Uncertainty in Economics

11 Key excerpts on "Uncertainty in Economics"

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  Foundations of Trusted Autonomy

  • Jason Scholz, Hussein A. Abbass, Darryn J. Reid(Authors)
  • 2018(Publication Date)
  • Springer Open

    (Publisher)

  The economic literature goes even further, however, by also proposing an ontological notion of uncertainty that asserts that, beyond limits to knowing, questions about outcomes pertaining to agent decision-making are simple unanswerable at all. 21.4 Epistemic and Ontological Uncertainty To see how uncertainty plays out in economic settings, consider the essential problem of human agents operating in an economy: they must make investment choices that will play out a future they can neither predict nor really control. Knight [ 27 ] famously formalised a distinction between risk and uncertainty on the basis that economic agents operating in a dynamic environment must do so with imperfect knowledge about the future. Knight distinguished risk, as applying to the situation when the outcome is unknown yet the chances are measurable, from uncertainty, when the information needed to measure the chances cannot be known in advance of the outcome; we do not have a sufficiently long history with the system as it currently stands to be able to establish a measure. Knight maintained that risk can be converted into an effective certainty 2 ; the practice of setting hurdle rates as the rate of return on the next best option having a comparable risk profile as a mechanism for soft capital rationing is one example of this kind of conversion of a risk into a cost. In Knight’s conception, uncertainty is distinct from stochastic risk in that it is not amenable to measurement and consequently cannot be meaningfully converted to a cost in this manner. Note that this conception of uncertainty effectively raises to a more general setting the first objection that was discussed earlier in relation to Bayesian assumption of a prior, which insists that a measure can be defined over the space of possible relevant states. 3 Knight’s uncertainty is epistemological: we lack knowledge of what future outcomes might occur, but at least we can be aware that we lack it.

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  Econophysics and Capital Asset Pricing

  Splitting the Atom of Systematic Risk

  • James Ming Chen(Author)
  • 2017(Publication Date)
  • Palgrave Macmillan

    (Publisher)

  Chen, Econophysics and Capital Asset Pricing, Quantitative Perspectives on Behavioral Economics and Finance, DOI 10.1007/978-3-319-63465-4_10 190 J.M. CHEN “uncertainty” for short—is traceable to Frank Knight’s 1921 book, Risk, Uncertainty, and Profit. 5 Whereas “‘risk’ means … a quantity suscep- tible of measurement,” Knight treated truly “unmeasurable … ‘uncer- tainty’ … of [a] non-quantitative type” as a “radically distinct” category. 6 Epistemic probability promises “accurate foreknowledge of the future [through] quantitative knowledge of the probability of every possible outcome [that] can be had.” 7 By contrast, imperfectly informed agents “perceive the world before [they] react to it, and [they] react not to what [they] perceive, but always to what [they] infer.” 8 Value “in a world of change …, and a world of uncertainty” arises from “action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge.” 9 Commenting on his own General Theory of Employment, Interest, and Money, 10 John Maynard Keynes endorsed a similar definition of uncertainty: By “uncertain” knowledge, … I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty; nor is the prospect of a Victory bond being drawn. Or, again, the expectation of life is only slightly uncertain. Even the weather is only moderately uncertain. The sense in which I am using the term is that in which the prospect of a European war is uncer- tain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention, or the position of private wealth own- ers in the social system in 1970. About these matters there is no scientific basis on which to form any calculable probability whatever.

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  Spatio-Temporal Methods in Environmental Epidemiology

  • Gavin Shaddick, James V. Zidek(Authors)
  • 2015(Publication Date)
  • Chapman and Hall/CRC

    (Publisher)

  Chapter 3 The importance of uncertainty 3.1 Overview Uncertainty is a topic that permeates all scientific inquiry and its importance is mag-nified when the results are applied in decision-making, which in this setting will involve legislation, regulations and designing public policy. Despite the general importance of the concept of ‘uncertainty’, its meaning lacks a universally agreed on definition. In fact it shares its general lack of definition with ‘information’, as described by the late Debabrata Basu (Basu, 1975): “But what is information? No other concept in statistics is more elusive in its meaning and less amenable to a generally agreed definition.” It has been described in various ways including ‘incomplete knowledge in relation to a specified objective’ which arises ‘due to a lack of knowledge regarding an unknown quantity’ (Bernardo & Smith, 2009). However, the lack of a clear cut definition has not stopped people from taxonomizing it! Thus we have for example the distinction between aleatory (stochastic) and epistemic (subjective) uncertainty (Helton, 1997). It seems generally agreed that some aspects of uncertainty are quantifiable while oth-ers are inherently qualitative, that is not subject to quantification. The latter would, for example, include framing the problem to be investigated by defining the system boundaries and explicating the role of values (van der Sluijs et al., 2005). Both qual-itative and quantitative aspects of uncertainty need to be taken into account within environmental risk analyses. There will often be intangible sources of uncertainty which will arise through the subjective judgements that are sometimes required to estimate the nature and mag-nitude of empirical quantities where other methods are not appropriate. Uncertainty may arise as a result of imprecise language in describing the quantity of interest and disagreement about interpretation of available evidence.

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  Philosophy and the Precautionary Principle

  Science, Evidence, and Environmental Policy

  • Daniel Steel(Author)
  • 2014(Publication Date)
  • Cambridge University Press

    (Publisher)

  Furthermore, rather than being defined as the absence of probability, uncertainty is often quantified by means of a probability distribution. In cost–benefit analysis, for instance, it is not unusual to first generate predictions of costs and benefits with a deterministic model. Uncertainty is then taken into account by introducing a probability distribution over some important inputs or parameters of the model and comparing the results of the probabilistic model to the original deterministic one. This practice is illustrated by some of the economic 100 Scientific uncertainty analyses of climate change mitigation discussed in section 2.3 (see Nordhaus 2008, pp. 123–5). I think Aven is entirely correct that, in ordinary usage, uncertainty is understood as an aspect of risk rather than something that exists only when risk is absent. In addition, the decision-theoretic definition of uncertainty departs from the ordinary concept in several other significant ways. First, the decision-theoretic definition makes knowledge of all possible outcomes of potential actions a necessary condition of uncertainty. Yet it would surely be odd to assert, for instance, that the effects of climate change mitigation are not uncertain because there may be some outcomes that have not been anticipated. To the contrary, the potential for surprises is more naturally regarded as a factor that intensifies uncertainty. Second, knowledge of prob- ability of outcomes is not normally regarded as sufficient for eliminating uncertainty. For example, one is uncertain which slot in a roulette wheel the ball will land in, despite knowing that the ball has a uniform proba- bility of 1/38 of landing in each. The practice in risk analysis of treating probability as a means for quantifying uncertainty seems much closer to ordinary understanding in this regard.

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  Economic Reform Now

  A Global Manifesto to Rescue our Sinking Economies

  • H. Flassbeck, P. Davidson, J. Galbraith, R. Koo, Jayati Ghosh(Authors)
  • 2017(Publication Date)
  • Palgrave Macmillan

    (Publisher)

  CHAPTER 1 Uncertainty and Austerity Policy Paul Davidson Decision-Making in Economics The economy is a process in historical time. Time is a device that prevents everything from happening at once. The production of commodities takes time; and the consumption of goods, espe- cially durables, takes considerable time. Economics is primarily the study of how households and firms make decisions regard- ing production and consumption activities that have an outcome (payoff) occurring at some time in the future. Any explanation of the behavior of economic decision-makers, therefore, requires the analyst to make some assumptions regard- ing (a) what decision-makers expect to be the future outcome of any decision they make today; and (b) whether those expectations will be met. This is most obvious in decisions about investment in plant and equipment, where the realized rate of return will be achieved, and therefore known, only years after the decision to invest is made. Once the decision is made, the decision-maker is stuck with the investment over its useful life. Investment in 2 ● Paul Davidson plant and equipment is like marriage in a conservative society— investor and investment united “till death do them part.” Will the rate of return actually made over the life of the invest- ment be the same as that the entrepreneur anticipated at the moment the investment decision was made? And how did the entrepreneur initially arrive at his/her expected future return? A majority of economists assume that uncertainty about the future can be measured by an objective probability distribution that can be identified today from existing market data. These analysts are essentially assuming that investors have “rational expectations” and therefore the ability to know the future with actuarial accuracy.

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  Foundations for New Economic Thinking

  A Collection of Essays

  • S. Dow(Author)
  • 2012(Publication Date)
  • Palgrave Macmillan

    (Publisher)

  We consider how both economists and economic actors can deal with uncertainty, introducing partial, provisional closures in order to con- struct knowledge about an open system. We conclude by taking further the explicit question of monetary policy-making under uncertainty. The nature and source of uncertainty By uncertainty, we mean here unquantifiable risk, although quantifi- able risk is often referred to in economics as uncertainty. If risk is quan- tifiable, we can insure against it. It is of limited interest because it allows the focus to continue to be on the core prediction. Thus, much of the macroeconomics which provided the foundation for policy advice, for a long time, effectively ignored the size of error variance. As long as 1 See Aikman et al (2010) for a recent discussion of the increasing frequency of references to uncertainty in central bank publications. The Issue of Uncertaint y in Economics 199 the error term had zero mean and it was normally distributed, the sto- chastic nature of the system could effectively be ignored, and certainty equivalence assumed. However, greater quantifiable risk is relevant to decision-making when the potential loss arising from outlying outcomes is taken into account. Indeed, much of the monetary policy literature dating from Brainard (1967) and Poole (1970) focused on the significance of higher variance in the error terms of equations representing the transmission of monetary policy. But as attention to Brainard’s idea of parametric (model) uncertainty increased, the robust control theory solution was found in ever-more elaborate structures for the error term, that is, con- tinuing to allow quantification of uncertainty as risk (see further Dow, 2004b). Quantifiable risk more generally has been the main focus of econom- ics, rather than unquantifiable risk, because of the attractions of math- ematical formalism.

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  Fundamental Uncertainty

  Rationality and Plausible Reasoning

  • Silva Marzetti Dall'aste Brandolini, Silva Marzetti Dall''aste Brandolini, R. Scazzieri(Authors)
  • 2016(Publication Date)
  • Palgrave Macmillan

    (Publisher)

  It was essential to analyse them in order to be able to understand the evolution of the actual economy. An example of such a situation would be the formulation of a plan of action based on the proposition ‘investing in technology y is profitable’. In the absence of any prior quantitative knowledge or experience of the operation of technology y, the businessman’s evaluation of the returns to be earned from adopting the technology can be based only on personal intuition. Knight contrasted such cases with what he called ‘risk’, a situation in which there was ‘measurable uncertainty’ (see Knight, 1921, pp. 224–5). Here it was possible to formulate ‘a priori probabilities’ (determined mathematically) or ‘statistical probabilities’ (determined by empiri- cal observation of frequency of occurrence). By drawing this sharp distinction between risk and uncertainty, Knight sought to highlight the important characteristics of uncertainty that he believed standard theory had neglected. Thus, Knight’s concerns are very similar to what we have identified as Keynes’s attempt to outline a more ‘general’ approach to decision making in the face of uncertainty as applying to cases in which the future is not perfectly known so that probabilities can be defined over the set of all possible results. 1 Keynes makes a distinction between events that are uncertain and events that are only probable in his famous article in the 1937 Quarterly Journal of Economics: By ‘uncertain’ knowledge, let me explain, I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty .... The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention .... About these matters, there is no scientific basis on which to form any calculable probability whatever.

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  Uncertainty and Economics

  A Paradigmatic Perspective

  • Christian Müller-Kademann(Author)
  • 2019(Publication Date)
  • Routledge

    (Publisher)

  Objective processes such as lotteries lack this reflexive-transformative aspect and must therefore not be targeted by economic policies. Consequently, research should advise what kind of problem causes an economic slump: is it resulting from a lack of confidence or from bad luck?

  In conclusion, the two-stage concept of decision making under uncertainty offers a range of empirical tests that may shed light on actual decision making. A reasonable set of research issues would address the validity of Keynes’s (1936) two-step view, the factors affecting the level of confidence, means for separating risk from uncertainty and general measures of confidence.

  The biggest advantage of the new perspective, therefore, is the opportunity to investigate human behaviour by asking open questions, that is, without having to know the “correct” answers in advance.

  2.4.4 Elements of an uncertainty paradigm

  If one would compile a list of paradigmatic hypotheses the items subjective rationality, subjective utility maximisation, risk (ambiguity) and equilibrium (optimality, ergodicity) would certainly be considered indispensable (see 2.3.2). Since uncertainty dominates reality, replacing risk with uncertainty is imperative for economics to be, remain or become a relevant social science depending on the degree of one’s pessimism about economics’ achievements at large.

  Substituting risk with uncertainty will affect the paradigms of economics in more than one way. First of all, with uncertainty, economic analysis will inevitably become more empirical as it will be forced to move away from the simplistic perspective of objectively knowable event spaces implied by probability distribution functions and theory-driven equilibria. Instead of objectively defined equilibria, equilibrium must be shown to emerge

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  Intermediate Microeconomics and Its Application

  • Walter Nicholson, Christopher Snyder(Authors)
  • 2021(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  Figure 5.5 Choices by Individual Investors Copyright 2022 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 152 PART 3 ● Uncertainty and Strategy SUMMARY In this chapter, we have briefly surveyed the economic theory of uncertainty and information. From that survey, we reached several conclusions that have relevance throughout the study of microeconomics. ● In uncertain situations, individuals are concerned with the expected utility associated with various outcomes. If individuals have a diminishing marginal utility for in-come, they will be risk averse. That is, they will generally refuse bets that are actuarially fair in dollar terms but re-sult in an expected loss of utility. ● Risk-averse individuals may purchase insurance that al-lows them to avoid participating in fair bets. Even if the premium is somewhat unfair (in an actuarial sense), they may still buy insurance in order to increase utility. ● Diversification among several uncertain options may reduce risk. Such risk spreading may sometimes be costly, however. ● Buying options is another way to reduce risk. Because the buyer has the right, but not the obligation, to complete a market transaction on specified terms, such options can add flexibility to the ways people plan in uncertain situations. Options are more valuable when the expected value of the underlying market transaction is more valu-able, the value of that transaction is more variable, and the duration of the option is longer. ● A final way to reduce risk is to obtain more precise infor-mation about the future.

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  Harvard Business Essentials, Decision Making

  5 Steps to Better Results

  • (Author)
  • 2005(Publication Date)
  • Harvard Business Review Press

    (Publisher)

  The Uncertainty Problem Key Topics Covered in This Chapter • A three-step process for dealing with decision uncertainty • Business tactics for dealing with uncertainty • How and when to follow your intuition How to Deal with Unknowns 7 I f you are like most people, uncertainty—that is, risk— is a major impediment to making good decisions—or to making decisions at all. Every decision involves a trip through foggy patches of uncertainty because decisions are about the future, an unwritten story for which there are no facts. Most of us rely on what we know about the past to provide in-sights into the future. What we know of the past and the present can help us understand where we are, where we have been, and what the general trajectory of our journey looks like. But the past and present provide nothing more than hints about the future. As Coleridge put it, “History is a lantern at the stern of a ship, revealing only where it has been,” casting only a dim light on the course ahead. Consider these typical examples of decision uncertainties: Yes, we can raise our prices, but how will our customers respond? Should I order one thousand units or five thousand? I’ll get a volume discount on the larger order, and that will save me money on every unit.That’s certain. But I’m not sure I can sell five thousand without cutting the price. We like this strategic plan. It seems sound, and it will differentiate us in a way that customers will appreciate. But our competitors aren’t stupid; they won’t sit on their hands and let us run all over them. Can anyone tell me what our competitors are likely to do to counter our new strategy? Perhaps they are already making moves we don’t know about. Given the current exchange rate, I should buy a $100,000 ten-year U.S.Treasury note. My euros can buy more in the United States for less than they could last year. But if the U.S. dollar continues to weaken against the euro, the buying power of my interest income here in Europe will drop with it.

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  Real R & D Options

  • Dean Paxson(Author)
  • 2002(Publication Date)
  • Butterworth-Heinemann

    (Publisher)

  Chapter 3 Investment under economic and implementation uncertainty ANDRIANOS E. TSEKREKOS SUMMARY Some investment decisions are exposed to uncertainty over their implementation phase apart from the underlying economic uncer-tainty. We provide a general way of introducing implementation uncertainty, which includes prior research as a special case. The generality of our treatment stems from the fact that implementa-tion uncertainty is allowed to affect both the level and the timing of project profitability. In a case explicitly addressed, implementation uncertainty might even cause earlier investment if the probability of uncertainty resolution exceeds the opportunity cost of delaying invest-ment. Investment will be earlier, the higher the effect of uncertainty resolution on project profitability. 3.1 INTRODUCTION When a firm is contemplating entry into a new market or investment in a research project, its decision must be made in an uncertain environment and in most cases it entails costs, which are at least partly irreversible. Uncertainty arises from the stochastic nature of the economic value of the investment. Since the return to a new product design or production process is derived from product market profitability, the value of the investment is affected by fluctuations in expected cash flows or market demand. On the other hand, R&D or investment expenditures may be sunk costs either because of the specificity of their nature in a particular firm/industry or because of what is termed the ‘lemons’ effect (see Akerlof, 1970). Economic and implementation uncertainty 31 A growing line of research known as ‘real options’, by exploiting the analogy between real and financial investment decisions, has stressed the fact that uncer-tainty and irreversibility give rise to option values which must be taken into account when making optimal investment/entry decisions.

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