Decision Science/Behavioral Economics vs Neo-Classical Economics
Two opposing schools of thought on human decision making.
Decision Science focuses on HOW humans decide and it factors in very important dynamics
• Heuristics and Biases
• Predictable violation of choice axioms
This is a Descriptive Model.
On the other hand we have Neo-Classical Economics which focuses on how human SHOULD decide based on:
• Bayes Theorem
• Subjective experiences
• Utility Theory
This utilizes a Prescriptive/Normative Model
The shortcoming of the Neo-Classical view omits HOW humans actually decide which is paramount.
And HOW do humans actually decide?
In order to become decision architects we know that we want to increase our judgement’s accuracy in order to increase the expected value from our choices. Therefore we must become aware of the importance of biases and heuristics that affect our decision making.
We want to decrease biases, increase accuracy, improve decision environment, improve decision outcomes.
We have bounded rationality.
Herb Simon, known as founder of Decision Sciences (behavioral economics) said, “A wealth of information creates a poverty of attention.”
2 conditions to become aware of:
Condition 1: Opaque Processes
We are not aware of our bounded conditions. We do not see everything that is there and we do not realize it or overlook it.
Condition 2: Bandwidth Constraint
The human mind is a serial, not parallel, information processor. Our minds cope with information overload by processing in automatic mode. More System 1 thinking which facilitates use of heuristics and biases much more than System 2 thinking.
Therefore, the more information we have the less our attention. Simplicity is key.
That is why in trading we want to eliminate the noise and focus on price, what is right in front of us. I like my charts to be as simple as possible. Less is more.
System 1 thinking is Heuristic Thought:
• Fast
• Automatic
• Effortless
• Associative
• Assumes what you see is all there is and that is simply not true
System 2 thinking is Systematic Thought:
• Slow
• Learned/Controlled
• Effortful
• Rule-based
• Asks if what you see is all there is
Some common heuristics that affect our Risk Perception vs the Actual Risk Reality
Availability Heuristic:
A tendency to judge the probability of events by how easy it is to think of examples.
This happens all the time and in my opinion is one of the top, most common mistakes made when deciding quickly. Our minds quickly refer to a past occurrence that is easily retrieved whether it is correct or not. It just makes sense in that moment, however, when we slow down we may realize that it actually does not. A quick example is to think of shark attacks when swimming in a local beach. Well most great white sharks do not usually swim to the beach. Overall shark attacks are so rare, however, the Jaws movies have altered our view on swimming for many, not all, of course.
Another recent example is something I noticed the other day in my medicine cabinet. A box of sticky bandages (I will avoid using the brand name that gets used as a noun). Across the top it states “top choice by hospitals”. What a good marketing label to place at top. Why? When shopping for bandages we are usually in a hurry and we have so many options. Have you looked lately? So with all the vast choices and being in a hurry, I doubt you go to your system 2 thinking and read most of the labels and think deeply. Therefore we rely on fast system 1 thinking which is associative. So what is a quick association we make. With availability heuristic we quickly think of hospitals being the place that aids and bandages the worst of all cuts so if this brand is the “top choice by hospitals” brand then it has to be the best, right? So I quickly grabbed this box and left. I felt good about now owning the “top choice by hospitals”. It must be the best, right? Not so fast! Why? When we take the time to think it through, I come up with many drawbacks. 1. just because it is the same company they can have many different levels of quality. Maybe this is the least costly and lowest quality of this brand (we know most brands do this. we see this with luxury car brands and other). 2. who ran this poll that it was the “top choice” do we know? 3. many other counters Conclusion: This is the “fall guy” and decoy and this brand should be avoided! No, not that extreme but you see how we apply our logic now and the headline label looks a little fishy and doesn’t really mean that much but to sway us while in the drugstore making a fast decision and that title can quickly allow this brand to stand out among the other suitors?
2 other important heuristics to mention are:
• Controllability Heuristic: When we perceive something is more risky with less control.
Ex. Flying in an airplane over driving a car. We know the statistics here!
• Certainty Heuristic: We have a tendency to reject options that are associated with uncertainty.
Ex. We are more fearful of nuclear power over coal due to its uncertainty.
Risk Neutrality: we want to close that gap between the perceived and actual risks. Make it neutral.
People are risk neutral if they are indifferent between a gamble and a sure outcome of equal expected value.
Ex. if someone is indifferent between a coin flip that pays either $20 or $0 or a sure outcome of $10. These 2 outcomes have the same expected value: .5(20) + .5(0) = 10 OR 1(10) = 10
Humans tend to be more risk seeking when framed in losses as we have Loss Aversion. We avoid the sure “loss”, however, we prefer the sure “win” when expressed in gains.
What affects this is another important bias…Framing Effect!
Framing Effect: How or the way data/information is presented affects our perception of it and as a result our decisions.
Therefore our risk communication goals are:
Close the gap between the perceived risk and actual risk by helping people make more informed decisions
Neutral Frame. If presented in survival frame switch to mortality frame and vice versa to frame it in a neutral way.
Convey likelihood with frequency over probability.
What does this mean?
Probability is p = .5 to win $20 and p = .5 to lose $20
Frequency is 50 flips in every 100 will win you $20 and the other 50 flips will lose you $20
So change the mathematical formulas into words. more explanatory.