The transdisciplinary nature and flexibility of Active Inference makes the framework ideal for practical, theoretical, and interoperable work across myriad use-cases. In the use case of learning in systematized settings (i.e. Education) the conventional planning frames take on wheels (π, as in policy selection) in order to function as a platform enabling translational “spinning” (i.e. helicity) across contexts of greater scale (learning generalization as transfer). With the inertia from the spin as your stability mechanism, the addition of policy selection by the learner as a self-organizing system (i.e. learning agent) within larger variability retained settings, introduces uncertainties to test the which and the where of when trans-disciplinary experience (i.e. real world experience, real dynamism, real problems) requires practical/pragmatic (i.e. action) solution(s). Comparatively speaking, conventional frame containment as stabilizer, only provides a variability reduced-reductionism environment ubiquitously held up as constructing learning where the product is a wide base as “foundational” retentions, and relatively smaller “crowning” states, as in Maslow’s Hierarchy.
Before describing what the mechanics of this inclusion of policy selection is, and can look like for you, it is best to point out that going forward, the acceptance that policy selection plays a role in how we learn, is not necessarily easy to incorporate as strategy applied. "I find this policy selection part hard to understand" is often heard when something new and/or unfamiliar is introduced into a messaging exercise. This is understandable when a proposal uses terminology that isn’t part of the newcomer’s current lexicon (and sometimes even when the term is already used). To take up new labels (and the ideas behind them) requires taking a step back from centuries of the accepted definition of what providing an education...is: define and refine via a process of packaging and delivery of information (so deliver to me, the learner, what I can recognize). Sustainers and defenders of that (status quo) strategy will argue (correctly) there is much more going on than that minimum of two of define and refine, and the Active Institute’s argument would be...maybe, possibly, but not certainly.
There too many examples, practiced both currently and historically in academia, to deny that at the core of educational practice, there is a reinforcement and incentivisation firmly established around practices focused on defining (i.e. agreement around an external ontology/standards) and refining (i.e. moving to smaller and smaller divergence(s) from what we see/do, and what we think we're doing/seeing). That being the case, new terminology like Prediction Matter Expert is the surprise given that phrase’s like this that are introduced, lack consensus around meaning and precision. Time is then spent working through where the introduced term/label/idea can fit (appropriately) within contexts of particular study/focus/research. This is an effortful exercise, that can often lead people new to Active Inference and the FEP, to wonder “where exactly is the Institute going with this idea/terminology/set of formalisms?” That’s a fair question, and in asking, we open a portal to the navigational aspects of resolving the “where” of learning as orientation process. This is the “where am I?” action - not just wonder - as Active Inference.
Applying Active Inference and the FEP to educational programming - “you are now here, but you’re not staying here, you’re going back out there” - has thus far struggled to gain much traction in many legacy (read hierarchical Pyramid Model) educational systems. Given most education systems’ tendencies to want to place the certainty of keeping systems accountable ahead of determining how agents learn when prediction-as-skill under uncertainty is given equal priority with subject matter expertise (as skill), we continue to find that active inference as functional compliment needs time for mass academic uptake (to scale). One of the core differences between subject matters and prediction matters exists at the waypoint called Updating. Currently, legacy education systems interpret “updating” as a cumulative-constructive-classical exercise, and therefore it is surprising for those vested in that method, when someone with formal active inference priors, proclaims the need to incorporate statistical and probability functions into the praxis and pedagogy design. This non-binary nature of probability (i.e. could be zero, or one, or something between) aspect dependent on “what I as agent…thinks will happen,” does exist as a teaching strategy, but is only applied within the variability reduced frame, pre-selected by the course/activity/lesson plan designer who is the subject matter expert.
And, active inference prediction modelling begins with the concept that the learning agent is first and foremost a self-organizer, self-designer who wants (self-identifies) minimization of any divergence between their own model and what the niche continually signals. Under this circumstance, updating as a process may take on constructive attributes, but it will also require some exposure to de-constructing processes (i.e. the most basic being, when change in the situation is apparent, will the agent 1) accept that change and 2a) either modify their surroundings or 2b) modify their model?). This is a fundamentally different type of branching - change the model, change the environment, change both - to pass/fail or even rubric induced accounts. This then necessitates a different (second) definition of “updating” as a result of starting with a predictive probability of achieving an ad hoc and post hoc processing threshold (could be described as ALL moves cardinal vs. NEXT moves ordinal/sequential), before “right and wrong” or even “75% correct” as assessed (as the 25% “wrong” usually doesn’t carry forward past the filters of constructive practices).
So why does this difference matter? In arriving at a threshold minimum, the active inference learning agent needs to reconcile while also keeping records. That “25% wrong” for example, is actually valuable information (not to be discarded) if divergence minimization is one of the stated goals. Now the question becomes “do I let go of what I predicted wrong because it didn’t affect my pass/fail status, do I let what I got wrong change my aspirations because I haven’t achieved perfection, or, do I look at Right-Wrong as a proportional measure from which to make future decisions?” (more on this shortly). Taking accounts and making reconciliations, is the process of modifications byandto which the updating of the active inference generative model, evolves. The conventional view of update as build-up, build-forth (Subject Matter Expertise, SME), is now complimented with a Prediction Matter Expert (PME) view of “what can I as learning agent let go, in order to arrive at a new know?” as policy selection to be determined. Borrowing from Chris Fields’ Identity Operator presentation, PME’s cope better with the undecideability in the frame problem - what doesn’t change as a result of an action. Using Chris’ terminology, “circumscribing what I don’t have to worry about”…means “I” can now take my “eye(s)” off of certain contents so as to increase availability for new [to me as agent] contents. Under this condition, the forensics come before, and not just after, a learning episode, making policy selection (π) now one part agent domain, one part external plan designer/niche reducer domain - with All Moves now meaning all of the puzzle pieces are present, and each is connected regardless of order application.
Of course, once the differences between legacy systems perspectives and active inference perspectives are held up as the parameterized space, the ability for the learning agent to oscillate between perspectives (i.e. perspective swap as action) becomes available. This oscillating process - first back, then forth...and never forth-only - is not uncommon. Agents swap perspectives when pairing science with fiction, active with inference, math with art as comparative with collective proportional measuring (as minimum) processing (unit of) analysis.
Which leaves the Institute with a challenge: how do we continue to attract Subject Matter Experts and point to the fact that Subject Matter Expertise alone can only take one so far as a navigator in variability retained settings? Another way of putting this could be stated as, as an institute, can we afford to not talk about the gorilla in the room: how we learn (define and refine...and retain) needs a co-pilot (what can I let go...to arrive at a new know?). This being asked as AI and LLM's train on far more information than humans can, to derive that synthesis (here's your answer!) that defining and refining puts out (outputs).
Let’s look at a real world example already introduced to the officers of the Institute where subject matter expertise attracted agents to the institute, and, the institute had to find a way to help the “experts” let go of what they already know. In this case example, Active Inference has been linked to the process of early childhood education (Montessori programming). Under Montessori philosophy, teacher’s are described as “directors” with a focus on “independent learning.” Comparisons can then be made to other early childhood education approaches. The Reggio Emilia early learning method holds up their philosophy of teachers roles as “partners” and “guides.”
The question then becomes one of: as the learning agent ages - enters different “grades”, stages and phases of Updating as a result of predictive processing (probability now based on increased temporal depth) - does the teacher as multi-hat wearing director/partner/guide/coach/facilitator still fit the needs of the self-organizing learner going forward? Perhaps, if the learning is organized as an adventure as a proxy for authentic - where once again authentic is trans-disciplinary real world experience, real dynamism, real problems) requiring practical/pragmatic (i.e. action) solution(s), while an adventure is a simulation.
Or, as a PME enabler (Not trainer), does the teacher SWAP titles - by subjecting themselves to the Identity Operator process - of Teacher with Way Finder (navigator), initiating their own perspective exchanging process of self-identifying (minimizer of divergence between their own model, now as minimum(2) dual-state swap able [i.e. Gripper & Gripped - BY and TO - simultaneously], with what the niche continually signals) resulting in an SME + PME hybrid triangulating with ANY niche (not just their subject specialty)? This would require teachers to both teach and co-learn interchangeably.
As the reader can appreciate, this is a different condition than teachers staying close (closed) to what they know (SME dilemma) and thus self-selecting away from “what can I let go, in order to arrive at a new know?” This is where the Institute’s role as director/partner/guide/coach/facilitator ends, and a co-piloting triangulation exercise (i.e. simulations to actualizations and Back) begins.
Going forward, it is the Institute’s ambition to make clear that the channel (i.e. gap) between legacy systems developing subject matter experts and what we view as new affordances that can be realized when uncertainty-as-learning-tool is perceived as a feature - as prediction matter expertise - is a potential exponentiator of a learner’s predictive capacities within and beyond systematized and variability reduced settings. We choose to be partners in this enterprise, as we feel serving in that capacity is closer to co-piloting than co-hosting in a flight simulator. Every organization wonders where the “stay afloat” energy will come from. In our case, we policy select to work with people vested in research with a specialty focus who also want to be able to generalize (play in “Scale Free”) with higher degrees of confidence when necessary (be a trans-disciplinarian when the niche is open, and variability retained).