**Links to**: [[Agency]], [[Agent]], [[Individual]], [[Free energy principle]], [[Neologistics]], [[Vantagepointillism]], [[Recursivity]], [[Entropomorfismo]], [[Perspectivism]], [[Perspectival anxiety]], [[Polycomputation]], [[Constraint]], [[Neologistics]], [[05 Prediction]], [[World model]], [[Self]]. ### [[Postulate]]: if _prediction_ is what agents do, then agents are better aptly termed _xpectators_.^[Pronounced *expectator*, or _ks-pectator_ or however you want to pronounce it.] The idea in this neologism is that we go against the _agent_ as something _agentially_ voluntarist, as repeatedly repeated through this work, towards something more passive. And: that we also hammer in the biased condition of knowing, knowing is _always_ in expectation of something in particular (with cosmic desire at the bottom, the bottom of which is currently unknowable). All organisms are xpectators, in the sense proposed by the framework of active inference under the free energy principle. The idea here is that _Bayes-optimal_ action-perception-cognition is observed across all living organisms. The optimization imperative being the minimizing of surprise in sensed input.^[“Surprise has to be interpreted in a technical sense: it measures how much an agent’s current sensory observations differ from its preferred sensory observations—that is, those that preserve its integrity (e.g., for a fish, being in the water). Importantly, minimizing surprise is not something that can be done by passively observing the environment: rather, agents must adaptively control their action-perception loops to solicit desired sensory observations. This is the active bit of Active Inference.” (Pezzulo, Parr, Friston 2022, p. 6).] In essence, thus, we are talking about a measure of _difference_, which this project deals with in the sense of chasing after the question: _is difference reducible to something more foundational than itself?_ All organisms are thus, difference engines. Computers have historically enjoyed the description of _difference engines_, but all organisms can be described as difference engines, in the sense that they (make) _sense_ (of) difference (in the context of active inference: error, surprise (Pezzulo, Parr, Friston 2022, p. 6)), and they are engines: etymologically, from _ingenium_: “that which is inborn.”^[From in- _in_ + _gignere_: “to beget, produce” (from PIE: _gene-_ “give birth, beget” — source: etymonline.com, accessed Feb 2023). The battle/war connotations of the term (from Late Latin “a war _engine_, battering ram” (Tertullian, Isidore of Seville)) are interesting to consider too, in light of the hostile, bellicose influences of war-developments on human-machines.] That is, that which is _given_ in the organism being what it is: the qualities of [[Pipa Pipa]] are specific to that type of animal, and from that ensue its umwelt and capacities. Additional notes will be added here ( #todo ), but a few additional remarks can be made about the connotations of the neologism for now: - As mentioned, agents are better framed as _expectant_ rather than _agential_ (this also, hopefully, moves us farther away from a paradigm of agent-exceptionalism, which is also often _anthropomorphism_ and _centrism_); - In line with our proposal of [[Vantagepointillism]], _xpectator_ also promotes the connotation that all agents are witnesses; - The ocular-centric connotation (in _spectator_) is unfortunate but can be accepted if we buy into my claim that the **X** at the beginning of the neologism _cancels_ this effect; - The term _expectation_ has connotations of desire (see: [[Desire]]) and of progeny (see: [[05 Prediction]]). All organisms enjoy these properties/qualities as a common denominator. ### Footnotes %% Agency, defense of, Kevin Miller and H Potter, relates to the way Levin frames agency, compare and defend the mechanism metaphor by saying how mechanism is just intelligibility, not boring reductionism https://www.mdpi.com/1099-4300/24/4/472 [[Bayesian inference]]: “Second, Bayesian inference is optimal. Optimality is defined in relation to a cost function that is optimized (i.e., minimized), which, for Bayesian inference, is known as variational free energy—closely related to surprise. We return to this in section 2.5. By explicitly considering the full distribution over hidden states, it naturally handles uncertainty, hence avoiding the limitations of alternative approaches that only consider point estimates of hidden states (e.g., the mean value of x). One such alternative would be maximum likelihood estimation, which simply selects the hidden state most likely to have generated the data at hand. The problem with this is that such estimates ignore both the prior plausibility of the hidden state and the uncertainty surrounding the estimation. Bayesian inference does not suffer these limitations. However, despite the use of surprise in objectively assessing whether the model is fit for purpose, it is important to appreciate that inference itself is subjective. The results of inference are not necessarily accurate in any objective sense (i.e., the organism’s belief may not actually correspond to reality) for at least two important reasons. First, biological creatures operate on the basis of limited computational and energetic resources, which render exact Bayesian inference intractable.4 This requires approximations that preclude guarantees of exact Bayesian optimality. These approximations include the notion of a variational posterior—based on something called a mean field approximation—which is central to chapter 4.” (Parr et al. 2022, p. 22)