Cultural Psychology

Archive for the ‘Mathemetical models’ Category

Using Artificial Intelligence in Just War Deliberations

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UNLESS one is a pure pacifist, the general assumption is that some wars are justified. For centuries a body of literature called just war theory has developed concerning what distinguishes a just from an unjust war.  The criteria come under several headings, like (1) just cause, (2) right intention, (3) last resort, (4) legal authority, (5) probability of success, and (6) that the war not produce greater harms than it intends to solve.

If these criteria, which conform to common sense and moral philosophy alike, were applied scrupulously, most wars would be avoided. The problem comes in practice:  governments, if they consider these criteria at all, typically pay mere lip service to them. For example, to satisfy the just cause criteria, threats posed by foreign powers are greatly exaggerated; and the predicted costs of a war, both economically and in terms of human life and suffering, are greatly minimized. Further, as happened in the case of the 2001 Afghan War and the 2003 Iraq War, intellectuals spend more time arguing tedious fine points about the precise technical meanings of just war criteria than in applying them in a practical and sensible way.

Considering this, it struck me how there is a close similarity between the decision to make war and a medical decision to perform some drastic and risky procedure  say, a dangerous operation. In the latter case, because of the complexity of the choices involved and the fallibility of human decision-makers, expert systems and artificial intelligence have been used as decision support tools. In fact, I’ve developed one or two such systems myself.


Computerized medical decision-support systems offer several benefits. First, they can help a physician decide how to treat a particular patient. For example, based on such variables as the patient’s age, health, genes, and physiology, the system might supply the physician with the estimated probabilities of success for several treatment options (e.g., surgery, medication, naturalistic treatment, or perhaps no treatment at all). The physician isn’t required to follow the recommendation but he or she can take it into account. Usually it is found that, in the long run, incorporating such a system into medical practice reduces the number of unnecessary procedures and improves practice overall.

Second, and perhaps more importantly, the process of developing of a medical decision support system is itself very valuable. It requires physicians and medical scientists to focus attention on how actual treatment decisions are made. Ordinarily, diagnosis and treatment selection can be a very subjective and ad hoc thing  something physicians do based on habit, wrong practices, or anecdotal evidence. Developing an expert system forces physicians to explicitly state how and why they make various decisions  and this process not infrequently reveals procedural errors and forces people to re-think and improve their practices.

Both of these advantages might accrue were we to similarly develop a computerized support system to decide whether a war is just. From the technical standpoint, it would not be difficult to do this; a functional prototype could easily be developed in, say, 6 weeks or less. Off-the-shelf software packages enable the rapid development of such a system.

Another advantage of such systems is that they do not produce yes/no results, but rather a probability of success. That is, they are inherently probabilistic in nature. All inputs  for example, whether a foreign power has weapons of mass destruction  would be supplied as probabilities, not definite facts. Probabilities can be estimated based on mathematical models, or expert consensus (e.g., the Delphi method).

A decision support system helps one see how uncertainties accumulate in a complex chain of inferences. For example, if the success of choice C depends on facts A and B both being true, and if A and B are only known as probabilities, then a system accordingly takes uncertainty concerning A and B into account in estimating the probability of C’s success. In a medical decision based on a dozen or more variables, none known with complete certainty, the net uncertainty concerning success or failure of a particular treatment option can be considerable. In that case, a physician may elect not to perform a risky procedure for a particular patient. The same principle would apply for a just war decision support system.

Such, then, is my proposal. From experience, I’ve learned that it is better to start with a simpler decision support system, and then to gradually increase its complexity. Accordingly, I suggest that we could begin with a system to model only one part of just war theory  say, just cause, or ‘no greater harms produced.’ I further propose that we could take the decision to invade Iraq in 2003 as guiding example. My guess is that were such a model produced, it would show that the likelihood of success, the immediate necessity, and the range of possible harms were all so uncertain in 2003 that we should have not intervened as we did.

A final advantage of such a system is that it would connect moral philosophy with science. Science is cumulative: one scientific or mathematical advance builds on another. The same is not true of moral philosophy. Philosophers can go back and forth for centuries, even millennia, rehashing the same issues over and over, and never making progress.

Perhaps this is a project I should pursue myself. Or it might be an excellent opportunity for a young researcher. In any case,  I’m throwing it out into cyber-space for general consideration. If anyone reads this and finds it interesting, please let me know.

Incidentally, military analysts have developed many such computerized systems to aid combat decisions.  (When working at the RAND Corporation, I worked on a system to help US forces avoid accidentally shooting at their own aircraft  something called fratricide.) Since it is clearly in the interests of the military to avoid pursuing unwinnable wars, possibly it is they who could take a lead in developing the line of research proposed here.  US Naval War College and West Point are you listening?

White Paper: Materialism, Idealism, and Higher Education in California

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I’ve just completed a new White Paper on public higher education policy in California.  Here is an abstract:

For the last 50 years, a belief that building a robust and competitive state economy should predominate California’s public higher education goals has become increasingly prevalent, and today it is taken as an unchallenged assumption. This White Paper emphatically rejects that assumption, and argues that broader cultural and social goals are of equal, if not greater importance for Californians’ well-being than purely economic ones; and that to achieve these broader social goals we must place more emphasis on humanities and the classical model of liberal education.

A more detailed Executive Summary is included with the paper.   You can download a copy to read here, at the Californians for Higher Education Reform website.

Prioritizing Flu Vaccine: Individual Patient vs. Aggregate Rules

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This is a fairly big issue and I’ll probably devote more than one post to it.

In recent days newspaper headlines have made misleading statements that relate to how to ‘prioritize’ swine flu vaccine (potentially demand will exceeed supply).

For example, one headline today ran “Flu vaccine to pregnant women first“. What the study in question actually showed (or, rather, suggested) is that pregnant women may, on average and as a group, be at greater risk for swine flu and swine flu complications.

But to keep in mind is that each person’s circumstances are unique. Assessment of flu vaccine candidacy, assuming there’s a vaccine shortage, must consider all relevant factors of a person: medical history, age, risk of exposure, health status, risk of complications, risk of infecting others, etc.

While being pregnant is a factor to consider, so are others. It’s not as simple as putting all pregnant women ahead of all non-pregnant women in the ‘queue’, as the headline seems to suggest.

Another headline this week similarly suggested “Antiviral drugs for swine flu patients may be wasted on the elderly.” Again this is an incorrect and misleading statement. On average, elderly people perhaps respond less well to antiviral flu medicine (they do show somewhat lower immunoresponse to flu vaccination on average than younger people) ; they areprobably less likely, again on average, to have a lot of contact with children .

But there are plenty of people above age 65 who vary from the average. Some respond well to vaccines, some have a lot of contact with children, etc.

Decisions to administer antivirals or to give swine flu vaccinations have to be made on a case-by-case basis, considering all relevant aspects of the person and their circumstances.

It can easily be shown that approaching vaccine allocation by a blanket rule like “only young people and pregnant women should get the vaccine” would be extremely suboptimal. The degree of suboptimality associated with such rules — or what could be technically termed marginal prediction — can be estimated; in this case such faulty prediction would likely produce considerable excess mortality and morbidity,  reduced overall quality of life, and unnecessary loss of many millions of dollars.

One alternative is to construct a simple statistical decision tool that  would compute a score for each person (e.g., 1 = lowest priority to 100 = highest priority) based on the person’s individual data. This could be put online for people or doctors to use, for example.

The data to construct such a tool exists in various places, but would need to be collated and analyzed. At present I’m tentatively planning to develop a prototype tool, hopefully in the next couple of weeks.

Written by John Uebersax

July 30, 2009 at 1:02 am

Protect Yourself from the Flu – Video

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Protect Yourself from the Flu

A leading flu vaccine producer, GlaxoSmithKline (GSK), has generously released this audio-visual presentation, originally developed for their employees, to the public:

GSK flu prevention video

When the new window opens, press the “Next” button on the lower right to continue.

This is the best presentation of its kind available today. Watch it yourself and show it to your family and friends.

If enough people follow the simple, common-sense steps outlined here, it can have a significant effect on reducing the swine flu pandemic. Because pandemic disease transmission follows an exponential pattern, even minor preventive steps like those explained here can have a major impact on total disease incidence.

Written by John Uebersax

July 24, 2009 at 11:39 pm