Perils of first order thinking

From Marks to Mauboussin

Picture this: It's the late 1950s, and Mao Zedong, the leader of China, is on a mission to rapidly transform his country from an agricultural society into an industrial one. One of the ways he hopes to achieve this is through the Four Pests Campaign, a large-scale attempt to eradicate four types of pests that were believed to be harming agricultural productivity: rats, flies, mosquitoes, and sparrows.

Let's focus on the campaign's targeting of sparrows, which were believed to eat crops and thus harm agricultural productivity. The Chinese were encouraged to kill sparrows, and as a result, millions of sparrows were killed. But here's the thing: sparrows also eat insects, especially locusts and without sparrows to control the insect population, insect populations exploded. This led to widespread crop damage and decreased agricultural productivity which in turn contributed to a famine that is estimated to have killed more than fifteen million people in China. (Maybe more)

It's hard to overstate the magnitude of this policy failure. The Four Pests Campaign was meant to improve agricultural productivity, but it ended up doing the opposite and giving rise to the worst ecological disaster ever!

In a similar way, policymakers who rely solely on quantitative analysis without considering the potential for unintended consequences risk making the same mistake as Mao's campaign. In the case of Medicare, policymakers relied on actuarial studies to estimate the cost of the program but failed to consider the incentive effects of the program's design. They created a reimbursement system that paid doctors and hospitals based on a percentage of their costs, which incentivized these providers to offer more services to patients, whether or not they were truly necessary. This was great for the hospitals and doctors, but bad for the patients and the taxpayers who footed the bill. By failing to think through the full consequences of their policy decisions, the policymakers created a system that ultimately cost far more than they had anticipated.

Investor and writer Howard Marks beautifully highlights the dangers of relying on first-order thinking in his book, The Most Important Thing.

First-level thinking says, “It’s a good company; let’s buy the stock.” Second level thinking says, “It’s a good company, but everyone thinks it’s a great company, and it’s not. So the stock’s overrated and overpriced; let’s sell.”

First-level thinking is simplistic and superficial, whereas second-level thinking is deep, complex, and convoluted. A related concept to second-order thinking is "expectation investing," popularized by Michael Mauboussin. This approach involves focusing on what the market is assuming, rather than just one's own opinions. It forces investors to think in terms of second-order thinking, as they need to consider not just what they think about a company or asset, but also what the market is expecting. Over time Ive realized that quite a few tools that may seem mundane, actually force us to think through consequences, which in turn try to help us minimize first-level thinking.

1. Investment checklists ensure that investors don't miss something obvious, forcing them to double-check their analysis.

2. Scenario and sensitivity analysis helps investors prepare for potential risks and uncertainties, forcing them to think through what could go wrong.

3. Porter's Five Forces help investors better understand competitive advantage, and view a business from different perspectives.

4. A reverse DCFs encourage investors to consider what the market is assuming rather than trying to value something.

These tools and frameworks force investors to think through consequences and help them avoid the pitfalls of first-order thinking. For more on this, I recommend you check out Howard Marks's book or Peter Bevelin’s book “From Darwin to Munger”.

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