The path towards understanding and appreciating conservatism in Silicon Valley is through complexity. Here, let's start at the beginning. Complexity as it is understood here starts with MIT Professor Edward Lorenz and his discovery of the error amplification effects of feedback-based systems. While attending complexity conferences, one hears the team "complexity" repeated too frequently, almost like a mantra. For Lorenz though, the complex combination of nonlinearity, stock-flow (integrative), and feedback effects in his 1960s-era different equation-based models led to unexpected amplification of small initial differences.
At approximately the same time, in 1965, Friedrich Hayek applied new notions of complexity to politics and economics. Those who have been paying attention know Hayek from two career highlights: (1) The Road to Serfdom; and (2) his becoming a Nobel laureate in Economics in 1974. Rather than running though this work, let us instead focus on the matter at hand, how does complexity impact and inform conservatism? Complexity generally, and the complexity of socio-economic systems specifically, provides hard limits to what is knowable about these systems. So as political entrepreneurs and purported innovators make claims in the marketplace of ideas, complexity places limits on what can be said.
The problem comes in that these same entrepreneurs and innovators don't want to be constrained by what they can say. Hayek explains at length how such claims, especially as they relate to and regard socialism, have demonstrated themselves to be false. Your correspondent agrees with Hayek's conclusion but will leave that argument for a later time.
What is especially informative is how these political entrepreneurs respond to complexities limitations: in the words of Tom Wolfe, they scream like weenies roasting over a fire. The best example I've found is Herman Finer's The Road to Reaction. It's a remarkable example of what I call an "affect storm," or what others call "argument by outrage." While purporting to be logical and rational, Finer's book is a collection of half-thought-through accusations, what Hayek called, ""a specimen of abuse and invective which is probably unique in contemporary academic discussion." It's instructive to consider the incentives behind Finer's comments, and here we need to distinguish between good reasons and real reasons. Finer, being a Fabian socialist, portrays a concern for society's poorest. Here your correspondent draws insight from Tom Wolfe who credits his prescience to a focus on status. So in your correspondent's opinion, Finer's affect-laden response to Hayek is driven by the implications and consequences of complexity: that is, he can no longer claim special insight into socio-economic systems, which would limits his status and power. This in Finer's view, is clearly unacceptable.
But much of the politics since the 1940s has been driven by just this tension between what is knowable and what is not as limited by complexity, which is a conservative concept. And that is what we see amplified in 21st century politics: this analytic tension between what can be known, and what is not knowable. The computer revolution, and represented by Silicon Valley, has something useful and important to say about this complexity by pushing the boundaries of what is knowable. However, this can only happen if senior policy makers and those interested in policy acknowledge these limitations, which is controversial and problematic.
Friday, August 12, 2016
What's more conservative, EE or CS?
As a Silicon Valley (SV) conservative, I was thinking to
myself, “Which is more conservative, electrical engineering (EE) or computer
science (CS)?” Let me allow the reader – whoever that might be – to consider
the question briefly before continuing. (Jeopardy theme)
Before diving into the answer, let’s first establish the conceptual
context. The foundation of conservatism -- and through inverse causality,
liberalism as well – is the fact-value
distinction, with conservatism associated with the fact side and liberalism
with values.
So how do EE and CS compare in the fact-value distinction.
The answer should be pretty obvious to anybody who’s studied both, EE is more
fact oriented because it is a branch of and grounded in physics, while CS is
more virtual, conceptual, and abstract. With EE, if you’re unsure about the ohms
(units of resistance), farads (capacitance), or henrys (inductance) of a device,
one can measure it. With CS, if you need more of whatever it is – memory, objects,
or agents – one just defines more. Now there are limits to this argument, but
CS is related more to math than physics – and then it doesn’t even need to be
that related to math.
These observation might explain why SV is so overwhelming
liberal, which is to say Democrat. EE is a discipline of design within
constraint, which CS and the products associated with it, are comparatively
less constrained by physics, or reality for that matter.
Monday, August 8, 2016
What does it mean to be an SV conservative?
One of the questions I cannot quite shake is this: Why is Silicon Valley so gosh-darned liberal? I grew up there and became a conservative of sorts. I grew up reading the San Francisco Chronicle and wondered at the insane decisions of so-called liberal adults whose politics and policies were ever so easy to criticize. But increasingly, instead of merely criticizing liberals, I increasingly wonder what it means to be a conservative?
There are a couple of insights that illuminate for me what being a conservative is. The first comes from the Gospels according to Matthew and Luke in which Jesus says that, "a tree is known by its fruits." My interpretation, in the "here" and "now", that names and reputations are based on the long-term consequences of a person, policy, or population. Note that this is very different from our modern-day media-driven democratic world of reputation based on short-term group opinion.
Second, Tom Wolfe's "The Great Relearning," provides multiple examples of the progressive ideas of intellectuals being implemented only to end in disaster. Wolfe's primary example is the sexual revolution, which was implemented with such fanfare but generated a of unanticipated, and sometimes anticipated, consequences.
Third, this focus on consequences leads, in turn, to a focus on complexity generally and causal complexity specifically. That is, what makes consequences "unintended" is that human cognition is not very good at anticipating the consequences of large policy changes because of the complexity of the social systems involved. However, power computers, the kinds created and used by Silicon Valley engineers, can be used to address and account for that complexity.
However, there is a division in Silicon Valley between analysis used for accurate prediction, such as that used for engineering design and corporate operations, and that used for sales and marketing. That is, sales and marketing rely more on affect and emotion rather than cognition and accurate analysis. And here is the key division between standard liberal and much rarer conservative beliefs in Silicon Valley: the former generates sales and votes based on affect, while the latter generates more accurate analysis based on accurate cognition and accounting for complexity.
As the costs of affect and profit-driven policy analysis mount and become ever more apparent, the need to engage in accurate, engineering design-like policy analysis will increase, but given the tenor of America's 2016 presidential election, that time is still some ways off.
There are a couple of insights that illuminate for me what being a conservative is. The first comes from the Gospels according to Matthew and Luke in which Jesus says that, "a tree is known by its fruits." My interpretation, in the "here" and "now", that names and reputations are based on the long-term consequences of a person, policy, or population. Note that this is very different from our modern-day media-driven democratic world of reputation based on short-term group opinion.
Second, Tom Wolfe's "The Great Relearning," provides multiple examples of the progressive ideas of intellectuals being implemented only to end in disaster. Wolfe's primary example is the sexual revolution, which was implemented with such fanfare but generated a of unanticipated, and sometimes anticipated, consequences.
Third, this focus on consequences leads, in turn, to a focus on complexity generally and causal complexity specifically. That is, what makes consequences "unintended" is that human cognition is not very good at anticipating the consequences of large policy changes because of the complexity of the social systems involved. However, power computers, the kinds created and used by Silicon Valley engineers, can be used to address and account for that complexity.
However, there is a division in Silicon Valley between analysis used for accurate prediction, such as that used for engineering design and corporate operations, and that used for sales and marketing. That is, sales and marketing rely more on affect and emotion rather than cognition and accurate analysis. And here is the key division between standard liberal and much rarer conservative beliefs in Silicon Valley: the former generates sales and votes based on affect, while the latter generates more accurate analysis based on accurate cognition and accounting for complexity.
As the costs of affect and profit-driven policy analysis mount and become ever more apparent, the need to engage in accurate, engineering design-like policy analysis will increase, but given the tenor of America's 2016 presidential election, that time is still some ways off.
Saturday, July 16, 2016
Hail Caesar!
Philosophy, region, and the philosophy of religion can appear in the most unexpected places. Why just the other week, I was watching the movie Hail Caesar!, when a insight hit me. The movie features both priests discussing the Trinity and communists, led by Herbert Marcuse, discussing the dialectic, which is itself unusual for a movie. The latter made me nostalgic for graduate school. However, the nature of both concepts -- the Trinity and Hegelian dialectic -- are complex, which means "hard to explain." In both cases people -- both Christians and communists -- argue hilariously. The sum total of both debates can be summed up by the Rabbi: "These men are screwballs!"
But the key insight is that, viewed from a certain perspective, neither the Christians nor the communists are screwballs. Instead, both are using the ill-suited but until recently the only tools available -- propositional logic and prose -- are inadequate for the task. Instead there are what are called feedback relationships that pervade, comprise, and control social systems including political systems. These relationships, being complex and essentially mathematical (actually differential equations), are pervasive in electrical engineering but not so much in religion, politics, philosophy, or economics (PPE).
The question then becomes, how best to bring this insight into PPE? There are several insights that bear mentioning. First, the temporal and causal perspective is increased beyond what science traditionally can handle. Successful science experiments usually are rigorously controlled so that their causal factors and results can be clearly identified. While a useful enterprise when skillfully executed, such experiments are of limited utility for complex social systems that evolve over extended, multi-generation time periods. Recent results on wicked problems have begun to characterize how to think about complex, temporally extended policy problems. Narrative techniques are especially helpful to think about social structuring and policy problems, as with the Bible, but more about that later.
Sunday, July 3, 2016
Dogmatic Complexity
I've been reading Francis Hall's 10-part Dogmatic Theology -- though certainly not all of it -- and Ian Barbour's Religion and Science. As I read these two books, I find them, in some sense, fundamentally unsatisfying because they're primarily descriptive. That is, they decompose or split the problem into lots of separate parts and offer lots of definitions, but while useful, it doesn't really address the problem. It doesn't get at the underlying pattern, connections, or reality that underlies religious thought. These different pieces must instead be lumped together. Now critics will say it's impossible, and I get that, but I have an intuition that we know enough about complexity, complex system, and complex social systems that we can say something innovative and useful about both religion and policy.
They key to this project is lumping instead of splitting, but how does one do that? One must first identify a "way in," and that's provided through philosophy. There are a number of words that for me provide a way in though:
Negative, balancing feedback relationships result from an odd number of negative, which means change in the opposite direction, causal relationships. For example, the more people, the more deaths, but the more deaths, the fewer people. Marxists tend to say such relationships are "internally inconsistent," though a proper understanding of feedback relationships shows that they are both ubiquitous and confusing. Hence the lack of clarity that surrounds religion, policy, and the social sciences. These fields are all doubtlessly complex, but only recently, with the advent of powerful computing, are the tools available to address and account for this complexity. One limitation is that those who study social science usually have little expertise in the computational and mathematical techniques that underlie complexity. How far the complexity sciences can uncover, reveal, and inform social systems remains to be seen, but it is an endeavor that will certainly be worth the effort.
They key to this project is lumping instead of splitting, but how does one do that? One must first identify a "way in," and that's provided through philosophy. There are a number of words that for me provide a way in though:
- Teleology, which has been criticized as confusing causes and effects
- Trinitarianism, the concept of Father, Son, and Holy Ghost which postdates the Bible
- Dialecticalism, Hegel's concept of thesis, antithesis, and synthesis
Negative, balancing feedback relationships result from an odd number of negative, which means change in the opposite direction, causal relationships. For example, the more people, the more deaths, but the more deaths, the fewer people. Marxists tend to say such relationships are "internally inconsistent," though a proper understanding of feedback relationships shows that they are both ubiquitous and confusing. Hence the lack of clarity that surrounds religion, policy, and the social sciences. These fields are all doubtlessly complex, but only recently, with the advent of powerful computing, are the tools available to address and account for this complexity. One limitation is that those who study social science usually have little expertise in the computational and mathematical techniques that underlie complexity. How far the complexity sciences can uncover, reveal, and inform social systems remains to be seen, but it is an endeavor that will certainly be worth the effort.
Saturday, June 25, 2016
Complex Christianity
The desire to reconcile science and religion has been a goal since at least the Renaissance. Machiavelli's modernism broke with Europe's religious past by explicitly distinction between positive and normative -- doing what works as opposed to what the Church says one should do. Swedenborg sought to reconcile explicitly reconcile science and religion. Today others undertake the project to reconcile science and religion from a literary perspective, while others, such as Sam Harris, adopt a scientistic -- that is, an exaggerated and inappropriately applied -- perspective to attack faith and religion.
The problem, as Sam Harris should understand, is that science as it is practiced is generally inapplicable to the social sciences, philosophy, and the humanities. Science is based on the experimental method, which Richard Feynman pithily defined as first guessing a new law, second performing an experiment, and third, comparing the guess to the experiment's results. If the guess doesn't square with experiment, then it's wrong, and that is the key to science.
What's great for particle physics though may not be appropriate for public policy due to the complexity of the latter. That is, with longer time-frames and more moving parts than a physics experiment, public policy in the real world is a different animal altogether. Christianity understands and accounts for this when Jesus noted that a tree is known by its fruits in both the Gospel according to Luke 6:43-45 and Matthew 7:15-20. These two passages recognize that there are short- and long-term consequences that can work in opposition to each other, and it is the long-term consequences by which policies and actions should be judged. This are confounded and made unclear by the inherent complexity of social systems.
There are multiple examples that could be developed the demonstrate the importance and unpredictability of long-term consequences:
The problem, as Sam Harris should understand, is that science as it is practiced is generally inapplicable to the social sciences, philosophy, and the humanities. Science is based on the experimental method, which Richard Feynman pithily defined as first guessing a new law, second performing an experiment, and third, comparing the guess to the experiment's results. If the guess doesn't square with experiment, then it's wrong, and that is the key to science.
What's great for particle physics though may not be appropriate for public policy due to the complexity of the latter. That is, with longer time-frames and more moving parts than a physics experiment, public policy in the real world is a different animal altogether. Christianity understands and accounts for this when Jesus noted that a tree is known by its fruits in both the Gospel according to Luke 6:43-45 and Matthew 7:15-20. These two passages recognize that there are short- and long-term consequences that can work in opposition to each other, and it is the long-term consequences by which policies and actions should be judged. This are confounded and made unclear by the inherent complexity of social systems.
There are multiple examples that could be developed the demonstrate the importance and unpredictability of long-term consequences:
- Cathedral of Christ the Savior in Moscow
- Tom Wolfe's Great Relearning
- California and its Democrat excesses
- Venezuela
Friday, June 24, 2016
Engineering as Policy Dogma
One of the enduring questions that I have is this: Why are intellectuals generally and Silicon Valley specifically so darned liberal. I don't have any real good answers to this, but I do have some thoughts regarding why engineers should be conservative if you consider that to mean being on the fact side of the fact-value distinction.
The foundation for this argument rests in dogma, a set of beliefs that are not questioned. For me, as an engineer, my dogma is engineering. I believe what I learned as an engineer because I've proved to myself that these rules work. Specifically, I refer to control theory, information theory, and computational theory, all of which impact policy and influence policy resistance through complexity. Now I've had arguments with philosophy professors who take as their dogma democracy theory, social justice theory, or critical legal theory, but none of these can come close to the trust or reliability offered by engineering. If the engineering 3 are incompatible with the philosophy 3, then that says more about the latter than the former. After all, the engineering 3 are all based on logic and rules, so why should they be incompatible with philosophy?
So how this argument manifest itself? First, Hayek in his 1967 essay "Complexity" talked about how advanced math and computation impacted policy and analysis. Second, Jay Forrester's system dynamics explicitly applied engineering to policy problems by accounting for aspects of causal complexity such as feedback relationships, stock-flow (integrative) relationships, and nonlinear relationships. Each of these causal relationships can confuse the human mind, but all three combined make even fairly simple policy problems hard to predict.
But you see politicians, professors, and policy professionals regularly acting like they know it all when they don't. This lesson was brought home to me when I had coded up a fairly large software system--over 5000 lines--and I didn't know how it would react when I changed it. So I made changes very carefully and always ensured I could get back to a knowable state when making changes--that is, I took pains to conserve the system. I took these precautions even though I knew more about that code base than anybody else on the planet and the code system was simple compared to social systems. And yet so-called experts and elite leaders regularly recommend massive, huge, and irreversible changes to systems about which they know very little. Any disinterested analysis would reveal that such changes are good for them but probably not so good for the system itself.
The foundation for this argument rests in dogma, a set of beliefs that are not questioned. For me, as an engineer, my dogma is engineering. I believe what I learned as an engineer because I've proved to myself that these rules work. Specifically, I refer to control theory, information theory, and computational theory, all of which impact policy and influence policy resistance through complexity. Now I've had arguments with philosophy professors who take as their dogma democracy theory, social justice theory, or critical legal theory, but none of these can come close to the trust or reliability offered by engineering. If the engineering 3 are incompatible with the philosophy 3, then that says more about the latter than the former. After all, the engineering 3 are all based on logic and rules, so why should they be incompatible with philosophy?
So how this argument manifest itself? First, Hayek in his 1967 essay "Complexity" talked about how advanced math and computation impacted policy and analysis. Second, Jay Forrester's system dynamics explicitly applied engineering to policy problems by accounting for aspects of causal complexity such as feedback relationships, stock-flow (integrative) relationships, and nonlinear relationships. Each of these causal relationships can confuse the human mind, but all three combined make even fairly simple policy problems hard to predict.
But you see politicians, professors, and policy professionals regularly acting like they know it all when they don't. This lesson was brought home to me when I had coded up a fairly large software system--over 5000 lines--and I didn't know how it would react when I changed it. So I made changes very carefully and always ensured I could get back to a knowable state when making changes--that is, I took pains to conserve the system. I took these precautions even though I knew more about that code base than anybody else on the planet and the code system was simple compared to social systems. And yet so-called experts and elite leaders regularly recommend massive, huge, and irreversible changes to systems about which they know very little. Any disinterested analysis would reveal that such changes are good for them but probably not so good for the system itself.
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