The main idea of this book is to provide explanation of difference between uncertainty, radical uncertainty, probabilities, and ambiguities. It is also about communication when information transferred via narrative or statistical representations could mislead people into believing false narrative. Book also provides some tools on how to deal with different uncertainties.
Part I: Introduction: The Nature of Uncertainty
1. The Unknowable Future
Authors begins by defining notion of “unknown unknowns” and provides examples such as Columbus discovery of America, Financial crisis, and so on, and then critic the idea that risk could always be measured, referring to statements of financial leaders about extremely low probability of events of 2008. Authors make this important point: “Economists (used to) distinguish risk, by which they meant unknowns which could be described with probabilities, from uncertainty, which could not. They had already adopted mathematical techniques which gave the term ‘risk’ a different meaning from that of everyday usage. In this book we will describe the considerable confusion and economic damage which has arisen as a result of the failure to recognize that the terms ‘risk’, ‘uncertainty’ and ‘rationality’ have acquired technical meanings in economics which do not correspond to the everyday use of these words. And over the last century economists have attempted to elide that historic distinction between risk and uncertainty, and to apply probabilities to every instance of our imperfect knowledge of the future.”
“We have chosen to replace the distinction between risk and uncertainty deployed by Knight and Keynes with a distinction between resolvable and radical uncertainty. Resolvable uncertainty is uncertainty which can be removed by looking something up (I am uncertain which city is the capital of Pennsylvania) or which can be represented by a known probability distribution of outcomes (the spin of a roulette wheel). With radical uncertainty, however, there is no similar means of resolving the uncertainty – we simply do not know. Radical uncertainty has many dimensions: obscurity; ignorance; vagueness; ambiguity; ill-defined problems; and a lack of information that in some cases but not all we might hope to rectify at a future date.”
Authors also define 3 main propositions of this book:
- the world of economics, business and finance is ‘non-stationary’ – it is not governed by unchanging scientific laws. Most important challenges in these worlds are unique events, so intelligent responses are inevitably judgements which reflect an interpretation of a particular situation.
- individuals cannot and do not optimize; nor are they irrational, victims of ‘biases’ which describe the ways they deviate from ‘rational’ behavior. The meaning of rational behavior depends critically on the context of the situation and there are generally many different ways of being rational. We distinguish axiomatic rationality, as used by economists, from evolutionary rationality, as practiced by people. Many so-called ‘biases’ are responses to the complex world of radical uncertainty.
- humans are social animals and communication plays an important role in decision-making. We frame our thinking in terms of narratives. And able leaders – whether in business, in politics, or in everyday life – make decisions, both personal and collective, by talking with others and being open to challenge from them.
2. Puzzles and Mysteries
Here authors compare NASA probe processes, which are based on calculations and well-known equations with processing in political or economic world where there is no stable equation that would provide framework for effective decision making. They also provide some technical language:” Engineers also distinguish between puzzles and mysteries, and give them technical names – ‘aleatory’ and ‘epistemic’ uncertainty, respectively. Meteorological records will describe the regular tides and winds to which a bridge is likely to be exposed (aleatory uncertainty), but since every bridge and every bridge location is different the effect of these conditions on the structure is never completely known (epistemic uncertainty). The tides and winds are the subject of known frequency distributions (tables showing how frequent are particular values of tide and wind speed); uncertainty remains because every complex structure is necessarily idiosyncratic. This distinction between the uncertainty which can be described probabilistically and the uncertainty which surrounds every unique project or event is important in all applications of practical knowledge and central to the argument of this book.”
Authors discuss several examples of uncertainties and them formulate another important point:” The claim of the modern science of decision theory is that most mysteries can be reduced to puzzles by the application of probabilistic reasoning. Such reasoning can provide solutions to puzzles, but not to mysteries. How to think about and cope with mysteries is the essence of managing life in the real world and is what this book is all about.”
3 Radical Uncertainty is Everywhere
In this chapter authors move to define scope of probabilistic reasoning and impossibility of its application to “unknown unknown” or “black swans.” Here is where authors defy popular ideas of behavior and mathematical economics clearly stating that:” Real households, real businesses and real governments do not optimize; they cope. They make decisions incrementally. They do not attain the highest point on the landscape, they seek only a higher place than the one they occupy now. They try to find outcomes that are better and avoid outcomes that are worse.”
Part II: The Lure of Probabilities
4 Thinking with Probabilities
Here authors discuss probabilistic approach to prediction. They start with mortality table and life insurance and then proceed to probability as frequency pointing out that one needs history of repetitive occurrences to calculate probability of event. They also discuss Bayes work and “The Bayesian dial is a visual representation of what is known as Bayesian reasoning. We deal with uncertainties by attaching ‘prior probabilities’ to uncertain events.”
The last part of chapter dedicated to the Monty hall problem, Indifference principle, and massive use of Bayesian algorithm in consulting and analysis using Gigerenzer’s analysis of breast cancer recognition technic.
5 A Forgotten Dispute
This refer to dispute between John Stuart Mill and Pierre-Simon Laplace about application of probability technic to unknowable sequences of events. Author discuss subjective probabilities and demonstrate quite convincingly that this is not a valid approach.
6 Ambiguity and Vagueness
In this chapter authors look at two notions that often mixed and provide definition for each one:” There is often vagueness or ambiguity in the description of future states of the world. Concepts are called vague when the ‘law of the excluded middle’ – either it is so, or it is not so – is not satisfied. Either it is Saturday, or it is not Saturday. But we are less confident whether it is warm, or not warm. Such vagueness is not necessarily a matter of loose or sloppy reasoning. Many descriptions are useful, but necessarily vague in this sense.” Correspondingly: “Although the term ‘ambiguity’ is often employed to describe many kinds of uncertainty, we prefer to limit its use to genuine linguistic ambiguity. The word ‘bank’ has a different meaning depending on whether the context is fishing or financial regulation.”
At the end authors review difficulties of communicating uncertainty and demonstrate it graphically:
7 Probability and Optimization
This is another problem, which is based on assigned values. Authors review use of this approach in economics with its move from values to utility. Authors also allocate significant space discussing notion of risk and its application to various problems.
Part III: Making Sense of Uncertainty
8 Rationality in a Large World
Authors start this part with discussion of what is rational and what is not. They provide useful definition for styles of reasoning:”
Deductive reasoning reaches logical conclusions from stated premises. For example, ‘Evangelical Christians are Republican. Republicans voted for Donald Trump. Evangelical Christians voted for Donald Trump.’ This syllogism is descriptive of a small world. As soon as one adds the word ‘most’ before either evangelical Christians or Republicans, the introduction of the inevitable vagueness of the larger world modifies the conclusion.
Inductive reasoning is of the form ‘analysis of election results shows that they normally favor incumbent parties in favorable economic circumstances and opposition parties in adverse economic circumstances. Since economic conditions in the United States in 2016 were neither particularly favorable nor unfavorable, we might reasonably have anticipated a close result. Inductive reasoning seeks to generalize from observations, and may be supported or refuted by subsequent experience. Abductive reasoning seeks to provide the best explanation of a unique event. For example, an abductive approach might assert that Donald Trump won the 2016 presidential election because of concerns in particular swing states over economic conditions and identity, and because his opponent was widely disliked.
After that authors provide examples of bias, discuss arrogant ideas of Nudge. Finally, they review Herbert Simon’s idea of Bounded Rationality, meaning search for “good enough” solution rather than optimal, by limiting area of search to some feasible solutions rather than all conceivable solutions.
9 Evolution and Decision-Making
This chapter challenges behavior economics by rejecting idea that people’s heuristics are illogical and inferior to mathematical approach with formal logic. The point is that evolution occurred in environment of radical uncertainty, so formal logic could not possibly work. Then authors go through specifics: Altruism, kinship, and mutuality; multilevel selection; superiority of loss aversion, value of confidence and optimism even if they could not be formally justified. At the end authors discuss Kahneman’s dual system, and human versus machine intellect.
10.The Narrative Paradigm
In this chapter authors move from analysis to communications and discuss narrative method. They look at its application in business schools, strategy sessions, and diagnosis. They also review use of historical narratives of various forms.
11.Uncertainty, Probability and the Law
Here authors discuss use of probabilities in criminal cases, review two cases one British and another O.J. Simpson to demonstrate how it could be misused. They also look at differences between probabilistic and legal reasoning:” Balance of probabilities’ means that it is more likely that the proposition is true than that it is not. The probability that it is true must exceed 50%. But to demonstrate a proposition ‘beyond reasonable doubt’ is to establish its truth to some very high degree of probability – perhaps at a level of 95% or beyond. Yet conversations with lawyers establish that things are not really like that. Indeed, when US judges were asked what probability was required to meet the requirement of ‘preponderance of the evidence’, not only did they offer a wide variety of answers, but most answers were higher – sometimes much higher – than 50%. US jurors’ estimates differed by more.”
12. Good and Bad Narratives
This is continuation of discussion about narratives. First authors look at whether narratives are true or false, then, regardless of this, at these narratives being good or bad. Authors also discuss some narrative tools such as metaphors, narratives of the future, and so on.
13. Telling Stories Through Numbers
This chapter is about numbers and statistics. Authors discuss various statistical data and then talks about power law:” The American linguist George Zipf studied word frequencies long before computers could take over such tasks, and formulated what is known as Zipf’s Law.7 When word frequency is plotted on a logarithmic scale, the result is more or less a straight line, with a stable relationship between the popularity of a word and the number of words of similar popularity. The nth most frequently used word appears with frequency 1/n times that of the most frequently used word.” After that authors move to discuss polling and specifically sample selection processes. The last part of chapter is about false information derived from statistics sometime because of statistical illiteracy, but sometimes intentionally for disinformation.
14. Telling Stories Through Models
Here authors look at models and they start with very wise observation:” All models are wrong, but some are useful.” Author discuss such examples of economic models as Adam Smith’s in “Wealth of Nations” and David Ricardo. Then they move close to our time and review efficient market theory, Akerlof’s lemons, and auction theory, eventually explaining why these theoretical constructions do not really work for real live.
15. Rationality and Communication
This chapters starts with comparison of Obama’s and Carter’s decisions to send special forces: one success and another failure. Authors suggest that in reality the difference maybe more depended on luck than on decision makers. After that they present interesting notion of Resulting – situation when evaluation of decision quality depends on result, which may or may not be driven by external circumstances and luck. They also discuss here communicative rationality and collective intelligence, which they differentiate from human intelligence.
This chapter is about need for variety of models and need to challenge narratives, which author illustrate with a number of examples and a great quote from A. Sloan:” ‘Gentlemen, I take it we are all in complete agreement on the decision here. Then, I propose we postpone further discussion of this matter until the next meeting to give ourselves time to develop disagreement, and perhaps gain some understanding of what the decision is all about.’
Part IV: Economics and Uncertainty
17. The World of Finance
In this chapter authors bring another differentiation between models and reality: small-world models in a large world. The point here is that theoretical models are always include assumptions, which to significant degree restrict area of model application define its outcome. In real world there is not such restrictions. Author then provide example of failed model in finance, pension models, and others, demonstrating limits of financial theory.
18.Radical Uncertainty, Insurance and Investment;
This is another detailed look at “science” of economics, specifically in areas of insurance, pensions, and how certainty is not the same as security. Authors complete this with note that volatility is good for investors.
19. (Mis)Understanding Macroeconomics
Here authors move to discuss dangerous combination of macroeconomics with powerful computers that allowed create super complex models very good in creating illusion of understanding reality without possessing such understanding at all. Eventually authors return to important theme of need to understand that economics way too complex for engineering approach, which result in severe mismanagement of economics.
20. The Use and Misuse of Models
Here authors provide examples of failure of models’ use. It starts with prediction for UK coal usage, and quite a few others. More importantly they provided suggestion on how do it properly:”
First, deploy simple models to identify the key factors that influence an assessment. A common response to criticisms of the kind we have described above is an offer to add to the model whatever we think is missing. But this is another reflection of the mistaken belief that such models can describe ‘the world as it really is’. The useful purpose of modelling is to find ‘small world’ problems which illuminate part of the large world of radical uncertainty.
Second, having identified the parameters which are likely to make a significant difference to an assessment, undertake research to obtain evidence on the value of these parameters. For example, what value do rail passengers attach to a faster journey? Quantification can often serve as a reality check even when precise quantification is obviously spurious. The preservation of the beautiful and well-preserved Norman church at Stewkley in England (close to a proposed new high-speed rail line) is worth something, but surely not a billion pounds. Often this kind of calibration is enough to resolve some aspects of a decision.
Third, simple models provide a flexibility which makes it much easier to explore the effects of modifications and alternatives. For example, the WHO demographic model not only diverted attention from the key issue but its complexity made it harder to investigate alternative specifications of the model structure and parameters. Scenarios are always useful in conditions of radical uncertainty. How might this policy decision look in five years’ time – or fifty?
Fourth, under radical uncertainty, the options conferred by a policy may be crucial to its evaluation. Faced with a choice as to which of London’s two major airports, Gatwick or Heathrow, should be chosen for expansion, recognition that the topography of Gatwick allows piecemeal adaptation of the development of facilities in the light of uncertain future demand, while that of Heathrow does not, should be an important factor in the choice. Options may be positive or negative in value – facilitating policies not directly connected to the initial objectives, or excluding possible attractive alternatives.”
Part V: Living with Uncertainty
21. Practical Knowledge
Here authors present what they believe to be proper approach to dealing with uncertainty: treat economics as practical knowledge, meaning measuring validity of theories by practice rather that by opinion of authoritative economists. Consequently, authors discuss use of models as tools with limitations rather as final word of oracle and always apply then to resolve specific practical problems.
22. Adapting to Radical Uncertainty
In this chapter authors discuss how to adapt to prevalence of radical uncertainty in life and provide some guidance:
- Recognize that live is not stationary so no algorithm could provide valid response to all future events.
- Remember that humans are social animals and analyze at the level of groups rather than individuals
- Challenge narratives
Authors also clearly express their believe that groups are better decision makers because groups have more information than individuals.
23. Embracing Uncertainty
In this final chapter authors call to embrace uncertainty by clearly differentiating it from risk and actively hone robustness and resilience, while trying to avoid path dependency. Finally, they link uncertainty with evolution, correctly stating that latter could not exist without former. They also link uncertainty to entrepreneurship.
MY TAKE ON IT:
I think it is useful book and authors very nicely classify different risks, uncertainties, and probabilities. They also define methods of dealing with them and how to apply different approaches for it could have some practical usage. I wish more people were educated in different forms of uncertainty and how to handle it and have much better understanding of models and their use. Maybe if it were the case, we would not have to deal with great many craziness of our time from global warming to universal everything. The one point that I completely disagree with authors is their believe in superiority of group decisions over individual decisions. I believe that there is not such thing as a group decision – all decisions are made by individuals. Sure, multiple people are in possession of more information than one individual and any decision made via interaction of multiple individuals would be better if all this information is taken into account. The problem is that any group is formal or informal hierarchy and an individual at the top of hierarchy is the one who makes “group” decision. It is true that in condition of equal power of a few individuals at the top a decision could be a compromise, but it still would be individual decision of each group member whether to accept of reject somebody’s decision rather than actually participate in decision making.