On Risk I

"The Risk of losing any Sum is the reverse of Expectation; and the true measure of it is, the product of the Sum adventured multiplied by the Probability of the Loss." (Abraham de Moivre, "The Doctrine of Chances", 1718)

"The modern theory of decision making under risk emerged from a logical analysis of games of chance rather than from a psychological analysis of risk and value. The theory was conceived as a normative model of an idealized decision maker, not as a description of the behavior of real people." (Amos Tversky & Daniel Kahneman, "Rational Choice and the Framing of Decisions", The Journal of Business Vol. 59 (4), 1986)

"Conversely, there are few features of life, the universe, or anything, in which chance is not in some way crucial. Nor is this merely some abstruse academic point; assessing risks and taking chances are inescapable facets of everyday existence. It is a trite maxim to say that life is a lottery; it would be more true to say that life offers a collection of lotteries that we can all, to some extent, choose to enter or avoid. And as the information at our disposal increases, it does not reduce the range of choices but in fact increases them." (David Stirzaker, "Probability and Random Variables: A Beginner's Guide", 1999)

"Players must accept the cards dealt to them. However, once they have those cards in hand, they alone choose how they will play them. They decide what risks and actions to take." (John C Maxwell, "The Difference Maker: Making Your Attitude Your Greatest Asset", 2006)

"When confronted with multiple models, I find it revealing to pose the resulting uncertainty as a two-stage lottery. For the purposes of my discussion, there is no reason to distinguish unknown models from unknown parameters of a given model. I will view each parameter configuration as a distinct model. Thus a model, inclusive of its parameter values, assigns probabilities to all events or outcomes within the model’s domain. The probabilities are often expressed by shocks with known distributions and outcomes are functions of these shocks. This assignment of probabilities is what I will call risk. By contrast there may be many such potential models. Consider a two-stage lottery where in stage one we select a model and in stage two we draw an outcome using the model probabilities. Call stage one model ambiguity and stage two risk that is internal to a model." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Without context, data is useless, and any visualization you create with it will also be useless. Using data without knowing anything about it, other than the values themselves, is like hearing an abridged quote secondhand and then citing it as a main discussion point in an essay. It might be okay, but you risk finding out later that the speaker meant the opposite of what you thought." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Mental Modeling enables discovery of people’s mental models in a structured, rigorous, respectful manner. Mental Modeling has been recognized as one of the premier methods for informing the development of strategies and communications that precisely address people’s current thinking, judgment, decision making, and behavior on complex issues , including risk issues. Broadly, Mental Modeling works from the"inside out," starting with an in-depth understanding of people’s mental models, and then using that insight to develop focused strategies and communication that builds on where people are at in their thinking today, reinforcing what they know about a topic and addressing critical gaps. Broadly stated, the goal is to help people make well-informed decisions and take appropriate actions on the topic at hand." (Matthew D Wood, An Introduction to Mental Modeling, [in "Mental Modeling Approach: Risk Management Application Case Studies"], 2017)

"Premature enumeration is an equal-opportunity blunder: the most numerate among us may be just as much at risk as those who find their heads spinning at the first mention of a fraction. Indeed, if you’re confident with numbers you may be more prone than most to slicing and dicing, correlating and regressing, normalizing and rebasing, effortlessly manipulating the numbers on the spreadsheet or in the statistical package - without ever realizing that you don’t fully understand what these abstract quantities refer to. Arguably this temptation lay at the root of the last financial crisis: the sophistication of mathematical risk models obscured the question of how, exactly, risks were being measured, and whether those measurements were something you’d really want to bet your global banking system on." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Sample error reflects the risk that, purely by chance, a randomly chosen sample of opinions does not reflect the true views of the population. The 'margin of error' reported in opinion polls reflects this risk, and the larger the sample, the smaller the margin of error. […] sampling error has a far more dangerous friend: sampling bias. Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)