Showing posts with label social science. Show all posts
Showing posts with label social science. Show all posts

Wednesday, December 13, 2023

PISA 2023 and the Gloomy Prospect

I'm in the Philippines now. I flew here after the semester ended, in order to meet with outsourcing (BPO = Business Process Outsourcing) companies that run call centers for global brands. This industry accounts for ~8% of Philippine GPD (~$40B per annum), driven by comparative advantages such as the widespread use of English here and relatively low wages. 

I predict that AIs of the type produced by my startup SuperFocus.ai will disrupt the BPO industry in coming years, with dramatic effects on the numbers of humans employed in areas like customer support. I was just interviewed for the podcast of the AI expert at IBPAP, the BPO trade association - he is tasked with helping local companies adopt AI technology, and adapt to a world with generative LLMs like GPT4. I'll publish a link to that interview when it goes live. 


During my visit the latest PISA results were released. This year they provided data with students grouped by Socio-Economic Status [1], so that students in different countries, but with similar levels of wealth and access to educational resources, can be compared directly. See figures below - OECD mean ~500, SD~100. 


Quintiles are defined using the *entire* international PISA student pool. These figures allow us to compare equivalent SES cohorts across countries and to project how developing countries will perform as they get richer and improve schooling.

In some countries, such as Turkey or Vietnam, the small subset of students that are in the top quintile of SES (among all PISA students tested) already score better than the OECD average for students with similar SES. On the other hand, for most developing countries, such as the Philippines, Indonesia, Saudi Arabia, Brazil, Mexico, etc. even the highest quintile SES students score similarly to or worse than the most deprived students in, e.g., Turkey, Vietnam, Japan, etc.

Note the top 20% SES quintile among all PISA takers is equivalent to roughly top ~30% SES among Japanese. If the SES variable is even crudely accurate, typical kids in this category are not deprived in any way and should be able to achieve their full cognitive potential. In developing countries only a small percentage of students are in this quintile - they are among the elites with access to good schools, nutrition, and potentially with educated parents. Thus it is very bad news that even this subgroup of students score so poorly in almost all developing countries (with exceptions like Turkey and Vietnam). It leads to gloomy projections regarding human capital, economic development, etc. in most of the developing world. 

I had not seen a similar SES analysis before this most recent PISA report. I was hoping to see data showing catch up in cognitive ability with increasing SES in developing countries. The results indicate that cognitive gaps will be very difficult to ameliorate.

In summary, the results suggest that many of these countries will not reach OECD-average levels of human capital density even if they somehow catch up in per capita GDP.

This suggests a Gloomy Prospect for development economics. Catch up in human capital density looks difficult for most developing countries, with only a few exceptions (e.g., Turkey, Vietnam, Iran, etc.).
 

Here is the obligatory US students by ancestry group vs Rest of World graph that reflects: 1. strong US spending on education (vs Rest of World) and 2. selective immigration to the US, at least for some groups.
 

Tuesday, October 10, 2023

SMPY 65: Help support the SMPY Longitudinal Study


The Study of Mathematically Precocious Youth (SMPY) needs your help to support the Age-65 phase of their unique longitudinal study. 


For decades, co-directed by David Lubinski and Camilla P. Benbow, SMPY has been a beacon of enlightenment, tracking five cohorts comprising over 5,000 remarkably gifted individuals. In doing so, we have unraveled the secrets to nurturing brilliance. However, we are confronted with a disconcerting reality: the effective methods to identify and cultivate intellectual talent are under siege, threatened by political ideology. 

Our 14-minute documentary and the 3-page feature in Nature underscore the dire need to provide our most gifted youths with the educational opportunities they deserve. They are the architects of solutions and the architects of the future itself. 

Here are some compelling longitudinal findings from SMPY's extensive research:
• Prodigies destined for eminent careers can be identified as early as age 13. 
• There is no plateau of ability; even within the top 1%, variations in mathematical, spatial, and verbal abilities profoundly impact educational, occupational, and creative outcomes. 
• The blend of specific abilities, such as mathematical, spatial, and verbal aptitudes, shapes the nature of one's accomplishments and career trajectory.



More information:

Long

Short


DONATE HERE

Indicate "Please designate this gift to Study of Mathematically Precocious Youth" in the Special Instructions.


Thursday, August 24, 2023

Aella: Sex Work, Sex Research, and Data Science — Manifold #42

 

Aella is a sex worker, sex researcher, and data scientist. 


Interviews with ex-prostitutes on the pimp life (Las Vegas) 

An earlier Aella interview with Reason: 


Audio-only and Transcript:

Steve and Aella discuss: 

(00:00) - Introduction 
(01:22) - Aella's background and upbringing 
(12:45) - Aella's experiences as a sex worker and escorting 
(29:52) - Pimp culture 
(38:01) - Seeking Arrangement 
(43:50) - Cheating 
(46:50) - OnlyFans, farming simps 
(51:49) - Incels and sex work 
(56:24) - Porn and Gen-Z 
(01:12:43) - Embryo screening 
(01:21:43) - How far off is IVG?

Thursday, July 13, 2023

Richard Hanania & Rob Henderson: The Rise of Wokeness and the Influence of Civil Rights Law — Manifold #39

 

Richard Hanania, Rob Henderson, and I were scheduled for a June 2023 panel as part of the University of Austin (UATX) Forbidden Courses series. I missed the panel due to travel issues, but we gathered on this podcast to recreate the fun! 


Topics: 

0:00 Introduction 
1:20 The University of Austin and forbidden courses 
17:37 Will woke campus culture change anytime soon? 
29:57 Common people vs elites on affirmative action 
35:42 Why it’s uncomfortable to disagree about affirmative action 
41:22 Fraud and misrepresentation in higher ed 
44:20 The adversity carveout in the Supreme Court affirmative action ruling 
50:10 Standardized testing and elite university admissions 
1:06:18 Divergent views among racial and ethnic groups on affirmative action; radicalized Asian American males 
1:10:00 Differences between East and South Asians in the West 
1:23:03 Class-based preferences and standardized tests 
1:31:57 Rob Henderson’s next move 



LINKS 

Richard Hanania’s new book: 

The Origins of Woke: Civil Rights Law, Corporate America, and the Triumph of Identity Politics 

Richard Hanania’s newsletter: 

The Center for the Study of Partisanship and Ideology: 

Rob Henderson’s newsletter: https://www.robkhenderson.com/ 

Rob Henderson’s new book: 

Troubled: A Memoir of Foster Care, Family, and Social Class 

Thursday, March 02, 2023

Prof. Gilles Saint-Paul (Ecole Normale): the Yellow Vests, French Politics, and Hypergamy (Manifold #31)

 

Audio (podcast only)


Gilles Saint-Paul is Professeur à l'Ecole Normale Supérieure. He is a graduate of Ecole Polytechnique in Engineering and received his PhD from MIT in Economics. Gilles and Steve discuss the French elite education system, the Yellow Vest movement, French politics and populism, and Saint-Paul's paper on marriage markets and hypergamy. 

0:00 Introduction 
1:43 Gilles Saint-Paul's background and education 
6:31 French and American elite education - Les Grandes Ecoles 
14:44 The Yellow Vests 
41:46 Mating and Hypergamy 

Links: 

On the Yellow Vest Insurrection 

Genes, Legitimacy and Hypergamy: Another Look at the Economics of Marriage https://ideas.repec.org/p/ide/wpaper/9118.html

Thursday, January 19, 2023

Dominic Cummings: Vote Leave, Brexit, COVID, and No. 10 with Boris — Manifold #28

 

Dominic Cummings is a major historical figure in UK politics. He helped save the Pound Sterling, led the Vote Leave campaign, Got Brexit Done, and guided the Tories to a landslide general election victory. His time in No. 10 Downing Street as Boris Johnson's Chief Advisor was one of the most interesting and impactful periods in modern UK political history.  Dom and Steve discuss all of this and more in this 2-hour episode. 

0:00 Early Life: Oxford, Russia, entering politics 
16:49 Keeping the UK out of the Euro 
19:41 How Dominic and Steve became acquainted: blogs, 2008 financial crisis, meeting at Google 
27:37 Vote Leave, the science of polling 
43:46 Cambridge Analytica conspiracy; History is impossible 
48:41 Dominic on Benedict Cumberbatch’s portrayal of him and the movie “Brexit: The Uncivil War” 
54:05 On joining British Prime Minister Boris Johnson’s office: an ultimatum 
1:06:31 The pandemic 
1:21:28 The Deep State, talent pipeline for public service 
1:47:25 Quants and weirdos invade No.10 
1:52:06 Can the Tories win the next election? 
1:56:27 Trump in 2024? 



References: 

Dominic's Substack newsletter: https://dominiccummings.substack.com/

Wednesday, September 28, 2022

The Future of Human Evolution -- excerpts from podcast interview with Brian Chau



1. The prospect of predicting cognitive ability from DNA, and the consequences. Why the main motivation has nothing to do with group differences. This segment begins at roughly 47 minutes. 

2. Anti-scientific resistance to research on the genetics of cognitive ability. My experience with the Jasons. Blank Slate-ism as a sacralized, cherished belief of social progressives. This segment begins at roughly 1 hour 7 minutes. 


1. Starts at roughly 47 minutes. 

Okay, let's just say hypothetically my billionaire friend is buddies with the CEO of 23andMe and let's say on the down low we collected some SAT scores of 1M or 2M people. I think there are about 10M people that have done 23andMe, let's suppose I manage to collect 1-2M scores for those people. I get them to opt in and agree to the study and da da da da and then Steve runs his algos and you get this nice predictor. 

But you’ve got to do it on the down low. Because if it leaks out that you're doing it, People are going to come for you. The New York Times is going to come for you, everybody's going to come for you. They're going to try to trash the reputation of 23andMe. They're going to trash the reputation of the billionaire. They're going to trash the reputation of the scientists who are involved in this. But suppose you get it done. And getting it done as you know very well is a simple run on AWS and you end up with this predictor which wow it's really complicated it depends on 20k SNPs in the genome ... 

For anybody with an ounce of intellectual integrity, they would look back at their copy of The Mismeasure of Man which has sat magisterially on their bookshelf since they were forced to buy it as a freshman at Harvard. They would say, “WOW! I guess I can just throw that in the trash right? I can just throw that in the trash.” 

But the set of people who have intellectual integrity and can process new information and then reformulate the opinion that they absorbed through social convention – i.e., that Gould is a good person and a good scientist and wise -- is tiny. The set of people who can actually do that is like 1% of the population. So you know maybe none of this matters, but in the long run it does matter. … 

Everything else about that hypothetical: the social scientists running the longitudinal study, getting the predictor in his grubby little hands and publishing the validation, but people trying to force you to studiously ignore the results, all that has actually already happened. We already have something which correlates ~0.4 with IQ. Everything else I said has already been done but it's just being studiously ignored by the right thinking people. 

 … 

Some people could misunderstand our discussion as being racist. I'm not saying that any of this has anything to do with group differences between ancestry groups. I'm just saying, e.g., within the white population of America, it is possible to predict from embryo DNA which of 2 brothers raised in the same family will be the smart one and which one will struggle in school. Which one will be the tall one and which one will be not so tall. 



2. Starts at roughly 1 hour 7 minutes. 

I've been in enough places where this kind of research is presented in seminar rooms and conferences and seen very negative attacks on the individuals presenting the results. 

I'll give you a very good example. There used to be a thing called the Jasons. During the cold war there was a group of super smart scientists called the Jasons. They were paid by the government to get together in the summers and think about technological issues that might be useful for defense and things like war fighting. … 

I had a meeting with the (current) Jasons. I was invited to a place near Stanford to address them about genetic engineering, genomics, and all this stuff. I thought okay these are serious scientists and I'll give them a very nice overview of the progress in this field. This anecdote takes place just a few years ago. 

One of the Jasons present is a biochemist but not an expert on genomics or machine learning. This biochemist asked me a few sharp questions which were easy to answer. But then at some point he just can't take it anymore and he grabs all his stuff and runs out of the room. ...

Thursday, June 16, 2022

Greg Clark: Genetics and Social Mobility — Manifold Episode #14

 

Gregory Clark is Distinguished Professor of Economics at UC-Davis. He is an editor of the European Review of Economic History, chair of the steering committee of the All-UC Group in Economic History, and a Research Associate of the Center for Poverty Research at Davis. He was educated at Cambridge University and received a PhD from Harvard University. His areas of research are long-term economic growth, the wealth of nations, economic history, and social mobility. 

Steve and Greg discuss: 

0:00 Introduction 
2:31 Background in economics and genetics 
10:25 The role of genetics in determining social outcomes 
16:27 Measuring social status through marriage and occupation 
36:15 Assortative mating and the industrial revolution 
49:38 Criticisms of empirical data, engagement on genetics and economic history 
1:12:12 Heckman and Landerso study of social mobility in US vs Denmark 
1:24:32 Predicting cognitive traits 
1:33:26 Assortative mating and increase in population variance 

Links: 

For Whom the Bell Curve Tolls: A Lineage of 400,000 English Individuals 1750-2020 shows Genetics Determines most Social Outcomes http://faculty.econ.ucdavis.edu/faculty/gclark/ClarkGlasgow2021.pdf 


A Farewell to Alms: A Brief Economic History of the World https://en.wikipedia.org/wiki/A_Farewell_to_Alms 


Thursday, March 03, 2022

Manifold Podcast #6: Richard Sander on Affirmative Action, Mismatch Theory, and Academic Freedom

 

Richard Sander is Jesse Dukeminier Professor at UCLA Law School. 
AB Harvard, JD, PhD (Economics) Northwestern. 

Sander has studied the structure and effects of law school admissions policies. He coined the term "Mismatch" to describe negative consequences resulting from large admissions preferences. 

Topics discussed: 

1. Early life: educational background and experience with race and politics in America. 

2. Mismatch Theory: basic observation and empirical evidence; Law schools and Colleges; Duke and UC data; data access issues. 

3. CA Prop 209 and Prop 16. 

4. SCOTUS and Harvard / UNC admissions case 

5. Intellectual climate on campus, freedom of speech 

Resources: 

Faculty web page, includes links to publications: 

A Conversation on the Nature, Effects, and Future of Affirmative Action in Higher Education Admissions (with Peter Arcidiacono, Thomas Espenshade, and Stacy Hawkins), University of Pennsylvania Journal of Constitutional Law 683 (2015) 

About Prop. 16 and Prop. 209, University of Chicago Law Review Online (2020) 

Panel at Stanford Intellectual Diversity Conference, April 8, 2016, Stanford Law School 

ManifoldOne podcast (transcript).

Monday, January 10, 2022

Recent Papers on Socio-Economic Status and Student Achievement: Marks and O'Connell

I received the message below from Michael O'Connell, University College Dublin, and Gary Marks, University of Melbourne. 

See also this recent post: Social and Educational Mobility: Denmark vs USA (James Heckman), and links therein. 
Dear Scholar, 
 
There is a widely-held perception that many of life’s key outcomes are fundamentally driven by people’s socio-economic status (SES). More specifically, there is a view that children’s educational attainment is largely a by-product of their familial SES. As a consequence of this pervasive paradigm, much of the energy in seeking to ameliorate or resolve poor educational attainment is based around trying to use SES as a social lever. 

However, in the six papers listed below, published between 2019-2022, evidence has been gathered demonstrating that SES is only very modestly correlated with educational attainment. Furthermore, once a child’s cognitive ability is taken into account, even the modest link between SES and attainment diminishes to slight influence. This is true of datasets drawn from international groups of young people, as well as those from the US, UK, or Ireland. Future attempts to aid and study young people experiencing difficulty with educational attainment should be built on an awareness of the limited role of SES. 

Gary N Marks   Michael O’Connell 


1. O’Connell, M. and Marks, G.N. (2022) 
Cognitive ability and conscientiousness are more important than SES for educational attainment: An analysis of the UK Millennium Cohort Study
Personality and Individual Differences, 188 
https://doi.org/10.1016/j.paid.2021.111471 
Highlights Antecedents of educational attainment of great interest Dominant paradigm focuses on SES of children. Cognitive ability and conscientiousness have stronger record in research findings. Using new UK MCS longitudinal survey data, GCSE state exam performance assessed Cognitive ability and conscientiousness explained far more than SES measures 

 

2. Marks, G. N. (2021) 
Is the relationship between socioeconomic status (SES) and student achievement causal? Considering student and parent abilities
Educational Research and Evaluation, 10.1080/13803611.2021.1968442: 1-24. 
Abstract Most studies on the relationship between students’ socioeconomic status (SES) and student achievement assume that its effects are sizable and causal. A large variety of theoretical explanations have been proposed. However, the SES–achievement association may reflect, to some extent, the inter-relationships of parents’ abilities, SES, children’s abilities, and student achievement. The purpose of this study is to quantify the role of SES vis-à-vis child and parents’ abilities, and prior achievement. Analyses of a covariance matrix that includes supplementary correlations for fathers and mothers’ abilities derived from the literature indicate that more than half of the SES–achievement association can be accounted for by parents’ abilities. SES coefficients decline further with the addition of child’s abilities. With the addition of prior achievement, the SES coefficients are trivial implying that SES has little or no contemporaneous effects. These findings are not compatible with standard theoretical explanations for SES inequalities in achievement. 

 

3. Marks, G. N. and O’Connell, M. (2021) 
No Evidence for Cumulating Socioeconomic Advantage. Ability Explains Increasing SES Effects with Age on Children’s Domain Test scores 
Intelligence, 88    
https://doi.org/10.1016/j.intell.2021.101582 
Highlights Data analysed for five domains for children of the NLSY79 mothers study. SES effects increase for only some domains and not substantially. No increase in SES effects when considering mother's or children's prior ability. Effects of child's prior ability on test scores increase substantially with age. SES effects are small net of mother's ability. 
 
4. Marks, G. N. and O'Connell, M. (2021) 
Inadequacies in the SES–Achievement model: Evidence from PISA and other studies 
Review of Education, 9(3): e3293. 
https://doi.org/10.1002/rev3.3293 
Abstract Students’ socioeconomic status (SES) is central to much research and policy deliberation on educational inequalities. However, the SES model is under severe stress for several reasons. SES is an ill-defined concept, unlike parental education or family income. SES measures are frequently based on proxy reports from students; these are generally unreliable, sometimes endogenous to student achievement, only low to moderately intercorrelated, and exhibit low comparability across countries and over time. There are many explanations for SES inequalities in education, none of which achieves consensus among research and policy communities. SES has only moderate effects on student achievement, and its effects are especially weak when considering prior achievement, an important and relevant predictor. SES effects are substantially reduced when considering parent ability, which is causally prior to family SES. The alternative cognitive ability/genetic transmission model has far greater explanatory power; it provides logical and compelling explanations for a wide range of empirical findings from student achievement studies. The inadequacies of the SES model are hindering knowledge accumulation about student performance and the development of successful policies. 
 
5. O'Connell, M. and Marks, G. N. (2021) 
Are the effects of intelligence on student achievement and well-being largely functions of family income and social class? Evidence from a longitudinal study of Irish adolescents
Intelligence, 84: 101511. 10.1016/j.intell.2020.101511 
Highlights Power of cognitive ability and social class contrasted. Large representative sample from longitudinal study, waves 1–3, of 6216 children Outcomes were attainments, difficulties and relationships. Cognitive ability explained large amounts of variance. Social background only minor effects 
 
6. O'Connell, M. (2019) 
Is the impact of SES on educational performance overestimated? Evidence from the PISA survey 
Intelligence, 75: 41-47 
https://doi.org/10.1016/j.intell.2019.04.005 
Highlights Policy-makers overly attribute differences in educational performance to SES. PISA survey used to assess roles of parental education and household income. Combining them concealed differences in outcomes between rich and poor countries. Household income important in poor countries, parental education in rich countries.

Saturday, November 27, 2021

Social and Educational Mobility: Denmark vs USA (James Heckman)




Despite generous social programs such as free pre-K education, free college, and massive transfer payments, Denmark is similar to the US in key measures of inequality, such as educational outcomes and cognitive test scores. 

While transfer payments can equalize, to some degree, disposable income, they do not seem to be able to compensate for large family effects on individual differences in development. 

These observations raise the following questions: 

1. What is the best case scenario for the US if all progressive government programs are implemented with respect to child development, free high quality K12 education, free college, etc.?

2. What is the causal mechanism for stubborn inequality of outcomes, transmitted from parent to child (i.e., within families)? 

Re #2: Heckman and collaborators focus on environmental factors, but do not (as far as I can tell) discuss genetic transmission. We already know that polygenic scores are correlated to the education and income levels of parents, and (from adoption studies) that children tend to resemble their biological parents much more strongly than their adoptive parents. These results suggest that genetic transmission of inequality may dominate environmental transmission.
  
See 



The Contribution of Cognitive and Noncognitive Skills to Intergenerational Social Mobility (McGue et al. 2020)


Note: Denmark is very homogenous in ancestry, and the data presented in these studies (e.g., polygenic scores and social mobility) are also drawn from European-ancestry cohorts. The focus here is not on ethnicity or group differences between ancestry groups. The focus is on social and educational mobility within European-ancestry populations, with or without generous government programs supporting free college education, daycare, pre-K, etc.

Lessons for Americans from Denmark about inequality and social mobility 
James Heckman and Rasmus Landersø 
Abstract Many progressive American policy analysts point to Denmark as a model welfare state with low levels of income inequality and high levels of income mobility across generations. It has in place many social policies now advocated for adoption in the U.S. Despite generous Danish social policies, family influence on important child outcomes in Denmark is about as strong as it is in the United States. More advantaged families are better able to access, utilize, and influence universally available programs. Purposive sorting by levels of family advantage create neighborhood effects. Powerful forces not easily mitigated by Danish-style welfare state programs operate in both countries.
Also discussed in this episode of EconTalk podcast. Russ does not ask the obvious question about disentangling family environment from genetic transmission of inequality.
 

The figure below appears in Game Over: Genomic Prediction of Social Mobility. It shows SNP-based polygenic score and life outcome (socioeconomic index, on vertical axis) in four longitudinal cohorts, one from New Zealand (Dunedin) and three from the US. Each cohort (varying somewhat in size) has thousands of individuals, ~20k in total (all of European ancestry). The points displayed are averages over bins containing 10-50 individuals. For each cohort, the individuals have been grouped by childhood (family) social economic status. Social mobility can be predicted from polygenic score. Note that higher SES families tend to have higher polygenic scores on average -- which is what one might expect from a society that is at least somewhat meritocratic. The cohorts have not been used in training -- this is true out-of-sample validation. Furthermore, the four cohorts represent different geographic regions (even, different continents) and individuals born in different decades.




The figure below appears in More on SES and IQ.

Where is the evidence for environmental effects described above in Heckman's abstract: "More advantaged families are better able to access, utilize, and influence universally available programs. Purposive sorting by levels of family advantage create neighborhood effects"? Do parents not seek these advantages for their adopted children as well as for their biological children? Or is there an entirely different causal mechanism based on shared DNA?

 


 

Saturday, October 30, 2021

Slowed canonical progress in large fields of science (PNAS)




Sadly, the hypothesis described below is very plausible. 

The exception being that new tools or technological breakthroughs, especially those that can be validated relatively easily (e.g., by individual investigators or small labs), may still spread rapidly due to local incentives. CRISPR and Deep Learning are two good examples.
 
New theoretical ideas and paradigms have a much harder time in large fields dominated by mediocre talents: career success is influenced more by social dynamics than by real insight or capability to produce real results.
 
Slowed canonical progress in large fields of science 
Johan S. G. Chu and James A. Evans 
PNAS October 12, 2021 118 (41) e2021636118 
Significance The size of scientific fields may impede the rise of new ideas. Examining 1.8 billion citations among 90 million papers across 241 subjects, we find a deluge of papers does not lead to turnover of central ideas in a field, but rather to ossification of canon. Scholars in fields where many papers are published annually face difficulty getting published, read, and cited unless their work references already widely cited articles. New papers containing potentially important contributions cannot garner field-wide attention through gradual processes of diffusion. These findings suggest fundamental progress may be stymied if quantitative growth of scientific endeavors—in number of scientists, institutes, and papers—is not balanced by structures fostering disruptive scholarship and focusing attention on novel ideas. 
Abstract In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.
See also Is science self-correcting?
A toy model of the dynamics of scientific research, with probability distributions for accuracy of experimental results, mechanisms for updating of beliefs by individual scientists, crowd behavior, bounded cognition, etc. can easily exhibit parameter regions where progress is limited (one could even find equilibria in which most beliefs held by individual scientists are false!). Obviously the complexity of the systems under study and the quality of human capital in a particular field are important determinants of the rate of progress and its character. 
In physics it is said that successful new theories swallow their predecessors whole. That is, even revolutionary new theories (e.g., special relativity or quantum mechanics) reduce to their predecessors in the previously studied circumstances (e.g., low velocity, macroscopic objects). Swallowing whole is a sign of proper function -- it means the previous generation of scientists was competent: what they believed to be true was (at least approximately) true. Their models were accurate in some limit and could continue to be used when appropriate (e.g., Newtonian mechanics). 
In some fields (not to name names!) we don't see this phenomenon. Rather, we see new paradigms which wholly contradict earlier strongly held beliefs that were predominant in the field* -- there was no range of circumstances in which the earlier beliefs were correct. We might even see oscillations of mutually contradictory, widely accepted paradigms over decades. 
It takes a serious interest in the history of science (and some brainpower) to determine which of the two regimes above describes a particular area of research. I believe we have good examples of both types in the academy. 
* This means the earlier (or later!) generation of scientists in that field was incompetent. One or more of the following must have been true: their experimental observations were shoddy, they derived overly strong beliefs from weak data, they allowed overly strong priors to determine their beliefs.

Saturday, May 08, 2021

Three Thousand Years and 115 Generations of 徐 (Hsu / Xu)

Over the years I have discussed economic historian Greg Clark's groundbreaking work on the persistence of social class. Clark found that intergenerational social mobility was much less than previously thought, and that intergenerational correlations on traits such as education and occupation were consistent with predictions from an additive genetic model with a high degree of assortative mating. 

See Genetic correlation of social outcomes between relatives (Fisher 1918) tested using lineage of 400k English individuals, and further links therein. Also recommended: this recent podcast interview Clark did with Razib Khan. 

The other day a reader familiar with Clark's work asked me about my family background. Obviously my own family history is not a scientific validation of Clark's work, being only a single (if potentially illustrative) example. Nevertheless it provides an interesting microcosm of the tumult of 20th century China and a window into the deep past...

I described my father's background in the post Hsu Scholarship at Caltech:
Cheng Ting Hsu was born December 1, 1923 in Wenling, Zhejiang province, China. His grandfather, Zan Yao Hsu was a poet and doctor of Chinese medicine. His father, Guang Qiu Hsu graduated from college in the 1920's and was an educator, lawyer and poet. 
Cheng Ting was admitted at age 16 to the elite National Southwest Unified University (Lianda), which was created during WWII by merging Tsinghua, Beijing, and Nankai Universities. This university produced numerous famous scientists and scholars such as the physicists C.N. Yang and T.D. Lee. 
Cheng Ting studied aerospace engineering (originally part of Tsinghua), graduating in 1944. He became a research assistant at China's Aerospace Research Institute and a lecturer at Sichuan University. He also taught aerodynamics for several years to advanced students at the air force engineering academy. 
In 1946 he was awarded one of only two Ministry of Education fellowships in his field to pursue graduate work in the United States. In 1946-1947 he published a three-volume book, co-authored with Professor Li Shoutong, on the structures of thin-walled airplanes. 
In January 1948, he left China by ocean liner, crossing the Pacific and arriving in San Francisco. ...
My mother's father was a KMT general, and her family related to Chiang Kai Shek by marriage. Both my grandfather and Chiang attended the military academy Shinbu Gakko in Tokyo. When the KMT lost to the communists, her family fled China and arrived in Taiwan in 1949. My mother's family had been converted to Christianity in the 19th century and became Methodists, like Sun Yat Sen. (I attended Methodist Sunday school while growing up in Ames IA.) My grandfather was a partner of T.V. Soong in the distribution of bibles in China in the early 20th century.

My father's family remained mostly in Zhejiang and suffered through the communist takeover, Great Leap Forward, and Cultural Revolution. My father never returned to China and never saw his parents again. 

When I met my uncle (a retired Tsinghua professor) and some of my cousins in Hangzhou in 2010, they gave me a four volume family history that had originally been printed in the 1930s. The Hsu (Xu) lineage began in the 10th century BC and continued to my father, in the 113th generation. His entry is the bottom photo below.
Wikipedia: The State of Xu (Chinese: 徐) (also called Xu Rong (徐戎) or Xu Yi (徐夷)[a] by its enemies)[4][5] was an independent Huaiyi state of the Chinese Bronze Age[6] that was ruled by the Ying family (嬴) and controlled much of the Huai River valley for at least two centuries.[3][7] It was centered in northern Jiangsu and Anhui. ...

Generations 114 and 115:


Four volume history of the Hsu (Xu) family, beginning in the 10th century BC. The first 67 generations are covered rather briefly, only indicating prominent individuals in each generation of the family tree. The books are mostly devoted to generations 68-113 living in Zhejiang. (Earlier I wrote that it was two volumes, but it's actually four. The printing that I have is two thick books.)




Sunday, March 21, 2021

The Contribution of Cognitive and Noncognitive Skills to Intergenerational Social Mobility (McGue et al. 2020)

If you have the slightest pretension to expertise concerning social mobility, meritocracy, inequality, genetics, psychology, economics, education, history, or any related subjects, I urge you to carefully study this paper.
The Contribution of Cognitive and Noncognitive Skills to Intergenerational Social Mobility  
(Psychological Science https://doi.org/10.1177/0956797620924677)
Matt McGue, Emily A. Willoughby, Aldo Rustichini, Wendy Johnson, William G. Iacono, James J. Lee 
We investigated intergenerational educational and occupational mobility in a sample of 2,594 adult offspring and 2,530 of their parents. Participants completed assessments of general cognitive ability and five noncognitive factors related to social achievement; 88% were also genotyped, allowing computation of educational-attainment polygenic scores. Most offspring were socially mobile. Offspring who scored at least 1 standard deviation higher than their parents on both cognitive and noncognitive measures rarely moved down and frequently moved up. Polygenic scores were also associated with social mobility. Inheritance of a favorable subset of parent alleles was associated with moving up, and inheritance of an unfavorable subset was associated with moving down. Parents’ education did not moderate the association of offspring’s skill with mobility, suggesting that low-skilled offspring from advantaged homes were not protected from downward mobility. These data suggest that cognitive and noncognitive skills as well as genetic factors contribute to the reordering of social standing that takes place across generations.
From the paper:
We believe that a reasonable explanation of our findings is that the degree to which individuals are more or less skilled than their parents contributes to their upward or downward mobility. Behavioral genetic and genomic research has established the heritability of social achievements (Conley, 2016) as well as the skills thought to underlie them (Bouchard & McGue, 2003). Nonetheless, these associations may be due to passive gene–environment correlation, whereby high-achieving parents both transmit genes and provide a rearing environment that promotes their children’s social success (Scarr & McCartney, 1983). Our within-family design controlled for passive gene–environment correlation effects. Although offspring inherit all of their genes from their parents, they inherit a random subset of parental alleles because of meiotic segregation. Consequently, some offspring inherit a favorable subset of their parents’ alleles, whereas others inherit a less favorable subset. We found, as did previous researchers (Belsky et al., 2018), that the inheritance of a favorable subset of alleles was associated with an increased likelihood of upward mobility... 
...In summary, our analysis of intergenerational social mobility in a sample of 2,594 offspring from 1,321 families found that (a) most individuals were educationally and occupationally mobile, (b) mobility was predicted by offspring–parent differences in skills and genetic endowment, and (c) the relationship of offspring skills with social mobility did not vary significantly by parent social background. In an era in which there is legitimate concern over social stagnation, our findings are noteworthy in identifying the circumstances when parents’ educational and occupational success is not reproduced across generations.

See also Game Over: Genomic Prediction of Social Mobility (PNAS July 9, 2018: 201801238). Both papers provide out of sample validation of polygenic predictors for cognitive ability, specifically of the relationship to intergenerational social mobility.


Friday, March 05, 2021

Genetic correlation of social outcomes between relatives (Fisher 1918) tested using lineage of 400k English individuals

Greg Clark (UC Davis and London School of Economics) deserves enormous credit for producing a large multi-generational dataset which is relevant to some of the most fundamental issues in social science: inequality, economic development, social policy, wealth formation, meritocracy, and recent human evolution. If you have even a casual interest in the dynamics of human society you should study these results carefully...

See previous discussion on this blog. 

Clark recently posted this preprint on his web page. A book covering similar topics is forthcoming.
For Whom the Bell Curve Tolls: A Lineage of 400,000 English Individuals 1750-2020 shows Genetics Determines most Social Outcomes 
Gregory Clark, University of California, Davis and LSE (March 1, 2021) 
Economics, Sociology, and Anthropology are dominated by the belief that social outcomes depend mainly on parental investment and community socialization. Using a lineage of 402,000 English people 1750-2020 we test whether such mechanisms better predict outcomes than a simple additive genetics model. The genetics model predicts better in all cases except for the transmission of wealth. The high persistence of status over multiple generations, however, would require in a genetic mechanism strong genetic assortative in mating. This has been until recently believed impossible. There is however, also strong evidence consistent with just such sorting, all the way from 1837 to 2020. Thus the outcomes here are actually the product of an interesting genetics-culture combination.
The correlational results in the table below were originally deduced by Fisher under the assumption of additive genetic inheritance: h2 is heritability, m is assortativity by genotype, r assortativity by phenotype. (Assortative mating describes the tendency of husband and wife to resemble each other more than randomly chosen M-F pairs in the general population.)
Fisher, R. A. 1918. “The Correlation between Relatives on the Supposition of Mendelian Inheritance.” Transactions of the Royal Society of Edinburgh, 52: 399-433
Thanks to Clark the predictions of Fisher's models, applied to social outcomes, can now be compared directly to data through many generations and across many branches of English family trees. (Figures below from the paper.)





The additive model fits the data well, but requires high heritabilities h2 and a high level m of assortative mating. Most analysts, including myself, thought that the required values of m were implausibly large. However, using modern genomic datasets one can estimate the level of assortative mating by simply looking at the genotypes of married couples. 

From the paper:
(p.26) a recent study from the UK Biobank, which has a collection of genotypes of individuals together with measures of their social characteristics, supports the idea that there is strong genetic assortment in mating. Robinson et al. (2017) look at the phenotype and genotype correlations for a variety of traits – height, BMI, blood pressure, years of education - using data from the biobank. For most traits they find as expected that the genotype correlation between the parties is less than the phenotype correlation. But there is one notable exception. For years of education, the phenotype correlation across spouses is 0.41 (0.011 SE). However, the correlation across the same couples for the genetic predictor of educational attainment is significantly higher at 0.654 (0.014 SE) (Robinson et al., 2017, 4). Thus couples in marriage in recent years in England were sorting on the genotype as opposed to the phenotype when it comes to educational status. 
It is not mysterious how this happens. The phenotype measure here is just the number of years of education. But when couples interact they will have a much more refined sense of what the intellectual abilities of their partner are: what is their general knowledge, ability to reason about the world, and general intellectual ability. Somehow in the process of matching modern couples in England are combining based on the weighted sum of a set of variations at several hundred locations on the genome, to the point where their correlation on this measure is 0.65.
Correction: Height, Educational Attainment (EA), and cognitive ability predictors are controlled by many thousands of genetic loci, not hundreds! 


This is a 2018 talk by Clark which covers most of what is in the paper.



For out of sample validation of the Educational Attainment (EA) polygenic score, see Game Over: Genomic Prediction of Social Mobility.

 

Wednesday, October 14, 2020

Election 2020: quant analysis of new party registrations vs actual votes

I think we should ascribe very high uncertainty to polling results in this election, for a number of reasons including the shy Trump voter effect as well as the sampling corrections applied which depend heavily on assumptions about likely turnout. 

Graphs below are from a JP Morgan quant analysis of changes in number of registered voters by party and state, and the correlation with actual votes in subsequent election. Of course it is possible that negative covid impact has largely counteracted the effect discussed below (which is an integrated effect over the last 4 years) -- i.e., Trump was in a strong position at the beginning of 2020 but has declined since then. 

This is an unusual election for a number of reasons so it's quite hard to call the outcome. There's also a good chance the results on election night will be heavily contested.

The author of this analysis is Marko Kolanovic, Global Head of Macro Quantitative and Derivatives Strategy at J.P. Morgan. He graduated from New York University with a PhD in theoretical high-energy physics.

Anyone with high conviction about the election is welcome to post their analysis in the comments.

Tuesday, June 02, 2020

Re-Post: Joe Cesario on Police Decision Making and Racial Bias in Deadly Force Decisions (Manifold Episode #11)

Re-posting this because of its relevance to the terrible events in Minneapolis.

Manifold Episode #11: Joe Cesario on Police Decision Making and Racial Bias in Deadly Force Decisions




Manifold Show Page    YouTube Channel

Corey and Steve talk with Joe Cesario about his recent work which argues that, contrary to activist claims and media reports, there is no widespread racial bias in police shootings. Joe discusses his analysis of national criminal justice data and his experimental studies with police officers in a specially designed realistic simulator. He maintains that racial bias does exist in other uses of force such as tasering but that the decision to shoot is fundamentally different: it is driven by specific events and context, rather than race.

Cesario is associate professor of Psychology at Michigan State University. He studies social cognition and decision-making. His recent topics of study include police use of deadly force and computational modeling of fast decisions. Cesario is dedicated to reform in the practice, reporting, and publication of psychological science.

Is There Evidence of Racial Disparity in Police Use of Deadly Force? Analyses of Officer-Involved Fatal Shootings in 2015–2016
https://journals.sagepub.com/doi/abs/...

Example of officer completing shooting simulator
https://youtu.be/Le8zoqk-UVo

Overview of Current Research on Officer-Involved Shootings
https://www.cesariolab.com/police

Joseph Cesario Lab
https://www.cesariolab.com/


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.


Thursday, March 05, 2020

Kaja Perina on the Dark Triad: Narcissism, Machiavellianism, and Psychopathy - Manifold Podcast #36



Kaja Perina is the Editor in Chief of Psychology Today. Kaja, Steve, and Corey discuss so-called Dark Triad personality traits: Narcissism, Machiavellianism, and Psychopathy. Do these traits manifest more often in super successful people? What is the difference between Sociopathy and Psychopathy? Are CEOs often "warm sociopaths"? Can too much empathy be a liability? Corey laments Sociopathy in academic Philosophy. Kaja explains the operation of Psychology Today. Steve reveals his Hypomania diagnoses.

2:33 - Psychopathology and the Dark Triad
11:34 - Do these traits manifest more often in super successful people?
17:52 - Can too much empathy be a liability?
35:16 - Corey laments Sociopathy in academic Philosophy
50:32 - Kaja explains the operation of Psychology Today
1:01:06 - Steve reveals his Hypomania diagnoses

Transcript

Kaja Perina (Psychology Today)

Related: Nice Guys Finish Last (2012 post), more Hypomania


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Wednesday, October 02, 2019

Harvard Discrimination Lawsuit: Judge Burroughs on Racial Balancing and "Unhooked" Applicants

As has been widely reported (WSJ):
U.S. District Judge Allison Burroughs found that Harvard’s practices were “not perfect” and could use improvements, including implicit bias training for admissions officers, but said “the Court will not dismantle a very fine admissions program that passes constitutional muster, solely because it could do better.”
I anticipate that this case will end up before the Supreme Court.

While I have not read the entire decision (PDF), I was curious to see how two important arguments made by the plaintiffs (Students For Fair Admissions, SFFA) were addressed. You can evaluate Burroughs' logic and use of evidence for yourself. In the excerpts below I first quote from the SFFA filing, and then from the decision.

Issue #1: Racial Balancing:
SFFA: ... Harvard is engaging in racial balancing. Over an extended period, Harvard’s admission and enrollment figures for each racial category have shown almost no change. Each year, Harvard admits and enrolls essentially the same percentage of African Americans, Hispanics, whites, and Asian Americans even though the application rates and qualifications for each racial group have undergone significant changes over time. This is not the coincidental byproduct of an admissions system that treats each applicant as an individual; indeed, the statistical evidence shows that Harvard modulates its racial admissions preference whenever there is an unanticipated change in the yield rate of a particular racial group in the prior year. Harvard’s remarkably stable admissions and enrollment figures over time are the deliberate result of systemwide intentional racial discrimination designed to achieve a predetermined racial balance of its student body.
This is a relevant figure from the Economist. It shows the increase in Asian representation at Caltech (mostly race-neutral admissions), tracking the overall population of college age Asian Americans, versus the suspicious Ivy league convergence at 15-20% of each class.


From page 80 of the decision:
Although Harvard tracks and considers various indicators of diversity in the admissions process, including race, the racial composition of Harvard’s admitted classes has varied in a manner inconsistent with the imposition of a racial quota or racial balancing. See [Oct. 31 Tr. 119:10–121:10; DX711]. As Figures 1 and 2 show, there has been considerable year-to-year variation in the portion of Harvard’s class that identifies as Asian American since at least 1980.   [ italics mine ]
Figure 1 seems merely to show that admittance by race tends to fluctuate by 5-10% from year to year. No attempt at analysis of correlations across years -- i.e., to detect racial balancing.


Figure 2 seems to show that Asian American applicants are a smaller fraction of the class relative to their share of the applicant pool, whereas, e.g., this ratio is reversed for African Americans. Racial balancing would be found only in detailed comparisons of these ratios across several years, adjusting for strength of application, etc.


Rather than giving a serious analysis of racial balancing (is it actually happening?), Burroughs seems to explicitly support the practice in her comments on racial diversity:
p.30 To summarize the use of race in the admissions process, Harvard does not have a quota for students from any racial group, but it tracks how each class is shaping up relative to previous years with an eye towards achieving a level of racial diversity that will provide its students with the richest possible experience. It monitors the racial distribution of admitted students in part to ensure that it is admitting a racially diverse class that will not be overenrolled based on historic matriculation rates which vary by racial group. [ Isn't this just a definition of racial balancing? ]
Quota Bad, Soft-Quota Good! Is this now the law of the land in the United States of America? SCOTUS here we come...


Issue #2: Is discrimination against Asian Americans especially obvious when one considers "unhooked" applicants separately?
SFFA: ... The task here is to determine whether “similarly situated” applicants have been treated differently on the basis of race; “apples should be compared to apples.” SBT Holdings, LLC v. Town of Westminster, 547 F.3d 28, 34 (1st Cir. 2008). Because certain applicants are in a special category, it is important to analyze the effect of race without them included. Excluding them allows for the effect of race to be tested on the bulk of the applicant pool (more than 95% of applicants and more than two-thirds of admitted students) that do not fall into one of these categories, i.e., the similarly situated applicants. For special-category applicants, race either does not play a meaningful role in their chances of admission or the discrimination is offset by the “significant advantage” they receive. Either way, they are not apples.

Professor Card’s inclusion of these applicants reflects his position that “there is no penalty against Asian-American applicants unless Harvard imposes a penalty on every Asian-American applicant.” But he is not a lawyer and he is wrong. It is illegal to discriminate against any Asian-American applicant or subset of applicants on the basis of race. Professor Card cannot escape that reality by trying to dilute the dataset. The claim here is not that Harvard, for example, “penalizes recruited athletes who are Asian-American because of their race.” The claim “is that the effects of Harvard’s use of race occur outside these special categories.” Professor Arcidiacono thus correctly excluded special-category applicants to isolate and highlight Harvard’s discrimination against Asian Americans. Professor Card, by contrast, includes “special recruiting categories in his models” to “obscure the extent to which race is affecting admissions decisions for those not fortunate enough to belong to one of these groups.” At bottom, SFFA’s claim is that Harvard penalizes Asian-American applicants who are not legacies or recruited athletes. Professor Card has shown that he is unwilling and unable to contest that claim.
The judge seems to have ignored or rejected the claim that discrimination within the pool of unhooked applicants (95% of the total!) is worth considering on its own. This seems to be an entirely legal (as opposed to statistical) question that may be tested in the appeal. (ALDC = Athletes, Legacies, Deans interest list (donors), and Children of Harvard faculty.)
p.52 Although ALDCs represent only a small portion of applicants and are admitted or rejected through the same admissions process that applies to other applicants, they account for approximately 30% of Harvard’s admitted class. [Oct. 30 Tr. 153:6–154:8, DX706; DD10 at 38, 40]. For reasons discussed more fully infra at Section V.F, the Court agrees with Professor Card that including ALDCs in the statistics and econometric models leads to more probative evidence of the alleged discrimination or lack thereof.
See also Former Yale Law Dean on Harvard anti-Asian discrimination case: The facts are just so embarrassing to Harvard... Quotas and a climate of dishonesty and comments therein.

Thursday, September 05, 2019

Manifold podcast #18: Rebecca Campbell on Identifying Serial Perpetrators, Rape Investigations, and Untested Rape Kits



Dr. Rebecca Campbell is Professor of Psychology at Michigan State University. Her research focuses on violence against women and children with an emphasis on sexual assault. Steve and Corey discuss her recent National Institute of Justice-funded project to study Detroit’s untested rape kits. Dr. Campbell describes the problem of untested kits and her work with police departments around the country to reduce the backlog. She explains how the use of the national CODIS database has led to sharply higher estimates of the proportion of rapes committed by serial perpetrators and how many rapists appear to be criminal “generalists” -- committing a wide range of offenses. She describes the dynamics of sexual assault investigations, the factors that lead police to put more effort into investigating certain cases over others, and how police questioning of women can lead them to disengage from the process. Other topics include the incentives at work in law enforcement, the slow pace at which new research in DNA testing and treatment of victims is incorporated into police training, and Dr. Campbell’s efforts to engage with law enforcement agencies to improve investigative practices.

Transcript

Additional links to research articles and media coverage


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

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