Weight training experiment – Week 20
In January of 2010, I was 10 weeks into a workout experiment. In the last ten weeks, strength gain in smaller muscle groups has clearly leveled off and the rapid increase in strength for the larger muscle groups has started to slow as well. Here are the charts updated with data up through today’s workout.
Week 20 - Strength as fraction of first workout (%).
A couple of features jump out:
- Progress with a pull downs flattened quickly. I backed off and tried to concentrate on negatives, but progress was slow. This exercise uses biceps, triceps and abs. The strategy going forward is to isolate the weakest area(s).
- Bench press progress appears to be tapering off to about 80% increase from mid-November. Leg press is nearly 90% and there is still week-to-week progress. The lat row progress, while only 45% from November, continues to progress a few pounds form week to week. I did not expect these results and am very pleasantly surprised!
- This represents 18 workouts averaging 23 min each, for a total workout time of about 7 hours of workout time. Time under load averages about 40% of the workout or about 2.8 hours of actually pushing the weights. I am satisfied that this workout method is very efficient!
- (The dip in leg press trend around February 20 is due to changing machines–I surpassed the capacity of the normal leg press machine and had to move a machine that holds free weights. This machine is at an incline so it took a couple of weeks to recalibrate. I added a conversion factor based on the angle of the inclined machine to adjust the last 6 points on the leg press line.)
- I gained a few pounds during this time period. Since I did not measure body fat ratios, I don’t know the details of weight redistribution. But the changes are in the right direction.
In terms of absolute weight, I am still a fairly weak desk jockey…
Week 20 - Weight trend for 18 workouts.
The results seem really great based on the 2 hours and 45 minutes I spent in the gym pushing weights. Recommended.
eBook readers want portability
Dear Author Reader Survey results I posted earlier indicates ebook readers want ebooks and devices characteristics that allow them to read conveniently on more than one device. Many solutions provide this functionality with some limits (both Barnes and Noble and Amazon offer dedicated devices [nook and Kindle], desk top applications and iPhone eReader options; Amazon uses proprietary book and DRM formats). And readers are using them.
Over half of the responses of those indicating they read ebooks indicate ebooks are read on more than one device.
The top multi-device combinations for 2 and 3 devices are shown below. Desktop plus iPhone is an important combination for publishers, ebook sellers and device makers to consider in their product plans.
Top three device combinations for users of 2 and 3 devices.
When readers expressed their preferences for using multiple devices and keeping them in sync, they responded consistently with the results above.
Readers value multi-device support.
Readers value synchronization between their devices.
Free eBook promotions drive sales
Dear Author Reader Survey results I posted earlier contain a couple of questions about free eBook promotions. The survey asks if readers have downloaded a free promotional eBook and whether the promotion prompted them to make purchase. The results for this audience are clear: they respond to the promotions and they make purchases based on downloads.
EBook promotions and resulting purchases.
EBook promotions and resulting purchases data.
eBook piracy about access, price, portability
Digging into the Dear Author Reader Survey results I posted yesterday a little more, I wanted to compare responses to a few questions by readers who indicated they have illegally downloaded an eBook to those who indicated they have not.
Of the 2724 responses to the survey, 681 indicated they have illegally downloaded an eBook (25%). It is difficult to judge from the audience whether this is a greater or smaller proportion than the general population of readers (even defining that is difficult 1+ books per year?). But the comparison of the two groups within the population that took the survey is interesting.
Here is how the “Illegal Downloaders” compared to the only legal downloaders (I call them “Non-Downloaders” in the charts below.)
Piracy skews young. The crossover seems to be mid-30s somewhere. More on this point later.
Age distribution of Illegal Downloaders vs. users who do not engage in illegal downloads (Non Downloaders)
Looking at complaints about eBooks, Illegal Downloaders are proportionally less satisfied in every category except the requirements for technical proficiency and “Other.” Illegal Downloaders are significantly less satisfied with Selection (access to books), eBook Quality and the Ability to share eBooks. The last is a little surprising since Illegal Downloaders have access to DRM-free versions of the books they downloaded and can share more easily than others–does this indicate they would rather be sharing legally?
Compare eBook complaints Illegal Downloaders vs. only Legal Downloaders (Non Downloaders)
When answering the question about the maximum price for an eBook they would be willing to pay, Illegal Downloaders have nearly the same distribution as Non Downloaders with a slightly higher fraction expressing they would be willing to pay on $5.99 for an eBook.
Compare max price distribution for Illegal Downloaders vs. those who do not engage in illegal downloading of eBooks (Non-Downloaders).
When asked what changes would stop or curtail illegal downloading, Illegal Downloaders expressed that both removal of DRM and a decrease in price would have an effect, while sharing would have less influence. Non Downloaders who answered this question showed that they had the same perceptions based on their imagined downloading influences–I like that they went out on a limb to predict their feelings about their hypothetical behavior.
Compare responses to influences on downloading behavior for Illegal Downloaders and Non Downloaders
Summary
The age distribution poses challenges for the future. There is no reason to think that illegal downloading of eBooks is static generational problem for publishers. As the population ages, older people will be downloading as much as younger people. eBook piracy will become a feature of the business landscape for all age groups.
Piracy seems to be about access and content portability. These complaints mirror the complaints about music. The quality and portability issues with music are clearing up slowly (e.g. iTunes now sells DRM-free, near CD quality tracks).
Piracy also seems to be about price. This survey doesn’t seem to indicate a solid floor. While one may be able to price non-DRM or portable eBooks higher than the alternatives, the pressure on naked price seems unrelentingly down. Most readers indicated they believe $9.99 to be a fair eBook price while a higher proportion of Illegal Downloaders than Non-Downloaders indicated that $5.99 is a fair eBook price. This survey doesn’t explore readers perceptions of eBook pricing over the life of the book (and there is no “used” eBook market in which to explore that question).
It isn’t clear that the current structure of authors, publishers and book sellers can make it all work well at $9.99 so this seems a real problem of perceived value. Readers seem to intuitively know that copying eBooks is free (you can get people to volunteer their time and resources to do it) and assume that means that eBooks should be very nearly free as well. But the same might be said for music where track prices seem to be finding some equilibrium between $.89 and $1.29. eBook pricing seems much more up in the air. We’ll see…
Dear Author Survey Results
In February, Dear Author sponsored a survey of readers with lots of questions about eReaders and eBooks. I found the the survey because TeleRead encouraged their readers to take the survey as well. Dear Author is community minded and released the raw results (kudos!) as well as a brief set of slides from a presentation they gave.
To do something beside coast on the hard work of others, I spent a few nights working up the results from the survey. My motivation was to dig into the questions about eReader functionality and eBook piracy. Dear Author was kind enough to give me permission to put some of the results here. Check in over the next few days for posts on observations conclusions from the survey data.
In the meantime, here are the complete results from the survey. Enjoy!
Dear Author 2010 Survey Report (pretty charts and such)
If you prefer to see the data at one of the lower levels, visit one of these.
Dear Author 2010 Survey raw data download
Saving the world with games
A few weeks ago, I waxed enthusiastic (for me, at least in writing here) about learning and games. McGonigal’s TED talk builds this out with descriptions and metrics of game play vs other kinds of problem solving. Formal education is already playing second place (time and attention) to game playing.
What problems are game players solving? This question warrants more investigation to understand what is happing now. But it is also a call to creativity and design.
The emotional and intellectual content as well as the complexity of the problems solved in these games beats out learning the 3Rs sitting quietly at a desk from here on. McGonigal outlines how the complex problems gamers are learning to tackle are important are deeply engaging:
- Urgent Optimism. Act now with a reasonable hope of success.
- Social Fabric. We like people better after game interactions, win or loose.
- Blissful Productivity. This kind of problem solving is fulfilling.
- Epic Meaning. Large scale, complex problems.
Residential energy monitoring payback?
Last year I made a cocktail-napkin calculation of the payoff challenges of home energy monitoring. My question then was whether the granularity of measurement required to make energy decisions in the home was mismatched to the amount energy used, and therefore, the amount that could be saved. It seems others are arriving at this question at even a slightly higher level of granularity–the (smart) meter.
From earth2tech comes an article on California utilities struggling to get data to customers. The last paragraph brings up the challenge of payoff periods being out of line with available savings,
There are signs that the smart meter backlash is spreading beyond California —Duke Energy is being ordered by Indiana regulators to justify the costs of an 800,000 smart meter deployment in that state, and Dominion Virginia Power is delaying a $600 million smart meter rollout to do more testing, after state regulators questioned whether the meters will cost customers more in increased rates than they will help them save in reduced energy usage.
Designing Outcomes (Applied game theory – Part 2)
It seems natural to extend the basic idea from the Predictioneer’s Game introduced in the last post in two ways.
First, what happens if subsets of the group meet and some of those parties sway others or reach compromises in order to form coalitions?
Second, taking this idea further, can we investigate a variety of decision processes to find an optimal process for a desired outcome and design coalitions in order to reach a desired outcome? In terms of the example situation, this is the problem of finding coalitions that will drive a desired result from those we predicted last time.
Again, here is the prediction from the last post:
party : Pos Inf(Norm) Sal ----------------------------------------------- d2 : 15 0.03774 80 ctcust : 35 0.09434 5 ctsal : 35 0.03774 5 er : 50 0.01887 20 eng : 60 0.07547 99 me : 75 0.15094 99 d1 : 75 0.15094 99 adv : 100 0.15094 20 legal : 100 0.13208 95 inv : 100 0.15094 5 ----------------------------------------------- Position (weighted avg): 76.4 Position (balance of power): 71.7
TABLE 1
To answer the first question, we need to build a simple model that replaces a coalition with a single new entity. In the model, the choice was to add the Influence of the parties of a coalition. The new position was the weighted average of the positions as described before. For the Salience, we need to reflect the idea that the most interested party in a coalition will drive the others and use their influence to support the new positions but with some skewing toward their Salience. To model this simple, replace the new entity’s salience with 20% of the average Salience + 80% of the max Salience. Many other choices can be made and explored using the tools here.
To answer the second question, we need a way to generate all of the possible coalitions. This amounts to generating a set partitioning of all the parties. TABLE 2 shows a simple example with only 4 parties.
Parties : ['a', 'b', 'c', 'd']
<<< 15 Partitions >>> ----------------------------- [['a', 'b', 'c', 'd']] [['a', 'b', 'c'], ['d']] [['a', 'b', 'd'], ['c']] [['a', 'b'], ['c', 'd']] [['a', 'b'], ['c'], ['d']] [['a', 'c', 'd'], ['b']] [['a', 'c'], ['b', 'd']] [['a', 'c'], ['b'], ['d']] [['a', 'd'], ['b', 'c']] [['a'], ['b', 'c', 'd']] [['a'], ['b', 'c'], ['d']] [['a', 'd'], ['b'], ['c']] [['a'], ['b', 'd'], ['c']] [['a'], ['b'], ['c', 'd']] [['a'], ['b'], ['c'], ['d']]
TABLE 2
For 10 parties, there are 115,974 games to play out. (See Donald Knuth’s The Art of Computer Programming Vol 4 F3). You can download output of all 115,974 games. The interesting games are the extremes: the game that results in the maximum position and the one that results in the minimum position.
Game A maxPos : 83.22147 party : Pos Inf(Norm) Sal ---------------------------------------------------------------- eng : 60 0.07547 99 _d1+d2_ : 64 0.18868 95 _er+me_ : 74 0.16981 83 _adv+ctcust+ctsal+inv+legal_ : 97 0.56604 77 ----------------------------------------------------------------- Position (weighted avg): 83.2 Position (balance of power): 77.1
(Game # : 2089)
Game B minPos : 66.27527 party : Pos Inf(Norm) Sal ------------------------------------------------------------ _ctcust+ctsal+d2+inv_ : 32 0.32075 65 _d1+eng+me_ : 72 0.37736 99 _er+legal_ : 98 0.15094 80 adv : 100 0.15094 20 ------------------------------------------------------------ Position (weighted avg): 66.3 Position (balance of power): 51.2
(Game # : 47615)
TABLE 3
In Game A, a coalition of d1+d2 and er+me results in the most extreme high-number position. The final position realized was 100 and these two coalitions were instrumental in the outcome. To design for to most extreme high position, these are the coalitions to nurture.
In this case, however, d2, the biggest stakeholder in the low-number position, would have achieved more of their goals had they designed the decision process around the coalitions shown in Game B. By building a relatively strong coalition around ctcust+ctsal+d2+inv and a diffusive coalition around er+legal, a much lower position is achieved.
There is a second hint here too. If the decision process could be more focused on Balance of Power rather than a salience/influence weighted average, a position of around 50 might be reached. This might be done, for example, by getting all the parties to agree to a final vote with 1 vote per person, winning position takes all votes, then voting in rounds sequentially through the coalitions of Game B top to bottom.
Download the Python model modules: MultiPlayerGame.py MultiPlayerNegotiationsModel.py Partitioner.py
Applied game theory – Part 1
Bruce Bueno De Mesquita’s Predictioneer’s Game describes work by De Mesquita’s team of applied game theorists and his students to business and political negotiations and decision making. Predictioneer’s Game covers some of the very basic ides, but De Mesquita leaves nearly all of the details of his models a mystery.
I recently had the opportunity to observe a business negotiation between about a dozen parties that seemed ripe for PG analysis. Among the basic ideas explained in the book are the basic inputs used by De Mesquita’s models and how to make estimates of “game” outcomes. In a future post the model here will be expanded to model more of the details of interactions between the parties and sub-groups. As the book contains few hints to how De Mesquita does this, those models will be striking off on our own.
But first, back to the negotiations and simple estimates of the outcome. All of the parties below are actual people involved in the negotiation. Negotiations covered about 2 months and are completed with respect to these positions.
The object is to calculate two estimates of the outcome of the negotiations following Predictioneer’s Game. Here are the steps:
(1) Identify the stake-holding parties involved in the negotiations. Anyone who takes a definite position and has some stake in the outcome should be included. This model includes only the most obvious players.
Party [Position, Influence, Salience]
-----------------------------------------
data = {
'd2': [ 15, 20, 80],
'ctcust': [ 35, 50, 5],
'ctsal': [ 35, 20, 5],
'er': [ 50, 10, 20],
'eng': [ 60, 40, 99],
'd1': [ 75, 80, 99],
'me': [ 75, 80, 99],
'adv': [100, 80, 20],
'inv': [100, 80, 5],
'legal': [100, 70, 95]
}
TABLE 1
(3) Describe the relative influence of the parties. Below, the influence will be normalized to add up to 100%, so just choose a scale that is convenient to get the relative numbers right. In the example, I estimated the influence of each party at the same time as I estimated positions.
(4) Now it is time to the estimate cost/benefit to each party. Salience is a score starting with ambivalence=0 moving up to everything there is to gain or loose is staked on the outcome scoring 100.
Predictioneer’s Game describes students and analysts researching positions and salience for political problems through interviews, CIA analyst records, newspapers, eye-witness reports etc. These steps could take a great deal of work. But also some discipline to hear and interpret what people are trying to accomplish.
Time to estimate outcomes. The first estimate is the weighted average position given by
Plugging in the numbers above gives a position just above parties d1 and me at 76.4. See Table 2 below.
The second estimate of outcome is the point along the position line where the cumulative total influence is 50%. That is, there is as much influence pulling the final position to the left as to the right. In this case the the balance-of-power point is at position 71.7.
The results are summarized on the table below. Influence has been normalized to add up to 100%.
Party : Pos Inf(Norm) Sal ------------------------------------------------------------ d2 : 15 0.03774 80 ctcust : 35 0.09434 5 ctsal : 35 0.03774 5 er : 50 0.01887 20 eng : 60 0.07547 99 me : 75 0.15094 99 d1 : 75 0.15094 99 adv : 100 0.15094 20 legal : 100 0.13208 95 inv : 100 0.15094 5 ------------------------------------------------------------ Position (weighted avg): 76.37 Position (balance of power): 71.72 TABLE 2
Both of these predictions give a position that was discussed as a plausible compromise between the more extreme positions.
This is not how the negotiations worked out. The final outcome was at position 100. In fact, the middle ground evaporated about 7 days after I first made this calculation. This situation moved very quickly to position 100 with everyone from position 50 and above accepting the 100 was the only possible outcome and the majority of the influence below position 50 also insisting that, although undesirable, 100 was the only possible outcome.
I am fairly confident that any proposal from the influential parties in the positions below 50 that landed at or slightly above 50 would have lead to further negotiations and resolution. In this case, some of the parties decided not to fight for their positions directly. This means I overestimated the salience of their positions?
Weight training experiment – Week 10
Last fall I happened across Body by science in the bookstore. I tend to see the weight loss and fitness industries as genetically above average humans telling genetically average humans that they aren’t doing it right. But these enthusiastic trainers don’t usually have much in the way of systematic research to back up their claims.
The first things I read when leafing through BBS was the research on the genes one needs to build a body like Arnold Schwarzenegger used to have. It turns out that we can currently identify 4. This means that there of some variations in genetic disposition to build large muscles with people having all four in the “on” position having a clear, measurable advantage when it comes to building muscle. This knowledge undermines much of the current body building industry–if you don’t have the genes, there is not workout regime, personal trainer, magical powdered drink or incantation that will make you grow big muscles. (Steroids appear to be the exception.) Given the constraints, however, most people can become stronger than they are now. Body by Science tries to answer how that is most effectively done.
After reading the sections on metabolism and muscle growth, it seemed clear that BBS was serious about recommendations based on evidence so I committed to try the workouts. I hate lifting weights. I learned the high-school “state of the art” 20 years ago, but never really caught the bug. The routine then was 3 sets with tapering reps 25, 20, 10 or something like that. Recommendations were to work out 3-5 times per week for 30-45 min and do lots of different exercises. You need to work out hard and stick with it.
Body by Science starts by recognizing that there are only two phases to muscle growth: first work a muscle very intensely, then rest long enough for the muscle to recover and grow.
“Work” in this case means one set of 9-11 repetitions done very slowly (10+s per rep) with muscle failure coming after about 90 seconds. Work large muscle groups and use machines (since working to complete failure.) The recommendation is to work only 5 muscle groups during the first 15 weeks.
This means the schedule is to workout for about 23 minutes per week.
Rest means 7-10 days between workouts–that’s 1 or fewer work outs per week. One obstacle to this program is that you don’t feel like you paid your dues. The big advantage is that the investment of time and effort is sustainable. If progress slows, add a day of rest.
Is the science sound? One anecdote is not evidence that it works for everyone, but it seems to be working for me. I just completed week 10. Results charted below.
The unanticipated challenge of this system is finding the weight that results in muscle failure at 90-100 seconds. This is because one’s strength changes quite a lot from week to week.
Percent change in strength
From the percent change chart, it is clear that there are 2 (maybe 3, see below) different growth rates for muscle groups. The chest and leg muscles gain strength most rapidly, with arms and shoulders growing more slowly.
To give some reference, here are the actual weights moved in the workouts. As you can see by the very steep rise at the beginning, I started with too light of weights and had to increase a lot the first week to reach failure in less than 12 reps.
Weights (machine) week by week.
One mistake I persisted in was with Pull Downs. I increased the weight too quickly in week three and didn’t realize how important thorough muscle fatigue is to strength building. The weight was too heavy and I was only able to do 4-5 reps. I thought I would improve enough during the week (based on my other exercises) to use the same weight the next week and reach failure in 9 or 10 reps. But strength didn’t improve nearly enough. In my last workout, I decided to reduce the weight down to 175 lbs in order to completely fatigue the muscle. The next few weeks should tell if this diagnosis is correct. If so, the red line should start taking off like the other smaller muscle groups.
On hearing about this experiment, a friend pointed me to an older book with essentially the same message. Check out The power of ten if you want a second opinion.
