[MUSIC] Welcome to the first video of week four of our course on unethical decision making. In the last week, we have introduced you to the concept of framing and looked at two cases that illustrated how people construct their reality. In this video, we will focus on how people can make and do make decisions by using simple heuristics. In this session, you will learn what simple heuristics are and you will see how effective and efficient these decision strategies are. How do people make decisions and how should they make decisions? Well, if you ask economists, most would say when making decisions, people should strive to be rational. Economists are, of course, framed. When they look at someone making a decision, they see homo economicus. Homo economicus cares about economic outcome, cares about subjective expected utility. Here we have John von Neumann and Oskar Morgenstern. Who formulated the axioms of this theory and who made operational a notion of rational behavior. For them, being rational meant being consistent with logical schemes and it meant choosing the alternative that maximizes subjective expected utility. To compute which course of action maximizes subjective expected utility, homo economicus takes all relevant and available information into account to update probability estimates of various events. And here we have Leo Savage, a proponent of this statistical, or say number-crunching, approach to decision making. This view has soon been challenged by Herbert Simon, who basically said, calculating expected utilities in order to optimize behavior may be possible in a small and stable world. But it's not feasible in the real world, which is large. That is, described on much more dimensions than one con handle computationally. It is dynamic and it contains a lot of interdependencies and uncertainties. So Herbert Simon questioned the psychological possibility of SEU theory. In his Nobel Prize lecture, he said that the classical model calls for knowledge of all the alternatives that are open to choice. It calls for complete knowledge of the consequences that will follow on each of the alternatives. It calls for certainty in the decision-maker's present and future evaluation of these consequences. And it calls for the ability to compare consequences in term of some consistent measure of utility. We can contrast these two views here in this illustration. We have two visions of cognition. One is the unbounded rationality, then, that includes optimization under constraints. On the other hand, we have Herbert Simon's view, who coined the term bounded rationality. In Herbert Simon's view, people do not optimize, but they use simple heuristics. And here we have an illustration of what we call the adaptive toolbox. This adaptive toolbox consists of simple heuristics that people can use to make decisions. It is not optimization under constraint and it doesn't mean that people are irrational when they use these heuristics. To the contrary, as we would see, these heuristics do a fairly good job. Let me now use one of these heuristics to illustrate the notion of bounded rationality in more detail. Imagine you're sitting on a field on some playground and your task is catch a ball. It comes high in the air, so you want catch the ball. How do you do this? It's simple, you may say, you just go there, catch it, that's it. But if you tried to figure out what's actually going on in the human brain, you see it's not so simple. Here we have a quote of Richard Dawkins. When a man throws a ball high in the air and catches it again, he behaves as if he had solved a set of differential equations in predicting the trajectory of the ball. As some subconscious level, something functionally equivalent to the mathematical calculation is going on. Do people use mathematics? Do they solve differential equations when catching balls? Here's what Peter McLeod and Zoltan Dienes found out in their research. Peter, fielders, players, they actually use a very simple heuristic. They look at the ball, the person throwing the ball up in the air, and they start running immediately. They do not do any calculation and predict where the ball would land. They start running before they even know where the ball would land. And they address their running speed so that they fixate the ball with a constant angle and, at some point, they'll have it even though they do not know where it would land. But that's not the goal. The goal is not to predict where the ball would land, the ball, goal is to catch it. There's a lot of empirical evidence that people and animals actually use this gaze heuristic. Let me give you another very powerful illustration of the gaze heuristic. That's actually an event that made it to the front news of all major newspapers worldwide. It's an event that happened on January 15th, 2009. It was US Airways flight 1549. Two minutes after takeoff at LaGuardia Airport, there were birds hitting the engines. The engine burned and the airplane basically turned into a glider. And the pilots had ten seconds, 15 seconds, to make a decision where to bring down the airport, airplane. They considered Titerboro Airport or at some point they then wondered, should we go back to La Guardia. How did they make this decision? Here's what Jeffrey Skiles later said in a talk show. It is not so much a mathematical calculation as visual, in that when you're flying in an airplane, a point that you can't reach will actually rise in your windshield. A point that you're going to overfly will descend in your windshield. So it's more of visual calculation. To get understand this, let's look at the following animation. This is the perspective of the pilot as the pilot have seen from the cockpit. And here we see the airport. Question, will they make it? If they look at the scratch in their windshield, they see the airport is rising. So at that moment, they knew we will not be able to make it. And they actually use the gaze heuristic]. Field players adjust the running speed so that they can always have the same angle. The pilots, who are not in the position where they could manipulate the speed of the airplane anymore, it was a glider, but they could look at the angle. And they could figure out, was the angle stable? Was it the same? And they've seen no, the angle changed. So they knew their speed was not fast enough and this is why they decided to go down into the Hudson and this later became known as the miracle of the Hudson River. The gaze heuristic is a very powerful illustration that the simple heuristic can guide behavior so that an organism can reach his goal. And note that most of the information out there and work knowledge can be ignored. No complex calculation is needed. No differential equations needs to be solved. Similar thing can be said about other simple heuristics that people have or may have in their adaptive toolbox. When one has to make a choice between a recognized and an unrecognized object, the recognition heuristic goes for the one that is recognized. No information about the alternative is processed. In a choice task, Take The Best considers one attribute after the other, starting with the best one and stops once an attribute is found that discriminates between the alternatives and makes the decision only based on this attribute. All others are ignored. Quickest is a simple heuristic to make numerical estimates. Fast and frugal trees can be used to categorize objects or to decide among courses of actions. And often, one piece of information may be enough for this purpose. All other information is ignored. Tallying sums up the arguments, speaking for each alternative, but does not weigh them. Eliminaton-by-aspects simply eliminates alternatives based on thresholds until only one is left. Often, we satisfies, that is, we are satisfied with something that is sufficiently good. Most often, we do not make the effort to find the optimum. And in most cases, it is not even clear what the optimum is. Often, we do not make much effort to find out what is best. We simply imitate others hoping that this is not too bad. And often, we do not make decisions each time anew, but we follow routines and go with what we did in the past in a similar situation. Often, we do not decide at all, but adapt defaults set by others. So let us conclude this session. Economists tend to frame decision making in terms of unbounded rationality. Homo economicus is an optimizer. The classical approach has been challenged by the concept of boundedly rationality. People have a repertoire of simple heuristics in their Adaptive Toolbox. These heuristics allow them to make good decisions even though people have limited knowledge, limited memory, and limited computational capacity. These heuristics may also be applied in situations that can be evaluated from an ethical point of view. And here comes the danger. These heuristics ignore information. If ethical dimensions are ignored, outcomes may be unethical. We will come to this in the next sessions. Thanks for watching today. [MUSIC]
