Mike A. writes: How do we make clear the core of a problem through constructing an illustration of our own beliefs and assumptions and say that's exactly what both great science and great art do. Science then has the obligation to challenge it against new instances of the problem in the classic Popperian way.
One trouble with Popper is, of course, that people just dont think that way. We engage in induction no matter how illogical it may be. Somebody I knew had a small animal skin.... ferret or something .... nailed to a board at one end. When you petted it, it arched its back, so to speak. Should we conclude that that is why cats arch their backs when you pet them???? Probably not. The other trouble with Popper is, as David Stove pointed out, that EVERY DEDUCTIVE INFERENCE REQUIRES INDUCTION TO GET IT INTO THE REALM OF PRACTICAL EXPERIMENT. So, for instance, as we are busily nailing our live cat to a board to test our deductive inference, we must assume that all our operations have the same effects in the live cat and in the ferret skin case, and this assumption is an INDUCTIVE STEP subject to all of Popper's doubts about the possibility of induction. I think Stove concluded that we just had to suck it up and go back to making rules for inductive inference, dubious as the whole enterprise is. So then the question would be, under what conditions do we accept that when the simple agents that we send forth to do battle in our models product the same collective behavior as the apparently real agents we see around us, that the real agents actually behave by the same underlying rules as the our created ones? Stove wrote a subsequent book on induction, but I havent read it. Has anybody??? > [Original Message] > From: <[EMAIL PROTECTED]> > To: <[email protected]> > Date: 8/14/2006 12:00:21 PM > Subject: Friam Digest, Vol 38, Issue 29 > > Send Friam mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > http://redfish.com/mailman/listinfo/friam_redfish.com > or, via email, send a message with subject or body 'help' to > [EMAIL PROTECTED] > > You can reach the person managing the list at > [EMAIL PROTECTED] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Friam digest..." > > > Today's Topics: > > 1. the odd question (Phil Henshaw) (Nicholas Thompson) > 2. The art of agent-based modeling (Jochen Fromm) > 3. Re: The art of agent-based modeling (Marcus G. Daniels) > 4. Re: The art of agent-based modeling (Jochen Fromm) > 5. Re: The art of agent-based modeling ([EMAIL PROTECTED]) > 6. Re: The art of agent-based modeling (Michael Agar) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sun, 13 Aug 2006 23:40:29 -0400 > From: "Nicholas Thompson" <[EMAIL PROTECTED]> > Subject: [FRIAM] the odd question (Phil Henshaw) > To: "Friam" <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset="us-ascii" > > Phil, > > I hate it when one of my topics gets dropped, and therefore feel guilty for being one of the DROPPERS, here. > > Sometimes the discussions get so far reaching and technical that I am forced to "pass over them in silence" as Wittegenstein said. > > the only piece of your message that I have anything nearly competent to say about is your .... > > > "when modern science took an interest in complex systems it, concentrated on theory rather than on carefully documenting the physical phenomenon." > > I wonder if this isnt a common occcurence in science. Think of Evolutionary Biology Darwinism has a much stronger hand on its theories than it does on the things those theories explain. Think for a moment about our realtive grasp on "natural selection" and "adaptation". Natural selection is supposed to the be "cause" of adaptation, yet we seem to understand the cause much better than we understand the effect. Ask an evolutionary biologist to define adaptation: 90 percent will use the word natural selection in their definitions, because they dont have clue what they mean by adaptation. > > Thus, it doesnt surprise me that wise and sophisticated people can talk about the theory of complexity without having a clue what they mean by it. > > I got a group of people to gether at Clark a few years back to start a research project on emergence in human social groups. We were NEVER able to come up with a phenomenon that everybody agreed was an instance of emergence. > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: /pipermail/friam_redfish.com/attachments/20060813/be24b22f/attachment-0001.h tml > > ------------------------------ > > Message: 2 > Date: Mon, 14 Aug 2006 14:14:55 +0200 > From: "Jochen Fromm" <[EMAIL PROTECTED]> > Subject: [FRIAM] The art of agent-based modeling > To: "'The Friday Morning Applied Complexity Coffee Group'" > <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset="US-ASCII" > > > One question I meet again and again if I try to > make meaningful agent-based simulations is: > - How do we simulate the core of a problem > without merely constructing an illustration > of our own beliefs and assumptions ? > In other words: How detailed should an agent-based > simulation be ? If the goal is "to capture the principal > laws behind the exciting variety of new phenomena that become > apparent when the many units of a complex system interact", as > Tamas Vicsek says in http://angel.elte.hu/~vicsek/images/complex.pdf > then how do we design models that are complex enough but not too > complex ? > > -If the simulation is too simple and matches your > own theoretical ideas, then no matter how good these > ideas are it is always easy to criticize that the > simulation is either not realistic enough or only > constructed to illustrate your own ideas and assumptions. > -If the simulation is too complex and matches > official experimental data, everything takes a > lot amount of time (creation, setup and execution of > the experiment and finally the cumbersome analysis > of the complex outcomes), and it becomes increasingly > difficult to identify the principal laws, because it is > easy to get lost in the data or bogged down in details > > The "art of agent-based modeling" looks really like an art > to me, something only mastered by a few scientists (for instance > Axelrod). Grimm et al. propose 'pattern-oriented modeling', > Macy and Willer say "Keep it simple" and "Test validity". > What do you think is the best solution for this problem ? > > Macy and Willer > "From Factors to Actors: Computational Sociology and Agent-Based Modeling" > http://www.casos.cs.cmu.edu/education/phd/classpapers/Macy_Factors_2001.pdf > > Grimm et al. > "Pattern-oriented modeling of agent-based complex systems" > Science Vol. 310. no. 5750 (2005) 987-991 > http://www.ufz.de/index.php?de=4976 > > -J. > > -----Original Message----- > From: Michael Agar > Sent: Saturday, August 12, 2006 5:05 PM > To: The Friday Morning Applied Complexity Coffee Group > Subject: [FRIAM] complexity and society > > [...] If you are considering a model, I like Axelrod's way of thinking > about them. He sees them as "thought experiment labs" for a > conclusion based on social research. So first of all the social > research has to be solid to really do it properly. More often than > not it isn't. > > The lab let's you test arguments of the form, if people do things in > particular ways properties will emerge at the level of society. By > "test" I mean it lets you see if the conclusion can be "generated," > to use Epstein and Axtell's concept, in just the way your social > research suggests that it can. It's a way of making the argument that > underlies the conclusion explicit so it can be better evaluated, and > it allows for exploration of the space of results that the same > argument produces and alternative spaces given control parameter > changes. It's a test of plausibility and an exercise in clarity, > nothing more, nothing less. [...] > > > > > ------------------------------ > > Message: 3 > Date: Mon, 14 Aug 2006 08:07:05 -0600 > From: "Marcus G. Daniels" <[EMAIL PROTECTED]> > Subject: Re: [FRIAM] The art of agent-based modeling > To: The Friday Morning Applied Complexity Coffee Group > <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > Jochen, > > -If the simulation is too complex and matches > > official experimental data, everything takes a > > lot amount of time (creation, setup and execution of > > the experiment and finally the cumbersome analysis > > of the complex outcomes), and it becomes increasingly > > difficult to identify the principal laws, because it is > > easy to get lost in the data or bogged down in details > > > This may be a false choice. In the case of having some data of > moderate resolution, there's no point in making a hugely elaborate model > and simulation, because you'll never be able to validate beyond your > data anyway. And if you don't validate, although the modeling still > may be useful as an thought experiment, it isn't science. You have to > be able to say something that can be shown to be wrong. If you do aim > to learn things about the world and then predict them it's not desirable > to have giant black box with lots of moving parts. It's better, if at > all possible, to have a simple story and make the simulation nothing > more than apparatus to help extend the data so that the dynamics can be > studied by theoreticians. > > Another mode of use for ABMs is to lower expectations of theoretical > traction and opportunistically look for ways a model makes useful > predictions and then modify the model in that direction over time. > This is a risky and expensive craft, but one that might have high enough > payoffs to consider (e.g. national security). > > It depends on the data and what is of interest. If the data tells you > about a number of rare events, and it is these events is what you really > care about, then it may make sense to loosely model everyday behaviors > and focus on model microstructure that can create the rare events you > care about. > > Finally, sometimes microstructure is known with clearly defined degrees > of freedom, and the dynamics are of interest. Consider modeling a > factory where different assembly regimes are to be evaluated.. There's > no need to validate here because the whole exercise is to answer > what-ifs about realizable specific systems. > > Marcus > > > > > > ------------------------------ > > Message: 4 > Date: Mon, 14 Aug 2006 17:04:40 +0200 > From: "Jochen Fromm" <[EMAIL PROTECTED]> > Subject: Re: [FRIAM] The art of agent-based modeling > To: "'The Friday Morning Applied Complexity Coffee Group'" > <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset="US-ASCII" > > > Of course it is the essence of science to verify hypotheses > by experiments. Yet sometimes we have neither suitable > experimental data nor a solid theory, for example > in the case of very large agent-based systems (for instance > for the self-organization and self-management of large > internet applications on planetary scale, or the modeling > of historical processes with millions of actors). It is > hardly possible to examine these systems without simplified > models, and in this case the questions I mentioned seem to > be justified. > > In traditional "factor-based" or "equation-based modeling" > we use differential equations and everything is based > on a soild theory: mathematics. This traditional modeling > has a century-long history and we know the suitable parameters, > equations and models. Agent-based modeling has a short history, > we don't know exactly the suitable parameters, agents and models, > and worst of all it is not based on a solid theoretical > theorem-lemma-proof science or calculus like mathematics. > > What is missing is a solid science of ABM or a new science of > complexity - something in the direction of Wolfram's NKS idea > (exploring computational universes in a systematic way). Just > as formal, symmetrical and regular systems can be described by > mathematics and 'equation-based modeling', complex systems can > in principle be described by a 'NKS' and agent-based modeling > - which seems to be more an art than a science. > > -J. > > > > > ------------------------------ > > Message: 5 > Date: Mon, 14 Aug 2006 09:50:59 -0600 > From: [EMAIL PROTECTED] > Subject: Re: [FRIAM] The art of agent-based modeling > To: The Friday Morning Applied Complexity Coffee Group > <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=ISO-8859-1 > > Quoting Jochen Fromm <[EMAIL PROTECTED]>: > > > Just as formal, symmetrical and regular systems can be described by > > mathematics and 'equation-based modeling', complex systems can > > in principle be described by a 'NKS' and agent-based modeling > > - which seems to be more an art than a science. > > In context, I think is a verification issue. > > ABMs are useful for poking around a complicated system to see what matters and > what doesn't by using a familiar and direct way of describing things, and to > leave the abstractions for later. ABMs complement traditional techniques of > analysis by extending data. > > The imperative programming languages that are typically used to make the > simulations are prone to a variety of programming mistakes but the continue to > be used because 1) they are common and 2) they provide an easy way to think > about side effects (e.g. modifications to a landscape). > > Equation-based modelling is more like functional programming, e.g. programming > languages like Haskell that are side-effect free. I see ABMs moving to these > kinds of programming languages so that components of a simulation can be shown > to be correct, and preferably by automated means. > > As a practical matter, I think it isn't a big deal. Unit testing during > development by experienced programmers/modelers does a good job of shaking out > bugs. > > Marcus > > > > ------------------------------ > > Message: 6 > Date: Mon, 14 Aug 2006 09:58:05 -0600 > From: Michael Agar <[EMAIL PROTECTED]> > Subject: Re: [FRIAM] The art of agent-based modeling > To: The Friday Morning Applied Complexity Coffee Group > <[email protected]> > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed > > > On Aug 14, 2006, at 6:14 AM, Jochen Fromm wrote: > > > > > > > - How do we simulate the core of a problem > > without merely constructing an illustration > > of our own beliefs and assumptions ? > > > I'd change this to > > How do we make clear the core of a problem through constructing an > illustration of our own beliefs and assumptions > > and say that's exactly what both great science and great art do. > Science then has the obligation to challenge it against new instances > of the problem in the classic Popperian way. > > Mike > > > > ------------------------------ > > _______________________________________________ > Friam mailing list > [email protected] > http://redfish.com/mailman/listinfo/friam_redfish.com > > > End of Friam Digest, Vol 38, Issue 29 > ************************************* ============================================================ 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
