One of my projects
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Not sure what happened to my last post to try to clarify the question, but another thought occurred to me this AM Maybe a way to look at an epidemic from the active management point of view, and an epidemic as an autonomous agent itself, is to consider it as exploiting the passive resource of infection pathways in a community. Any particular epidemic may be using the familiar ones, or some unfamiliar ones. It may discover new ones in the course of events. The question is how to use models to help people a) identify the interruptible links in the pathways an epidemic is exploiting?, and b) how to tell when the epidemic has changed to exploit some new unseen pathway that a new intervention strategy will be needed for? -- Phil Henshaw ????.?? ? `?.???? ~~~~~~~~~~~~~~~~~~~~~~~~ 680 Ft. Washington Ave NY NY 10040 tel: 212-795-4844 e-mail: sy at synapse9.com explorations: www.synapse9.com Re: [FRIAM] One of my projects Douglas Roberts Sat, 31 Mar 2007 09:05:26 -0800 Phil, I did read your question, repeated below: Cool, do you include any comparative natural system component? Perhaps working with better ways to identify system structures in natural systems and early signs of when they are inventing new ones would be helpful in developing tests for models that approximate the complexity of nature. However, I found it to be sufficiently ambiguous that I had absolutely no idea what was being asked, and thus found myself at a complete loss for a response. -- Doug Roberts, RTI International [EMAIL PROTECTED] [EMAIL PROTECTED] 505-455-7333 - Office 505-670-8195 - Cell |
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Well, ok.
1. I have no idea what an "active management point of view" means. 2. An epidemic is not an agent. The epidemic is the emergent behavior of the system in response to a pathogen being introduced into the population of agents (people, it this case) in the system being simulated. 3. I have no idea what "exploiting the passive resource of infection pathways" means. Phil, I strongly recommend that before you invest much more time asking questions about agent based models and their use that you actually build one yourself. And then run it. Until then, I suspect your ability understand the basic underlying principles of ABM technology will be somewhat limited. -- Doug Roberts, RTI International droberts at rti.org doug at parrot-farm.net 505-455-7333 - Office 505-670-8195 - Cell On 4/2/07, Phil Henshaw <sy at synapse9.com> wrote: > > > Not sure what happened to my last post to try to clarify the question, but > another thought occurred to me this AM > > Maybe a way to look at an epidemic from the active management point of > view, > and an epidemic as an autonomous agent itself, is to consider it as > exploiting the passive resource of infection pathways in a community. > Any > particular epidemic may be using the familiar ones, or some unfamiliar > ones. > It may discover new ones in the course of events. The question is how > to > use models to help people a) identify the interruptible links in the > pathways an epidemic is exploiting?, and b) how to tell when the epidemic > has changed to exploit some new unseen pathway that a new intervention > strategy will be needed for? > > > -- > Phil Henshaw ????.?? ? `?.???? > ~~~~~~~~~~~~~~~~~~~~~~~~ > 680 Ft. Washington Ave > NY NY 10040 > tel: 212-795-4844 > e-mail: sy at synapse9.com > explorations: www.synapse9.com > Re: [FRIAM] One of my projects > Douglas Roberts > Sat, 31 Mar 2007 09:05:26 -0800 > > Phil, > > I did read your question, repeated below: > > Cool, do you include any comparative natural system component? Perhaps > working with better ways to identify system structures in natural systems > and early signs of when they are inventing new ones would be helpful in > developing tests for models that approximate the complexity of nature. > > > However, I found it to be sufficiently ambiguous that I had absolutely no > idea what was being asked, and thus found myself at a complete loss for a > response. > > -- > Doug Roberts, RTI International > [EMAIL PROTECTED] > [EMAIL PROTECTED] > 505-455-7333 - Office > 505-670-8195 - Cell > > > > ============================================================ > 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 > An HTML attachment was scrubbed... URL: http://redfish.com/pipermail/friam_redfish.com/attachments/20070402/610bd451/attachment.html |
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Ok, I'll try another approach. I looked into EpiSims a little. It
really does seem like a fully formed modeling environment for how we think epidemics propagate. It's missing the scientific reality component though, the step of identifying how what we think is *different from* reality. That comparison is just not there that I could see. One of the obvious problems is that the old method of classical mechanics does not work at all for complex systems. This kind of modeling simply does not describe reality as following mathematical curves anymore, so recording the shapes of nature's curves and adjusting formulas to fit them no longer helps to validate our best models. Still, don't you need some sort of method of a) validation of results and b) finding patterns in the discrepancy in the results found? Phil Henshaw ????.?? ? `?.???? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 680 Ft. Washington Ave NY NY 10040 tel: 212-795-4844 e-mail: pfh at synapse9.com explorations: www.synapse9.com <http://www.synapse9.com/> -----Original Message----- From: [hidden email] [mailto:[hidden email]] On Behalf Of Douglas Roberts Sent: Monday, April 02, 2007 11:54 AM To: Phil Henshaw; The Friday Morning Applied Complexity Coffee Group Subject: Re: [FRIAM] One of my projects Well, ok. 1. I have no idea what an "active management point of view" means. 2. An epidemic is not an agent. The epidemic is the emergent behavior of the system in response to a pathogen being introduced into the population of agents (people, it this case) in the system being simulated. 3. I have no idea what "exploiting the passive resource of infection pathways" means. Phil, I strongly recommend that before you invest much more time asking questions about agent based models and their use that you actually build one yourself. And then run it. Until then, I suspect your ability understand the basic underlying principles of ABM technology will be somewhat limited. -- Doug Roberts, RTI International droberts at rti.org doug at parrot-farm.net 505-455-7333 - Office 505-670-8195 - Cell On 4/2/07, Phil Henshaw <sy at synapse9.com> wrote: Not sure what happened to my last post to try to clarify the question, but another thought occurred to me this AM Maybe a way to look at an epidemic from the active management point of view, and an epidemic as an autonomous agent itself, is to consider it as exploiting the passive resource of infection pathways in a community. Any particular epidemic may be using the familiar ones, or some unfamiliar ones. It may discover new ones in the course of events. The question is how to use models to help people a) identify the interruptible links in the pathways an epidemic is exploiting?, and b) how to tell when the epidemic has changed to exploit some new unseen pathway that a new intervention strategy will be needed for? -- Phil Henshaw ????.?? ? `?.???? ~~~~~~~~~~~~~~~~~~~~~~~~ 680 Ft. Washington Ave NY NY 10040 tel: 212-795-4844 e-mail: sy at synapse9.com explorations: www.synapse9.com Re: [FRIAM] One of my projects Douglas Roberts Sat, 31 Mar 2007 09:05:26 -0800 Phil, I did read your question, repeated below: Cool, do you include any comparative natural system component? Perhaps working with better ways to identify system structures in natural systems and early signs of when they are inventing new ones would be helpful in developing tests for models that approximate the complexity of nature. However, I found it to be sufficiently ambiguous that I had absolutely no idea what was being asked, and thus found myself at a complete loss for a response. -- Doug Roberts, RTI International [EMAIL PROTECTED] [EMAIL PROTECTED] 505-455-7333 - Office 505-670-8195 - Cell ============================================================ 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://redfish.com/pipermail/friam_redfish.com/attachments/20070403/4838a9c0/attachment.html |
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Phil Henshaw wrote:
> It's missing the scientific reality component though, the step of > identifying how what we think is *different from* reality. If it's possible to identify how a model is thought be not representative of reality, then new mechanisms can be added. Then, differences in model behavior can be quantified both relative to the simpler model and relative to some set of data metrics that characterize known `reality'. > Still, don't you need some sort of method of a) validation of results e.g. Doug mentioned the 1918 pandemic flu: http://www.mail-archive.com/friam at redfish.com/msg01646.html and b) finding patterns in the discrepancy in the results found? Suppose one posited that temperature had some role in the distribution of a pathogen in some environment. Data could be collected from actual cool and warm environments (by experiment or from a historical account). Meanwhile a model could implement the hypothesis about the role of temperature. If the distribution of the pathogen in the model doesn't match the data, then the model can be elaborated or changed. Or one can consider collecting more precise data if the model suggests finer distinctions in outcomes than could otherwise be witnessed. Rinse and repeat until the model acts like reality. Now try predicting distributions from new datasets. No earthshaking changes to scientific method here. It's just that the predictions can be high dimensional if needed and model mechanisms aren't constrained to be analytically tractable. |
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Correct, and perhaps a better way of saying that same thing.
I have a bundle of natural system derived metrics for use in comparing the behavior of models with individual instances of physical systems. There's still a gap in making the system features I can extract from nature connect with what statistical modelers will want to plug into their models, however. The gap seems to represent a significant real disconnect between the designs of individual physical systems and the designs of computer models of them. One of the features I think would help the most to make computer models more similar to individual instances of physical systems is that every sub-system act as an individual, and be given the behavior of 'exploring' it's domain. System 'exploration' is a property that I think could be given mathematical definition, but has not yet as far as I know. It basically means 'variation at the fringe' where the number of experiments in the region of successful experiments is variously self-controlled. The problem for modeling that I see is the question 'fringe of what??'. Because a computer model is iterative there are loops of effects. In nature, loops of effects like that bundle into individuals that act as wholes. I know how to identify them and a little about how to explore them, but not how to write programs to emulate them. Perhaps that's because I see their every feature to be essential, and maybe that's not quite necessary for building somewhat useful models that carry more of their authentic structures? Phil Henshaw ????.?? ? `?.???? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 680 Ft. Washington Ave NY NY 10040 tel: 212-795-4844 e-mail: pfh at synapse9.com explorations: www.synapse9.com > -----Original Message----- > From: friam-bounces at redfish.com > [mailto:friam-bounces at redfish.com] On Behalf Of Marcus G. Daniels > Sent: Wednesday, April 04, 2007 6:04 AM > To: The Friday Morning Applied Complexity Coffee Group > Subject: Re: [FRIAM] One of my projects > > > Phil Henshaw wrote: > > It's missing the scientific reality component though, the step of > > identifying how what we think is *different from* reality. > If it's possible to identify how a model is thought be not > representative of reality, then new mechanisms can be added. Then, > differences in model behavior can be quantified both relative to the > simpler model and relative to some set of data metrics that > characterize > known `reality'. > > Still, don't you need some sort of method of a) validation > of results > e.g. Doug mentioned the 1918 pandemic flu: > and b) finding patterns in the discrepancy in the results found? Suppose one posited that temperature had some role in the distribution of a pathogen in some environment. Data could be collected from actual cool and warm environments (by experiment or from a historical account). Meanwhile a model could implement the hypothesis about the role of temperature. If the distribution of the pathogen in the model doesn't match the data, then the model can be elaborated or changed. Or one can consider collecting more precise data if the model suggests finer distinctions in outcomes than could otherwise be witnessed. Rinse and repeat until the model acts like reality. Now try predicting distributions from new datasets. No earthshaking changes to scientific method here. It's just that the predictions can be high dimensional if needed and model mechanisms aren't constrained to be analytically tractable. ============================================================ 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 |
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Oh,... a little correction. The statement "variation at the fringe" is not
entirely original, though I did develop it before knowing about the others who use essentially the same thing. It's one of the things that makes the work of three others I know of unique and advanced in evolutionary systems thinking. Rob Ulanowitz, Peter M. Allen and Kirschner & Gerhart. Sorry for the omission. Rob refers to it with his concept of 'autocatalysis', Allen using the word 'explore', and Kirschner & Gerhart as 'facilitated', all refering to locally stimulated evolution within a larger system. For me it's been something I've noticed in closely watching how natural system pathways evolve for a long time, and specifically offered as a mechanism to explain the apparent genetic feedback dynamics in the G. tumida species transition I demonstrated. On 4/4/07, Phil Henshaw <sy at synapse9.com> wrote: > > Correct, and perhaps a better way of saying that same thing. > > I have a bundle of natural system derived metrics for use in comparing > the behavior of models with individual instances of physical systems. > There's still a gap in making the system features I can extract from > nature connect with what statistical modelers will want to plug into > their models, however. The gap seems to represent a significant real > disconnect between the designs of individual physical systems and the > designs of computer models of them. > > One of the features I think would help the most to make computer models > more similar to individual instances of physical systems is that every > sub-system act as an individual, and be given the behavior of > 'exploring' it's domain. System 'exploration' is a property that I > think could be given mathematical definition, but has not yet as far as > I know. It basically means 'variation at the fringe' where the number > of experiments in the region of successful experiments is variously > self-controlled. The problem for modeling that I see is the question > 'fringe of what??'. Because a computer model is iterative there are > loops of effects. In nature, loops of effects like that bundle into > individuals that act as wholes. I know how to identify them and a > little about how to explore them, but not how to write programs to > emulate them. Perhaps that's because I see their every feature to be > essential, and maybe that's not quite necessary for building somewhat > useful models that carry more of their authentic structures? > > > Phil Henshaw ????.?? ? `?.???? > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > 680 Ft. Washington Ave > NY NY 10040 > tel: 212-795-4844 > e-mail: pfh at synapse9.com > explorations: www.synapse9.com > > > > -----Original Message----- > > From: friam-bounces at redfish.com > > [mailto:friam-bounces at redfish.com] On Behalf Of Marcus G. Daniels > > Sent: Wednesday, April 04, 2007 6:04 AM > > To: The Friday Morning Applied Complexity Coffee Group > > Subject: Re: [FRIAM] One of my projects > > > > > > Phil Henshaw wrote: > > > It's missing the scientific reality component though, the step of > > > identifying how what we think is *different from* reality. > > If it's possible to identify how a model is thought be not > > representative of reality, then new mechanisms can be added. Then, > > differences in model behavior can be quantified both relative to the > > simpler model and relative to some set of data metrics that > > characterize > > known `reality'. > > > Still, don't you need some sort of method of a) validation > > of results > > e.g. Doug mentioned the 1918 pandemic flu: > > > http://www.mail-archive.com/friam at redfish.com/msg01646.html > > and b) finding patterns in the discrepancy in the results found? > > Suppose one posited that temperature had some role in the distribution > of a pathogen in some environment. > Data could be collected from actual cool and warm environments (by > experiment or from a historical account). Meanwhile a model could > implement the hypothesis about the role of temperature. If the > distribution of the pathogen in the model doesn't match the data, then > the model can be elaborated or changed. Or one can consider collecting > more precise data if the model suggests finer distinctions in outcomes > than could otherwise be witnessed. Rinse and repeat until the model > acts like reality. Now try predicting distributions from new datasets. > > No earthshaking changes to scientific method here. It's just that the > predictions can be high dimensional if needed and model mechanisms > aren't constrained to be analytically tractable. > > ============================================================ > 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 > > > > > ============================================================ > 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 > > > An HTML attachment was scrubbed... URL: http://redfish.com/pipermail/friam_redfish.com/attachments/20070404/b2ad8d86/attachment.html |
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