Privatizing Public Lands – Claim your 0.3 acres now!

BLM Public Lands Statistics show that the federal government holds about 643 million acres – about 2 acres for each person.

But what would you really get if these lands were transferred to the states and privatized by sale? Asset sales would distribute land roughly according to the existing distribution of wealth. Here’s how that would look:

The Forbes 400 has a net worth of $2.4 trillion, not quite 3% of US household net worth. If you’re one of those lucky few, your cut would be about 44,000 acres, or 69 square miles.

Bill Gates, Jeff Bezos, Warren Buffet, Mark Zuckerberg and Larry Ellison alone could split Yellowstone National Park (over 2 million acres).

The top 1% wealthiest Americans (35% of net worth) would average 70 acres each, and the next 19% (51% of net worth) would get a little over 5 acres.

The other 80% of America would split the remaining 14% of the land. That’s about a third of an acre each, which would be a good-sized suburban lot, if it weren’t in the middle of Nevada or Alaska.

You can’t even see the average person’s share on a graph, unless you use a logarithmic scale:


Otherwise, the result just looks ridiculous, even if you ignore the outliers:


Remembering Jay Forrester

I’m sad to report that Jay Forrester, pioneer in servo control, digital computing, System Dynamics, global modeling, and education has passed away at the age of 98.


I’ve only begun to think about the ways Jay influenced my life, but digging through the archives here I ran across a nice short video clip on Jay’s hope for the future. Jay sounds as prescient as ever, given recent events:

“The coming century, I think, will be dominated by major social, political turmoil. And it will result primarily because people are doing what they think they should do, but do not realize that what they’re doing are causing these problems. So, I think the hope for this coming century is to develop a sufficiently large percentage of the population that have true insight into the nature of the complex systems within which they live.”

I delve into the roots of this thought in Election Reflection (2010).

Here’s a sampling of other Forrester ideas from these pages:

The Law of Attraction

Forrester on the Financial Crisis

Self-generated seasonal cycles

Deeper Lessons



Market Growth

Urban Dynamics

Industrial Dynamics

World Dynamics




Dynamics of Term Limits

I am a little encouraged to see that the very top item on Trump’s first 100 day todo list is term limits:

* FIRST, propose a Constitutional Amendment to impose term limits on all members of Congress;

Certainly the defects in our electoral and campaign finance system are among the most urgent issues we face.

Assuming other Republicans could be brought on board (which sounds unlikely), would term limits help? I didn’t have a good feel for the implications, so I built a model to clarify my thinking.

I used our new tool, Ventity, because I thought I might want to extend this to multiple voting districts, and because it makes it easy to run several scenarios with one click.

Here’s the setup:


The model runs over a long series of 4000 election cycles. I could just as easily run 40 experiments of 100 cycles or some other combination that yielded a similar sample size, because the behavior is ergodic on any time scale that’s substantially longer than the maximum number of terms typically served.

Each election pits two politicians against one another. Normally, an incumbent faces a challenger. But if the incumbent is term-limited, two challengers face each other.

The electorate assesses the opponents and picks a winner. For challengers, there are two components to voters’ assessment of attractiveness:

  • Intrinsic performance: how well the politician will actually represent voter interests. (This is a tricky concept, because voters may want things that aren’t really in their own best interest.) The model generates challengers with random intrinsic attractiveness, with a standard deviation of 10%.
  • Noise: random disturbances that confuse voter perceptions of true performance, also with a standard deviation of 10% (i.e. it’s hard to tell who’s really good).

Once elected, incumbents have some additional features:

  • The assessment of attractiveness is influenced by an additional term, representing incumbents’ advantages in electability that arise from things that have no intrinsic benefit to voters. For example, incumbents can more easily attract funding and press.
  • Incumbent intrinsic attractiveness can drift. The drift has a random component (i.e. a random walk), with a standard deviation of 5% per term, reflecting changing demographics, technology, etc. There’s also a deterministic drift, which can either be positive (politicians learn to perform better with experience) or negative (power corrupts, or politicians lose touch with voters), defaulting to zero.
  • The random variation influencing voter perceptions is smaller (5%) because it’s easier to observe what incumbents actually do.

There’s always a term limit of some duration active, reflecting life expectancy, but the term limit can be made much shorter.

Here’s how it behaves with a 5-term limit:


Politicians frequently serve out their 5-term limit, but occasionally are ousted early. Over that period, their intrinsic performance varies a lot:


Since the mean challenger has 0 intrinsic attractiveness, politicians outperform the average frequently, but far from universally. Underperforming politicians are often reelected.

Over a long time horizon (or similarly, many districts), you can see how average performance varies with term limits:


With no learning, as above, term limits degrade performance a lot (top panel). With a 2-term limit, the margin above random selection is about 6%, whereas it’s twice as great (>12%) with a 10-term limit. This is interesting, because it means that the retention of high-performing politicians improves performance a lot, even if politicians learn nothing from experience.

This advantage holds (but shrinks) even if you double the perception noise in the selection process. So, what does it take to justify term limits? In my experiments so far, politician performance has to degrade with experience (negative learning, corruption or losing touch). Breakeven (2-term limits perform the same as 10-term limits) occurs at -3% to -4% performance change per term.

But in such cases, it’s not really the term limits that are doing the work. When politician performance degrades rapidly with time, voters throw them out. Noise may delay the inevitable, but in my scenario, the average politician serves only 3 terms out of a limit of 10. Reducing the term limit to 1 or 2 does relatively little to change performance.

Upon reflection, I think the model is missing a key feature: winner-takes-all, redistricting and party rules that create safe havens for incompetent incumbents. In a district that’s split 50-50 between brown and yellow, an incompetent brown is easily displaced by a yellow challenger (or vice versa). But if the split is lopsided, it would be rare for a competent yellow challenger to emerge to replace the incompetent yellow incumbent. In such cases, term limits would help somewhat.

I can simulate this by making the advantage of incumbency bigger (raising the saturation advantage parameter):


However, long terms are a symptom of the problem, not the root cause. Therefore it probably necessary in addition to address redistricting, campaign finance, voter participation and education, and other aspects of the electoral process that give rise to the problem in the first place. I’d argue that this is the single greatest contribution Trump could make.

You can play with the model yourself using the Ventity beta/trial and this model archive:

Climate and Competitiveness

Trump gets well-deserved criticism for denying having claimed that the Chinese invented climate change to make  US manufacturing non-competitive.


The idea is absurd on its face. Climate change was proposed long before (or long after) China figured on the global economic landscape. There was only one lead author from China out of the 34 in the first IPCC Scientific Assessment. The entire climate literature is heavily dominated by the US and Europe.

But another big reason to doubt its veracity is that climate policy, like emissions pricing, would make Chinese manufacturing less competitive. In fact, at the time of the first assessment, China was the most carbon-intensive economy in the world, according to the World Bank:


Today, China’s carbon intensity remains more than twice that of the US. That makes a carbon tax with a border adjustment an attractive policy for US competitiveness. What conspiracy theory makes it rational for China to promote that?

Feedback and project schedule performance

Yasaman Jalili and David Ford look take a deeper look at project model dynamics in the January System Dynamics Review. An excerpt:

Quantifying the impacts of rework, schedule pressure, and ripple effect loops on project schedule performance

Schedule performance is often critical to construction project success. But many times projects experience large unforeseen delays and fail to meet their schedule targets. The failure of large construction projects has enormous economic consequences. …

… the persistence of large project delays implies that their importance has not been fully recognized and incorporated into practice. Traditional project management methods do not explicitly consider the effects of feedback (Pena-Mora and Park, 2001). Project managers may intuitively include some impacts of feedback loops when managing projects (e.g. including buffers when estimating activity durations), but the accuracy of the estimates is very dependent upon the experience and judgment of the scheduler (Sterman, 1992). Owing to the lack of a widely used systematic approach to incorporating the impacts of feedback loops in project management, the interdependencies and dynamics of projects are often ignored. This may be due to a failure of practicing project managers to understand the role and significance of commonly experienced feedback structures in determining project schedule performance. Practitioners may not be aware of the sizes of delays caused by feedback loops in projects, or even the scale of impacts. …

In the current work, a simple validated project model has been used to quantify the schedule impacts of three common reinforcing feedback loops (rework cycle, “haste makes waste”, and ripple effects) in a single phase of a project. Quantifying the sizes of different reinforcing loop impacts on project durations in a simple but realistic project model can be used to clearly show and explain the magnitude of these impacts to project management practitioners and students, and thereby the importance of using system dynamics in project management.

This is a more formal and thorough look at some issues that I raised a while ago, here and here.

I think one important aspect of the model outcome goes unstated in the paper. The results show dominance of the rework parameter:

The graph shows that, regardless of the value of the variables, the rework cycle has the most impact on project duration, ranging from 1.2 to 26.5 times more than the next most influential loop. As the high level of the variables increases, the impact of “haste makes waste” and “ripple effects” loops increases.


Yes, but why? I think the answer is in the nonlinear relationships among the loops. Here’s a simplified view (omitting some redundant loops for simplicity):


Project failure occurs when it crosses the tipping point at which completing one task creates more than one task of rework (red flows). Some rework is inevitable due to the error rate (“rework fraction” – orange), i.e. the inverse of quality. A high rework fraction, all by itself, can torpedo the project.

The ripple effect is a little different – it creates new tasks in proportion to the discovery of rework (blue). This is a multiplicative relationship,

ripple work ≅ rework fraction * ripple strength

which means that the ripple effect can only cause problems if quality is poor to begin with.

Similarly, schedule pressure (green) only contributes to rework when backlogs are large and work accomplished is small relative to scheduled ambitions. For that to happen, one of two things must occur: rework and ripple effects delay completion, or the schedule is too ambitious at the outset.

With this structure, you can see why rework (quality) is a problem in itself, but ripple and schedule effects are contingent on the rework trigger. I haven’t run the simulations to prove it, but I think that explains the dominance of the rework parameter in the results. (There’s a followup article here!)

Update, H/T Michael Bean:

Update II

There’s a nice description of the tipping point dynamics here.

Paul Romer on The Trouble with Macroeconomics

Paul Romer (of endogenous growth fame) has a new, scathing critique of macroeconomics.

For more than three decades, macroeconomics has gone backwards. The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque. Macroeconomic theorists dismiss mere facts by feigning an obtuse ignorance about such simple assertions as “tight monetary policy can cause a recession.” Their models attribute fluctuations in aggregate variables to imaginary causal forces that are not influenced by the action that any person takes. A parallel with string theory from physics hints at a general failure mode of science that is triggered when respect for highly regarded leaders evolves into a deference to authority that displaces objective fact from its position as the ultimate determinant of scientific truth.

Notice the Kuhnian finish: “a deference to authority that displaces objective fact from its position as the ultimate determinant of scientific truth.” This is one of the key features of Sterman & Wittenberg’s model of Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution:

The focal point of the model is a construct called “confidence.” Confidence captures the basic beliefs of practitioners regarding the epistemological status of their paradigm—is it seen as a provisional model or revealed truth? Encompassing logical, cultural, and emotional factors, confidence influences how anomalies are perceived, how practitioners allocate research effort to different activities (puzzle solving versus anomaly resolution, for example), and recruitment to and defection from the paradigm. …. Confidence rises when puzzle-solving progress is high and when anomalies are low. The impact of anomalies and progress is mediated by the level of confidence itself. Extreme levels of confidence hinder rapid changes in confidence because practitioners, utterly certain of the truth, dismiss any evidence contrary to their beliefs. ….

The external factors affecting confidence encompass the way in which practitioners in one paradigm view the accomplishments and claims of other paradigms against which they may be competing. We distinguish between the dominant paradigm, defined as the school of thought that has set the norms of inquiry and commands the allegiance of the most practitioners, and alternative paradigms, the upstart contenders. The confidence of practitioners in a new paradigm tends to increase if its anomalies are less than those of the dominant paradigm, or if it has greater explanatory power, as measured by cumulative solved puzzles. Confidence tends to decrease if the dominant paradigm has fewer anomalies or more solved puzzles. Practitioners in alternative paradigms assess their paradigms against one another as well as against the dominant paradigm. Confidence in an alternative paradigm tends to decrease (increase) if it has more (fewer) anomalies or fewer (more) solved puzzles than the most successful of its competitors.

In spite of its serious content, Romer’s paper is really quite fun, particularly if you get a little Schadenfreude from watching Real Business Cycles and Dynamic Stochastic General Equilibrium take a beating:

To allow for the possibility that monetary policy could matter, empirical DSGE models put sticky-price lipstick on this RBC pig.

But let me not indulge too much in hubris. Every field is subject to the same dynamics, and could benefit from Romer’s closing advice.

A norm that places an authority above criticism helps people cooperate as members of a belief field that pursues political, moral, or religious objectives. As Jonathan Haidt (2012) observes, this type of norm had survival value because it helped members of one group mount a coordinated defense when they were attacked by another group. It is supported by two innate moral senses, one that encourages us to defer to authority, another which compels self-sacrifice to defend the purity of the sacred.

Science, and all the other research fields spawned by the enlightenment, survive by “turning the dial to zero” on these innate moral senses. Members cultivate the conviction that nothing is sacred and that authority should always be challenged. In this sense, Voltaire is more important to the intellectual foundation of the research fields of the enlightenment than Descartes or Newton.

Get the rest from Romer’s blog.

Tax cuts visualized

Much has been made of the fact that Trump’s revised tax plan cuts its implications for deficits in half (from ten to five trillion). Oddly, there’s less attention to the equity implications, which border on the obscene. Trump’s plan gives the top bracket a tax cut ten times bigger (as percentage of income) than that given to the bottom three fifths of the income distribution.

That makes the difference in absolute $ tax cuts between the richest and poorest pretty spectacular – a factor of 5000 to 10,000:


Trump tax cut distribution, by income quantile.

To see one pixel of the bottom quintile’s tax cut on this chart, it would have to be over 5000 pixels tall!

For comparison, here are the Trump & Clinton proposals. The Clinton plan proposes negligible increases on lower earners (e.g., $4 on the bottom fifth) and a moderate increase (5%) on top earners:


Trump & Clinton tax cut distributions, by income quantile.


Politics & growth

Trump pledges 4%/yr economic growth (but says his economists don’t want him to). His economists are right – political tinkering with growth is a fantasy:


Source: Maddison

The growth rate of real per capita GDP in the US, and all leading industrial nations, has been nearly constant since the industrial revolution, at about 2% per year. Over that time, marginal tax rates, infrastructure investments and a host of other policies have varied dramatically, without causing the slightest blip.

On the other hand, there are ways you can screw up, like having a war or revolution, or failing to provide rule of law and functioning markets. The key is to preserve the conditions that allow the engine of growth – innovation – to function. Trump seems utterly clueless about innovation. His view of the economy is zero-sum: that value is something you extract from your suppliers and customers, not something you create. That view, plus an affinity for authoritarianism and conflict and neglect of the Constitution, bodes ill for a Trump economy.

Models, data and hidden hockey sticks

NPR takes a harder look at the much-circulated xkcd temperature reconstruction cartoon.


The criticism:

Epic Climate Cartoon Goes Viral, But It Has One Key Problem


As you scroll up and down the graphic, it looks like the temperature of Earth’s surface has stayed remarkably stable for 10,000 years. It sort of hovers around the same temperature for some 10,000 years … until — bam! The industrial revolution begins. We start producing large amounts of carbon dioxide. And things heat up way more quickly.

Now look a bit closer at the bottom of the graphic. See how all of a sudden, around 150 years ago, the dotted line depicting average Earth temperature changes to a solid line. Munroe makes this change because the data used to create the lines come from two very different sources.

The solid line comes from real data — from scientists actually measuring the average temperature of Earth’s surface. These measurements allow us to see temperature fluctuations that occur over a very short timescale — say, a few decades or so.

But the dotted line comes from computer models — from scientists reconstructing Earth’s surface temperature. This gives us very, very coarse information. It averages Earth’s temperature over hundreds of years. So we can see temperature fluctuations that occur only over longer periods of time, like a thousand years or so. Any upticks, spikes or dips that occur in shorter time frames get smoothed out.

So in a way the graphic is really comparing apples and oranges: measurements of the recent past versus reconstructions of more ancient times.

Here’s the bit in question:


The fundamental point is well taken, that fruit are mixed here. The cartoon even warns of that:


I can’t fault the technical critique, but I take issue with a couple aspects of the tone of the piece. It gives the impression that “real data” is somehow exalted and models are inferior, thereby missing the real issues. And it lends credence to the “sh!t happens” theory of climate, specifically that the paleoclimate record could be full of temperature “hockey sticks” like the one we’re in now.

There’s no such thing as pure, assumption free “real data.” Measurement processes involve – gasp! – models. Even the lowly thermometer requires a model to be read, with the position of a mercury bubble converted to temperature via a calibrated scale, making various assumptions about physics of thermal expansion, linearity, etc.

There are no infallible “scientists actually measuring the average temperature of Earth’s surface.” Earth is a really big place, measurements are sparse, and instruments and people make mistakes. Reducing station observations to a single temperature involves reconstruction, just as it does for longer term proxy records. (If you doubt this, check the methodology for the Berkeley Earth Surface Temperature.)

Data combined with a model gives a better measurement than the raw data alone. That’s why a GPS unit combines measurements from satellites with a model of the device’s motion and noise processes to estimate position with greater accuracy than any single data point can provide.

In fact, there are three sources here:

  1. recent global temperature, reconstructed from land and sea measurements with high resolution in time and space (the solid line)
  2. long term temperature, reconstructed from low resolution proxies (the top dotted line)
  3. projections from models that translate future emissions scenarios into temperature

If you take the recent, instrumental global temperature record as the gold standard, there are then two consistency questions of interest. Does the smoothing in the long term paleo record hide previous hockey sticks? Are the models accurate prognosticators of the future?

On the first point, the median temporal resolution of the records contributing to the Marcott 11,300 year reconstruction used is 120 years. So, a century-scale temperature spike would be attenuated by a factor of 2. There is then some reason to think that missing high frequency variation makes the paleo record look different. But there are also good reasons to think that this is not terribly important. Marcott et al. address this:

Our results indicate that global mean temperature for the decade 2000 – 2009 ( 34 ) has not yet exceeded the warmest temperatures of the early Holocene (5000 to 10,000 yr B.P.). These temperatures are, however, warmer than 82% of the Holocene distribution as represented by the Standard 5×5 stack, or 72% after making plausible corrections for inherent smoothing of the high frequencies in the stack. In contrast, the decadal mean global temperature of the early 20th century (1900 – 1909) was cooler than >95% of the Holocene distribution under both the Standard 5×5 and high-frequency corrected scenarios. Global temperature, therefore, has risen from near the coldest to the warmest levels of the Holocene within the past century, reversing the long-term cooling trend that began ~5000 yr B.P.

Even if there were hockey sticks in the past, that’s not evidence for a natural origin for today’s warming. We know little about paleo forcings, so it would be hard to discern the origin of those variations. One might ask, if they are happening now, why can’t we observe them? Similarly, evidence for higher natural variability is evidence for less damping of the climate system, which favors higher climate sensitivity.

Finally, the question of the validity of model projections is too big to tackle, but I should point out that the distinction between a model that generates future projections and a model that assimilates historic measurements is not as great as one might think. Obviously the future hasn’t happened yet, so future projections are subject to an additional source of uncertainty, which is that we don’t know all the inputs (future solar output, volcanic eruptions, etc.), whereas in the past those have been realized, even if we didn’t measure them. Also, models the project may have somewhat different challenges (like getting atmospheric physics right) than data-driven models (which might focus more on statistical methods). But future-models and observational data-models also have one thing in common: there’s no way to be sure that the model structure is right. In one case, it’s because the future hasn’t happened yet, and in the other because there’s no oracle to reveal the truth about what did happen.

So, does the “one key problem” with the cartoon invalidate the point, that something abrupt and unprecedented in the historical record is underway or about to happen? Not likely.