When the interactions are complex it's fairly rare for people to look
carefully to see if the intended outcome actually occurs, for example whether not your aid of people in trouble is actually competently aiding them, or perhaps multiplying the people and their troubles. It's really great that you're asking the question this way, and about one of the areas where there do seem to be a lot of resources put into sophisticated growth system modeling. It seems to me that the key to this one is recognizing that all interventions will necessarily be experimental and that the two main modes of failure would be guessing wrong in the fist place and not noticing very quickly in the second place. Certainly using simpler models that are easier to put though a variety of trials can help with failure type 1, but failure type 2 is largely benefited by skillful watching of what is happening and picking up on and correctly identifying the divergences. For that perhaps the need for better observation technique takes precedence, rather than models and planning. > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA512 > > Colleagues, > > Two questions re: pandemic flu simulation development. Thoughts? > > 1. Many of the models of pandemic flu are complex and model reality to a > relatively high-level of granularity. I was thinking that a more > simplistic model (SD with some ABM) might be suitable for creating a > simulation that could be fed real-time data in the event of an actual > pandemic. Data would come from surveillance with a goal of trying to > determine (a) transmissibility and (b) antiviral resistance. Are others > working on more simplistic models of pandemic flu that can be linked to > surveillance data? What sort of surveillance data would be appropriate > to flow into such a simple model for trying to ascertain probable values > on the above factors? > > 2. I am struck by the fact that many simulations of pandemic flu focus > only on key performance indicators related to influenza-related > outcomes, while ignoring the broader costs that would clearly result > from targeted layered containment strategies. That is, could the costs > associated with trying to 'contain' a pandemic exceed the costs of just > letting it run it's course? So, for example, we close the schools to > reduce the number of infected, but this results in many children > becoming malnourished because they rely on free school lunches for a key > portion of their daily calories? How do we simulate the impact of > well-meaning policies that decrease the spread of the disease (a > benefit) but that also have tremendous costs when viewed outside the > perspective of the spread of the disease? That is, how do we create a > simulation to test policy decisions to ensure that well-meaning policies > don't actually make a tough situation even worse. > > > - -- > Best regards, > > Justin Lyon > M: +44 781 480 2797 (London, UK) > E: justin at simudyne.com > W: http://www.simudyne.com > -----BEGIN PGP SIGNATURE----- > Version: GnuPG v1.4.5 (MingW32) > Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org > > iD8DBQFFvgNQhfoqghrmIrARCpduAJ9ybJDsZwCs5sD9xWJ/1fKyAHGqcwCgtX/Q > jQGgLZDN1IdcA/YMeMyHlOw= > =jwfl > -----END PGP SIGNATURE----- > > ============================================================ > FRIAM Applied Complexity Group listserv > Meets Fridays 9a-11:30 at cafe at St. John's College > lectures, archives, unsubscribe, maps at http://www.friam.org > > -- Phil Henshaw ????.?? ? `?.???? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ tel: 212-795-4844 e-mail: sy at synapse9.com explorations: www.synapse9.com |
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