Posts tagged ChatGPT
Pro-natal fertility policies and eugenics

I am reading this volume at the moment, which presents fertility case studies across a number of countries. It was published in 2015, so it is a bit out of date relative to the past decade’s ongoing and broadening fertility decline. But I was struck by the chapter on Singapore and the initial phase of the country’s pro-natal policies in the mid-1980s, which were strongly influenced by so-called positive eugenics. From the relevant chapter:

“ (… ) in 1984, the state implemented selective pro-natalist policies, described as the “eugenic phase” of Singapore’s population policy. Educated women were given incentives to reproduce under the “Graduate Mother Scheme,” while sterilization cash incentives were offered to less educated women.”

As the authors—Gavin W. Jones and Wajihah Hamid—go on to explain, this overtly eugenic policy was quickly abandoned. Still, it made me to think more broadly about the extent to which eugenics have driven pro-natal policies, either overtly or implicitly, since WWII, and what this history can tell us about modern policy design as governments attempt to raise birth rates for women facing different opportunity costs of childrearing depending on educational attainment, and opportunities in the labour force.

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Things to think about #10 - Ukraine, the Endgame, and Coding with AI

Glenn Loury and John McWhorter are at their best when they disagree, and I enjoyed their discussion about the disastrous exchange between Trump, Vance, and Zelensky at the White House. Both agree that the U.S. is right to push for a negotiated settlement, which involves pressuring Ukraine to acknowledge its precarious position. However, they diverge on how this pressure was communicated and its potential repercussions. Glenn argues that Trump and the vice president rightly prioritized American interests by applying pressure on Zelensky, while John takes the opposite stance, framing his argument within a broader critique of the U.S. president and his administration.

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The BIS gets it wrong on AI/LLM and feminism & reproduction

The BIS has a Bulletin out on the usefulness of AI and large language models. They’re not terribly impressed.

When posed with a logical puzzle that demands reasoning about the knowledge of others and about counterfactuals, large language models (LLMs) display a distinctive and revealing pattern of failure. 

The LLM performs flawlessly when presented with the original wording of the puzzle available on the internet but performs poorly when incidental details are changed, suggestive of a lack of true understanding of the underlying logic. 

Our findings do not detract from the considerable progress in central bank applications of machine learning to data management, macro analysis and regulation/supervision. They do, however, suggest that caution should be exercised in deploying LLMs in contexts that demand rigorous reasoning in economic analysis.

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Stock market signals with Chat GPT 4

This is my third use case for how to do quantitative analysis with Chat GPT 4. The two others, on Eurozone inflation and times series regression with macro data, can be found here and here. I started in the industry as Head of Research for Variant Perception, a research shop that specialises, among other things, in quantitative trading models, asset allocation tools, and trade signalling analysis. One tool that came up again and again in my analyses was binary signals to identify turning points in asset classes, stocks or economic data series. The idea is simple. First, you create a binary indicator which takes the value of 1, if a certain threshold in the data is breached to the upside or downside, and zero otherwise. Secondly, you investigate what happens after such a signal has gone off, either in the original data set or mapped to a separate data set. You can combine signals across datasets to get a rolling series of signals, which can be compared to asset prices or economic data. You can see an example of such an analysis with the Nasdaq here.

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