Chat Transcripts from Property Experiments

I’m reading The Property Species by Bart Wilson. I like chapter 4 “What is Right is Not Taken Out of the Rule, but Let the Rule Arise Out of What Is Right,” partly because I got to play a small part in this line of research.

Along with several coauthors, Bart Wilson has run experiments in which players have the ability to make and consume goods. According to the instructions that all players read at the beginning of the experiment, “when the clock expires… you earn cash based upon the number of red and blue items that have been moved to your house.”

Property norms can emerge in these environments, and sometimes subjects take goods from each other in an action that could be called “stealing.” The experimental instructions do not contain any morally loaded words like “stealing,” but subjects use that word to describe the activities of their counterparts.

Here is a conversation from the transcript of the chat room players can use to communicate while they produce and trade digital goods:

E: do you want to do this right way?

F: wht is the right way

E: the right way is I produce red you make blue then we split it nobody gets 100 percent profit but we both win

F: tht wht I been doing then u started stealing

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Chesterton Views on Work in 20th Century America

One hundred years ago, the British writer G.K. Chesterton traveled to the United States for a lecture tour. He published his observations of America in What I Saw in America (1922). In an essay titled “The American Businessman”, Chesterton notes with surprise how passionate Americans appear about their professional work.

Chesterton recognizes this enthusiasm for work as more than mere greed.

This is the intro to my latest article for the OLL Reading Room. I discuss the American work ethic and Chesterton’s prescient insight into American economic dynamism compared to Britain. (Relatedly, Alex on British stagnation this week.)

Here’s a fun bit of the book that I didn’t include in the OLL article. Chesterton wrote this about seeing New York City for the first time:

But there is a sense in which New York is always new; in the sense that it is always being renewed. A stranger might well say that the chief industry of the citizens consists of destroying their city; but he soon realises that they always start it all over again with undiminished energy and hope. At first I had a fancy that they never quite finished putting up a big building without feeling that it was time to pull it down again; and that somebody began to dig up the first foundations while somebody else was putting on the last tiles. This fills the whole of this brilliant and bewildering place with a quite unique and unparalleled air of rapid ruin. 

Interested New Yorkers can find the rest online at Project Gutenberg. Delightful throughout if you like history. Amazon link to the book here.

Highlights from ASSA 2023

I expected the meetings would shrink, but I was still surprised by how much they did:

That said, I mostly didn’t notice the smaller numbers on the ground, because most of the missing people are those on the job market, who used to spend most of their time shut away doing interviews anyway. There was still a huge variety of sessions and most seemed well-attended. ASSAs is also still unparalleled for pulling in top names to give talks; I got to talk to Nobel laureate Roger Myerson at a reception. But there may be a trend of the big names being more likely to stay remote:

The big problem with attendance falling to 6k is that they’ve planned years worth of meetings with the assumption of 12k+ attendance. Getting one year further from Covid and dropping mask and vaccine mandates might help some, but the core issue is that 1st-round job interviews have gone remote and aren’t coming back. The best solution I can think of is raising the acceptance rate for papers, which in recent history has been well under 20%.

In terms of the actual economic research, two sessions stood out to me:

How many factors are there in the stock market? Classic work by Fama and French argues for 3 (size, value, and market risk), but the finance literature as a whole has identified a “zoo” of over 500. Two papers presented one after the other at ASSA argued for two extremes. “Time Series Variation in the Factor Zoo” argues that the number of factors varies over time, but is quite high, typically over 20 and sometimes over 100:

In contrast, “Three Common Factors” argues that there really are just 3 factors, though they are latent and not the same as the Fama-French 3 factors. In this case, the whole zoo of factors in the literature is mostly non-robust results driven by p-hacking and a desire to find more factors (fortune and fame potentially await those who do). Overall these asset pricing papers make me want to look into all this myself; when reading them I’m always struck by an odd mix of reactions- “I don’t understand that”, “why would you do it that way, it seems wrong and unnecessarily complicated”, and “why didn’t the field settle such a seemingly basic question decades ago?”.

Hayek: A Life this session covered the new book by Bruce Caldwell (who taught me much of what I know of the history of economic thought) and Hansjoerg Klausinger. Discussants Emily Skarbek and Stephen Durlauf agreed it is surprisingly readable for a long work of original scholarship, calling it a beautifully written 800p pageturner. Vernon Smith asked Caldwell if Hayek read the Theory of Moral Sentiments. Caldwell: “he cited it.” Smith: “but did he read it? Seems like he didn’t understand it very well.” Caldwell agreed he may not have, or if he did it was a German translation.

Vernon Smith’s own talk featured great comments on market instability: instability in markets comes from retrading. Markets are stable when consumers just value goods for their use, like haircuts and hamburgers. The craziness and potential for bubbles and crashes comes in when people are thinking about reselling something, whether it be tulips, stocks, houses, or crypto.

I asked Bruce Caldwell at a reception how he was able to finish writing such a big book that involved lots of archival work and original research. He said “one chapter at a time”, and noted that its fine to write the easiest chapters first to get the ball rolling.

Overall, while ASSA is diminished from the pre-Covid days and I often disagree with the AEAs decisions, its still a top-tier conference, especially when in New Orleans.

New Textbook for Game Theory and Behavioral Economics

Game Theory and Behavior is extremely readable. Carpenter and Robbett have a great set of examples (e.g. the poison drink dilemma from The Princess Bride). I think the book has been developed from teaching a course that resonates with undergraduates today. The authors are both experimental economists, so there is natural integration with lab results from experiments with games.

Topics covered include:

Game Theory and standard definitions

Solving Games

Sequential Games

Bargaining

Markets

Social Dilemmas

Voting

Behavioral Extensions of Standard Theory

In their words:

This book provides a clear and accessible formal introduction to standard game theory, while at the same time addressing how people actually behave in these games and demonstrating how the standard theory can be expanded or updated to better predict the behavior of real people. Our objective is to simultaneously provide students with both the theoretical tools to analyze situations through the logic of game theory and the intuition and behavioral insights to apply these tools to real world situations. The book was written to serve as the primary textbook in a first course in game theory at the undergraduate level and does not assume students have any previous exposure to game theory or economics. 

Not every book on game theory would be described as extremely readable. The authors do present mathematical concepts and solutions and practice problems. I want to be clear that I’m not implying that their book is not rigorous. They present game theory as primarily an intuitive and important framework for decisions instead of as primarily a mathematical object, which should go over well with most undergraduate students.

The following are questions that occurred to me as I was writing this post, with ChatGTP replies.

Mises’s Interventionism, A Recap

I suspect that Mises may have felt somewhat restless after writing Socialism. He had taken a very good stab at describing the socialist economy and its inadequacy for the promotion of human flourishing. By 1940 fascism had arisen in both Italy and in Germany, who Mises considered the clear antagonists of World War II. Further, the communist Soviets were allied with Germany at the time of writing Interventionism.

A communist-fascist alliance may seem strange to idealogues, but it appeared quite natural to Mises that the two distasteful versions of socialism should find cooperation convenient to achieve their own ends. In America, the revelations of German atrocities had yet to arrive and there were many sympathizers with both Russia and Germany. In Britain, union leaders were promoting the idea of socialism as a reward to the public who would be bearing the costs of the war.

Mises thought that the disfunction of socialism was adequate to describe its ultimate failure as an economic system. However, socialist tendencies were pervasive in the liberal market economies among both idealogues and demagogues enough to make the transition to socialism a very real threat. After all, while socialism may not be a stable regime in a dynamic world, certain features within specific market economies may nonetheless tend toward it. What is the cause of such tendencies?

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Joy Recommends Stuff for Kids 2022

I recommend two games for teaching kids to read their “sight words”. In early school grades, learning sight words can mean doing boring homework or rote memorization of flash cards. Instead use

Zingo Sight Words

and

 Sight Word Swat 

These are both fun interactive games that will get kids reading and talking about sight words. Zingo Sight Words is easier, so I recommend starting there. It’s a lot like bingo with a fun plastic dispenser. Kids can do the matching task to win the game even if they are not yet confident with reading.

Sight Word Swat is a little more advanced but good for expanding vocabulary past the first 50 words. It’s fast paced and fun. Someone yells out a word and then two players compete to “swat” with a plastic mallet the correct “fly” that has the word. Also, if the kid isn’t competitive, they could swat the correct word without time pressure.

Next, I’ll recommend a game that will not remotely feel like an educational exercise. “Spot It” is a genius card game. The tin is small, so you can store it easily and travel with it. The game is easy to teach to new friends because it’s just matching visual patterns. Spot It requires zero reading – not even reading numbers. So, a kid as young as 4 could potentially jump in and start trying to get matches. One of the great things about Spot It is that you play a series of mini games. It’s not the nightmare of a Monopoly game that could take multiple days to finish. So, if you are a parent with limited time to spend on card games, you can parachute in and out quickly.

All of these items are under $20 and potentially all of them could make fun holiday gifts, although your mileage may vary for gifting books and getting smiles. Personally, I bought the sight word games when we needed them for learning instead of trying to make them Christmas gifts.

I had been looking forward to reading the Phantom Tollbooth with my kids for a long time. This is the kind of book that you should read as soon as they are ready to understand most of the action, but not before. If too much is going over their heads, then it isn’t fun. In my case, this book prompted a lot of questions and great conversations with the 7-year-old. The book will teach kids a lot, but if you keep your tone light it feels like just another adventure story.

Svante Pääbo: The Surprising Science behind Who We Are and How We Got Here

Its Nobel Prize season- the economics prize will be announced Monday, while most prizes are announced this week. My favorite so far is the Medicine prize being awarded to Svante Pääbo “for his discoveries concerning the genomes of extinct hominins and human evolution”. He figured out how to sequence DNA from Neanderthal remains despite the fact that they were 40,000 years old.

As recently as 2010 it was controversial to suggest that Neanderthals might have mixed with humans, until Pääbo’s DNA definitively settled the debate, showing that “Neanderthals and Homo sapiens interbred during their millennia of coexistence. In modern day humans with European or Asian descent, approximately 1-4% of the genome originates from the Neanderthals”

While the Neanderthal genome settled an existing controversy, Pääbo’s other big discovery came entirely unlooked for. The Nobel Foundation explains:

In 2008, a 40,000-year-old fragment from a finger bone was discovered in the Denisova cave in the southern part of Siberia. The bone contained exceptionally well-preserved DNA, which Pääbo’s team sequenced. The results caused a sensation: the DNA sequence was unique when compared to all known sequences from Neanderthals and present-day humans. Pääbo had discovered a previously unknown hominin, which was given the name Denisova. Comparisons with sequences from contemporary humans from different parts of the world showed that gene flow had also occurred between Denisova and Homo sapiens. This relationship was first seen in populations in Melanesia and other parts of South East Asia, where individuals carry up to 6% Denisova DNA.

Pääbo’s discoveries have generated new understanding of our evolutionary history. At the time when Homo sapiens migrated out of Africa, at least two extinct hominin populations inhabited Eurasia. Neanderthals lived in western Eurasia, whereas Denisovans populated the eastern parts of the continent. During the expansion of Homo sapiens outside Africa and their migration east, they not only encountered and interbred with Neanderthals, but also with Denisovans

The same techniques that enabled these discoveries have been applied much more widely throughout the field of Paleogenomics, which continues to rewrite what we thought we knew about history and pre-history. The field has been advancing so quickly over the last decade that its hard to keep up with it. I’ve found the best introduction to be David Reich’s Who We Are and How We Got Here, though again the field is moving so fast that a 2018 book is already a bit out of date. Razib Khan is always writing about the latest updates at Unsupervized Learning. If you haven’t kept up with this stuff since school, this post and diagram give a quick introduction to how much our understanding of human origins has recently changed:

Business Analytics Textbook plus Discussion Book

Many undergraduates take at least one business analytics course at the 200 course level. A book that I and other professors at our business school have selected to teach business statistics is by Albright and Winston

Business Analytics: Data Analytics and Decision Making (Amazon link)

This book provides three essential ingredients to a successful course:

  1. Covering core concepts like descriptive statistics and optimization
  2. Providing relevant examples in a business context (e.g. how much inventory should a retail store order)
  3. Showing step-by-step instructions for how to do applications in a specific software which in this case is Excel

Microsoft Excel is essential for business school graduates (arguably all college graduates). No one is born knowing how to select cells or enter formulas. The book does not assume anything, so the professor does not have to require supplementary material on how to use Excel. There are lots of exercise and examples that teach proficiency in the tool while demonstrating the concepts. Analytics courses should be hands-on.

Sometimes statistics courses do not feel like they allow for critical thinking or discussions. There is only one correct formula for an average, and it is merely and exactly what the formula determines it to be. Therefore, an interesting addition to a technical class is the book by Muller

The Tyranny of Metrics (Amazon link)

Muller spends most of the book pointing out cases where measuring results backfired. He is not so much against “analytics” as he is skeptical of pay-for-performance management schemes. Many of these schemes were sold to the public as incredible technocratic improvements, such as No Child Left Behind. I do not always agree with Muller, but he gives students something to debate. Note that only select chapters should be assigned so that it does not take up too much time from the other course material.

Data Analytics with R Textbook

For an advanced undergraduate analytics class for business school students, I use a textbook by Saltz and Stanton called

Data Science for Business with R (Amazon link)

This textbook teaches R and analytics at the same time. The professor does not have to provide a separate R curriculum or require students to buy a second book.

The running example in the textbook is an airline business scenario that is interesting and builds with the complexity of the subject matter. The authors provide the dataset that students can work with for the airline case study. Many examples in the textbook use data that is available online and therefor can be imported to R with just a few lines of code.

One semester is not enough time to cover every chapter in the book. I emphasize predictive analytics, so I skip the chapters on maps and shiny apps.

I do some supplemental lectures on concepts in predictive analytics before students reach the chapters on regression and decision trees. For example, overfitting is a new concept to undergraduates. I want them to have a more intuitive grasp of that subject before learning the R code to separate data into training and validation sets.

Note that these students have already taken what has traditionally been called Business Statistics, so they already understand basic descriptive statistics and graphing. The book is no substitute for that primary class.

There are free supplementary materials online for learning R. Students find message boards especially helpful in pinpointing answers for questions that come up while coding.

Book Review: Big Data Demystified

Last year, our economics department launched a data analytics minor program. The first class is a simple 2 credit course called Foundations of Data Analytics. Originally, the idea was that liberal arts majors would take it and that this class would be a soft, non-technical intro of terminology and history.

However, it turned out that liberal arts majors didn’t take the class and that the most popular feedback was that the class lacked technical challenge. I’m prepping to teach the class and it will have two components. A Python training component where students simply learn Python. We won’t do super complicated things, but they will use Python extensively in future classes. The 2nd component is still in the vein of the old version of the course.

I’ll have the students read and discuss “Big Data Demystified” by David Stephenson. He spends 12 brief chapters introducing the reader to the importance of modern big data management, analytics, and how it fits into an organization’s key performance indicators. It reads like it’s for business majors, but any type of medium-to-large organization would find it useful.

Davidson starts with some flashy stories that illustrate the potential of data-driven business strategies. For example, Target corporation used predictive analytics to advertise baby and pregnancy products to mothers who didn’t even know that they were pregnant yet. He wets the appetite of the reader by noting that the supercomputers that could play Chess or Go relied on fundamentally different technologies.

The first several chapters of the book excite the reader with thoughts of unexploited potentialities. This is what I want to impress upon the students. I want them to know the difference between artificial intelligence (AI) and machine learning (ML). I want them to recognize which tool is better for the challenges that they might face and to see clear applications (and limitations).

AI uses brute force, iterating through possible next steps. There are multiple online tic-tac-toe AI that keep track records. If a student can play the optimal set of strategies 8 games in a row, then they can get the general idea behind testing a large variety of statistical models and explanatory variables, then choosing the best.

But ML is responsive to new data, according to what worked best on previous training data. There are multiple YouTubers out there who have used ML to beat Super Mario Brothers. Programmers identify an objective function and the ML program is off to the races. It tries a few things on a level, and then uses the training rounds to perform quite well on new levels that it has never encountered before.

There are a couple of chapters in the middle of the book that didn’t appeal to me. They discuss the question of how big data should inform a firm’s strategy and how data projects should be implemented. These chapters read like they are written for MBAs or for management. They were boring for me. But that’s ok, given that Stephenson is trying to appeal to a broad audience.

The final chapters are great. They describe the limitations of big data endeavors. Big data is not a panacea and projects can fail for a variety of what are very human reasons.

Stephenson emphasizes the importance of transaction costs (though he doesn’t say it that way). Medium sized companies should outsource to experts who can achieve (or fail) quickly such that big capital investments or labor costs can be avoided. Or, if internals will be hired instead, he discusses the trade-offs between using open source software, getting locked in, and reinventing the wheel. These are a great few chapters that remind the reader that data scientists and analysts are not magicians. They are people who specialize and can waste their time just as well as anyone else.

Overall, I strongly recommend this book. I kinda sorta knew what machine learning and artificial intelligence were prior to reading, but this book provides a very accessible introduction to big data environments, their possible uses, and organizational features that matter for success. Mid and upper level managers should read this book so that they can interact with these ideas prudentially. Those with a passing interest in programming should read it for greater clarity and to get a better handle on the various sub-fields. Hopefully, my students will read it and feel inspired to be on one side or the other of the manager- data analyst divide with greater confidence, understanding, and a little less hubris.