Scale-Free networks nonsense or Science vs Pseudo-Science

(this article’s title is a nod to Lior Pachter vitriolic arc of 3 articles with similar title)

Over the last couple of days I was engaged in a debate with LĂȘ from Science4All about what exactly science was, that spun off from his interview with an evolutionary psychologist and my own vision of evolutionary psychology in its current state as a pseudo-science.

While not necessarily always easy and at times quite movemented, this conversation was quite enlightening and let me to trying to lay down

Following the recent paper about scale-free networks not being that spread in the actual environment (that I first got as a gist from Lior Pachter’s blog back in 2015) helped me to formalize a little bit better what I believe I feel a pseudo-science is.

Just as the models and theories within the scientific method itself, something being a scientific approach is not defined or proved. Instead, similarly to the NIST definition of random numbers through a series of tests that all need to be successfully passed, the definition of a scientific approach is a lot of time defined from what it’s not, whereas pseudo-science is defined as something that tries to pass itself as a scientific method but fails one or several tests.

Here are some of my rules of thumb for the criteria defining pseudo-science:

The model is significantly more complicated that what the existing data and prior knowledge would warrant. This is particularly true for generative models not building on the deep pre-existing knowledge of components.

The theory is a transplant from another domain where it worked well, without all the correlated complexity and without justifying that the transposition is still valid. Evolutionary psychology is a transplant from molecular evolutionary theory,

The success in another domain is advanced as the main argument for the applicability/correctness of the theory in the new domain.

The model claims are non-falsifiable.

The model is not incremental/emergent from a prior model.

There are no closely related, competing models that are considered upon application to choices.

The cases where the model fails are not defined and are not acknowledged. Evo psy – modification of the environment by humans. Scale-Free networks.

Back-tracking on the claims, without changing the final conclusion. This is different with regards to affining the model where the change in the model gets propagated to the final conclusion and that conclusion is then re-compared with reality. Sometimes mends are done to that model for it to align with the reality again, but at least during a period, the model is still considered as false.

Support by a cloud of plausible, but refuted claims rather than a couple of strong, hard to currently attack the claims.

The defining feature of pseudo-science however, epsecially compared to the faulty science is its refusal to accept the criticism/limitations to the theory and change its prediction accordingly. It always needs to fit the final maxim, no matter the data.

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