Category: biology

Fantastical Places and the Ethics of Architecture

Lemuria was a hypothetical answer to the problem of lemurs in Madagascar and India. It was a connective tissue for the naturalism observed during the formative years of naturalism itself. Only a few years had passed since Darwin’s Origin of the Species came out and the patterns of observations that drove Darwin’s daring hypothesis were resonating throughout the European intellectual landscape. Years later, the Pangaea supercontinent would replace the temporary placeholder of Lemuria and the concept would be relegated to mythologized abstractions alongside Atlantis and, well, Hyperborea.

I’m in Lemuria right now, but it is a different fantastical place. In this case, I’m in the Lemuria Earthship Biotecture near Taos, New Mexico. I rented it out on a whim. I needed to travel to Colorado to drop off some birthday cards for our son and thought I might come by and observe this ongoing architectural experiment that I’ve been tracking for decades but never visited. I was surprised to find that I could rent a unit.

First, though, you have to get here, which involves crossing the Rio Grande Gorge:

Once I arrived, I encountered throngs of tourists, including an extended Finnish family that I had to eavesdrop on to guess the language they were speaking. The Earthship project has a long history, but it is always a history of trying to create sustainable, off-the-grid structures that maximize the use of disposable aspects of our society. So the walls are tires filled with dirt or cut wine bottles embedded in cement. Photovoltaics charge batteries and gray water (shower and washing water) is reused to flush toilets and grow food plants. Black water (toilet water) flows into leachfields that support landscape plants. Rainwater is captured from the roof to fill the gray water reservoirs. And, amazingly, it all works very well.

Here’s my video on arrival at Lemuria. There is wind noise when I’m on the roof, but it dies off when I get inside.

Architecture and ethics have always had an uneasy truce. At the most basic, there are the ethical limits of not deceiving clients about materials, costs, or functionality. But the harder questions build around aesthetic value versus functional value. A space that is sculptural like a Calatrava train station or Frank Gehry music hall is a space that values aesthetics at least as highly as functionality. There is no reusability in curved magnesium panels.

Where experiements like the Earthship thrive is in finding a weighted balance that gives functional and sustainable solutions precedence over the purely conceptual aspects of architecture. What could be is grounded by ethical stewardship.

Lemuria is standing up to a heavy downpour quite well right now as the monsoonal storms lash over the high plateau. I think I can hear the water flowing into the cisterns and an occasional pump pushing water through filters. It almost seems more fantastical that we don’t build houses like this.

Less Dead

I’m feeling less dead than I could be. Here’s the rattlesnake that struck and bounced off my running shoe this morning:

He started rattling after the initial strike, which seems like an evolutionary spandrel. At least he didn’t have a machine gun. I’ve named him Bartholomew and wish him the best on his future journeys. And here is the juvenile oryx who was laughing at the situation nearby:

Zebras with Machine Guns

I was just rereading some of the literature on Plantinga’s Evolutionary Argument Against Naturalism (EAAN) as a distraction from trying to write too much on ¡Reconquista!, since it looks like I am on a much faster trajectory to finishing the book than I had thought. EAAN is a curious little argument that some have dismissed as a resurgent example of scholastic theology. It has some newer trappings that we see in modern historical method, however, especially in the use Bayes’ Theorem to establish the warrant of beliefs by trying to cast those warrants as probabilities.

A critical part of Plantinga’s argument hinges on the notion that evolutionary processes optimize against behavior and not necessarily belief. Therefore, it is plausible that an individual could hold false beliefs that are nonetheless adaptive. For instance, Plantinga gives the example of a man who desires to be eaten by tigers but always feels hopeless when confronted by a given tiger because he doesn’t feel worthy of that particular tiger, so he runs away and looks for another one. This may seem like a strange conjunction of beliefs and actions that happen to result in the man surviving, but we know from modern psychology that people can form elaborate justifications for perceived events and wild metaphysics to coordinate those justifications.

If that is the case, for Plantinga, the evolutionary consequence is that we should not trust our belief in our reasoning faculties because they are effectively arbitrary. There are dozens of responses to this argument that dissect it from many different dimensions. I’ve previously showcased Branden Fitelson and Elliot Sober’s Plantinga’s Probability Arguments Against Evolutionary Naturalism from 1997, which I think is one of the most complete examinations of the structure of the argument. There are two critical points that I think emerge from Fitelson and Sober. First, there is the sober reminder of the inherent frailty of scientific method that needs to be kept in mind. Science is an evolving work involving many minds operating, when at its best, in a social network that reduces biases and methodological overshoots. It should be seen as a tentative foothold against “global skepticism.”

The second, and critical take-away from that response is more nuanced, however. The notion that our beliefs can be arbitrarily disconnected from adaptive behavior in an evolutionary setting, like the tiger survivor, requires a very different kind of evolution than we theorize. Fitelson and Sober point out that if anything was possible, zebras might have developed machine guns to defend against lions rather than just cryptic stripes. Instead, the sieve of possible solutions to adaptive problems is built on the genetic and phenotypic variants that came before. This will limit the range of arbitrary, non-true beliefs that can be compatible with an adaptive solution. If the joint probability of true belief and adaptive behavior is much higher than the alternative, which we might guess is true, then there is a greater probability that our faculties are reliable. In fact, we could argue that using a parsimony argument that extends Bayesian analysis to the general case of optimal inductive models (Sober actually works on this issue extensively), that there are classes of inductive solutions that, through eliminating add-ons, outperform predictively those solutions that have extra assumptions and entities. So, P(not getting eaten | true belief that tigers are threats) >> P(not getting eaten | false beliefs about tigers), especially when updated over time. I would be remiss if I didn’t mention that William of Ockham of Ockham’s Razor-fame was a scholastic theologian, so if Plantinga’s argument is revisiting those old angels-head-pin-style arguments, it might be opposed by a fellow scholastic.

The Obsessive Dreyfus-Hawking Conundrum

I’ve been obsessed lately. I was up at 5 A.M. yesterday and drove to Ruidoso to do some hiking (trails T93 to T92, if interested). The San Augustin Pass was desolate as the sun began breaking over, so I inched up into triple digit speeds in the M6. Because that is what the machine is made for. Booming across White Sands Missile Range, I recalled watching base police work with National Park Rangers to chase oryx down the highway while early F117s practiced touch-and-gos at Holloman in the background, and then driving my carpool truck out to the high energy laser site or desert ship to deliver documents.

I settled into Starbucks an hour and a half later and started writing on ¡Reconquista!, cranking out thousands of words before trying to track down the trailhead and starting on my hike. (I would have run the thing but wanted to go to lunch later and didn’t have access to a shower. Neither restaurant nor diners deserve an après-run moi.) And then I was on the trail and I kept stopping and taking plot and dialogue notes, revisiting little vignettes and annotating enhancements that I would later salt in to the main text over lunch. And I kept rummaging through the development of characters, refining and sifting the facts of their lives through different sets of sieves until they took on both a greater valence within the story arc and, often, more comedic value.

I was obsessed and remain so. It is a joyous thing to be in this state, comparable only to working on large-scale software systems when the hours melt away and meals slip as one cranks through problem after problem, building and modulating the subsystems until the units begin to sing together like a chorus. In English, the syntax and semantics are less constrained and the pragmatics more pronounced, but the emotional high is much the same.

With the recent death of Hubert Dreyfus at Berkeley it seems an opportune time to consider the uniquely human capabilities that are involved in each of these creative ventures. Uniquely, I suggest, because we can’t yet imagine what it would be like for a machine to do the same kinds of intelligent tasks. Yet, from Stephen Hawking through to Elon Musk, influential minds are worried about what might happen if we develop machines that rise to the level of human consciousness. This might be considered a science fiction-like speculation since we have little basis for conjecture beyond the works of pure imagination. We know that mechanization displaces workers, for instance, and think it will continue, but what about conscious machines?

For Dreyfus, the human mind is too embodied and situational to be considered an encodable thing representable by rules and algorithms. Much like the trajectory of a species through an evolutionary landscape, the mind is, in some sense, an encoded reflection of the world in which it lives. Taken further, the evolutionary parallel becomes even more relevant in that it is embodied in a sensory and physical identity, a product of a social universe, and an outgrowth of some evolutionary ping pong through contingencies that led to greater intelligence and self-awareness.

Obsession with whatever cultivars, whatever traits and tendencies, lead to this riot of wordplay and software refinement is a fine example of how this moves away from the fears of Hawking and towards the impossibilities of Dreyfus. We might imagine that we can simulate our way to the kernel of instinct and emotion that makes such things possible. We might also claim that we can disconnect the product of the effort from these internal states and the qualia that defy easy description. The books and the new technologies have only desultory correspondence to the process by which they are created. But I doubt it. It’s more likely that getting from great automatic speech recognition or image classification to the general AI that makes us fearful is a longer hike than we currently imagine.

Tweak, Memory

Artificial Neural Networks (ANNs) were, from early on in their formulation as Threshold Logic Units (TLUs) or Perceptrons, mostly focused on non-sequential decision-making tasks. With the invention of back-propagation training methods, the application to static presentations of data became somewhat fixed as a methodology. During the 90s Support Vector Machines became the rage and then Random Forests and other ensemble approaches held significant mindshare. ANNs receded into the distance as a quaint, historical approach that was fairly computationally expensive and opaque when compared to the other methods.

But Deep Learning has brought the ANN back through a combination of improvements, both minor and major. The most important enhancements include pre-training of the networks as auto-encoders prior to pursuing error-based training using back-propagation or  Contrastive Divergence with Gibbs Sampling. The critical other enhancement derives from Schmidhuber and others work in the 90s on managing temporal presentations to ANNs so the can effectively process sequences of signals. This latter development is critical for processing speech, written language, grammar, changes in video state, etc. Back-propagation without some form of recurrent network structure or memory management washes out the error signal that is needed for adjusting the weights of the networks. And it should be noted that increased compute fire-power using GPUs and custom chips has accelerated training performance enough that experimental cycles are within the range of doable.

Note that these are what might be called “computer science” issues rather than “brain science” issues. Researchers are drawing rough analogies between some observed properties of real neuronal systems (neurons fire and connect together) but then are pursuing a more abstract question as to how a very simple computational model of such neural networks can learn. And there are further analogies that start building up: learning is due to changes in the strength of neural connections, for instance, and neurons fire after suitable activation. Then there are cognitive properties of human minds that might be modeled, as well, which leads us to a consideration of working memory in building these models.

It is this latter consideration of working memory that is critical to holding stimuli presentations long enough that neural connections can process them and learn from them. Schmidhuber et. al.’s methodology (LSTM) is as ad hoc as most CS approaches in that it observes a limitation with a computational architecture and the algorithms that operate within that architecture and then tries to remedy the limitation by architectural variations. There tends to be a tinkering and tweaking that goes on in the gradual evolution of these kinds of systems until something starts working. Theory walks hand-in-hand with practice in applied science.

Given that, however, it should be noted that there are researchers who are attempting to create a more biologically-plausible architecture that solves some of the issues with working memory and training neural networks. For instance, Frank, Loughry, and O’Reilly at University of Colorado have been developing a computational model that emulates the circuits that connect the frontal cortex and the basal ganglia. The model uses an elaborate series of activating and inhibiting connections to provide maintenance of perceptual stimuli in working memory. The model shows excellent performance on specific temporal presentation tasks. In its attempt to preserve a degree of fidelity to known brain science, it does lose some of the simplicity that purely CS-driven architectures provide, but I think it has a better chance of helping overcome another vexing problem for ANNs. Specifically, the slow learning properties of ANNs have only scant resemblance to much human learning. We don’t require many, many presentations of a given stimulus in order to learn it; often, one presentation is sufficient. Reconciling the slow tuning of ANN models, even recurrent ones, with this property of human-like intelligence remains an open issue, and more biology may be the key.

Traitorous Reason, Facts, and Analysis

dinoObama’s post-election press conference was notable for its continued demonstration of adult discourse and values. Especially notable:

This office is bigger than any one person and that’s why ensuring a smooth transition is so important. It’s not something that the constitution explicitly requires but it is one of those norms that are vital to a functioning democracy, similar to norms of civility and tolerance and a commitment to reason and facts and analysis.

But ideology in American politics (and elsewhere) has the traitorous habit of undermining every one of those norms. It always begins with undermining the facts in pursuit of manipulation. Just before the election, the wizardly Aron Ra took to YouTube to review VP-elect Mike Pence’s bizarre grandstanding in Congress in 2002:

And just today, Trump lashed out at the cast of Hamilton for lecturing Mike Pence on his anti-LGBTQ stands, also related to ideology and belief, at the end of a show.

Astonishing as this seems, we live in an imperfect world being drawn very slowly away from tribal and xenophobic tendencies, and in fits and starts. My wife received a copy of letter from now-deceased family that contained an editorial from the Shreveport Journal in the 1960s that (with its embedded The Worker editorial review) simultaneously attacked segregationist violence, the rhetoric of Alabama governor George Wallace, claimed that communists were influencing John F. Kennedy and the civil rights movement, demanded the jailing of communists, and suggested the federal government should take over Alabama:

editorial-shreveport-60s-m

The accompanying letter was also concerned over the fate of children raised as Unitarians, amazingly enough, and how they could possibly be moral people. It then concluded with a recommendation to vote for Goldwater.

Is it any wonder that the accompanying cultural revolutions might lead to the tearing down of the institutions that were used to justify the deviation away from “reason and facts and analysis?”

But I must veer to the positive here, that this brief blip is a passing retrenchment of these old tendencies that the Millennials and their children will look back to with fond amusement, the way I remember Ronald Reagan.

Motivation, Boredom, and Problem Solving

shatteredIn the New York Times Stone column, James Blachowicz of Loyola challenges the assumption that the scientific method is uniquely distinguishable from other ways of thinking and problem solving we regularly employ. In his example, he lays out how writing poetry involves some kind of alignment of words that conform to the requirements of the poem. Whether actively aware of the process or not, the poet is solving constraint satisfaction problems concerning formal requirements like meter and structure, linguistic problems like parts-of-speech and grammar, semantic problems concerning meaning, and pragmatic problems like referential extension and symbolism. Scientists do the same kinds of things in fitting a theory to data. And, in Blachowicz’s analysis, there is no special distinction between scientific method and other creative methods like the composition of poetry.

We can easily see how this extends to ideas like musical composition and, indeed, extends with even more constraints that range from formal through to possibly the neuropsychology of sound. I say “possibly” because there remains uncertainty on how much nurture versus nature is involved in the brain’s reaction to sounds and music.

In terms of a computational model of this creative process, if we presume that there is an objective function that governs possible fits to the given problem constraints, then we can clearly optimize towards a maximum fit. For many of the constraints there are, however, discrete parameterizations (which part of speech? which word?) that are not like curve fitting to scientific data. In fairness, discrete parameters occur there, too, especially in meta-analyses of broad theoretical possibilities (Quantum loop gravity vs. string theory? What will we tell the children?) The discrete parameterizations blow up the search space with their combinatorics, demonstrating on the one hand why we are so damned amazing, and on the other hand why a controlled randomization method like evolutionary epistemology’s blind search and selective retention gives us potential traction in the face of this curse of dimensionality. The blind search is likely weakened for active human engagement, though. Certainly the poet or the scientist would agree; they are using learned skills, maybe some intellectual talent of unknown origin, and experience on how to traverse the wells of improbability in finding the best fit for the problem. This certainly resembles pre-training in deep learning, though on a much more pervasive scale, including feedback from categorical model optimization into the generative basis model.

But does this extend outwards to other ways in which we form ideas? We certainly know that motivated reasoning is involved in key aspects of our belief formation, which plays strongly into how we solve these constraint problems. We tend to actively look for confirmations and avoid disconfirmations of fit. We positively bias recency of information, or repeated exposures, and tend to only reconsider in much slower cycles.

Also, as the constraints of certain problem domains become, in turn, extensions that can result in change—where there is a dynamic interplay between belief and success—the fixity of the search space itself is no longer guaranteed. Broad human goals like the search for meaning are an example of that. In come complex human factors, like how boredom correlates with motivation and ideological extremism (overview, here, journal article, here).

This latter data point concerning boredom crosses from mere bias that might preclude certain parts of a search space into motivation that focuses it, and that optimizes for novelty seeking and other behaviors.

Quantum Field Is-Oughts

teleologySean Carroll’s Oxford lecture on Poetic Naturalism is worth watching (below). In many ways it just reiterates several common themes. First, it reinforces the is-ought barrier between values and observations about the natural world. It does so with particular depth, though, by identifying how coarse-grained theories at different levels of explanation can be equally compatible with quantum field theory. Second, and related, he shows how entropy is an emergent property of atomic theory and the interactions of quantum fields (that we think of as particles much of the time) and, importantly, that we can project the same notion of boundary conditions that result in entropy into the future resulting in a kind of effective teleology. That is, there can be some boundary conditions for the evolution of large-scale particle systems that form into configurations that we can label purposeful or purposeful-like. I still like the term “teleonomy” to describe this alternative notion, but the language largely doesn’t matter except as an educational and distinguishing tool against the semantic embeddings of old scholastic monks.

Finally, the poetry aspect resolves in value theories of the world. Many are compatible with descriptive theories, and our resolution of them is through opinion, reason, communications, and, yes, violence and war. There is no monopoly of policy theories, religious claims, or idealizations that hold sway. Instead we have interests and collective movements, and the above, all working together to define our moral frontiers.

 

The Goldilocks Complexity Zone

FractalSince my time in the early 90s at Santa Fe Institute, I’ve been fascinated by the informational physics of complex systems. What are the requirements of an abstract system that is capable of complex behavior? How do our intuitions about complex behavior or form match up with mathematical approaches to describing complexity? For instance, we might consider a snowflake complex, but it is also regular in it’s structure, driven by an interaction between crystal growth and the surrounding air. The classic examples of coastlines and fractal self-symmetry also seem complex but are not capable of complex behavior.

So what is a good way of thinking about complexity? There is actually a good range of ideas about how to characterize complexity. Seth Lloyd rounds up many of them, here. The intuition that drives many of them is that complexity seems to be associated with distributions of relationships and objects that are somehow juxtapositioned between a single state and a uniformly random set of states. Complex things, be they living organisms or computers running algorithms, should exist in a Goldilocks zone when each part is examined and those parts are somehow summed up to a single measure.

We can easily construct a complexity measure that captures some of these intuitions. Let’s look at three strings of characters:

x = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

y = menlqphsfyjubaoitwzrvcgxdkbwohqyxplerz

z = the fox met the hare and the fox saw the hare

Now we would likely all agree that y and z are more complex than x, and I suspect most would agree that y looks like gibberish compared with z. Of course, y could be a sequence of weirdly coded measurements or something, or encrypted such that the message appears random. Let’s ignore those possibilities for our initial attempt at defining a complexity measure. We can see right away that an approach using basic information theory doesn’t help much. Algorithmic informational complexity will be highest for y, as will entropy:

for each sequence composed out of an alphabet with counts, s. So we get: H(x) = 0, H(y) = 3.199809, and H(z) = 2.3281. Here’s some sample R code using the “entropy” package if you want to calculate yourself:

> z = "the fox met the hare and the fox saw the hare"
> zt = table(strsplit(z, '')[[1]])
> entropy(zt, method="ML")

Note that the alphabet of each string is slightly different, but the missing characters between them don’t matter since their probabilities are 0.

We can just arbitrarily scale entropy by the maximum entropy possible for the same length string like this:

This is somewhat like channel efficiency in communications theory, I think. And then just turn this into a parabolically-scaled measure that centers at 0.5:

where is an arbitrary non-zero scaling parameter.

But this calculation is only considering the individual character frequencies, not the composition of the characters into groupings. So we can consider pairs of characters in this same calculation, or triples, etc. And also, just looking at these n-gram sequences doesn’t capture potentially longer range repetitious structures. So we can gradually ladle on grammars as the counting mechanism. Now, if our measure of complexity is really going to capture what we intuitively consider to be complex, all of these different levels of connections within the string or other organized piece of information must be present.

This general program is present in every one of Seth Lloyd’s complexity metrics in various ways and even comes into play in discussions of consciousness, though many use mutual information rather than entropy per se. Here’s Max Tegmark using a variation on Giulio Tinoni’s Phi concept from Integrated Information Theory to demonstrate that integration is a key component of consciousness and how that might be calculated for general physical systems.

Bayesianism and Properly Basic Belief

Kircher-Diagram_of_the_names_of_GodXu and Tenebaum, in Word Learning as Bayesian Inference (Psychological Review, 2007), develop a very simple Bayesian model of how children (and even adults) build semantic associations based on accumulated evidence. In short, they find contrastive elimination approaches as well as connectionist methods unable to explain the patterns that are observed. Specifically, the most salient problem with these other methods is that they lack the rapid transition that is seen when three exemplars are presented for a class of objects associated with a word versus one exemplar. Adults and kids (the former even more so) just get word meanings faster than those other models can easily show. Moreover, a space of contending hypotheses that are weighted according to their Bayesian statistics, provides an escape from the all-or-nothing of hypothesis elimination and some of the “soft” commitment properties that connectionist models provide.

The mathematical trick for the rapid transition is rather interesting. They formulate a “size principle” that weights the likelihood of a given hypothesis (this object is most similar to a “feb,” for instance, rather than the many other object sets that are available) according to a scaling that is exponential in the number of exposures. Hence the rapid transition:

Hypotheses with smaller extensions assign greater probability than do larger hypotheses to the same data, and they assign exponentially greater probability as the number of consistent examples increases.

It should be noted that they don’t claim that the psychological or brain machinery implements exactly this algorithm. As is usual in these matters, it is instead likely that whatever machinery is involved, it simply has at least these properties. It may very well be that connectionist architectures can do the same but that existing approaches to connectionism simply don’t do it quite the right way. So other methods may need to be tweaked to get closer to the observed learning of people in these word tasks.

So what can this tell us about epistemology and belief? Classical foundationalism might be formulated as something is a “basic” or “justified” belief if it is self-evident or evident to our senses. Other beliefs may therefore be grounded by those basic beliefs. And a more modern reformulation might substitute “incorrigible” for “justified” with the layered meaning of incorrigibility built on the necessity that given the proposition it is in fact true.

Here’s Alvin Plantinga laying out a case for why justified and incorrigibility have a range of problems, problems serious enough for Plantinga that he suspects that god belief could just as easily be a basic belief, allowing for the kinds of presuppositional Natural Theology (think: I look around me and the hand of God is obvious) that is at the heart of some of the loftier claims concerning the viability or non-irrationality of god belief. It even provides a kind of coherent interpretative framework for historical interpretation.

Plantinga positions the problem of properly basic belief then as an inductive problem:

And hence the proper way to arrive at such a criterion is, broadly speaking, inductive. We must assemble examples of beliefs and conditions such that the former are obviously properly basic in the latter, and examples of beliefs and conditions such that the former are obviously not properly basic in the latter. We must then frame hypotheses as to the necessary and sufficient conditions of proper basicality and test these hypothesis by reference to those examples. Under the right conditions, for example, it is clearly rational to believe that you see a human person before you: a being who has thoughts and feelings, who knows and believes things, who makes decisions and acts. It is clear, furthermore, that you are under no obligation to reason to this belief from others you hold; under those conditions that belief is properly basic for you.

He goes on to conclude that this opens up the god hypothesis as providing this kind of coherence mechanism:

By way of conclusion then: being self-evident, or incorrigible, or evident to the senses is not a necessary condition of proper basicality. Furthermore, one who holds that belief in God is properly basic is not thereby committed to the idea that belief in God is groundless or gratuitous or without justifying circumstances. And even if he lacks a general criterion of proper basicality, he is not obliged to suppose that just any or nearly any belief—belief in the Great Pumpkin, for example—is properly basic. Like everyone should, he begins with examples; and he may take belief in the Great Pumpkin as a paradigm of irrational basic belief.

So let’s assume that the word learning mechanism based on this Bayesian scaling is representative of our human inductive capacities. Now this may or may not be broadly true. It is possible that it is true of words but not other domains of perceptual phenomena. Nevertheless, given this scaling property, the relative inductive truth of a given proposition (a meaning hypothesis) is strictly Bayesian. Moreover, this doesn’t succumb to problems of verificationalism because it only claims relative truth. Properly basic or basic is then the scaled contending explanatory hypotheses and the god hypothesis has to compete with other explanations like evolutionary theory (for human origins), empirical evidence of materialism (for explanations contra supernatural ones), perceptual mistakes (ditto), myth scholarship, textual analysis, influence of parental belief exposure, the psychology of wish fulfillment, the pragmatic triumph of science, etc. etc.

And so we can stick to a relative scaling of hypotheses as to what constitutes basicality or justified true belief. That’s fine. We can continue to argue the previous points as to whether they support or override one hypothesis or another. But the question Plantinga raises as to what ethics to apply in making those decisions is important. He distinguishes different reasons why one might want to believe more true things than others (broadly) or maybe some things as properly basic rather than others, or, more correctly, why philosophers feel the need to pin god-belief as irrational. But we succumb to a kind of unsatisfying relativism insofar as the space of these hypotheses is not, in fact, weighted in a manner that most reflects the known facts. The relativism gets deeper when the weighting is washed out by wish fulfillment, pragmatism, aspirations, and personal insights that lack falsifiability. That is at least distasteful, maybe aretetically so (in Plantinga’s framework) but probably more teleologically so in that it influences other decision-making and the conflicts and real harms societies may cause.