- Solicit Ideas
Talk to customers, analyze usage data, talk to peers, managers, think, hold competitions, raffles,…
For each idea:
- Define the idea
Write out the idea for clarity — at least 5–10 sentences. Do some due diligence to see what else is there. Learn and revise (including abandon).
- Does the idea make business sense? Does it make sense to the developers (if the idea is mechanistic or implementation related)?
Ideally, peer review. But at the minimum: Talk about your idea with the manager(s) and the developers. If both manager(s) and developer(s) agree (for some mechanistic things, only developers need to agree), move to the next step.
This is optional, and for major, complex innovations that can be easily prototyped only. Write code. Produce Results. Does it make sense? Peer review, if needed. If not, abandon. If it does, move to the next step
- Write the specifications
Spend no less than 20% of the entire development time on writing the specs, including proposed functions, options, unit tests, concerns, implications. Run the specifications by developers, get this peer reviewed, improve, and finalize.
- Set Priority and Release Target
Talk to the manager about the priority order of the change, and assign it to a particular release cycle.
- Who Does What by When?
Create JIRA ticket(s)
General cycle = code, test -> peer review -> code, test -> peer review …
MVP of Expected Code or Aspects of good code: ‘Code your documentation’ (well-documented), modular, organized, tested, nice style, profiled. Do it once, do it well.
The best kind of insight is the ‘duh’ insight — catching something that is exceedingly common, almost routine, but something that no one talks about. I believe this is one such insight—the standards for citing congenial research (that supports the hypothesis of choice) are considerably lower than the standards for citing uncongenial research. It is an important kind of academic corruption. And it means that the prospects of teleological progress toward truth within science, as currently practiced, are bleak. An alternate ecosystem that provides objective ratings for each piece of research is likely to be more successful. (As opposed to the ‘echo-system’—here are the people who find stuff that ‘agrees’ with what I find—in place today.)
An empirical implication of the point is that the average ranking of journals in which congenial research that is cited is published is likely to be lower than in which uncongenial research is published. Though, for many of the ‘conflicts’ in science, all sides of the conflict will have top-tier publications —-which is to say that the measure is somewhat crude.
The deeper point is that readers generally do not judge the quality of the work cited for support of specific arguments, taking many of the arguments at face value. This, in turn, means that the role of journal rankings is somewhat limited. Or more provocatively, to improve science, we need to make sure that even research published in low ranked journals is of sufficient quality.
Limited Time Only
Lifespan ~ 80 years*52 weeks/year ~ 4k weeks
If you are 40: 2k weeks to go.
If you are 70: another 500–700 weeks.
Interpolate for rest.
If you read a book a week for 50 years, you will read ~ 2500 books.
Approximately 130M books have been published.
India’s PPP adjusted median per capita income was $1,497/yr in 2013. So ~ 550M people < $1500/yr.
- Since it is PPP wage, think about living on $1500/year in the US
- Another way to think about it:
Wage of median American working for a year ($26,964) > wage of 18 Indians working for a year
- Yet another way to think about it:
Avg. career ~ 35 years (30–65)
What median American will earn in 2 yrs = lifetime earning of an Indian earning median income
- Yet another way to think about it:
median CEO/median worker wage ~ 4x
median US Worker/median Indian worker ~ 18x
- Yet another way to think about it:
From a company’s perspective, median American has to be more than 45x productive (where shipping 0, as in lots of digital products) than median Indian or outsourcing or automation makes more sense.
Three goals: impart information, spur deep(er) thought about the topic (and the social world more generally), and inculcate care in thinking. As is perhaps clear, working towards achieving any one of these goals creates positive externalities that help achieve other goals. For instance, care in exposition, which is a necessary (though not sufficient) condition for imparting correct information, may also inadvertently produce —either through mimesis, or further thought—care in how students think about questions.
Supplement such synergies by actively seeking and utilizing pertinent opportunities during both, class-wide discussions about the materials, and one-to-one discussions about research projects, to raise (and clarify) relevant points. During discussions, encourage students to seriously consider questions about epistemology, fundamental to science but also more generally to reasoning and discourse, by weaving in questions such as, “What is the claim that we are making?”, and “When can we make this claim and why?”.
Some of the epistemological questions are most naturally (and perhaps best) handled when students are engaged in working on their own research projects. Guiding students as they collect and analyze their own data provides unique opportunities to discuss issues related to research design, and logic. And it is my hunch that students are more engaged with the material (and hence learn more of it, and think more about it) when they work on their own projects than when asked to learn the materials through lectures alone. For instance, undergraduates at Stanford often excel at knowing the points made in the text, but often have yet to spend time thinking about the topic itself. My sense is (and some experience corroborates it) that thinking broadly about an issue allows students to gain new insights, and helps them contextualize their findings better. It also spurs curiosity about the social world and the broader set of questions about society. Hence, in addition to the above, ask students to discuss the topics that they are working on more generally, and think carefully and deeply about what else could be going on.
The paucity of women in Computer Science, Math and Engineering in the US is justly widely lamented. Sometimes, the imbalance is attributed to gender stereotypes. But only a small fraction of men study these fields. And in absolute terms, the proportion of women in these fields is not a great deal lower than the proportion of men. So in some ways, the assertion that these fields are stereotypically male is in itself a misunderstanding.
For greater clarity, a contrived example: Say that the population is split between two similar sized groups, A and B. Say only 1% of Group A members study X, while the proportion of Group B members studying X is 1.5%. This means that 60% of those to study X belong to Group B. Or in more dramatic terms: activity X is stereotypically Group B. However, 98.5% of Group B doesn’t study X. And that number is not a whole lot different from 99%, the percentage of Group A that doesn’t study X.
When people say activity X is stereotypically Group B, many interpret it as ‘activity X is quite popular among X.’ (That is one big stereotype about stereotypes.) That clearly isn’t so. In fact, the difference between the preferences for studying X between Group A and B — as inferred from choices (assuming same choices, utility) — is likely pretty small.
Obliviousness to the point is quite common. For instance, it is behind arguments linking terrorism to Muslims. And Muslims typically respond with a version of the argument laid out above—they note that an overwhelming majority of Muslims are peaceful.
One straightforward conclusion from this exercise is that we may be able to make headway in tackling disciplinary stereotypes by elucidating the point in terms of the difference between p(X|Group A) and p(X| Group B) rather than in terms of p(Group A | X).
A few commercial publishers publish a large chunk of top flight of academic research. And earn a pretty penny doing so. The standard operating model of the publishers is as follows: pay the editorial board no more than $70-$100k, pay for typesetting and publishing, and in turn get copyrights to academic papers. And then go on and charge already locked in institutional customers—university and government libraries—and ordinary scholars extortionary rates. The model is gratuitously dysfunctional.
Assuming there are no long term contracts with the publishers, the system ought to be rapidly dismantled. But if dismantling is easy, creating something better may not be. It just happens to be. A majority of the cost of publishing is in printing on paper. Twenty first century has made printing large organized bundles on paper largely obsolete; those who need it can print on paper at home. Beyond that, open source software for administering a journal already exists. And the model of a single editor with veto powers seems anachronistic. Editing duties can be spread around much like peer review. As unpaid peer review can survive as it always has, though better mechanisms can be thought about. If some money is still needed for administration, it could be gotten easily by charging a nominal submission tax, waived where the author self identifies as being unable to pay.
Many companies offer employees ‘benefits.’ These include paying for healthcare, investment plans, company gym, luncheons etc. (Just ask a Silicon Valley tech. employee for the full list.)
But why ‘benefits’? And why not cash?
A company offering a young man zero down healthcare plan seems a bit like within-company insurance. Post Obamacare — it also seems a bit unnecessary. (My reading of Obamacare is that it just mandates companies pay for the healthcare but doesn’t mandate that how they pay for it. So cash payments ought to be ok?)
For investment, the reasoning strikes me as thinner still. Let people decide what they want to do with their money.
In many ways, benefits look a bit as ‘gifts’ — welfare reducing but widespread.
My recommendation: just give people cash. Or give them an option to have cash.
Many people conceive of intelligence as a CPU. To them, being more intelligent means having a faster CPU. And this is despairing as the clock speed is largely fixed. (Prenatal and childhood nutrition can make a sizable difference, however. For instance, iodine deficiency in children causes mental retardation.)
But people misconceive intelligence. Intelligence is not just the clock speed of the CPU. It is also the short-term cache and the OS.
Clock speed doesn’t matter much if there isn’t a good-sized cache. The size of short-term memory matters enormously. And the good news is that we can expand it with effort.
A super fast CPU with a large cache is still only as good as the operating system. If people know little or don’t know how to reason well, they generally won’t be smart. Think of the billions of people who came before we knew how to know (science). Some of those people had really fast CPUs. But many of them weren’t able to make much progress on anything.
The chances an ignoramus who doesn’t know correlation isn’t causation will come across as stupid are also high. In fact, we often mistake being knowledgeable and possessing rules of how to reason well for being intelligent. Though it goes without saying, it is good to say it: how much we know and knowledge of how to reason better is in our control.
Lastly, people despair because they mistake skew for the variance. People believe there is a lot of variance in processing capacity. My sense is that variance in the processing capacity is low, and the skew high. In layman’s terms, most people are as smart as the other with very few very bright people. None of this is to say that the little variance that exists is not consequential.
Toward a Better OS
Ignorance of relevant facts limits how well we can reason. Thus, increasing the repository of relevant facts at hand will help you reason smarter. If you don’t know about something, that is ok. Today, the world’s information is at your finger tips. It will take you some time to go through things but you can become informed about more things than you think possible.
Besides knowledge, there are some ‘frameworks’ of how to approach a problem. For instance, management consultants have something called MECE. This ‘framework’ can help you reason better about a whole slew of problems. There are likely others.
Besides reasoning frameworks, there are simple rules that can help you reason better. You can look up books devoted to common errors in thinking, and use those to derive the rules. The rules can look as follows:
1. Correlation is not the same as causation
2. Replace categorical thinking with continuous where possible, and be precise. For e.g., rather than claim that ‘there is a risk’, quantify the risk. Or replace the word possibility with probability where applicable
People seem to believe that increasing levels of education will reduce economic inequality. However, it isn’t clear if the policy is empirically supported. Here are some potential ways increasing levels of education can impact economic inequality:
- As Grusky argues, the current high wage earners, whose high wages depend on education and lack of competition from similarly educated men and women (High Education Low Competition or HELCO) from similarly highly educated, will start earning a lower wage because of increased competition (thereby reducing inequality). This is assuming that HELCO won’t respond by trying to burnish their education credentials, etc. This is also assuming that HELCO exists as a large class. What likely exists is success attributable to networks, etc. That kind of advantage cannot be blunted by increasing education of those not in the network.
- Another possibility is that education increases the number of high paying jobs available in the economy and it raises the boats of non-HELCO more than HELCO.
- Another plausible scenario is that additional education produces only a modest effect with non-HELCO still mostly doing low paying jobs. This may due to only a modest increase in overall availability of ‘good jobs.’ Already easy access to education has meant that many a janitor and store clerk walk around with college degrees (see Why Did 17 Million Students Go to College?, and The Underemployed College Graduate).
Without an increase in ‘good jobs,’ the result of an increase in education is an increased heterogeneity in who succeeds (random draw at the extreme) but no change in the proportion of successful people. Or, increasing equality of opportunity (a commendable goal) but not reduction in economic inequality (though in a multi-generation game, it may even out). Increasing access to education also has the positive externality of producing a more educated society, another worthy goal.
How plentiful ‘good’ jobs are depends partly on how the economic activity is constructed. For instance, there may have once have been a case for only hiring one ‘super-talented person’ (say ‘superstar’) for a top shelf job (say CEO). Now we have systems that can harness the wisdom of many. It is also plausible that that wisdom is greater than that of the superstar. It reasons then that the superstar is replaced; economic activity will be more efficient. Or else let other smart people who can contribute equally (if educated) be recompensed alternately for doing work that is ‘beneath them.’
The Internet has revolutionized the dissemination of misinformation. Easy availability of incorrect information, gullible and eager masses, and ease of sharing has created fertile conditions for misinformation epidemics.
While a fair proportion of misinformation is likely created deliberately, it may well spread inadvertently. Misinformation that people carry is often no different than fact to them. People are likely to share misinformation with the same enthusiasm as they would fact.
Attitude congenial misinformation is more likely to be known (and accepted as fact), and more likely to be enthusiastically shared with someone who shares the same attitude (for social, and personal rewards). Misinformation considered useful is also more likely to be shared, e.g. (mis)-information about health related topics.
The chance of acceptance of misinformation may be greater still if people know little about the topic, or if they have no reason to think that the information is motivated. Lastly, these epidemics are more likely to take place among those less familiar with technology.