Abstract
During the first part of the twentieth century, the theme of expectation formation was considered of secondary importance in economic theory. In most economic models, such as the input-output model, economic agents (producers, consumers, etc.) were assumed to react mechanically and passively to stimuli coming from endogenous economic shocks and/or government policies. One (notable) exception in the treatment of expectations in the first half of the century is delivered by Keynes (1936). He assumes that in order to optimize the level of output, entrepreneurs have to form long-term expectations on the return of investment. Defining expectation formation as a tool to establish grounds for belief leading to an action, Keynes had a different perspective on the use of probability associated with uncertainty. His view was that while quantified probability based on frequency distributions was a special case, the general case was uncertainty, where even nonquantifiable (ordinal) probability may not be identifiable. However, Keynes introduced the determinant of confidence on the outcomes of an event through the notion of weight of argument, where weight is generally higher, the higher the amount of relevant evidence brought to bear. But Dow (1995) noted that what is relevant is itself theoretically loaded, and theoretical understandings can change. Also, there is no direct correspondence between degree of confidence and degree of probability, so there may, for example, be high confidence that an outcome has low probability, and vice versa. What this leads to are two folds in the path: (1) entrepreneurs’ optimistic expectations and a willingness to ignore the uncertainty surrounding these expectations, and this is the meaning of Keynes’ animal spirits, and (2) public institutional conventions that promote public-spirited behavior, which would address the consequences of uncertainty in the economy and in society, albeit on the basis of uncertain knowledge (Davis 1994).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Neuroeconomists argue that the function of the brain is not likely to contain a single “reasoning” module, such as in the expectation formation process, and suggest that there are two, not one, mechanisms in the process of judging the likelihood of an event—some explicit, driven by controlled processes, others implicit driven by automatic processes (CLP, 45). This view is further supported by other experts in the field of neural/psychology. In his paper on neural mechanisms for economics, Craver (Craver Carl F. (2008), No Revolution Necessary: Neural Mechanisms for Economics, Economics and Philosophy, 24, 381–406, Cambridge University Press) wrote:
Many decision-making tasks, such as evaluation of utility , judgments of probability, choice, etc., straddle these quadrants. Moreover, automatic and affective dimensions, scientists are finding, influence judgment and behavior much more than one would expect given the standard economic theory’s emphasis on controlled and cognitive processes. For example, judgments about whether or not one agrees with a given editorial, how probable an event is, whether a given stimulus is positive or negative, whether consumption should be delayed or not, and many others, all take place at the intersection of several of the four quadrants (CLP). Yet only one of these processes, the controlled cognitive one, is captured by the expected utility theory.
- 2.
The “conjunction fallacy” in Linda’s problem defies the probability axiom which says in mathematical terms that the probability of combined events occurring together is always less than that of a single event.
- 3.
Herrmann, N. <http://www. Herrmannsolutions.com/what-is-whole-brain-thinking-2/>.
- 4.
“The name ‘theory of possibility’ was coined by Zadeh in 1978, in Fuzzy Sets and Possibility Theory”. Journal of Artificial Intelligence.
- 5.
Cf. Shafer (1976).
- 6.
The starting point is a unique representation of π by a possibility distribution function π: X → [0,1] via the formula π(A) = maxx ∈ A π(x), where the possibility of a state or an outcome A is the maximization of the possibility of x which is an element of A. If the possibility of a state π(s) = 0, it means that state s is rejected as impossible; if π(s) = 1, it means that state s is totally possible (= plausible). If, for example, the degree of plausibility x = s and denoted πx(s), and πx(s) = 0 iff x = s is impossible, then it is a total surprise as the surprise measure 1 − πx(s) = 1. But if πx(s) = 1 if x = s is normal and fully plausible, then it is unsurprising, though there is no certainty (Dubois).
For a simple example “does event A occur?”, where A is a subset of state S = [0,1], the possibility and necessity functions are: Π(A) = supμ ∊ Aπ(μ) and N(A) = inf s ∉ A 1 − π(μ), where Π(A) evaluates to what extent A is consistent with supremum value of π, while N(A) evaluates to what extent A is certainly implied by infimum value of π. The possibility-necessity duality is expressed by N(A) = 1 − Π(Ac), where Ac is the complement of A.
- 7.
Cf. Frydman and Goldberg (2013).
- 8.
Rajan, Raghuram, an IMF economist at the time, who correctly warned about the financial market before its crash in 2008.
- 9.
Cf. for example Ernan et al. (2007).
References
Betsch, T. 2016. The Nature of Intuition and Its Neglect in Research on Judgment and Decision Making. https://www.researchgate.net/profile/Tilmann_Betsch/publication/267701225_The_Nature_of_Intuition_and_Its_Neglect_in_Research_on_Judgment_and_Decision_Making/links/54d09c750cf29ca81101ed68.pdf
Camerer, C., G. Loewenstein, and D. Prelec. 2005. Neuroeconomics: How Neuroscience Can Inform Economics. Journal of Economic Literature 43 (1): 9–64.
Cosmides, L., and J. Tooby. 1994. Better than Rational: Evolutionary Psychology and the Invisible Hand. The American Economic Review 84 (2): 327–332.
Davis, J.B. 1994. Keynes’s Philosophical Development. Cambridge: Cambridge University Press.
Dow, S. 1995. Uncertainty about Uncertainty. In Keynes, Knowledge and Uncertainty, ed. S. Dow and J. Hillard. Aldershot: Edward Elgar.
Earl, P. 2012. Kahneman’s Thinking, Fast and Slow from the Standpoint of Old Behavioral Economics. https://shredecon.files.wordpress.com/2012/06/hetsa-2012-earl.pdf.
Ernan, H., Y. Lahav, and C.N. Noussair. 2007. Traders’ Expectations in Asset Markets: Experimental Evidence. American Economic Review 97 (5): 1901–1920.
Friedman, M. 1968. The Role of Monetary Policy. American Economic Review 58 (1): 1–17.
Frydman, R., and M. Goldberg. 2011. Beyond Mechanical Markets: Asset Price Swings, Risk, and the Role of the State. Princeton, NJ: Princeton University Press.
———. 2013. The Imperfect Knowledge Imperative in Modern Macroeconomics and Finance Theory. In Rethinking Expectations: The Way Forward for Macroeconomics, ed. R. Frydman and E. Phelps. Princeton University Press.
Fuster, A., D. Laibson, and B. Mendel. 2010. Natural Expectations and Macroeconomic Fluctuations. Journal of Economic Perspectives 24 (4): 67–84.
Harteis, C., and H. Gruber. 2007. Intuition and Professional Competence: Intuitive versus Rational Forecasting of the Stock Market. Vocations and Learning 1 (1): 71–85.
Kahneman, Daniel. 2013. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
Kahneman, D., and D. Miller. 1986. Norm Theory: Comparing Reality to Its Alternatives. Psychological Review 93 (2): 136.
Kahneman, D., and A. Tversky. 2002. Extensional Vs. Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment. In Heuristics and Biases: The Psychology of Intuitive Judgment, ed. T. Gilovich, D. Griffin, and D. Kahneman. Cambridge University Press.
Keynes, J.M. 1936. The General Theory of Employment, Interest and Money. London: Macmillan.
Levine, D., ed. 2012. Is Behavioral Economics Doomed? Cambridge: Open Book Publishers.
Mallard, G. 2016. Bounded Rationality and Behavioral Economics. Routledge.
Malmendiery, U., and S. Nagelz. 2013. Learning from Inflation Experiences. NBER and CEPR.
Muth, John F. 1961. Rational Expectations and the Theory of Price Movements. Econometrica 29 (3): 315–335.
Nerlove, M. 1958. Adaptive Expectations and Cobweb Phenomena. Quarterly Journal of Economics 72 (2): 227–240.
Phelps, E. 2008. Our Uncertain Economy. Wall Street Journal.
Ritholtz, B. 2011. Shiller: More Expectations Theory, Less Efficient Market Hypothesis. http://ritholtz.com/2011/06/shiller-more-expectations-theory-less-efficient-market-hypothesis/
Shackle, G. 1966. The Nature of Economic Thought. Cambridge University Press.
Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton: Princeton University Press.
Soros, G. 2013. Fallibility, Reflexivity, and the Human Uncertainty Principle. Journal of Economic Methodology 20 (4): 309–329.
Syll, L.P. 2012. David K. Levine is Totally Wrong on the Rational Expectations Hypothesis. Real-World Economics Review Blog. https://rwer.wordpress.com/2012/02/16/david-k-levine-is-totally-wrong-on-the-rational-expectations-hypothesis
Zadeh, L.A. 1978. Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems 1 (1): 3–28.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Ghisellini, F., Chang, B.Y. (2018). How Do People Form Expectations in the Real World?. In: Behavioral Economics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-75205-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-75205-1_6
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-319-75204-4
Online ISBN: 978-3-319-75205-1
eBook Packages: Economics and FinanceEconomics and Finance (R0)