Are there any significant alternatives to the theory that the mind exists?

Are there any psychological models which do not presuppose the existence of the mind?

In the same way that there are theories of physics which do not presuppose the existence of a luminiferous aether, is the sort of thing that I mean.

Can there be, or is it impossible by definition?

Theory of Preflop Opening Ranges

Question Summary: What are the theoretical drivers of pre-flop opening ranges?

I see so many charts online and in books, and they all differ slightly. They are very rarely theoretically justified, other than the obvious “as you get closer to the button, you should loosen up since you don’t have to fear raises ahead of you and you can be more confident you can steal the blinds”. (Another obvious thing people state: the higher your stack, the more you play for implied odds). This is all well and good, but it doesn’t tell us why specific hands make opening ranges and others don’t.

For example, here is the 40BB+ early position opening range from Mastering Small Stakes No Limit Holdem:

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  1. Why play 98s but not 32s? There is no way that 98s is EV+ against another person’s range in later position if all that’s driving this is fold equity + occasionally winning with middle pairs. So it must be that we play this hand due to its implied odds of catching straights and flushes. But if 98s is opened, why not 32s?

  2. Why not play KQo? Why is KQo (and other high broadways) not played? Is it because it can’t withstand 3-bets? If you’re at a table where no one 3-bets, can you play this hand (since they after all will be ahead of the range of those who call after you closer to the button)?

In general, what theoretically is causing these specific hands to be thrown away and other specific hands to be played? Are we trying to achieve a certain frequency and doesn’t matter what we pick (at the margins)? What is driving all of this this (other than the obvious and entirely unhelpful “we are trying to maximize EV”)?

Reference request for K-Theory linearization

I posted this question on math.se, but I think it may be more appropriate here, sorry if I am wrong about that.

In Waldhausen’s paper Algebraic K theory of Spaces(the long one) he proves the following:

$$A(X)simeq mathbb{Z}times Bwidehat{Gl}(Omega^{infty}Sigma^infty |G|)$$

Where $|G|$ is a loop group of $X$. My problem is that to apply $B$ we need $widehat{Gl}(Omega^{infty}Sigma^infty |G|)$ to be $A^infty$, but since $Omega^{infty}Sigma^infty |G|$ is only a ring up to homotopy this isn’t obvious to me. Waldhausen just brushes past this point, so I was hoping to get a reference to somewhere that shows this in detail. Thanks.

How to talk about theory

I realize this might be a contentious question, but this seemed like the right place to ask. Please redirect me if not.

The background is that I am a “practitioner” (PhD student, I don’t study CS theory) but I have a reasonable foundation in undergraduate algorithms and math. Nevertheless, discussions with theorists are usually very surface-level, as if they’re afraid to use mathematical terms with me in case I get scared. In reality, I’m perfectly comfortable with and interested in theory, but I’m just not used to discussing it so I probably don’t always use terms that would flag me as “theorist”. I find that the direct approach (“please tell me the details”) doesn’t always work, particularly if the theorist in question has assumed a condescending tone that sets a high bar for expertise (this happens often).

As theorists, if you filter people in this way, do you have recommendations for how a practitioner can avoid being “flagged” by your filter?

Is Game Theory Prescriptive or Descriptive?

I am trying to more clearly understand the objective of game theory.

I started off by reading papers in economics, where the main focus seems to be on finding equilibria under various behaviorial assumptions on the players (termed solution concepts, one of them being Nash equilibrium, for example). In this case, it seems a large part of the behavior is already embedded into the model and the question is how the agents reach an outcome, what is the quality of that outcome, etc. In my view, this seems descriptive, in the sense that the theory is describing what would happen if players played a certain way.

On the other hand, many of the papers I read from computer science tend to treat game theory as a more prescriptive theory. That is, how players should play given a particular objective. The most prominent example I can think of is the success of Google Deepmind’s AlphaGo algorithm in which the algorithm specified high quality actions (moves) for the player to take in order to win the game. No analysis regarding the equilibrium of this (in game theory terminology, dynamic perfect information) game was ever investigated, or even desired.

Can anyone shed some light on why the two fields seemingly have such different objectives?