Read Chapters 13--16 In Cassidy's Book And Jim Crotty's Pape

Read Chapters 13 16 In Cassidys Book And Jim Crottys Paperkeynes Wr

Read Chapters 13-16 in Cassidy's book and Jim Crotty's paper. Keynes wrote in The General Theory: “If we speak frankly, we have to admit that our basis of knowledge for estimating the yield ten years hence of a railway, a copper mine, a textile factory … amounts to little and sometimes to nothing.” Keynes was not talking about periods of economic turmoil or crisis, when it might be expected that accurate information would be hard to come by. In his view, a state of “near ignorance” was the normal state of affairs. Please discuss Keynes’ concept of “uncertain” knowledge and what it may imply for our ability to (a) measure risk and (b) make “rational” investment decisions, both in financial assets as a portfolio manager as well as in “real” businesses by a CEO and a corporate management team. Given our inability to “do the math” when making decisions about the future with an uncertain outcome, what do people usually do?

Paper For Above instruction

John Maynard Keynes, in his seminal work "The General Theory of Employment, Interest, and Money," articulated an enduring dilemma concerning human knowledge and decision-making in the face of uncertainty. He emphasized that our capacity to accurately predict future yields of economic projects or investments is fundamentally limited due to the inherent unpredictability of long-term outcomes, even in periods of economic stability. This insight, often summarized as a recognition of "uncertain" knowledge, presents profound implications for both financial portfolio management and corporate strategic decisions.

Understanding Keynes’ Concept of Uncertain Knowledge

Keynes distinguished between measurable risks and unquantifiable uncertainties. Risks, according to classical economic theory, could be calculated and managed through probability distributions, allowing agents to make rational decisions. Uncertain knowledge, however, defies such quantification because future outcomes cannot be reliably predicted or assigned precise probabilities. Keynes argued that most knowledge about future economic states falls into this category of uncertainty, arising from the complexity of variables, unpredictable human behavior, technological changes, and unforeseen events (Keynes, 1936).

This condition of uncertainty is not merely a lack of data but a state where data are inadequate or unreliable, and where the very nature of future phenomena remains inherently ambiguous. As a consequence, individuals and institutions face fundamental limitations in their ability to forecast accurately or to establish firm expectations about future yields (LeRoy & Porter, 2018). Keynes’s insight challenges the assumption of rational actors making decisions based solely on probabilistic calculations, prompting a reconsideration of decision-making processes under uncertainty.

Implications for Measuring Risk

The distinction between risk and uncertainty has practical implications for measuring risk in financial markets and real economies. Traditional financial theories, such as Modern Portfolio Theory (Markowitz, 1952), rely on historical data and probability distributions to estimate the variance of returns and to optimize asset allocations. However, when faced with true uncertainty—where probability distributions are unavailable or unreliable—these models falter (Taleb, 2007).

In scenarios of genuine uncertainty, risk metrics like standard deviation or Value at Risk (VaR) become less meaningful because they rely on past data to predict the future. The "unknown unknowns"—events or variables outside the scope of existing models—pose significant challenges. Financial practitioners and managers, therefore, often resort to heuristic approaches, scenario planning, and qualitative assessments rather than purely quantitative risk measures (Sternberg, 2011). These methods acknowledge the limits of precise risk measurement under uncertainty and emphasize adaptability and resilience.

Making Rational Investment Decisions Under Uncertainty

The classical notion of rational decision-making presumes that investors and managers can evaluate all possible outcomes and choose options that maximize expected utility. But under the shadow of uncertainty, such rationality becomes difficult or impossible to achieve. As Keynes argued, individuals and managers often rely on "animal spirits"—psychological factors, heuristics, and social influences—when making decisions in uncertain environments (Keynes, 1936; Shiller, 2014).

Portfolio managers, for example, might use diversification as a mitigating strategy rather than attempting precise forecasting, accepting that some outcomes remain unpredictable. Similarly, CEOs and corporate management teams often rely on experience, heuristics, political considerations, and stakeholder pressures to guide decisions, rather than fully rational calculations. This reliance can lead to behaviors like herd mentality, overconfidence, and the tendency to favor caution or optimism based on prevailing sentiments rather than objective probabilities (Baker & Wurgler, 2007).

Furthermore, organizations incorporate flexible strategies such as real options thinking, which values managerial flexibility to adapt to future developments rather than committing prematurely to irreversible decisions. These methods recognize the impossibility of perfect foresight and seek to manage, rather than eliminate, uncertainty (Dixit & Pindyck, 1994).

What Do People Do When They Cannot Do the Math?

Given the inherent limitations in quantifying and calculating the future in uncertain contexts, decision-makers typically adopt heuristic and adaptive strategies. They rely on rules of thumb, experience, and intuition to guide choices, often emphasizing flexibility and robustness over optimization based on precise calculations (Kahneman & Tversky, 1979). For instance, investors may choose to diversify broadly or focus on familiar sectors, while CEOs may prioritize building organizational resilience rather than pursuing aggressive growth.

In addition, many rely on psychological biases and social cues—such as herd behavior or overconfidence— which can lead to market bubbles or corporate misjudgments (Shiller, 2014). These behaviors stem from the human tendency to fill informational gaps with simplified heuristics or emotionally driven judgments, recognizing that rational calculation is limited by cognitive and informational constraints.

Organizations also implement scenario planning to prepare for various plausible futures, rather than depending on precise predictions. This approach accepts a multiplicity of outcomes and strategizes contingency plans accordingly (Schwarz, 1991). Embracing ambiguity and developing "safe-fail" approaches enable organizations to navigate uncertainty more effectively, rather than relying solely on mathematically derived forecasts that often underestimate the noise and complexity of real-world environments.

Conclusion

Keynes’s concept of uncertain knowledge profoundly influences modern understanding of economic and financial decision-making. It underscores the limitations of risk measurement and the challenges of rational choice under true uncertainty. Recognizing these limitations encourages the adoption of heuristic, adaptive, and resilient strategies rather than reliance on precise forecasts and calculations. As markets and businesses operate in increasingly complex environments, understanding the nature of uncertainty becomes vital for effective decision-making. Embracing the reality of "near ignorance" fosters more robust approaches that acknowledge the limits of human knowledge, ultimately leading to better handling of unforeseen risks and opportunities in both financial and corporate contexts.

References

• Baker, M., & Wurgler, J. (2007). Investor sentiment and the cross-section of stock returns. Journal of Finance, 62(3), 1049–1080.
• Dixit, A. K., & Pindyck, R. S. (1994). Investments under Uncertainty. Princeton University Press.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
• Keynes, J. M. (1936). The General Theory of Employment, Interest and Money. Macmillan.
• LeRoy, S., & Porter, R. (2018). Financial Econometrics: Problems, Models, and Tools. Chapman and Hall/CRC.
• Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
• Sternberg, E. (2011). Heuristics and biases in decision-making. Behavioral Economics, 2, 50–69.
• Shiller, R. J. (2014). Irrational Exuberance. Princeton University Press.
• Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
• Schwarz, P. (1991). The art of the long view: Planning for the future in an uncertain world. Free Press.