This session introduces how this course use the concept of risk. I'll offer a quick definition of risk and then we'll move beyond definitions to explore the very basics of how people evaluate risk and why we're so interested in the concept of risk in the first place. To start, if you use the term risk and search it, you'll find many definitions. There have been thousands of scholarly studies examining every dimension of the concept of risk and how risk differs from things like uncertainty and loss. In one famous example, University of Chicago economist Frank Knight separated the concept of risk from the concept of uncertainty. In his classic work, both risks and uncertainty involve an unknown outcome. But the difference is that with uncertainty, now called Knightian uncertainty, you don't even know how likely the possible outcomes are. With risk, there's an assumption that although the outcome is unknown, we at least know the probabilities of each outcome occurring. In this class, we're not going to dive deeply into questions like the distinction between risk and uncertainty. We're going to keep things simple and focus as we often do in reality, on risk as three-dimensions. A force or event that causes variability, the likelihood of the event happening, and the magnitude of that event. In other words, for us, risk is a cause and effect relationship between an event and the impact of the event on a relevant objective. For example, say we've got a virtual meeting and our objective is getting to the meeting on time. A risk event could be something like having an unstable Internet connection, which occurs with some probability and has a certain impact on the prospect of getting to the meeting on time. As we go through the class, we'll learn that none of these three elements are straightforward as they seem. That's why we're not going to focus as much on picking one definition or another as we will on building comfort with the human and organizational factors that help us address issues like the following. If you present a goal to a group of five people, and ask them to list some risks to that goal, it's unlikely that all five lists will be the same. Further, there are different ways to think about an events likelihood. Do we phrase it as historical accuracy? This thing happens one or two times per week or one or two times per month? Or we use a forward-looking probability judgment? It depends on the risk and the situation. Similarly, we could phrase impact as high, medium, or low. Or we could quantify in terms of dollars or lives lost, or lives saved. Picking the right unit of measure and the right classification system in general is more of an art than a science. Really, it's good to start at this point because humans often do not behave in the way that simple, easy to understand models would predict anyway. If nothing else, this class expects you to adopt a people centric view of the world. Take, for example, one of the most common behavioral oddities in evaluating risk, risk aversion. Risk aversion very simply is the tendency to prefer outcomes with low variability, over outcomes with high variability even if the high variability outcomes have equal or higher value. We can visualize risk aversion on a graph by depicting how a person's expected satisfaction with an outcome changes with the outcomes objective value. A lot of scholars use the term utility instead of satisfaction, but other term is fine for this class. The y-axis is satisfaction. The x-axis is objective value. As you move to the right, the outcomes get better. More money, more pieces of candy, more time to watch TV. You'd expect satisfaction to increase as value increases. If the two are aligned, then a person is said to be risk-neutral, an extra 10 bucks is valued as an extra 10 bucks. An extra piece of candy is worth an extra piece of candy. Risk aversion means that our satisfaction diminishes as the objective amounts get larger and larger. Called the law of diminishing marginal utility. Simply speaking, we get more satisfaction out of the first piece of candy than we do from the tenth, fourth, or twentieth. As a result, we are less willing to take a risk to increase our candy stash from 10 to 11 pieces then we are to increase our candy stash from zero to one. What this ultimately does is causes to prefer known outcomes to unknown outcomes. With each incremental increase in objective value translating into less and less satisfaction. There's one twist here that emphasizes the importance of the human side. It helped win a Nobel Prize for two psychologists named Daniel Kahneman and Amos Tversky. The premise is simple. People evaluate risk differently depending on whether they are evaluating a gain, an upside risk or a loss, a downside risk. In dozens of experiments, Kahneman and Tversky developed something called prospect theory. Arguing that people tend to be risk averse when evaluating gain prospects. But risk-seeking when evaluating loss prospects. That is to say, people prefer certain gains and uncertain losses. If a person has a choice between a certain gain of $100, and 10 percent chance to gain a $1,000, they'll choose the certain amount. But if they have a choice between a certain loss of $900 and a 90 percent chance to lose $1,000, they'll choose the uncertain gamble. The end result is changing how we visualize expected satisfaction, and really the effects of potential risk events. The visual we just discussed is now used to describe how people respond to upside risks. A big jump in satisfaction at first, which then decreases as the gain increases. The loss side is the opposite. Small increases in satisfaction, which increase as the loss decreases. The final twist is something called loss aversion. Over time, evidence grew that this curve is steeper for losses than for gains. People experience more dissatisfaction from a loss than satisfaction from an equally sized gain. Losing $5 feels worse than gaining $5 feels good. Losses loom larger than gains. People will take more risks to avoid losses than they will to realize gains. This is called a simple psychological effect called loss aversion. It has massive real-world consequences on everything from people's unwillingness to sell a home for less than what they paid to the years to sell a stock as soon as its price increases. If you're not yet convinced that understanding the human side is critical to risk management, what if I told you that a person's tendency towards loss aversion and risk aversion can depend on dozens of personality traits? Or with the environment in which they make a decision? They do. That's why we're bringing empathy to the data.
