Mathematics Naturalism Philosophy Science

Why the Scientific Imagination Matters

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One common criticism of the upcoming Alternatives to Methodological Naturalism conference has been that “scientists just follow the evidence where it leads.” Even among fellow ID’ers who disagree with methodological naturalism, they find it difficult to envision why we would need an alternative that is different from “just go with the evidence.”

The answer is simple – the scientific imagination.

One of the reasons why I started the conference is because Methodological Naturalism (hereafter, MN) constrains thinking in ways that I am not sure even people led entirely by the evidence are aware of.

Theory construction is often treated by both scientists and observers of science as an automatic given once the data is in.

In actuality, though, it is entirely philosophical. Every scientific theory is underdetermined by the evidence. Additionally, the ability to convert data into a theory is constrained by what scientists expect theories to look like.

Therefore, one’s imagination is actually the biggest constraining factor. This is the biggest aspect that needs to be worked on. However, ideas such as MN are what are constantly molding the imagination. Therefore, new ideas are needed in order to help people re-imagine what the world is like with a fresh perspective.

Taking the time to look into disciplines such as Austrian Economics which have explicit methodological dualism help people re-imagine the possibilities for their own disciplines. When we can see the possibilities played out in another context, we can grow our imaginations to see how those ideas can play in our contexts.

Mathematics itself is largely constrained by imaginations. I don’t have a problem per se with the way mathematics is taught, however, the way it is taught does bring forward a lot of incorrect expectations from mathematics. For instance, people commonly expect math to give them solvable problems, graphs with continuous lines, and everything smoothed out. As a matter of fact, there is nothing within mathematics that makes any of these a necessary truth. Therefore, people don’t think to look for strange graphs, and assume that everything, even if it is more complicated, has the general structure of the functions they worked with in high school and college. These sorts of assumptions limit imagination.

The biggest problem I have found with the proponents of MN is that they literally cannot imagine something else. They think that the alternative to naturalism is just making stuff up and pretending it to be true. This is also true of opponents of MN or PN. They may be able to see the problems with MN, but many have not been able to imagine an alternative way of doing things that has both rigorous and understandable methodologies.

That’s why having a conference on alternative methodologies is so important. We need to stretch the imagination of both our side and theirs, and to establish ways of thinking about the topic that are both new and rigorous. We need, more than anything, to have a rethink. And that’s why I think we need a conference.

10 Replies to “Why the Scientific Imagination Matters

  1. 1
    Bob O'H says:

    Theory construction is often treated by both scientists and observers of science as an automatic given once the data is in.

    Anyone who has looked at this seriously would disagree, of course. Partly because theory is often constructed before the data are in (indeed the data are collected to test the theory), but also because it’s accepted that ideas occur to people in different ways (Kekulé’s benzene dream is a famous example), and there’s no reason why that should be constrained by methodology. What is contrained it how those initial ideas are developed and tested.

    The biggest problem I have found with the proponents of MN is that they literally cannot imagine something else. They think that the alternative to naturalism is just making stuff up and pretending it to be true.

    If science is about explaining the real world, I’m curious to know how you do that without rooting it in a comparison with the real world. Isn’t MN just about how you root it in the real world?

    I guess the problem is one of constraints – if we don’t accept MN, we can use non-material factors whose existence cannot be probed by MN(gods, spirits, honest politicians etc.). But how do we constrain these factors so they cannot do anything?

  2. 2
    Zachriel says:

    johnnyb: Therefore, one’s imagination is actually the biggest constraining factor.

    While arguably true, methodological naturalism isn’t the constraint. While science is a process to match observation and theory, the hypothesis can have almost any source, including ‘deep-thought or vast experience in a field of study, but also from a hunch, a dream (Kekulé), serendipity, an inspiration, from fanciful thought-experiments (Einstein), a lucky guess, or even while playing cards (Mendeleev).’

  3. 3
    Neil Rickert says:

    One of the reasons why I started the conference is because Methodological Naturalism (hereafter, MN) constrains thinking in ways that I am not sure even people led entirely by the evidence are aware of.

    I doubt that.

    To say that MN constrains thinking is to say that a person first adopts MN, and then changes the way he thinks because of that. And that seems unlikely.

    Of course, a person’s nature constrains thinking. And some people might adopt MN because they think it a good way of describing how they already constrain their thinking. This seems more likely than that MN is what does the constraining.

    For myself, I have never found a need to MN. One goes by evidence, not by some external philosophy.

    Theory construction is often treated by both scientists and observers of science as an automatic given once the data is in.

    It’s hard to know what that says, because “often” is a rather vague term. As stated, it seems to imply that a mindless mechanical robot could construct theories from input data. Perhaps some AI proponents believe that, but I think most scientists and philosophers would disagree.

    In actuality, though, it is entirely philosophical.

    Assuming that the “it” refers to “theory construction”, I again disagree. There is a philosophical component, but it is far from entirely philosophical. Scientists are concerned about data, and ofteh the theory attempts to account for data. The theory sometimes gives the technical definition of the data type involved, which is why it often seems that data is theory laden.

    I agree with your general thesis, that imagination is involved. But you still cannot separate the theory construction from the data and the quest for more and better data.

  4. 4
    johnnyb says:

    In my article, I stated,

    The biggest problem I have found with the proponents of MN is that they literally cannot imagine something else. They think that the alternative to naturalism is just making stuff up and pretending it to be true.

    This is confirmed by the statements of Bob and Zachriel.

    Bob –

    You said,

    If science is about explaining the real world, I’m curious to know how you do that without rooting it in a comparison with the real world. Isn’t MN just about how you root it in the real world?

    This presumes that the “real word” = naturalism. What happens when the real world is not naturalistic? I *am* rooting it in a comparison with the real world, I just don’t have the same constraints as to what sorts of phenomena I allow to be included in “the real world.”

    Now, your next statement is much closer to the mark:

    I guess the problem is one of constraints – if we don’t accept MN, we can use non-material factors whose existence cannot be probed by MN(gods, spirits, honest politicians etc.). But how do we constrain these factors so they cannot do anything?

    This is precisely why such work is needed. We need to establish methodologies that help us know how such entities work so they can be better detected and understood, so that we cannot just arbitrarily assign causation to them.

    But let me give a hint (and we will discuss this more at the conference). Let’s look at the concept of “randomness”. Scientists feel comfortable assigning events as being random (such as QM) despite the fact that this violates deterministic causation, and actually would require infinite measurements to test. What they do is that they look for patterns which are indicative of randomness (such as mean=variance for the Poisson distribution) and use those.

    I have pointed out that non-material causation can be modeled through non-computational mathematics. What we need to do is to look for patterns that we would expect from non-physical entities, and then try to find secondary patterns that are indicative of these primary patterns being there.

    It is not foolproof, but then neither is anything in science. Especially since, with the introduction of QM, randomness can be a causal source, this actually opens the door up for all sorts of non-mechanical processes to be analyzed along similar lines.

    Zachriel –

    the hypothesis can have almost any source

    Tell that to the #handofgod authors. But my point isn’t about where the hypothesis comes from, but what the hypothesis is about.

  5. 5
    johnnyb says:

    Neil –

    It’s hard to know what that says, because “often” is a rather vague term. As stated, it seems to imply that a mindless mechanical robot could construct theories from input data. Perhaps some AI proponents believe that, but I think most scientists and philosophers would disagree.

    I would have thought so, too, except for many conversations with people, including those who are professional philosophers with science backgrounds. Additionally, if one believes in PN, then the above follows directly. Therefore, if one believes in MN, the only valid theories about how scientists form theories would be the above.

    I agree with your general thesis, that imagination is involved. But you still cannot separate the theory construction from the data and the quest for more and better data.

    I agree with you here – theory construction should certainly include lots of data.

    I think we are largely in agreement, except that I feel that imagination has a much stronger hold on us than we often realize. Again, going back to randomness, this is why Einstein had such problems with QM. He could not imagine a physics that was founded on probabilities rather than direct cause/effect relationships!

    If we are not explicit about trying to open up our imaginations to new ways of thinking about things, we can easily get stuck.

  6. 6
    GaryGaulin says:

    Theories simply explain how a device or system works. For example:

    https://en.wikipedia.org/wiki/Theory_of_operation

    A theory of operation is a description of how a device or system should work. It is often included in documentation, especially maintenance/service documentation, or a user manual. It aids troubleshooting by providing the troubleshooter with a mental model of how the system is supposed to work. The troubleshooter can then more easily identify discrepancies, to aid diagnosis of problem.

    This is the “theory” for explaining the ID Lab 5 model, which leads to more theory for explaining how “intelligent cause” works:

    THEORY OF OPERATION – HOW IT WORKS

    The Intelligence Design Lab-5 is a cognitive model with behavior that is guided by a navigational network system that maps out an internal representation of its external environment (an internal world model) using a 2D array where signal flow (magnitude and direction) vectors point out the shortest path to where they want to go. This is a vital part of our visual imagination. During human development it is common and expected to cause children to stretch out their arms and say “I can fly!” as they run around while visualizing themselves navigating the sky.

    Physical properties at each place in the external environment are mapped into a network according to whether they are safely navigable, an unnavigable boundary or border at a barrier, or place attracting it (in this case where the food is).

    An attracting location in the network provides an always signaling (action potential) signal that propagates outward in all directions and around barrier locations that do not signal at all (the signal stops there just as the critter would by bashing into a barrier). In math these directional activity patterns are shown using a vector map. The ID Lab provides this in the onscreen Navigation Network form that can show the signal direction through each place in the network.

    It is also possible to show the average Concordant and Discordant pairs ratio, which will be around 60% when the optimum amount of detail is being mapped into network. Too much information leads to navigation errors caused by being overwhelmed as in being “boxed in”. Too little information leads to navigation errors caused by not being able to in their mind “see” the invisible barrier that contains them inside the arena or the shock zone to avoid, as when the program first starts up with the critter unaware of all but a cue card revolving around them and the attracting food. It has accurate information but has no map yet to speak of to work from.

    Its confidence in motor actions (forward/reverse and left/right) depend on the magnitude and direction it is actually traveling matching the magnitude and direction of the signal flow at the corresponding place it is currently at. Where there is more than one pathway the shortest path dominates, will be the first to propagate to that point and be favored. Where there are two or more paths of equal distance it may become indecisive but will soon favor one path over the others.

    To test its place avoidance behavior a hidden moving shock zone slowly rotates counterclockwise, while the critter chases food in a clockwise direction heading straight towards the hazard. Although the test is demanding the confidence system of this intelligence strives for perfection, as does a human athlete. The relatively high confidence levels shown in the included line chart indicates that the virtual critter is having fun. In the research paper “Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames” (see notes) that the arena and some of the navigational network is based upon it was found that; some live rats preferred to chase after the treats even though they are not hungry enough to need to eat, while others preferred to remain in the shock free center zone. Even a live animal has to first be willing to accept the challenge. For the virtual critter several If-Then statements that compare actual travel magnitude and direction to that of the internal representation is enough to make it want nothing else but to chase the food around its arena.

    Intentionally getting out of the way of the approaching invisible shock zone requires the ability to (from past experience) predict future environmental events. This was added by alternating between current angular time (by default room angle is from 0 to 15) and the next angular time frame ahead. The places that will soon become a shock hazard periodically become a place to avoid. This sequential on and off signaling causes a (over time) temporal decision to be made. The same works for swarming bees. Scouts that find a possible new place to build a hive are one at a time allowed to dance out the location for other bees to inspect. This way each option is first considered, before making a final decision. Otherwise all the bees would either swarm to the first site found or to different ones (instead of staying together).

    http://io9.com/5866215/bee-swa.....uman-brain

    The virtual critter cannot (like a swarm of bees) divide itself then go separate ways, therefore appropriate actions are taken simply by repeatedly presenting (in any sequence) what must be considered.

    Exactly what it will choose to do at any given time is as hard to predict as it is in real animals. The only way to know for sure is read their mind, which (by adding RAM monitoring code) is possible to do to the ID Lab critter. But it’s still not at all like the easy predictable behavior of zombie-like “programmed” actions from an algorithm that uses math to make it go in a given direction in response to an approaching hazard instead of simply showing the options to consider then leaving the decision up to it to figure out, on its own.

    After avoiding being surrounded by the approaching zone it must have the common sense to go around to behind then wait for the food to be in the clear, while knowing where the food is located even when it’s surrounded by places to avoid that can (where signal timing is way off) block its signal activity. Where the signals from attract and avoid locations combine: the wanting to go both towards and away from the food results in it becoming nervously anxious, skittish, as are real animals with such a dilemma.

    The signal timing that was found to work best closely follows Hebbian Theory. Neighboring cells that fire together, wire together a network with activity patterns that recreate the physical properties of what is in the external environment. It can also be conceptualized as a conservation of energy strategy where at each place in the network an incoming charge is transferred to uncharged neighbors on the opposite side, outgoing direction. The signal energy is moved from place to place, not destroyed then regenerated all over again.

    To establish a benchmark that assumes error free signals from parts of the brain that use dead reckoning to convert what is seen through the eyes into spatial coordinates in its external environment the program simply uses the already calculated X,Y positions that are used to place things in the virtual environment. In the real world our brain oppositely converts visual signals to these spatial X,Y locations, which a virtual environment has to instead start with. Where this dead reckoning system were added to this model and working perfectly that’s what you would get for coordinates. Using the exact coordinates that the program already has provides ideal numbers to work from, which in turn gives this critter an excellent sense of where visible things are located around itself even though in this Lab its eyes cannot visually see them.

    This navigation system demonstrates how simple it is to organize a network that provides navigational intuition like we have. It helps explain why animals (insects are also animals) seem born with a navigational ability that is there from the start. The origin of this behavior in living animals does not have to be a learned instinct that slowly developed over many millions of years of time by blundering animals passing on slightly less blundering behavioral traits to offspring. It’s possible for these neural navigational networks to have existed when multicellular animals first developed, which set off the Cambrian Explosion. The origin of these inherent navigational behaviors may best explained by the activity patterns in these relatively simple cellular networks.

    The origin of our brain may in part be from subcellular networks that work much the same way in unicellular protozoans (single celled animals) such as paramecia, which have eye spots, antennae and other features once thought to only exist in multicellular animals. Testing such a hypothesis using this computer model requires additional theory, which may have a controversial title but going further into biology this way meets all of the requirements of the premise for an already proposed theory. In a case like this regardless of being controversial science requires developing already existing theory. Therefore see the TheoryOfID.pdf in Notes folder, for a testable operational definition for “intelligent cause” where each of the three emergent levels can be individually modeled. It is predicted to this way be possible to demonstrate a never before programmed intelligent causation event, which is still a further research goal and challenge for all to enjoy.

    No imagination is required, just an accurate as possible explanation of how something works or happened.

  7. 7
    Robert Byers says:

    OH. NOTHING is more wrong then saying scientific accomplishment follows the evidence. It does not. its a insight above the curve of knowledge.
    Not much different then masking successful music.
    Its wisdom/or understanding upon knowledge.Too use bible terms of human intellectual anatomy.
    Any person who did anything knows they deserve the credit. They were not just beating others by a few more facts they reached.
    This also means error is common.
    Its always mOST just memorize what the few discovered.
    Few deserve to be in wiki for science accomplishment.

    A british show IN OUR TIME deals with science history and is a good lesson on this.
    Its a spark, insight, eureka moment. Not a result of data collection.

  8. 8
    Zachriel says:

    johnnyb: Tell that to the #handofgod authors.

    The translated version makes a scientifically unsubstantiated claim about the Creator and the Creator’s motivation. The author’s say it was a mistranslation.

    johnnyb: But my point isn’t about where the hypothesis comes from, but what the hypothesis is about.

    Your claim was that methodological naturalism constrains imagination. We just pointed out that there is plenty of room for imagination within the confines of methodological naturalism.

    We don’t subscribe to methodological naturalism (except as a useful heuristic), but could you provide an example of a testable hypothesis that is not within the confines of methodological naturalism?

  9. 9
    bornagain77 says:

    One thing Darwinists do not suffer from is a lack of imagination. If anything Darwinists suffer from allowing too much imagination to influence their thinking and from not strictly adhering to the the empirical method (i.e. following the evidence). For prime example, even though no one has ever witnessed a molecular machine, or non-trivial functional information, being generated by unguided material processes, (and although intelligence has built molecular machines), Darwinists continue to imagine that molecular machines that greatly exceed, in terms of engineering parameters, any machine man has ever built, must have been built by unguided material processes.

    EVOLUTIONARY JUST-SO STORIES
    Excerpt: ,,,The term “just-so story” was popularized by Rudyard Kipling’s 1902 book by that title which contained fictional stories for children. Kipling says the camel got his hump as a punishment for refusing to work, the leopard’s spots were painted on him by an Ethiopian, and the kangaroo got its powerful hind legs after being chased all day by a dingo.
    Kipling’s just-so stories are as scientific as the Darwinian accounts of how the amoeba became a man.
    Lacking real scientific evidence for their theory, evolutionists have used the just-so story to great effect. Backed by impressive scientific credentials, the Darwinian just-so story has the aura of respectability.
    Biologist Michael Behe observes:
    “Some evolutionary biologists–like Richard Dawkins–have fertile imaginations. Given a starting point, they almost always can spin a story to get to any biological structure you wish” (Darwin’s Black Box).,,,
    http://www.wayoflife.org/datab.....ories.html

    “Grand Darwinian claims rest on undisciplined imagination”
    Dr. Michael Behe – 29:24 mark of following video
    http://www.youtube.com/watch?f.....fM#t=1762s

    In fact, I hold that the committed naturalist/atheist would much rather believe in pink unicorns, (i.e. believe in his undisciplined imagination), than believe in the living God who created the universe and all that is in it.

    Fine Tuning, Pink Unicorns, and The Triune God – video
    https://www.facebook.com/philip.cunningham.73/videos/vb.100000088262100/1145151962164402/?type=2&theater

    Further note:

    Theism compared to Materialism/Naturalism – an overview – video
    https://www.facebook.com/philip.cunningham.73/videos/vb.100000088262100/1139512636061668/?type=2&theater

  10. 10
    Origenes says:

    Bornagain77,

    Both your videos — fine tuning and theism compared to naturalism — are excellent. Thank you very much!

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