The website "WTF Happened In 1971?" presents a collection of graphs depicting various socio-economic indicators in the United States, primarily spanning from the post-World War II era to the present day. The overarching implication of the website is that a significant inflection point occurred around 1971, after which several key metrics seemingly diverged from their previously established trends. This divergence often manifests as a decoupling between productivity and compensation, a stagnation or decline in real wages, and a dramatic increase in metrics related to cost of living, such as housing prices and healthcare expenses.
The website does not explicitly propose a singular causative theory for this shift. Instead, it presents a compelling visual argument for the existence of a turning point in American economic history, inviting viewers to draw their own conclusions. The graphs showcase a variety of indicators, including, but not limited to:
Productivity and real hourly wages: These graphs illustrate a strong correlation between productivity and wages prior to 1971, with both rising in tandem. Post-1971, however, productivity continues to climb while real wages stagnate, creating a widening gap. This suggests that the benefits of increased productivity were no longer being equitably distributed to workers.
Housing prices and housing affordability: The website depicts a sharp escalation in housing costs relative to income after 1971. This is visualized through metrics like the house price-to-income ratio and the number of years of median income required to purchase a median-priced house. This indicates a growing difficulty for the average American to afford housing.
Healthcare costs: Similar to housing, the cost of healthcare exhibits a dramatic increase after 1971, becoming a progressively larger burden on household budgets.
Debt levels (both household and national): The website presents graphs showcasing a substantial rise in debt levels, particularly after 1971. This includes metrics like household debt as a percentage of disposable income and the national debt as a percentage of GDP, suggesting a growing reliance on borrowing to maintain living standards.
College costs and college tuition as a percentage of median income: The cost of higher education undergoes a significant increase post-1971, making college less accessible for many.
Income inequality: The website visually represents the growing disparity in income distribution, with the share of wealth held by the top 1% increasing significantly after 1971, further exacerbating the economic challenges faced by the majority of the population.
In essence, "WTF Happened In 1971?" visually argues that a fundamental change occurred in the American economy around that year, marked by decoupling of productivity and wages, exploding costs of essential goods and services like housing and healthcare, and a widening gap between the wealthy and the rest of the population. The website refrains from explicitly attributing this shift to any specific cause, leaving the interpretation and analysis to the observer.
The blog post "Kelly Can't Fail," authored by John Mount and published on the Win-Vector LLC website, delves into the oft-misunderstood concept of the Kelly criterion, a formula used to determine optimal bet sizing in scenarios with known probabilities and payoffs. The author meticulously dismantles the common misconception that the Kelly criterion guarantees success, emphasizing that its proper application merely optimizes the long-run growth rate of capital, not its absolute preservation. He accomplishes this by rigorously demonstrating, through mathematical derivation and illustrative simulations coded in R, that even when the Kelly criterion is correctly applied, the possibility of experiencing substantial drawdowns, or losses, remains inherent.
Mount begins by meticulously establishing the mathematical foundations of the Kelly criterion, illustrating how it maximizes the expected logarithmic growth rate of wealth. He then proceeds to construct a series of simulations involving a biased coin flip game with favorable odds. These simulations vividly depict the stochastic nature of Kelly betting, showcasing how even with a statistically advantageous scenario, significant capital fluctuations are not only possible but also probable. The simulations graphically illustrate the wide range of potential outcomes, including scenarios where the wealth trajectory exhibits substantial declines before eventually recovering and growing, emphasizing the volatility inherent in the strategy.
The core argument of the post revolves around the distinction between maximizing expected logarithmic growth and guaranteeing absolute profits. While the Kelly criterion excels at the former, it offers no safeguards against the latter. This vulnerability to large drawdowns, Mount argues, stems from the criterion's inherent reliance on leveraging favorable odds, which, while statistically advantageous in the long run, exposes the bettor to the risk of significant short-term losses. He further underscores this point by contrasting Kelly betting with a more conservative fractional Kelly strategy, demonstrating how reducing the bet size, while potentially slowing the growth rate, can significantly mitigate the severity of drawdowns.
In conclusion, Mount's post provides a nuanced and technically robust explanation of the Kelly criterion, dispelling the myth of its infallibility. He meticulously illustrates, using both mathematical proofs and computational simulations, that while the Kelly criterion provides a powerful tool for optimizing long-term growth, it offers no guarantees against substantial, and potentially psychologically challenging, temporary losses. This clarification serves as a crucial reminder that even statistically sound betting strategies are subject to the inherent volatility of probabilistic outcomes and require careful consideration of risk tolerance alongside potential reward.
The Hacker News post "Kelly Can't Fail" (linking to a Win-Vector blog post about the Kelly Criterion) generated several comments discussing the nuances and practical applications of the Kelly Criterion.
One commenter highlighted the importance of understanding the difference between "fraction of wealth" and "fraction of bankroll," particularly in situations involving leveraged bets. They emphasize that Kelly Criterion calculations should be based on the total amount at risk (bankroll), not just the portion of wealth allocated to a specific betting or investment strategy. Ignoring leverage can lead to overbetting and potential ruin, even if the Kelly formula is applied correctly to the initial capital.
Another commenter raised concerns about the practical challenges of estimating the parameters needed for the Kelly Criterion (specifically, the probabilities of winning and losing). They argued that inaccuracies in these estimates can drastically affect the Kelly fraction, leading to suboptimal or even dangerous betting sizes. This commenter advocates for a more conservative approach, suggesting reducing the calculated Kelly fraction to mitigate the impact of estimation errors.
Another point of discussion revolves around the emotional difficulty of adhering to the Kelly Criterion. Even when correctly applied, Kelly can lead to significant drawdowns, which can be psychologically challenging for investors. One commenter notes that the discomfort associated with these drawdowns can lead people to deviate from the strategy, thus negating the long-term benefits of Kelly.
A further comment thread delves into the application of Kelly to a broader investment context, specifically index funds. Commenters discuss the difficulties in estimating the parameters needed to apply Kelly in such a scenario, given the complexities of market behavior and the long time horizons involved. They also debate the appropriateness of using Kelly for investments with correlated returns.
Finally, several commenters share additional resources for learning more about the Kelly Criterion, including links to academic papers, books, and online simulations. This suggests a general interest among the commenters in understanding the concept more deeply and exploring its practical implications.
Summary of Comments ( 66 )
https://news.ycombinator.com/item?id=42711781
Hacker News users discuss potential causes for the economic shift highlighted in the linked article, "WTF Happened in 1971?". Several commenters point to the Nixon Shock, the end of the Bretton Woods system, and the decoupling of the US dollar from gold as the primary driver, leading to increased inflation and wage stagnation. Others suggest it's an oversimplification, citing factors like the oil crisis, increased competition from Japan and Germany, and the peak of US manufacturing dominance as contributing factors. Some argue against a singular cause, proposing a combination of these elements along with demographic shifts and the end of the post-WWII economic boom as a more holistic explanation. A few more skeptical commenters question the premise entirely, arguing the presented correlations don't equal causation and that the chosen metrics are cherry-picked. Finally, some discuss the complexities of measuring productivity and the role of technological advancements in influencing economic trends.
The Hacker News post titled "WTF Happened in 1971?" generated a significant amount of discussion, with many commenters offering various perspectives on the claims made in the linked article. While some expressed skepticism about the presented correlations, others offered supporting arguments, additional historical context, and alternative interpretations.
A recurring theme in the comments was the acknowledgment that 1971 was a pivotal year with numerous significant global events. The end of the Bretton Woods system, where currencies were pegged to gold, was frequently cited as a key factor contributing to the economic shifts highlighted in the article. Commenters debated the long-term consequences of this change, with some arguing it led to increased financial instability and inequality.
Several commenters pointed out potential flaws in the article's methodology, suggesting that simply correlating various metrics with the year 1971 doesn't necessarily imply causation. They argued that other factors, such as the oil crisis of the 1970s, increasing globalization, and technological advancements, could have contributed to the observed trends. Some suggested that focusing solely on 1971 oversimplifies a complex historical period and that a more nuanced analysis is required.
Some commenters offered alternative explanations for the trends shown in the article. One commenter proposed that the post-World War II economic boom, driven by reconstruction and pent-up demand, was naturally slowing down by the early 1970s. Another suggested that the rise of neoliberal economic policies, beginning in the 1970s and 80s, played a significant role in the growing income inequality.
Other commenters focused on the social and cultural changes occurring around 1971. They mentioned the rise of counterculture movements, the changing role of women in society, and the increasing awareness of environmental issues as potential factors influencing the trends discussed. Some argued that these societal shifts were intertwined with the economic changes, creating a complex and multifaceted picture of the era.
A few commenters delved deeper into specific data points presented in the article, challenging their accuracy or offering alternative interpretations. For example, the discussion around productivity and wages prompted debate about how these metrics are measured and whether they accurately reflect the lived experiences of workers.
While the article itself presents a particular narrative, the comments on Hacker News offer a broader range of perspectives and interpretations. They highlight the complexities of historical analysis and the importance of considering multiple factors when examining societal shifts. The discussion serves as a valuable reminder that correlation does not equal causation and encourages a critical approach to understanding historical trends.