Ramblemuse Touch Points

I was noticing in the tweets coming out of the American Meteorological Society meeting in New Orleans that NASA and NOAA have renamed the recently launched polar orbiter, NPOESS preparatory project, the Suomi NPP, after the late Verner Suomi.

This brought back a memory confirming in my own mind the suitability of the choice. Years ago in writing my PhD thesis, I quoted from a section by Suomi on The Need for Climate Monitoring from a larger National Academies Press Report on Energy and Climate: Studies in Geophysics (1977). Looking back through Suomi’s writing, I’m amazed by how pertinent his plea for adequate monitoring remains today.

The sentences I’d quoted also remain particularly pertinent. They focused on the need to gain information from the immense potential flows of data.

An extremely important aspect of the entire climate monitoring activity is the data-processing effort required. It is possible for the secrets of nature to be hidden in a flood of data as well as in nature. Clearly, we need information more than we need data.

Thanks for the insights, Verner Suomi

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One of the economic shortcomings that’s more or less obvious in “just letting the market handle it” is that the market often doesn’t include total costs of use. The situation is akin to letting someone buy supplies for a large party, holding the party on common land, and then simply walking off, leaving the trash and costs of clean-up to others. The costs of producing the party supplies are included in their price, but not their costs of use. The market can only achieve an equitable balance, one that does not foster costs onto third parties, if a way is found to include all costs within the price. In short, the costs of use need to be born by those receiving the benefits of use.

The Economist column Do economists all favour a carbon tax? addresses just this situation applied to use of fossil fuels and carbon emissions.

Carbon emissions represent a negative externality. When an individual takes an economic action with some fossil-fuel energy content—whether running a petrol-powered lawnmower, turning on a light, or buying bunch of grapes—that person balances their personal benefits against the costs of the action. The cost to them of the climate change resulting from the carbon content of that decisions, however, is effectively zero and is rationally ignored. The decision to ignore carbon content, when aggregated over the whole of humanity, generates huge carbon dioxide emissions and rising global temperatures. The economic solution is to tax the externality so that the socialwww.ramblemuse.comcted in the individual consumer’s decision.

The concept is far more general than just fossil fuel use. It applies to any situation in which side-effect costs of production or costs of disposal are not included in the cost accounting, be it slag from mining or disposal of chemicals or sewage into streams. Regulation and/or taxation is required, not to interfere with the market, but to ensure that the market correctly accounts for total costs.

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This past week I had a rare opportunity to explore using the virtual world of Second Life (SL) as an immersive means of visualizing and exploring real life topography. Generally, a SL sim already has set terrain and arbitrarily replacing that would break everything developed on it. When a friend is obtaining a new sim (Numantia Maris) and wants something with islands and a lot of water, however, it’s a fresh canvas on which to paint data and create an immersive experience of the topography.

The topography data were part of the dataset produced by the Shuttle Radar Topography Mission (SRTM). The location used was the Greek islands of Santorini. Santorini is interesting both historically and geologically in being the site of a massive volcanic eruption about 1600 BCE.The eruption is thought to have sent tsunami waves up to 75 ft high into the coast of Crete (68 miles distant), contributing to the end of the Minoan civilization. This explosion also may have been the source of the Atlantis legend. Because it was buried by the eruption, the excavation site of Akrotiri was exceptionally well-preserved and has yielded insights into both artwork and architecture.

The finished global data product is at three arc-second resolution (roughly 90 m) delivered in one-degree tiles. The area of interest is selectable using the Earth Explorer. Selecting an area, choosing the SRTM elevation data set, and requesting data makes one or more tile files available. In the case of Santorini, only the N36E25 tile was needed. This included Santorini and all or parts of several other islands as shown in this reduced resolution picture of the tile.

I used Python 2.6 to read in the BIL (Band Interleaved by Line) file and the corresponding Python Imaging Library (PIL) to display it. Once read as binary data, the 1,442,401 16-bit integers of the data tile could be unpacked into a numpy integer array and then reshaped into a 1201 by 1201 array. It was then trivial to sub-select the portion of the array containing Santorini. I then filled-in the small number of missing data points by diffusing in data from the sides of the missing area.

Under it’s “Miscellaneous Functions” category, SciPy contains several PIL interface routines that allow resizing and rotating images stored as arrays. These made it trivial to resample and rotate the selected data portion to a 256 by 256 array with the desired orientation. By happenstance, it turned out that the desired region for Santorini contained 256 x 256 measurements and didn’t have to be resampled.

The 256 by 256 image size was chosen because a SL sim is 256 by 256 meters. Given that measurements are about 90 m apart, mapping the measurements onto a sim one-for-one creates about a 1:90 scale model horizontally. Vertically, I took the log of the elevations and then scaled the result to 50 m. This compresses the vertical scale in a manner appropriate for SL terrain. Since the SRTM data did not provide ocean bottom values, I created a set of fractal noise varying about an initial value of 7.5 m. Note that SL uses 20 m as its nominal sea-level height. The noise was produced by successive doubling from an initial grid of one square, where the scale of vertical noise was reduced by 2^-1/2 at each iteration. The noise was then added in where the topography height was zero. Finally, the data were written out as a SL raw file.

One uploads the raw file onto the waiting sim. Nothing happens for a minute or two. Then, suddenly the land takes form with the shape of Santorini. A bit of playing with the four terrain textures height interpolated by the SL sim software, and there is reddish volcanic rock rising from the sea before us. It’s very definitely a scale model, but one large enough to sail a boat into the caldera.

My appreciation to Stonehedg Magic, owner of Numantia Maris, and to Desmond Shang, governor of the Victorian, steampunk realm of Caledon, for making this effort possible.

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I’ve been playing with uploading simulator (sim) terrain from Second Life and displaying it with Python and Matplotlib. The figure displays two snapshots of a sim taken a year apart. The major difference is that a deep hole has been “nuked” into the ridge near the northwest corner to make room to create caverns, caves, dungeons, and the like in the right-hand figure.

The basic terrain is of two ridges with a river valley between them. The western ridge angles slightly from NW to SE, leaving room for a beach in the SW corner. The eastern ridge eases into a flat plateau in the south. In the figure, just the terrain is shown (hence the post title), without the standard water level at 20 meters. I used the Matplotlib ‘copper’ color-table for both plots. The data for the left figure came from a Second Life raw file. The data for the right figure was obtained by scripting a surveyor prim to upload data via HTTP to a PHP script. Both plots are of a full sim of 256m by 256m at 2m by 2m resolution.

Given all that technical detail, the result captures an almost Rubenesque sense of the terrain.

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Models and simulations of many kinds are tools for dealing with reality; they are as old as humanity itself. Humans have always used mental models to better understand reality, to make plans, to consider different possibilities, to share their ideas with others, to try out changes and alternatives, to develop blueprints for realization of some ideas, or to convince themselves and others that certain ideas cannot be realized. — Hartmut Bossel, Modeling and Simulation

The thought thread for this post started from the amount of flak that’s been tossed out attacking the usefulness and results of climate models. Reflecting on this situation, I decided that one of the reasons that such negative characterizations can take hold is that few people realize the pervasive use of models in everyday life, thus thinking of climate models as something apart from what we, as human beings, do day-in and day-out. This is the first in what I plan as a series of posts on models and modeling.

Marvin Minsky, in The Society of Mind, defines a model as: “Any structure that a person can use to simulate or anticipate the behavior of something else.”

Gene Bellinger, on his website on Systems Thinking, defines a model as: “a simplified representation of a system at some particular point in time or space intended to promote understanding of the real system.” Bellinger goes on to say:

The most important question to ask should relate to the extent to which the models we develop promote the intentioned development of our understanding. The extent to which a model aids in the development of our understanding is the basis for deciding how good the model is. In developing models there is always a trade off. A model is a simplification of reality, and as such, certain details are excluded from it. The question is always what to include and what to exclude.

Bellenger’s statement that a model is a simplified representation of a system is important. Polish-American scientist and philosopher Alfred Korzybski stated this as “the map is not the territory,” encapsulating his view that an abstraction derived from something, or a reaction to it, is not the thing itself.

In Empirical Modeling-Building and Response Surfaces (1987), Box and Draper noted that: “essentially, all models are wrong, but some are useful” (p. 424) and “remember that all models are wrong; the practical question is how wrong do they have to be to not be useful” (p. 74).

Gregory Bateson, in “Form, Substance and Difference,” from Steps to an Ecology of Mind, elucidates the essential impossibility of knowing what the territory is, as any understanding of it is based on some representation:

We say the map is different from the territory. But what is the territory? Operationally, somebody went out with a retina or a measuring stick and made representations which were then put on paper. What is on the paper map is a representation of what was in the retinal representation of the man who made the map; and as you push the question back, what you find is an infinite regress, an infinite series of maps. The territory never gets in at all. […] Always, the process of representation will filter it out so that the mental world is only maps of maps, ad infinitum.

Bateson also points out that the usefulness of a map (a representation of reality) is not necessarily a matter of its literal truthfulness, but its having a structure analogous, for the purpose at hand, to the territory.

Before going further and deeper into modeling in the abstract, I wanted to bring up an example of mental modeling from Gary Klein’s book Sources of Power, in which he talks about field research on the nature of expertise and expert decision making. He also goes into this same material in a video on adaptive decision making. In the following, Klein is commenting on the observation that firefighter commanders don’t compare different solutions to a problem but pick one that matches the pattern of the current situation and that they expect to be a sufficient solution. Part of arriving at this expectation is a process of mental simulation, i.e. execution of a mental model.

The commanders’ secret was that their experience let them see a situation, even a nonroutine one, as an example of a prototype, so they knew the typical course of action right away. Their experience let them identify a reasonable reaction as the first one they considered, so they did not bother thinking of others. They were not being perverse. They were being skillful. We now call this strategy recognition-primed decision making…

To evaluate a single course of action, the lieutenant imagined himself carrying it out. Fire-ground commanders use the power of mental simulation, running the action through in their minds. If they spot a potential problem, like the rescue harness not working well, they will move on to the next option, and the next, until they find one that seems to work. Then they carry it out. As the example shows, this is not a foolproof strategy. The advantage is that it is usually better than anything else they can do.

Klein’s observations that the strategy is not foolproof coincides with models being approximations of reality not reality itself. The commander runs a mental simulation, but his or her model does not contain all details of the situations, only the ones that have been unconsciously flagged as being relevant. Nonetheless, in most cases the model is useful.

A simulation is a model-based experiment. An experiment is done using the model with the anticipation that the result will increase our understanding of how the real system would respond. This requires that the experiment be within the set (or space) of valid experiments for a given model; i.e. that the experiment is within the design range of the model. François Cellier, in Continuous System Modeling (pp. 5-6), stresses the connection between model and experiment.

If people say that “a model of a system is invalid” (as can be frequently read), they don’t know what they are talking about. A model of a system may be valid for one experiment and invalid for another, that is: the term “model validation” always relates to an experiment or class of experiments to be performed on a system rather than to the system alone. Clearly, any model is valid for the “null experiment” applied to any system (if we don’t want to get any answers out of a simulation, we can use any model for that purpose). On the other hand, no model of a system is valid for all possible experiments except the system itself or an identical copy thereof.

A model may take many forms: a mental model, a map or sketch on a piece of paper, a reduced-scale physical model of the system, an electrical circuit or hydraulic system that is an analog of the real system, or a computer model. The latter might be equation-based or agent-based or a hybrid of the two. Delving deeper into types of models will be another post. I’ll close this overview with a quote from Richard Hamming.

The purpose of computing is insight, not numbers.

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California does not require state licensing to practice massage therapy, but instead offers, via the California Massage Therapy Council (CAMTC), a state-law authorized, voluntary certification that exempts the holders from local licensing laws. In return, certificants undergo education verification and extensive background checks. Because of this voluntary regulatory structure, there’s no centralized means of reliably estimating the number of massage providers in California. Recent estimates range between 30,000 to 100,000.

What we can do, however, is check the consistency of such estimates with other statistics. The 5 year business survival rate for massage providers is estimated to be between about 30% to 40% (Grant and Forman, p. 20). This compares with general small business survival rates of 42% to 51% from data given by Headd (2000). Factors differentiating the massage therapy survival rates from general small business rates can include: entering massage practice without being fully prepared to run a business, physical demands, emotional/interpersonal demands, and need to provide health care for self and family (a concern of entrepreneurs in general).

We can take five-year survival rates of a cohort entering to profession to be the 5th power of yearly survival rates. Thus 30%, 35%, and 40% five-year rates correspond to yearly survival rates of 78.6%, 81.1%, and 83.3%, respectively. The corresponding yearly loss rates are 21.4%, 18.9%, and 16.7%. Also, the respective two-year retention rates would be, 61.8%, 65.8%, and 69.4%, which might constitute a range of expected renewal rates for two-year provider certification.

If we assume a balance between annual graduation rates (G) of massage students and yearly loss from the profession (i.e., an equilibrium assumption), then we get that the number of providers in practice is:

N_MT = G / L ,

where L is the annual loss rate expressed as a fraction. The loss rates above give (1/L) factors of 4.7, 5.3, and 6.0, respectively. Note also that G could include multiple sources of those entering practice in California, including net migration from other states.

At the 18 January CAMTC meeting, an estimate of annual graduates of 7000-8000 was informally stated. When I multiply 7500 by 5.3, I get an estimate of about 40,000 providers. The range in number of providers from the above is 33,000 (4.7×7000) to 48,000 (6.0×8000).

While nothing guarantees the truth or accuracy of the above, when we look at number of providers, graduation rates, and five-year business survival rates as being connected rather than just as separate numbers, we gain some insights we otherwise would miss.

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I recently had a massage teaching colleague ping me about: 1) Why we need mitochondria? and 2) How many molecules of ATP are used per second in typical muscle contractions? Both questions are a matter of energy, thus piquing my underlying physicist nature.

Glucose to ATP

The first question really was really along the lines of why, if adenosine diphosphate (ADP) and so extra phosphorous are available, they don’t just combine into adenosine triphosphate (ATP)? My answer was that the reaction is endothermic (energy absorbing). Think of it as being on a bike at the bottom of a hill. The bike isn’t just going to spontaneously move up the hill, you’re going to have to supply the energy to make it climb.

Being endothermic is one of two reasons that reactions often don’t “just happen”. The second reason is that a reaction, while exothermic (energy releasing), can have a substantial activation energy. This is, once again, like being on a bicycle, this time wanting to ride a couple of kilometers downhill to the beach. The problem is that, before the road starts downhill, it climbs to a place higher than where you are starting. So, you can’t just coast downhill, you have to put some energy in first to get over the hill in the middle before collecting on the downhill ride at the end. There’s a good short video from the Kahn Academy and also an even shorter animation.

As glycolysis breaks down glucose, it uses up two ATP early on and then churns out four ATP and two pyruvate in the end; a net gain of two ATP. Here’s a short animation and also material by Jon Maber covering glycolysis.

The mitochondria further convert ADP into ATP. They take the two pyruvate generated by glycolysis in the cytosol and oxidize it via the citric acid (Krebs) cycle to produce two further molecules of ATP, as shown in this short animation.

Most of the 36-38 ATP produced by breaking down glucose via both glycolysis and the Krebs cycle aren’t direct (i.e. substrate phosphorylation). They come via production of Nicotinamide adenine dinucleotide (NAD) as NADH and then the establishment of a proton gradient via the electron transport chain and transforming ADP to ATP via oxidative phosphorylation. When the net production of ATP energy is compared against the total possible energy from oxidation of glucose, it shows an efficiency of about 40%, the rest of the energy going into heat.

Note that pyruvate is the input fueling the Krebs Cycle. If glycolysis is occuring too rapidly for the aerobic Krebs cycle to process it, pyruvate will reversibly be converted into lactate. Clearly, lactate (aka lactic acid) isn’t a harmful waste product, but simply a means of temporarily storing partially processed fuel. Because it can be converted back into pyruvate, lactate is to glucose about what charcoal is to wood. Lactate may build up in the blood during anerobic exercise, but once that exercise is stopped, it’s concentration is halved every 15-25 minutes so that the lactate concentration rapidly returns to baseline, homeostatic levels (Plowman & Smith, Exercise Physiology for Health Fitness and Performance, 2007, p115).

So, the bottom line for production of ATP from glucose is that it is a multi-faceted, very energy intensive process. It takes a bit of “shoving” to get that third phosphate attached to yield the high-energy, unstable, molecule of ATP.

Using the ATP energy

While the nominal energy from breaking the bond of the third phosphate group in ATP is 30.5 kJ/mole, when the concentrations of ATP, ADP, phosphate, magnesium, and the pH are taken in to account, the effective free energy is approximately 50 kJ/mole, as discussed in terms of ATP hydrolysis and the free energy from such hydrolysis. Also the overall efficiency for producing mechanical energy from glucose in on the order of 20%, which, combined with the efficiency of 40% for producing ATP, means that the efficiency for converting ATP energy into mechanical work is about 50%.

There’s an estimate that working out on a rowing machine takes about 250 Watts of power (0.250 kJ/sec). I can also estimate the power required to lift my 750g ceramic cup full of coffee at a steady velocity of 2/3 m/sec against gravity (about 10 m/sec²). That comes to

P_cup = (0.750 kg) × (10 m/s²) × (2/3 m/s) = 5 Watts

For a known rate of mechanical power usage P, the rate of ATP breakdown (including a factor for 50% efficiency) is:

This is a rate of 2.4×10¹⁹ molecules of ATP per second per Watt of mechanical energy produced; 1.2×10²⁰ molec/sec to lift my coffee cup; and 6.0×10²¹ molec/sec to workout on the rowing machine. We need to continually make ATP because we use it up rapidly.

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Remembering on both sides, that civility is not a sign of weakness and sincerity is always subject to proof.

Let us never negotiate out of fear, but let us never fear to negotiate.

Let both sides explore what problems unite us, instead of belaboring those problems which divide us.

Let both sides, for the first time, formulate serious and precise proposals…

 

John Fitzgerald Kennedy, 20, January 1961 – Inaugural Speech

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The first of two starting points for this post is David Evans’s An ‘Unacceptable’ Degree? in the Chronicle of Higher Education. Evans is discussing hiring practices and the rejection of potential candidates who lack a “trophy degree.”

One of the commentators mentions several reasons that a candidate would be rejected that, even if shared, wouldn’t help the candidate prepare for future searches. The most striking of those is, “Technically you have an appropriate graduate credential and your presentation was OK, but the school you attended just isn’t acceptable to our administration.”

There’s an implication in this that academic departments and even businesses might, at times, be turning away more capable candidates in preference to those coming from “the right&lrdquo; institutions. My own reaction to this is, “Great! Let them!” The place to put pressure on leveling the playing field toward maximizing capability and return on investment isn’t in hiring practices but in funding, a point raised by one of those commenting on Evans’s article.

The place to push back against blind elitism is in the review and funding process — ensuring that it doesn’t just become a process of the elite donating to the elite. Given that, such preferential hiring practices can open entrepreneurial doors. Any commodity, including talent, undervalued by some is an opportunity for others. If capable persons are overlooked by some institutions, they become available to those less concerned with trophies and more willing to discern abilities to function in the trenches. Potentially having a lower level of debt, such person may come with somewhat lower salary needs. So, for someone trying to build a new department or new business, rejection by some may improve the ability to create a “knock their pants off” facility. In an educational world in which both new technologies and a need to contain costs will play an increasing role, ROI is going to be something to consider.

The second starting point for this post is another essay by William Deresiewicz, again come across via Don Vandergriff’s blog. This time it’s, The Disadvantages of an Elite Education. This time, Dereesiewicz in noting that, while an education at an elite institution may strengthen some capabilities, it can also cripple other capabilites and create perceptual blind spots.

You learn to think, at least in certain ways, and you make the contacts needed to launch yourself into a life rich in all of society’s most cherished rewards. To consider that while some opportunities are being created, others are being cancelled and that while some abilities are being developed, others are being crippled is, within this context, not only outrageous, but inconceivable.

I’m not talking about curricula or the culture wars, the closing or opening of the American mind, political correctness, canon formation, or what have you. I’m talking about the whole system in which these skirmishes play out. Not just the Ivy League and its peer institutions, but also the mechanisms that get you there in the first place: the private and affluent public “feeder” schools, the ever-growing parastructure of tutors and test-prep courses and enrichment programs, the whole admissions frenzy and everything that leads up to and away from it. …

The world that produced John Kerry and George Bush is indeed giving us our next generation of leaders. The kid who’s loading up on AP courses junior year or editing three campus publications while double-majoring, the kid whom everyone wants at their college or law school but no one wants in their classroom, the kid who doesn’t have a minute to breathe, let alone think, will soon be running a corporation or an institution or a government. She will have many achievements but little experience, great success but no vision. The disadvantage of an elite education is that it’s given us the elite we have, and the elite we’re going to have.

On the average, tunnel vision education and hiring gives us leaders who are less flexible and less perceptive than they might be, opening the door for others either in the U.S. or the increasingly global economy. To the worst, an elite education may give us leaders seriously out of touch with the world, such as highlighted by Michael Watkins in his Harvard Business Review article, I Want To Live Like Common People: BP and the Great PR Divide.

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In the serendipity of looking through various RSS feeds, I came across Don Vandergriff’s recent post, The Best Leadership Article I have Seen, which reprints and links to William Deresiewicz’s lecture Solitude and Leadership posted on The American Scholar. I agree with Vandergriff that it is a very timely and thought provoking piece of writing. I also find it also lends perspective both to an article on the German concept of Auftragstaktik (mission tactics) and to some some blog posts on disincentives to pursue scientific research that has a long time constant (i.e. takes a long time to get results). I’m going to juxtaposition these three pieces, mostly by quoting from them, starting with Deresiewicz commenting on the students he encountered while at Yale.

So what I saw around me were great kids who had been trained to be world-class hoop jumpers. Any goal you set them, they could achieve. Any test you gave them, they could pass with flying colors. They were, as one of them put it herself, “excellent sheep.” I had no doubt that they would continue to jump through hoops and ace tests and go on to Harvard Business School, or Michigan Law School, or Johns Hopkins Medical School, or Goldman Sachs, or McKinsey consulting, or whatever. And this approach would indeed take them far in life. They would come back for their 25th reunion as a partner at White & Case, or an attending physician at Mass General, or an assistant secretary in the Department of State.

That is exactly what places like Yale mean when they talk about training leaders. Educating people who make a big name for themselves in the world, people with impressive titles, people the university can brag about. People who make it to the top. People who can climb the greasy pole of whatever hierarchy they decide to attach themselves to.

But I think there’s something desperately wrong, and even dangerous, about that idea. … That’s really the great mystery about bureaucracies. Why is it so often that the best people are stuck in the middle and the people who are running things—the leaders—are the mediocrities? Because excellence isn’t usually what gets you up the greasy pole. What gets you up is a talent for maneuvering. Kissing up to the people above you, kicking down to the people below you. Pleasing your teachers, pleasing your superiors, picking a powerful mentor and riding his coattails until it’s time to stab him in the back. Jumping through hoops. Getting along by going along. Being whatever other people want you to be, so that it finally comes to seem that, like the manager of the Central Station, you have nothing inside you at all. Not taking stupid risks like trying to change how things are done or question why they’re done. Just keeping the routine going. …

We have a crisis of leadership in America because our overwhelming power and wealth, earned under earlier generations of leaders, made us complacent, and for too long we have been training leaders who only know how to keep the routine going. Who can answer questions, but don’t know how to ask them. Who can fulfill goals, but don’t know how to set them. Who think about how to get things done, but not whether they’re worth doing in the first place. What we have now are the greatest technocrats the world has ever seen, people who have been trained to be incredibly good at one specific thing, but who have no interest in anything beyond their area of exper­tise. What we don’t have are leaders.

What we don’t have, in other words, are thinkers. People who can think for themselves. People who can formulate a new direction: for the country, for a corporation or a college, for the Army—a new way of doing things, a new way of looking at things. People, in other words, with vision.

Now, it’s interesting to correlate Deresiewicz’s comments on leadership and the lack of it with a few quotes from Major General Werner Widder’s (German army) article on Auftragstaktik or “mission tacticts”.

Thus, Auftragstaktik is more than giving a mission to a subordinate and allowing him the latitude to execute it. Rather, it is the superior’s duty to specify the objective and the framework within which the subordinate has to accomplish the mission. The commander provides all resources required to carry out the mission. This, in turn, means that execution itself becomes the executor’s responsibility. His skills, creativity, and commitment will be the key elements of execution. Thus, Auftragstaktik is not merely a technique of issuing orders but a type of leadership that is inextricably linked to a certain image of men as soldiers.

The decisive foundation for Auftragstaktik is peacetime training with a deliberate focus on training soldiers to think independently and to act according to the superior commander’s intent. The superior’s specified objective, his confidence in his subordinates’ capabilities, his and his subordinates’ acceptance of their respective responsibilities, and their freedom to act are the four cornerstones of Auftragstaktik on the one hand and its secret on the other. The onus, nevertheless, still remains with the commander, who must provide the necessary means to accomplish the mission. …

Auftragstaktik is based on an image of man who values his individual dignity and freedom and who harnesses them to achieve superior strength. This concept is still valid for the 21st century. Based on the premise that leadership encompasses two aspects—being a role model and accepting responsibility—leadership requires competence, strength of character, trust, initiative, judgment, assertiveness, and decisionmaking ability at all command levels.

Because I have a primary interest in leadership as it pertains to the pursuit of good science, I want to bring the focus back to this arena by turning to Canadian GirlPostdoc’s blog posts on Slow Science gets the Shaft – Part I and Slow Science Gets the Shaft – Part Deux.

This anecdote is my way of saying, that as academic scientists we make choices on a daily basis to pursue what is expedient at the cost of what may turn out to be interesting, all because of the lack of time. This rushed time frame creates an environment that does not support slow science. …

An environment that uses a carrot (tenure) and stick (terminal contract) incentive model narrows people’s focus and destroys creativity. If you don’t believe me, listen to the facts put forward by Daniel Pink, in his TED talk (it’s worth 17 minutes of your time) on the science of motivation. Why would you spend time doing science that you think is worthwhile when it doesn’t get you the carrot. Instead, the choice is obvious, you do science that you know will work, ie get you the publications, the grants, all in the drive for tenure. This carrot and stick model leaves no room for innovation and creativity.

A second effect that this “if you do this then you will get this or else” atmosphere does is it creates and attracts a particular type of scientist to academia and selects against another. One academic I know, has said that he doesn’t participate in a project unless he sees a publication in it for himself. …

So the pursuit of science has become one of the bureaucracies that Deresiewicz refers to. One in which it is better to keep the routine going than to risk straying too far into the unknown. One in which there are disincentives to develop the kind of Auftragstaktik that Widder encourages of using local authority within the greater direction of endeavor of the scientific framework. It’s an open question, how we work to reverse the push toward the safety of mediocrity. It’s likely not going to happen unless the system of rewards are restructured to value the uncertain and ambiguous.

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