biases: Availability Examples: Native American Weather Forecast. Availability bias can lead to: Accumulation of bad data rather than finding the one piece of good data. Snap judgments Confirmation bias (see previous) Overconfidence bias (see previous) What to do about it: Evaluate the reasons why you use the data you use. Ask whether you need to know more about the underlying process before accepting the advice of experts.

Other Tendencies Distorted or motivated reasoning—interpreting information the way you want it to come out Hindsight—We tend to attribute greater ability to have predicted uncertain outcomes in the past. So, we think we can predict the future better than we actually can. Rush to solve—With limited resources and tight deadlines, every accountant has to face this. Self-serving bias—It is almost impossible to discount how the expected outcome would affect us. As a result, we tend to favor the alternative that works out best for us (especially in the short-run).

Environmental Constraints on Decision- Making Time pressure and deadlines Lack of resources Misaligned incentives and performance measurement Groupthink Closed channels of communication

Reasons Why Auditors Are Susceptible to Bias From Bazerman et Al. Ambiguity—opens the door for all of the biases. Attachment—Your fate is tied, somewhat to the fate of the client. Approval—special case of anchoring. We tend to make riskier assessments when we just have to approve it rather than come up with it ourselves. Familiarity—similar to attachment. The client is close. The public is distant. Discounting—problems with the client happen now. Problems with the public happen much later. The uncertain future outcome Escalation—Bad auditors are not usually bad people who set out to do bad audits. Instead they are auditors who might have made some tough calls. The auditor often feels compelled to continue to cover previous decisions.