It is probably well known that jurors should not independently research the case and evidence presented before them in a trial. Just to make that clear, four jury verdicts were overturned on appeal because a juror did just that--using the internet.
Thanks to the Juries Blog, which I follow, for the lead to this excellent story.
All kidding aside, if a person can have several representatives, each with different priorities, why can't some of these be organizations. I believe Grosof's courteous logic programming is the basis for that. This allows one to deal with different people creating different rule sets. Each might have a different priorities. Each voter would have several represenatives. Each voter would assign a priority to each represenative. Legislation is a set of rule sets. When they conflict, we look at which representatives voted for each and combine them based upon the priorities they gave them.
In the proposal, many individuals would give their employer as a representative, as they are concerned that it is prosperous. They would also give various entities such as the Sierra Club, their Church, etc. a vote. And they would give a priorities (One could see one giving their Church first, then their employer, and then the environmental group to which they felt most attached.) And, of course, on any bill, one could vote oneself. Thus, the system would extend to try participatory democracy, but only on those bills which one cares enough to read for oneself.
I have played with how to to combine the votes using Dr. Grosof's work. (This will be a subject of another Thoughtful Thursday Posting.)
Mr. Aaronovitch also said that a significant portion of the educated Pakistani's believe 9/11 was a hoax put on by the Bush Administration. A former President of Malaysia, Mahathir Mohamad, was in favor of this and said that if the country could make Avatar, they could create this conspiracy theory. The Wikipedia article on 9/11 Conspiracy Theories is very helpful on this subject.
I will put his book on my list of books to review for a Thoughtful Thursday post.
Obviously, one must be concerned about any democracy whether it be participatory of otherwise, when people believe things that are not true. However, I have found no evidence that a participatory democracy would be more affected than a conventional one where demagogues could whip up the frenzy.
ID3 is an algorithm to generate a decision tree from a set of examples. It is used in machine learning.
Many legal codes are some sort of function of several parameters and are of a nature that can be defined as a decision tree. For example, one is making the gun control law. Some of the parameters might be the type of gun. (Is this a BB-gun, an antique gun, a non-functional gun, an assault gun, a pistol, etc?) Is the weopon concealed? And there are the characteristics of the owner of the possessor. Are they mentally ill, convicted of a crime, have an order of protection against them, etc. Was the person's life threatened? Does the person job need it, e. g. security guard. And lastly, what is the place: a school, bar, a place where beer is sold such as some supermarkets.
The answer could be a simple boolean, "permitted" or "not permitted" Or we could get a classification such that the possession might be a misdemeanor or a felony. Many states have categories for felonies. For example, in the State of New York, a class A felonly, the term is life imprisonment, for class B, the term for a class B felony does not exceed twenty-five years, etc. (There are other special rules for dispositions, such as those who have prior convictions or are youthful offendors.) Then the bulk of the penal code defines offenses to be a class D felony, a class B felony, etc.
A possible decision tree
| Gun Type | Location | Age | Personal Status | Penalty, if any |
| antique | No Offense | |||
| BB Gun | N Offense | |||
| Pistol | 15-20 | A Class Misdemeanour | ||
| Pistol | 20-30 | B Class Felony | ||
| Pistol | 30-50 | Felony | ||
| Pistol | 50-70 | A Class Misdemeanour | ||
| Rifle | Bar | Class B Felony | ||
| Rifle | Schol | Class C Felony | ||
| Rifle | Place where Beer is Sold | Class A Misdemeanour | ||
| Rifle | other | No Offensse | ||
| Assualt | Class B felony | |||
| Rifle | Other | mentally ill | Class D Felonly | |
| Rifle | Other | Convicted of a Felony | Class C Felony | |
| Rifle | Other | Other | Class B Misdemeanor |
An ID3 algorithm would take a series of examples such as the one in the table above. It would create a decision tree that would give the same answers as the examples. It needs to pick which parameter to use for the root node--in the above tree, it was Gun Type. It picks one that divides a lot of examples between thee categories. Often the formula from information theory is used. In the above example, the tree was precisely determined from the input data. That is not always true. In a diagnosis application, there would likely be lines that contradict each other. Or there would might be several lines with very similar attributes. All might have the same classification or penalty, except one. Is that due to a real difference, or just a random error?
| A | B | C | D | Result |
| 1 | 2 | 3 | 44 | ii |
| 1 | 2 | 4 | 44 | ii |
| 1 | 2 | 5 | 44 | ii |
| 1 | 2 | 3 | 45 | ii |
| 1 | 2 | 4 | 45 | ii |
| 1 | 2 | 5 | 45 | ii |
| 1 | 2 | 3 | 46 | ii |
| 1 | 2 | 4 | 46 | ii |
| 1 | 2 | 5 | 46 | iii |
Mr. Yeluri set up an example to illustrate these ideas. Since it is long, I put it at the end in an appendix.
In participatory democracy, we do it a little bit differently. For each node in the tree, the users vote on what the next parameter should be. Thus, at the start the individuals would vote on which of the above questions regarding place, type of gun and person should be asked first. Then, the group for each resulting category would be classified again. The individuals can vote to decide for any node when it comes up to classify it once and for all. E. G., if the category of assault weopon shows up, individuals might vote to always ban it and for the category of BB-gun, they may vote not to consider any other factors and always permit it. This is a leaf node and the people will then vote on the classification from the possibilities available.
We established the following terminology, for the input to our experiment:
antique,
bb, assualt, rifle, assault
or shot gun
for example, the voting might bring us to making a decision on
what to do with those caught with an antique guns, a history of
mental illness, and who are sixteen to twenty years old.
This is a list of title (nature of gun) and type, antique,
mental illness status, and type of history of mental ilness
and age, which is divided into types or ranges.
this is defined as
After the root node, the users can assign a penalty. Observe in the figure that we immidetely assign "no offense" to those possessing antique guns. on the other hand, there arthree levels until we get to gun type ="rifle",m location="other" and "mental health status" = "felony conviction." at each node, the users have the option to "vote now" and if that gets more votes than any title, the users vote on a penalty for same.
How would we apply this to tax law? The ID3 algorithm is not designed to deal with cases that produce a continuous function, like the amount of tax that the taxpayer should payer. (Note, ID3 is used when there are input parameters that are numeric. The algorithm sorts the examples in numeric order of the parameter. It then finds the decision pont that gives the greatest information theory gain.)
Thus, for a tax system by ID3, at each leaf node, there would per be a formula. The titles in a tax problem might be various income ranghe atues, the number of children, marital status. And then the users might vote for those unmarried head-of-household with three children and in the range $20,000 to $40,000 to make the tax = $500.00 + 0.10*(Income - $20,000). That is $500.00 on the first $20,000 plus ten percent of the remaining income. They might put other components of the formulae in for the amount of state tax they paid, the mortgage interest, etc.
A, B and C.
The possible choices, or parameters for each of these
are:
| A | F, G, H
|
| B | I, J, K |
| C | M, N, P |
| A | B | C | Classification Yes /No |
| F | I | M | N |
| F | I | N | Y |
| F | I | P | N |
| G | I | M | N |
| G | I | P | Y |
| G | I | M | N |
| G | I | P | Y |
| G | I | N | Y |
| G | J | M | Y |
| G | J | P | Y |
| F | J | M | N |
| F | J | N | Y |
| F | J | P | Y |
| H | I | N | N |
| H | I | N | N |
| H | J | N | N |
| H | I | P | Y |
| H | J | M | Y |
| H | J | N | N |
| H | J | P | Y |
| H | K | M | N |
| H | K | N | Y |
| H | K | P | Y |
| F | K | M | N |
| F | K | N | Y |
| F | K | P | Y |
| A | B | C | Classification Yes /No |
| F | I | M | N |
| F | I | N | Y |
| F | I | P | N |
| F | J | M | N |
| F | J | N | Y |
| F | J | P | Y |
| F | K | M | N |
| F | K | N | Y |
| F | K | P | Y |
| A | B | C | Classification Yes /No |
| G | I | M | N |
| G | I | P | Y |
| G | I | N | Y |
| G | J | M | Y |
| G | I | M | N |
| G | I | P | Y |
| G | J | P | Y |
| A | B | C | Classification Yes /No |
| H | I | M | N |
| H | I | N | N |
| H | J | N | N |
| H | I | P | Y |
| H | J | M | Y |
| H | J | N | N |
| H | J | P | Y |
| H | K | M | N |
| H | K | N | Y |
| H | K | P | Y |
By comparison, the sample sets for classifying by C are:
C="M"
| A | B | C | Classification Yes /No |
| F | I | M | N |
| G | I | M | N |
| G | J | M | Y |
| F | J | M | N |
| H | I | M | N |
| H | I | N | N |
| H | J | M | Y |
| H | K | M | N |
| F | K | M | N |
| G | I | M | N |
| A | B | C | Classification Yes /No |
| F | I | M | N |
| F | J | M | N |
| F | K | M | N |
| A | B | C | Classification Yes /No |
| G | I | M | N |
| G | J | M | Y |
| G | I | M | N |
| A | B | C | Classification Yes /No |
| H | J | M | Y |
| H | K | M | N |
| A | B | C | Classification Yes /No |
| F | I | N | Y |
| G | I | N | Y |
| F | J | N | Y |
| H | I | N | N |
| H | J | N | N |
| H | K | N | Y |
| F | K | N | Y |
| A | B | C | Classification Yes /No |
| F | I | N | Y |
| F | J | N | Y |
| F | K | N | Y |
| A | B | C | Classification Yes /No |
| G | I | N | Y |
| A | B | C | Classification Yes /No |
| H | I | N | N |
| H | J | N | N |
| H | K | N | Y |
Or we can split it by B, giving us:
| A | B | C | Classification Yes /No |
| F | I | N | Y |
| G | I | N | Y |
| G | I | M | N |
| H | I | N | N |
| A | B | C | Classification Yes /No |
| F | J | N | Y |
| G | J | P | Y |
| H | J | N | N |
| A | B | C | Classification Yes /No |
| H | K | N | Y |
| F | K | N | Y |
The resulting tree chosen would be given by the below:
So much for using patient copays to reduce patient costs.
See our four-part participatory democracy framework to reform health care. The last part is the doctors are paid on the basis of the service they provide, after they provide it.
But proposed here in a share economy, and where sortition juries of share holders approve all expenditures. This helps prevent slippery definitions from allowing perks to go through. It does not single out speech for special attention will probably upset first-amendment purists. It simply says that a randomly-selected set of jurors, weighted by the number of shares they own, must approve each and every expenditure. This would do wonders for many accounting controls!
But the answer is not to get rid of the fillibuster. it is simply to say that fourty percent of one house or the other can propose a referendum. I proposed this in the second sentence of my second post in this blog. Note, that if there are several related proposals they would be resolved by approval voting.
And Congress can do this, I believe, without a Constitutional Amendment--although that would make it bullet proof. The United Supreme Court has required that Congress provide some standards in delgation of powers. And in Clinton versus City of new York, the Supreme Court stopped a line-item veto to the President. The question is whether this proposal, a delgation back tot he people is an unconstitutional delegation of powers. I would argue that there is a very definite standard, simply counting the number of votes of the populace.