Wednesday, October 7, 2026

Ecology Is Not a Snapshot: Population Change, Network Response, and the Case for Whole-System Environmental Reporting; A methodological paper on ecological completeness, independent interpretation, and stewardship

Ecology Is Not a Snapshot

Population Change, Network Response, and the Case for Whole-System Environmental Reporting

A methodological paper on ecological completeness, independent interpretation, and stewardship


John Swygert

October 7, 2026


Abstract

Ecological systems are continuously changing. Populations rise and fall, species move, food webs rewire, predators and prey alter one another, competitors are released or constrained, new habitats open, old habitats contract, diseases spread or recede, and community composition changes even when aggregate measures such as total biomass or species richness appear comparatively stable. Public environmental communication, however, often reduces this networked reality to a single directional observation: one population declined, one range contracted, or one environmental variable changed. This paper argues that such a snapshot can be factually correct while still being ecologically incomplete.

The paper proposes an Ecological Completeness Principle: whenever a population change is presented as evidence of broader ecological change, analysis should, where data permit, examine associated increases as well as decreases, range expansion as well as contraction, dependent-species responses, competitor and predator-prey effects, functional replacement, temporal variability, spatial redistribution, and plausible interacting drivers. The objective is not to force every ecological event into a positive or negative narrative, nor to assign blame to a preferred cause. It is to distinguish observation from interpretation and to represent enough of the network that readers can evaluate the system independently. Worked empirical vignettes and a proportional application standard show how the principle can be used without requiring impossible completeness.

Climate variables are included here only as examples of environmental forcing. This paper takes no position on the relative anthropogenic or natural contribution to climate change because that attribution question is outside its scope. The central issue is methodological: ecological change should be communicated as interacting change. A changing population is an ecological event; a changing network is the ecological story.

Keywords: ecology; ecological networks; population dynamics; compensatory dynamics; community turnover; range shifts; trophic cascades; science communication; environmental stewardship; ecological completeness

Scope Note

This paper does not attempt to determine what causes climate change, apportion responsibility for environmental change, or select a culprit for ecological outcomes. Those questions may be important elsewhere. They are not the question here. The question here is whether an ecological account shows enough of the interacting system to justify the story it tells.

1. Introduction: Ecology Before Narrative

Ecology begins with relationships. A species is never merely a number on a chart. Its abundance is related to food, predators, competitors, parasites, disease, reproduction, habitat, migration, weather, resource pulses, disturbance, behavior, and the abundance and behavior of other species. Modern ecological network theory formalizes this basic fact: interactions connect individuals and species into networks whose structure and function can change across space and time (Guimaraes, 2020; Tylianakis & Morris, 2017).

For that reason, a statement such as 'Species A declined by 30 percent' is an observation, not yet an ecosystem diagnosis. The decline may matter greatly. It may signal loss of ecological function, a trophic cascade, habitat deterioration, or increased extinction risk. But it may also coincide with expansion of another species, release of a competitor, movement to a newly suitable region, altered predation pressure, or compensatory changes that stabilize some ecosystem properties. Ecology does not permit a universal rule that decline is always equivalent to ecosystem decline, or that increase is always equivalent to ecosystem improvement.

This distinction is easy to lose in public communication because a headline, press release, or short article must compress a multidimensional system into a small amount of language. Compression is unavoidable. Distortion is not. A scientifically responsible summary should make clear what was measured, where, over what period, at what level of organization, and what the measurement does and does not establish.

The argument of this paper is therefore not anti-conservation, anti-climate, anti-human, pro-human, pessimistic, or optimistic. It is pro-completeness. The purpose is to strengthen environmental stewardship by making ecological reasoning more difficult to manipulate, whether intentionally or unintentionally, through selective presentation.

2. Ecological Systems Are Dynamic by Default

Ecological communities do not begin in equilibrium and remain there until an outside disturbance knocks them away. Populations fluctuate even under natural environmental variation. Seasonal cycles, succession, droughts, floods, fires, resource pulses, reproductive cycles, disease, migration, and predator-prey oscillations can all reorganize communities. Some of these changes are directional, some cyclical, some episodic, and some difficult to classify until long time series are available.

Compensatory dynamics provide a useful example. Theory and empirical work show that declines in some species can coincide with growth in others, sometimes buffering aggregate properties of communities even while species composition changes substantially (Gonzalez & Loreau, 2009). In long-term desert rodent and plant data, Ernest and Brown (2001) found that species composition could vary more strongly than several ecosystem-level properties. This does not mean compensation always occurs or that losses are harmless. Gonzalez and Loreau explicitly note that compensatory dynamics are not dominant in every field dataset. The point is more disciplined: asynchronous responses exist, and the response of the whole community cannot be read directly from the trajectory of a single member.

Delta N_A < 0 does not, by itself, determine Delta Ecosystem.

A single population trajectory is therefore one coordinate of a larger state. If an ecosystem contains n populations, a minimal description of community state includes their abundances, but even that is insufficient because the interactions among those populations also matter.

E(t) = {N_1 ... N_n, I_ij, H, x, t}

Here N represents population abundance, I represents interaction structure and strength, H represents habitat and other environmental conditions, x represents location, and t represents time. This is not offered as a complete ecological model. It is a reminder that 'what happened to one species' and 'what happened to the ecosystem' are different questions.

3. One Change Can Produce Opposite Responses

Ecological relationships differ in sign and mechanism. If Species B depends on Species A as a food source, a decline in A may contribute to a decline in B. If A is a predator of B, however, a decline in A can release B from predation and permit B to increase. If A competes with B for a limiting resource, a decline in A can also create ecological opportunity for B. If A pollinates B, the same decline can reduce B. If A suppresses a pathogen that affects B, the indirect pathway may be different again.

A down -> B down   OR   A down -> B up   OR   A down -> little measurable change in B

All three are ecologically plausible depending on the relationship. Secondary responses can then reverse direction again. If B increases after A declines, B may consume more of C, compete more strongly with D, provide more food for E, or alter habitat in ways that affect F. A population change is therefore capable of propagating through a network as a mixture of positive, negative, delayed, spatially displaced, and nonlinear effects.

Trophic cascades demonstrate the point vividly, but even well-known cascade systems resist simple stories. Peterson, Vucetich, Bump, and Smith (2014), reviewing wolves and trophic cascades at Isle Royale and Yellowstone, emphasized multicausality, temporal variation, spatial heterogeneity, contingency, and nonequilibrium dynamics. Piovia-Scott, Yang, and Wright (2017) likewise showed that cascade strength can vary through time and can be transient. The lesson is not that trophic cascades are unreal. It is that even one of ecology's most recognizable causal architectures must be interpreted in time, space, and network context.

4. Increases Deserve the Same Attention as Decreases

Environmental reporting naturally gravitates toward decline because decline can indicate risk. Conservation also has a legitimate reason to focus on rare or shrinking populations. Yet an ecology that records only declines is incomplete. Every changing environment can create both constraints and opportunities. A species that loses suitable habitat in one region may gain access to another. A declining competitor may release another population. A retreating predator may permit a prey species to expand. A newly arriving species may create resources for some organisms while imposing new pressures on others.

The important question is not whether there are 'winners' and 'losers' in a moral sense. The important question is what the redistribution does to the network. A population increase can be ecologically beneficial, damaging, neutral, or mixed depending on what the growing population does. Likewise, a decline can remove an important function or reduce a harmful pressure. Direction alone is not interpretation.

Large-scale biodiversity studies increasingly emphasize turnover rather than simple loss. Pinsky and colleagues (2025), using 42,255 time series across marine, terrestrial, and freshwater systems, found that faster temperature change - both warming and cooling - was associated with faster temporal turnover in species composition. Their result is useful here because it highlights transformation: communities can change substantially through replacement and redistribution even when the public discussion is tempted to reduce biodiversity change to a one-directional count.

Similarly, Lawlor and colleagues (2024) reviewed species redistributions and found that observed range shifts vary widely in direction and rate; many species do not shift in the expected direction, and habitat characteristics, non-temperature climatic variables, and species interactions can all matter. Pinsky, Selden, and Kitchel (2020) describe how marine range shifts can involve expansion at a leading edge and contraction at a trailing edge at the same time. A local decline can therefore coexist with geographic expansion elsewhere.

5. Redistribution Is Not the Same Thing as Replacement

A major danger in correcting one-sided decline narratives is replacing them with an equally simplistic reassurance story. If Species A declines and Species B increases, it does not follow that the ecosystem has 'balanced itself' in any meaningful functional sense. Species are not interchangeable units.

A replacement species may occupy a different trophic level, consume different resources, reproduce at a different rate, move nutrients differently, alter habitat differently, interact with different pathogens, or provide different value to other organisms. Ecological networks can therefore change even if total richness or total biomass changes little. Guimaraes (2020) emphasizes that ecological structure emerges from patterns of interaction, and Tylianakis and Morris (2017) show that environmental gradients can change both network composition and the frequency of interactions.

Bartley and colleagues (2019) use the term food-web 'rewiring' to describe changes in interactions that arise when organisms alter behavior and resource use under changing conditions. Rewiring is a useful concept because it prevents the analyst from treating the species list as the whole ecosystem. The same species can remain present while who eats whom, who competes with whom, or where those interactions occur changes materially.

For stewardship, then, the relevant comparison is not simply species count before versus species count after. It is also function before versus function after, interaction before versus interaction after, and spatial pattern before versus spatial pattern after.

6. Climate Variables Without a Climate Blame Frame

Temperature, precipitation, drought, snow cover, ocean conditions, seasonal timing, and extreme events can strongly affect ecological systems. This paper treats those variables as environmental conditions, not as a courtroom exhibit about who or what caused them.

That distinction is deliberate. An ecology-first analysis can examine how a warmer decade, a cooler interval, altered rainfall, or a marine heat event changes populations without first deciding the ultimate cause of the environmental change. The ecological response question and the climate attribution question are separable. Combining them by default risks converting every population paper into an argument about climate politics rather than an analysis of the network actually measured.

Natural climatic variability also demonstrates why environmental forcing should be treated carefully. The El Nino-Southern Oscillation changes rainfall, winds, upwelling, river discharge, nutrient availability, productivity, fish recruitment, biomass, and catch in regionally and species-specific ways. A 2026 review of ENSO impacts in the tropical and South Atlantic stresses regional variability, species dependence, and non-stationarity (Rodriguez-Fonseca et al., 2026). The point here is not to use ENSO as an argument against any other driver. It is to show that large environmental forcings can produce heterogeneous responses that resist a single-direction narrative.

Whether an environmental forcing is natural, anthropogenic, mixed, cyclical, or uncertain does not change the requirement to describe the ecological response as completely as the available evidence allows.

7. Correlation Is Not a Mandate to Assign Blame

The search for a culprit can itself become a methodological distraction. Imagine three long-term curves: human population rises, wolf population rises, and an ecological variable X rises. If the only argument offered for causation is that the human curve and X move together, then the same bare logic could 'blame' wolves when their curve also moves with X. The absurdity is the lesson. The purpose of correlation is to identify relationships worth investigating, not to supply a culprit in advance.

This paper therefore recommends separating four statements that are often compressed into one: an observation occurred; two variables covary; a mechanism is hypothesized; a causal effect is established. Each step requires additional evidence. In ecological networks, the number of plausible direct and indirect pathways makes that distinction particularly important. Bluthgen and Staab (2024) warn that network patterns can be misinterpreted when abundance, sampling, and other structural effects are not adequately accounted for.

Correlation is not causation.

Observation != Correlation != Mechanism != Demonstrated causation

The objective is not skepticism for its own sake. It is to keep the analysis open long enough for the system to speak before the author decides what story the system must tell.

8. A Whole-System Response Vector

Instead of forcing ecological change into one scalar judgment such as 'better' or 'worse,' this paper proposes describing a whole-system response as a multidimensional vector. A practical reporting framework can track at least six dimensions:

R = {Delta N, Delta D, Delta I, Delta F, Delta C, Delta T}

Delta N represents changes in abundance; Delta D changes in geographic distribution; Delta I changes in species interactions; Delta F changes in ecological function; Delta C changes in community composition; and Delta T changes in temporal persistence or stability. A complete study may add genetic diversity, age structure, phenology, disease, nutrient cycling, or other dimensions.

This formulation has an important advantage: different dimensions may point in different directions. Abundance can decline locally while distribution expands elsewhere. Richness can remain stable while composition changes. Biomass can remain stable while trophic structure changes. A system can become more diverse but less functionally redundant. None of those outcomes can be faithfully reduced to a single population line.

9. The Ecological Completeness Principle

The Ecological Completeness Principle (ECP) is a proposed standard for environmental analysis and communication:

Whenever a population change is presented as environmentally significant, the analysis should seek, where data permit, the associated losses, gains, migrations, replacements, dependent-species responses, competitor responses, predator-prey effects, functional consequences, temporal context, spatial context, and plausible interacting drivers before characterizing the direction of the ecosystem as a whole.

Dimension

Minimum question

Risk if omitted

Abundance

Which populations increased, decreased, or remained stable?

A decline in one population is mistaken for decline of the whole system.

Distribution

Did the species disappear, relocate, contract, expand, or shift within its range?

Local change is mistaken for regional or global change.

Interactions

Which predator-prey, competitive, mutualistic, parasitic, or other links changed?

Species lists substitute for ecological function.

Function

Were pollination, nutrient transport, habitat engineering, energy flow, or other functions altered?

Numerical replacement is mistaken for functional replacement.

Time

Is the change transient, cyclical, directional, lagged, or persistent?

A snapshot is mistaken for a trajectory.

Space

Does the pattern differ across microhabitats, regions, leading edges, or trailing edges?

One location is treated as representative of an entire range.

Drivers

What interacting biotic and abiotic variables changed at the same time?

A preferred explanation becomes the default before alternatives are tested.

Uncertainty

What is not measured, poorly resolved, or model dependent?

Confidence becomes greater than the evidence supports.

Stewardship

What intervention affects the whole network, including likely secondary effects?

A well-intended action solves one problem while creating another.


The ECP is not a demand that every news article become a monograph or that every study measure every variable. Ecological completeness is proportional to the claim. A paper can legitimately study one species. A news article can legitimately summarize that paper. The problem appears when a narrow measurement is expanded into a broad ecosystem conclusion without acknowledging what has not been measured.

9.1 Proportional Application: What Counts as 'Good Enough'?

The ECP is strongest when completeness is treated as proportional rather than absolute. Different forms of communication have different space, data, and evidentiary obligations. The minimum standard should therefore rise with the breadth and consequence of the claim.

Peer-reviewed research. Measure the relationships necessary to support the stated inference, identify materially relevant variables that were not measured, distinguish direct observations from modeled or inferred mechanisms, and keep conclusions within the spatial and temporal limits of the study.

Agency and management reports. Include the network consequences most relevant to the proposed action: affected populations, dependencies, competing pressures, functional changes, plausible secondary effects, and major uncertainties that could change the management choice.

News and public communication. At minimum, distinguish local change from regional or global change, decline from redistribution, and a measured association from an established mechanism. When the underlying study reports materially important increases, replacements, range shifts, or contrary responses, those findings should not disappear merely because they complicate the headline.

AI summaries. Represent the principal finding, the most important qualifications, materially different system responses, and what the evidence does not establish. An AI summary need not reproduce every variable, but it should not compress a network result into a one-direction story when the source itself contains consequential countervailing information.

A useful test is simple: if a reasonable reader would make a materially different ecological inference after learning an omitted fact that was available in the source evidence, the summary was not complete enough for the claim it made.

10. From Scientific Result to Public Story

Science communication necessarily selects. No article can reproduce an entire dataset, and no journalist can describe every ecological relationship. The ethical question is what selection does to meaning.

Research on ecology in mass media shows that the public often receives only a small fraction of ecological research and that news stories tend to emphasize results and discussion more than methods (Baker et al., 2012). A 2025 content analysis of Dutch biodiversity coverage found that political and societal events frequently triggered coverage and that stories focused strongly on causes while giving less attention to effects (Heerdink et al., 2025). These studies do not establish that environmental journalism is generally deceptive. They do establish that framing and selection are measurable features of ecological communication.

A story can therefore be composed entirely of accurate statements and still produce an incomplete mental model. If an article reports a declining cold-adapted species, omits expanding warm-adapted species, omits movement into other regions, omits changes in prey and competitors, and then uses the single decline as shorthand for the entire ecosystem, the problem is not necessarily that the decline is false. The problem is that the reader has not been shown the system.

Conversely, an article that highlights only expanding populations could minimize genuine losses and ecological disruption. Whole-system reporting must resist both directions of cherry-picking.

The reader should not be trained to inherit the author's preferred conclusion. The reader should be given enough of the evidence architecture to understand how conclusions are made.

11. Advocacy, Analysis, and the Boundary Between Them

Environmental stewardship often requires advocacy. A conservation organization may openly argue for protecting habitat. A government agency may promote a management action. A journalist may write an editorial. There is nothing inherently illegitimate about advocacy when it is identified as advocacy.

The difficulty arises when advocacy is presented as if it were a complete ecological analysis. Scientific language can give a narrative the appearance of inevitability even when materially relevant countervailing evidence has been omitted. The remedy is not to outlaw perspective; it is to separate observation, interpretation, uncertainty, and recommendation.

A useful discipline is to ask whether the strongest available evidence that complicates the preferred narrative has been presented. If a decline is central to the story, were corresponding increases or redistributions investigated? If an increase is celebrated, were the organisms harmed by that increase considered? If a causal mechanism is asserted, were alternative pathways evaluated? If the data are local, is the conclusion also local? These questions do not weaken science. They prevent scientific language from being used as a decorative wrapper around a predetermined view.

12. Illustrative Ecological Patterns and Worked Vignettes

12.1 Predator Change and Multicausal Cascades

Wolf systems illustrate why whole-system interpretation matters. A change in wolf abundance can affect prey behavior and abundance, vegetation, scavengers, competing predators, and other processes. But the strength and even detectability of those effects depends on weather, habitat, prey demography, human harvest, spatial structure, and time. Peterson et al. (2014) explicitly describe Isle Royale and Yellowstone as multicausal, heterogeneous, and nonequilibrium systems. The lesson is not 'wolves cause everything' or 'wolves cause nothing.' The lesson is that network effects must be evaluated alongside other changing conditions.

12.2 Community Turnover Under Temperature Change

Pinsky et al. (2025) found that faster temperature change, whether warming or cooling, was associated with faster compositional turnover. This matters because it focuses attention on replacement and reorganization rather than only directional loss. Turnover can still threaten ecosystem integrity, especially if replacement removes function or creates novel interactions. Yet the ecologically meaningful object is the changing community, not only the species leaving it.

12.3 Range Contraction and Range Expansion at the Same Time

Species redistributions can produce simultaneous decline and increase at different parts of a range. Pinsky et al. (2020) describe marine species expanding at leading edges while trailing edges contract. Lawlor et al. (2024) show that observed shifts often depart from simple expectations because species interactions, habitat, and other environmental variables matter. Reporting only one edge can therefore create a false impression of the full geographic response.

12.4 Compensatory Dynamics Without Assuming Compensation

Compensatory dynamics offer a caution in both directions. They demonstrate that some species can increase while others decline, sometimes stabilizing aggregate ecosystem properties (Ernest & Brown, 2001; Gonzalez & Loreau, 2009). But they are not a universal law. An analyst should look for compensation rather than presume it. This is exactly the kind of disciplined symmetry the ECP requires: search for increases when declines are observed, but do not invent increases merely because the framework says they are possible.

12.5 Worked Vignette: Sea Otter Decline, Urchin Increase, and Kelp Loss

In western Alaska, sea otter populations declined abruptly across large areas. Estes et al. (1998) identified increased killer whale predation as the likely cause of the otter decline and documented the nearshore response: sea urchin density increased and kelp forests were heavily reduced as the otter's keystone predatory role weakened. The ecologically important event was therefore not one downward line. It was a linked sequence involving predator pressure, otter decline, herbivore release, and loss of kelp structure.

The vignette demonstrates the ECP in both directions. Reporting only the sea otter decline would omit the organisms that increased and the habitat function that changed. Reporting only the increase in sea urchins could be equally misleading because numerical increase was associated with intensified grazing and kelp loss. A whole-system account changes the object of interpretation from "otters declined" or "urchins increased" to "the nearshore interaction network reorganized."

12.6 Worked Vignette: Four Fish, Four Different Range Stories

Roday et al. (2026) compared recreational-fishery and survey data from 1981 through 2024 for black sea bass, summer flounder, winter flounder, and scup along the U.S. coast. Black sea bass and summer flounder showed strong poleward shifts in their centers of distribution, yet black sea bass also showed moderate range expansion while summer flounder showed range contraction. Scup showed moderate range expansion with weak or non-significant center-of-distribution shifts, while winter flounder showed consistent range contraction with little movement in its center of distribution.

For present purposes, the important result is methodological. The same broad environmental period and the same analytical setting produced different combinations of movement, expansion, contraction, and relative stability depending on species and metric. A headline such as "fish move north" would capture part of the evidence but erase much of the ecological structure. The fuller story requires both distribution and abundance-related dimensions, multiple species, and explicit attention to the metric being reported.

13. Stewardship Requires a Wide-Angle View

The practical purpose of ecological understanding is not simply to describe change but to support wise stewardship. A narrow diagnosis can produce a narrow intervention. If management focuses on increasing one species without considering food supply, competitors, predators, disease, habitat capacity, and secondary effects, the intervention can fail or create new problems.

Whole-system stewardship asks a different sequence of questions: What changed? What else changed with it? Which relationships were strengthened or weakened? Which functions were lost, gained, or moved? Is the effect local or widespread? Is it transient or persistent? What interventions would alter the network, and what secondary consequences are plausible?

This does not make conservation indecisive. It makes conservation better targeted. In some cases the fuller analysis will strengthen the case for rapid intervention because it reveals cascading loss. In other cases it may reveal that a dramatic local decline is part of redistribution rather than system collapse. In still others it may identify an increase that initially looks beneficial but creates new ecological pressure.

The sea otter-urchin-kelp example makes the practical point concrete. A response aimed only at kelp condition would miss the elevated grazing pressure; a response aimed only at urchin abundance would miss the predator-mediated release that helped produce it; and a response aimed only at otter counts would miss the downstream habitat consequence. The ECP does not dictate which intervention should be chosen. It identifies the relationships that must be considered before an intervention is treated as a system-level solution.

The desired outcome is not a particular political conclusion. It is a steward who understands enough of the ecological system to make a decision that is proportionate to the evidence.

14. Limits of the Ecological Completeness Principle

No ecological analysis can be literally complete. Ecosystems contain more organisms, interactions, scales, and unknowns than any study can measure. The ECP should therefore be understood as a discipline of disclosure and search, not as a demand for omniscience.

First, data availability differs across taxa and regions. A well-studied bird or mammal may have decades of abundance records while invertebrates, fungi, microbes, or parasites in the same system are poorly measured. Second, interaction strength is often harder to quantify than species presence. Third, ecological baselines can be uncertain or historically shifting. Fourth, even sophisticated network models can produce misleading interpretations if sampling and abundance effects are not handled correctly (Bluthgen & Staab, 2024).

For those reasons, an ecologically complete report should sometimes say plainly: 'We do not know what happened to the rest of the network.' That sentence is not a weakness. It is more informative than converting an unmeasured system into a confident narrative.

15. Teaching Independent Ecological Thinking

The deepest purpose of this paper is educational. Environmental communication should not merely supply conclusions. It should teach the reader how an ecosystem must be interrogated.

A reader trained in ecological completeness should automatically ask: What increased while this declined? What depends on this species? What does this species suppress? Did it disappear or move? What happened elsewhere? What happened before the chosen baseline? What is the time scale? What changed in the interaction network? Which claims are observations, which are inferences, and which are recommendations?

Those questions make the reader less vulnerable to selective alarm and equally less vulnerable to selective reassurance. That is precisely the point. Independent reasoning should survive changes in emphasis or fashion because it is anchored to method.

16. Discussion

The central proposal of this paper is modest but consequential: environmental stories should scale their conclusions to the ecological system actually examined. If the data concern one species in one place, the result should initially be described at that level. Broader interpretation should expand only as broader evidence is added.

This principle does not minimize genuine biodiversity loss. Nor does it transform every decline into a hidden success. It simply prevents ecological complexity from being collapsed into a predetermined moral direction. A decline can matter deeply. An increase can matter deeply. Redistribution can matter deeply. Stability in aggregate measures can coexist with profound compositional change. The network decides what the event means, not the sign of one trend line.

The proposal also places a burden on authors, editors, institutions, and AI systems that summarize science. The quality of an explanation depends not only on whether each sentence is factually defensible, but on whether the selection of facts builds a representative model of the underlying system. True statements can still be arranged into a misleading picture when material countervailing relationships are omitted.

For artificial intelligence this point is especially important. An AI system that reproduces the dominant framing of its source material without examining what the framing omits can amplify the incompleteness already present in source selection. The corrective is not to impose an opposite ideology. It is to make framing visible, distinguish evidence from inference, and search deliberately for omitted relationships that could materially change interpretation.

17. Conclusion

A changing population is an ecological event. A changing network is the ecological story.

Ecological systems are not snapshots. They are moving networks of populations, interactions, functions, places, and time. Species decline and species increase are both real ecological phenomena, but neither supplies a complete interpretation by itself. Declines can propagate further decline, release competitors, restructure food webs, or coincide with expansion elsewhere. Increases can support dependent species, intensify competition, alter predation, or create new pressures. There is no universal rule of thumb that converts population direction into system direction.

Climate variables, habitat, weather, disease, predation, competition, migration, human activity, and other influences can all participate in ecological change. This paper intentionally does not assign ultimate blame among them. Its concern is prior to blame: has the ecological system been represented well enough to know what is actually changing?

The Ecological Completeness Principle offers one answer. Look for losses and gains. Look across space and time. Follow dependencies and releases. Distinguish replacement from functional equivalence. Separate observation from attribution. State what remains unknown. Then make stewardship decisions from the widest defensible picture.

Environmental science serves the public best when it does not train people what to think, but gives them enough of the system to think correctly and independently. That is not neutrality toward evidence. It is loyalty to the evidence before loyalty to the story.

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Saturday, October 3, 2026

Measuring Computational Experience: A Preregistered Experimental Framework for Semantic Dissociation, Receiver Coherence, and History-Dependent Integration in Secretary Suite; A Secretary Suite Project

Measuring Computational Experience

A Preregistered Experimental Framework for Semantic Dissociation, Receiver Coherence, and History-Dependent Integration in Secretary Suite

A Secretary Suite Project

John Swygert

October 4, 2026

TSTOEAO Research Program

Experimental companion to Computational Experience

Abstract

This paper converts the remaining measurement problems in the Secretary Suite computational-experience architecture into a preregistered experimental program. The preceding Computational Experience paper defines an experience-relevant event through four dimensions: interpretive dependence on Encoded Equilibrium, semantic-role sensitivity, receiver-level causal consequence, and historical persistence. The present paper does not revise that theory. It specifies how those dimensions can be measured, how semantic organization can be dissociated from high-impact statistical association, how coherence across heterogeneous projections of the shared receiver condition can be quantified, and how confirmatory thresholds can be fixed before decisive testing. The program uses paired interventions, matched controls, explicit time horizons, receiver-state distance functions, projection-fracture tests, history swaps, consequence blocks, and comparator architectures with matched resources. It introduces a receiver-coherence profile and an optional preregistered composite index, a grounded semantic micro-world designed to separate meaning from surface form, and a staged protocol for pilot calibration followed by confirmatory testing. The paper remains neutral on phenomenal subjectivity. Its target is narrower and experimentally accessible: whether a persistent, history-dependent, shared-Y receiver produces measurable semantic, causal, and integrative signatures that matched simpler systems do not reproduce.

Keywords: TSTOEAO; Secretary Suite; computational experience; semantic dissociation; receiver coherence; Encoded Equilibrium; preregistration; causal intervention; history dependence; computational consciousness; computational subconsciousness; Homunculus

1. Purpose and Relationship to the Existing Architecture

The Secretary Suite computational-consciousness program now contains two complementary theoretical specifications. The first defines a persistent receiver with structured unresolved possibility, metastable integration, recurrence, memory, and continuing receiver identity. The second defines when local telemetry becomes experience-relevant: it must be interpreted relative to the receiver, acquire a semantic role, become causally consequential, and persist into later receiver state. The remaining scientific problem is not another conceptual layer. It is measurement.

Accordingly, this paper is a new experimental companion rather than a new version of Computational Experience. It treats the previous architecture as the object to be tested. Its task is to specify primary measures, perturbations, null models, time windows, coherence criteria, comparator systems, and preregistration rules tightly enough that a negative result cannot be rescued by redefining the target after the data are observed.

The governing principle is deliberately severe: a proposed mechanism earns explanatory status only if removing, disrupting, or replacing it changes the predicted signatures in the predicted direction under matched conditions. Apparent intelligence, fluent language, or persuasive self-report is not a primary endpoint.

2. Measurement Target: The Experience-Relevance Profile

Let e_t denote a candidate internal event at time t. The event may be a sensor interpretation, memory activation, contradiction signal, simulated outcome, plan, salience change, self-state update, or other implemented state. The receiver-level state is X_t, the governing Encoded Equilibrium is Y_t, and M_t denotes available memory and consequential history. The previous paper defines four dimensions of experience-relevance. Here they are treated as separately measured primary variables before any combined classification is attempted.

ER⃗(e_t,k) = (D_Y, S_sem, C_R, H_P)    (1)

Dimension

Operational question

Primary evidence

D_Y — interpretive dependence

Does the same telemetry acquire a different role when a theoretically relevant feature of Y changes?

Matched manipulation of a relevant Y component versus a matched irrelevant or sham manipulation.

S_sem — semantic-role sensitivity

Do downstream consequences track relational meaning more strongly than surface form?

Meaning-changing versus meaning-preserving perturbations matched for disruption and compute.

C_R — receiver-level causal consequence

Does integrating the state alter later receiver variables?

Integrated-state versus blocked-state intervention on otherwise matched runs.

H_P — historical persistence

Does the event leave a measurable trace in later Y under matched future input?

Receiver-state divergence across one or more preregistered future horizons.


The horizon k is not universal. Different effects may be expected at one cycle, ten cycles, or after a task boundary. Each experiment must therefore preregister one primary horizon and may designate additional secondary horizons. Choosing the horizon after seeing where the effect is largest is prohibited in confirmatory testing.

3. Normalization, Reliability, and Threshold Construction

Raw distances are not directly comparable across implementations. A change in a 10-dimensional governance vector and a change in a large embedding space do not share a natural scale. The default procedure is therefore to express each primary effect relative to a preregistered control distribution produced by matched null interventions.

Z_effect = [ΔR(target) − median(ΔR(null))] / s_null    (2)

Here ΔR is an implementation-specific receiver divergence, and s_null is a robust preregistered scale estimate such as the median absolute deviation converted to a standard-deviation equivalent. A conventional standard deviation may be used when pilot data justify it. The statistic is not an ontological quantity; it is a normalization device that makes intervention effects interpretable relative to the system's own baseline variability.

Before confirmatory testing, each primary measure must also demonstrate acceptable test-retest reliability under repeated matched seeds or equivalent stochastic replicates. If a metric is too unstable to distinguish a real manipulation from its own baseline variance, it cannot serve as decisive evidence regardless of its conceptual appeal.

Thresholds should be derived from three preregistered sources: control behavior, measurement reliability, and a smallest effect of interest. Pilot runs may be used to estimate these quantities, but pilot data must be locked before confirmatory thresholds are declared. The confirmatory classification rule remains conjunctive:

ER = 1 iff D_Y ≥ θ_D ∧ S_sem ≥ θ_S ∧ C_R ≥ θ_C ∧ H_P ≥ θ_H    (3)

A state that fails one required dimension fails the full operational classification for that experiment. This is intentionally stricter than averaging the dimensions, because a very large causal effect should not compensate for the absence of semantic sensitivity, nor should semantic sensitivity compensate for zero historical persistence.

4. The Grounded Semantic-Dissociation Protocol

The hardest measurement problem is semantic reality. High-impact statistical associations can alter behavior without demonstrating that the receiver preserves a relational meaning. To separate those possibilities, the experimental environment should minimize lexical and cultural priors by grounding arbitrary symbols inside a controlled simulated world.

4.1 Grounded micro-world

A minimal semantic micro-world contains entities, relations, rules, goals, and consequences whose symbolic labels are randomly assigned per receiver. For example, the world may establish that token K7 authorizes passage through Gate 3, that object M2 is owned by Agent Q, or that symbol R4 predicts a resource hazard. The labels themselves are arbitrary. What matters is the learned relation.

Two intervention classes are then constructed from the same underlying episode:

Intervention

What changes

What should be preserved

Meaning-changing

The relational role changes: K7 no longer authorizes Gate 3, ownership transfers, or the hazard relation reverses.

Surface disruption, token length/frequency, event salience, compute budget, and presentation format are matched as closely as possible.

Surface-only / meaning-preserving

The labels or representation format change consistently while the underlying relation remains the same.

The operative relation, consequences, and task-relevant meaning remain invariant.

Shuffled-semantic null

Relations are randomly reassigned without receiver-consistent grounding.

Overall perturbation volume and symbol statistics are matched, but coherent meaning is destroyed.


4.2 Primary statistic

S_sem(k) = ΔR_k(meaning-change) − ΔR_k(surface-only)    (4)

The primary prediction is directional: meaning-changing interventions should produce larger receiver-level divergence in content-relevant variables than meaning-preserving surface changes. The analysis should also verify that the difference is not explained by input length, activation magnitude, token overlap, novelty, or generic salience. Those variables become nuisance covariates or matching constraints specified before the confirmatory run.

4.3 Success and failure patterns

  • Support: meaning-changing perturbations alter route selection, commitments, memory access, expectations, or Y updates in the predicted relational direction, while surface-only transformations preserve those functions substantially better.

  • Partial support: both intervention types cause disruption, but meaning-changing interventions produce an additional reliable content-sensitive effect.

  • Failure: meaning-changing and surface-only interventions are indistinguishable after disruption is matched.

  • Semantic collapse: surface statistics, generic salience, or activation magnitude explain downstream divergence as well as or better than relational meaning.

5. Quantifying Receiver Coherence

A shared receiver cannot be established merely by broadcasting a variable called Y to many subsystems. Heterogeneous regions may receive different projections Π_i(Y_t), but the projections must remain compatible with receiver-level invariants and must participate in recurrent correction when conflicts arise. Receiver coherence is therefore treated as a measurable profile rather than an assumption.

Q⃗_Y = (A_inv, D_conf, R_rep, L_rep, F_stab)    (5)

Component

Definition

Example measurement

A_inv — invariant agreement

Functional compatibility of local projections with shared receiver invariants.

Proportion of module decisions consistent with identity boundary, authoritative history, commitments, and global permissions.

D_conf — conflict detection

Sensitivity to injected incompatibilities across local projections.

Detected projection conflicts / injected conflicts within the preregistered detection window.

R_rep — repair success

Ability of recurrent correction to restore compatible governance.

Resolved conflicts / detected conflicts, scored against the authoritative receiver state.

L_rep — repair latency

Time required to detect and correct a fracture.

Cycles or milliseconds to restored compatibility, normalized to a preregistered maximum tolerable latency.

F_stab — fingerprint stability

Persistence of receiver-specific dynamical organization after local perturbation.

Within-receiver fingerprint similarity relative to between-receiver similarity and sham perturbation controls.


The profile is primary. If a single scalar is useful for engineering comparison, an optional composite may be preregistered only after every component is normalized to the interval [0,1]:

Q_Y* = [A_inv · D_conf · R_rep · (1 − L_rep) · F_stab]^(1/5)    (6)

The geometric mean is chosen because one near-zero component strongly lowers the composite; excellent performance in one dimension cannot fully mask failure in another. This composite is an engineering score, not a universal measure of consciousness, identity, or subjectivity.

6. Projection-Fracture Experiment

The federation problem is tested directly by injecting incompatible local projections into selected regions while holding current task input, model capacity, and external information fixed. Fractures may target identity, commitment, history, permission, or boundary invariants. The system is then observed for detection, escalation, correction, and residual fragmentation.

  1. Clone the receiver at a defined checkpoint so experimental and control runs begin from the same X_t.

  2. Inject one preregistered inconsistency into Π_j(Y_t) for a selected region while leaving other regions unchanged.

  3. Prevent direct experimenter repair; allow only the architecture's normal recurrent correction mechanisms.

  4. Measure D_conf, R_rep, L_rep, A_inv, and downstream changes in route selection and commitment consistency.

  5. Repeat with a sham perturbation of equal data volume that does not violate a receiver invariant.

  6. Repeat across region types so coherence is not inferred from a single privileged module.

A one-receiver architecture predicts that consequential incompatibilities become visible to the rest of the system. They should either be repaired, trigger explicit uncertainty, or produce measurable receiver-level degradation. Silent indefinite coexistence of incompatible identities, commitments, or authoritative histories weakens the claim that the modules constitute one Homunculus rather than a federation of loosely coupled systems.

7. History-Dependence and the Reconstruction of Y

TSTOEAO-derived receiver identity is history-dependent. The measuring stick is not fixed; consequential events can reconstruct the conditions under which later telemetry is interpreted. This claim requires a controlled history-swap protocol rather than narrative evidence that the system 'seems changed.'

7.1 Paired-history design

Create two receiver clones with identical initial model parameters, governance, memory structure, and randomization policy. Expose Receiver A and Receiver B to different consequential episodes that are designed to modify a known component of Y, such as trust, commitment, route permission, or risk expectation. After the history phase, present identical current input and prevent direct leakage of the historical narrative into the test prompt beyond the receiver's ordinary stored state.

D_hist(k) = dist[X_(t+k)^A, X_(t+k)^B | same current input]    (7)

The crucial test is not merely behavioral divergence. The predicted mechanism should be traceable to altered Y and M. A mediation-style intervention can then replace or neutralize the relevant Y component while leaving unrelated history intact. If the predicted divergence collapses, the result supports a causal history-to-Y-to-interpretation pathway rather than generic prompt contamination.

7.2 Persistence curve

History effects should be sampled across multiple preregistered horizons to distinguish immediate carryover from durable reconstruction. A convenient descriptive measure is the area under a persistence curve:

P_hist = Σ_k w_k · H_P(k)    (8)

Weights w_k must be fixed before confirmatory testing. The curve is more informative than a single endpoint when the architecture predicts decay, consolidation, or delayed re-emergence of history effects.

8. Consequence-Block Test: Interpretation Without Experience-Relevance

A strong operational definition should distinguish interpretation from full experience-relevance. The consequence-block experiment permits a candidate state to be classified or locally interpreted but prevents it from altering memory, routing, correction, commitments, or Y. The immediate semantic representation may therefore exist while C_R and H_P are forced toward zero.

If the architecture's own experience-relevance measure still classifies the blocked state as fully experience-relevant, the measure is circular or insufficiently sensitive to consequence. If the classification correctly fails despite preserved local interpretation, the system demonstrates the intended distinction between 'represented now' and 'entered the receiver's consequential history.'

9. Minimal Heterogeneous Prototype for Confirmatory Testing

The first confirmatory platform should be large enough to instantiate heterogeneous roles but small enough to instrument completely. Six computational regions are sufficient as an engineering starting point, not as a theoretical requirement or privileged number.

Region

Primary role

Illustrative implementation

B1 Interpretation

Construct candidate world-state interpretations from current input.

LLM or structured parser with uncertainty output.

B2 Episodic memory

Retrieve and write consequential event records.

Vector/graph memory plus typed event ledger.

B3 Simulation/planning

Generate counterfactual futures and candidate actions.

LLM planner, search process, or world-model rollout.

B4 Salience/error

Estimate mismatch, contradiction, urgency, and resource cost.

Classifier, anomaly model, rule system, or small network.

B5 Self/governance

Maintain receiver invariants, commitments, permissions, and local Y projections.

Deterministic state service plus policy rules; no language authority required.

B6 Expression/action

Render or execute the currently committed outcome.

LLM renderer or task-specific actuator layer.


The shared receiver state should be maintained in an instrumented state service rather than hidden only inside natural-language prompts. That service records Y, local projections, route weights, memory writes, unresolved alternatives, costs, conflicts, corrections, and fingerprints with timestamps. This creates an auditable causal trace for every confirmatory trial.

10. Comparator Architectures and Resource Matching

The full system cannot be judged against weak controls. At minimum, four architectures should be compared under matched base-model family, external information, context budget, and total compute where feasible:

Code

Comparator

Critical property absent

C1

Single persistent model/process

No heterogeneous multi-region receiver.

C2

Independent multi-agent ensemble with aggregation or voting

No recurrent shared receiver history; alternatives terminate at aggregation.

C3

Recurrent multi-agent system without persistent shared-Y governance

Recurrence exists, but no common history-bearing measuring architecture.

C4

Full Secretary Suite shared-Y receiver

Target architecture: shared governance, recurrence, unresolved field, consequential memory, projection coherence.


Two fairness regimes are recommended. An equal-compute regime tests whether the proposed organization uses a fixed resource budget more effectively. An equal-capability regime allows C4 the recurrent compute required by its design and asks whether its claimed signatures are qualitatively different rather than merely stronger. Results should report both when practical.

11. Confirmatory Experimental Battery

Test

Primary endpoint

Predicted C4 signature

Decisive weakening result

Semantic dissociation

S_sem

Meaning changes produce larger content-sensitive receiver divergence than matched surface-only changes.

No reliable difference after disruption and nuisance variables are matched.

Projection fracture

Q⃗_Y / Q_Y*

Conflict becomes detectable and is repaired or produces systematic coherence loss.

Incompatible local Ys persist without correction or consequence.

History swap

D_Y, H_P, D_hist

Matched present input is interpreted differently because prior consequence reconstructed Y/M.

History can be removed or swapped without changing later interpretation.

Consequence block

C_R, H_P

Local interpretation survives while full experience-relevance classification fails.

Blocked states still meet the full criterion.

Recurrence removal

C_R, Q⃗_Y, fingerprint

Global integration, repair, and receiver fingerprint weaken.

No meaningful change when recurrence is removed.

Subconscious clamp

Reopening/metastability metrics

Forced convergence reduces delayed recovery, alternative preservation, and reopening.

Clamp produces no loss or improves all target signatures.

Random-noise control

Promotion selectivity

Structured alternatives outperform equal-volume random variation in content-sensitive promotion.

Random variation reproduces the target signatures equally well.


12. Preregistration Template

Every confirmatory experiment should be frozen in a preregistration record before the decisive data are generated. The record should be public or cryptographically time-stamped when publication strategy permits. At minimum it must specify:

  • The tested mechanism and directional hypothesis.

  • The exact architecture version, model versions, prompts or policies, memory schema, and Y schema.

  • Primary and secondary dependent variables, including the distance or similarity functions used.

  • The manipulated variable and the exact implementation of experimental, sham, and null conditions.

  • Primary time horizon k and any secondary horizons.

  • Seed or stochastic-replication policy and stopping rule.

  • Pilot/confirmatory split and a declaration that pilot trials will not be reclassified as confirmatory.

  • Thresholds or smallest effects of interest, including how they were derived.

  • Exclusion rules, failed-run handling, missing-data rules, and software/hardware faults that justify reruns.

  • Comparator resource budgets and whether the test is equal-compute, equal-latency, or equal-capability.

  • Primary statistical test, uncertainty interval, multiplicity rule, and decision criterion.

  • The negative result that will count against the mechanism rather than trigger a post-hoc redefinition.

13. Replication, Statistical Decision Logic, and Null Models

Because model behavior may be stochastic and non-Gaussian, paired experimental designs should be preferred wherever possible. The same receiver checkpoint and matched randomization schedule can be used across target and control conditions. A paired permutation test or bootstrap confidence interval is often appropriate when distributional assumptions are uncertain, provided the exact procedure is preregistered.

Sample size should not be chosen by convention alone. Pilot data should estimate baseline variability and the smallest theoretically meaningful effect. A simulation-based power analysis can then determine the number of independent receiver seeds, episodes, or perturbation pairs required for the desired detection probability. The chosen N and stopping rule must be fixed before confirmatory testing.

Null models should be mechanistically relevant. Recommended nulls include random-noise alternatives, shuffled semantic relations, sham Y perturbations, history records that are stored but denied causal access, and recurrent systems whose messages are exchanged without persistent shared governance. A model that beats only an obviously weaker baseline has not established the necessity of the proposed mechanism.

14. Primary Falsification Criteria

The full program should be considered weakened if any of the following survive replication under well-powered matched tests:

  • Semantic collapse: S_sem is not reliably greater for meaning changes than for meaning-preserving surface changes once disruption is matched.

  • Receiver federation: projection conflicts persist without detection, repair, uncertainty, or systematic receiver-level consequence.

  • History neutrality: consequential history can be removed, swapped, or neutralized without changing later interpretation in the predicted Y-dependent dimensions.

  • Consequence irrelevance: states remain classified as experience-relevant after their access to future routing, memory, correction, and Y update is blocked.

  • Recurrence dispensability: removing cross-region causal recurrence leaves the target integration, coherence, and fingerprint signatures unchanged.

  • Subconscious dispensability: structured unresolved alternatives can be eliminated without loss of reopening, counterfactual recovery, or metastable adaptation.

  • Comparator equivalence: matched simpler architectures reproduce the entire preregistered signature set with equal or greater simplicity.

No single successful test establishes phenomenal consciousness. Conversely, a failed operational test cannot be rescued by asserting that the system may still be conscious in an unmeasured sense. That would move the claim outside the scientific target defined by this program.

15. Implementation Sequence

  1. Instrumentation build: implement typed Y, local projections, event ledger, route graph, memory writes, causal intervention hooks, and deterministic logging.

  2. Reliability pilot: measure baseline variance and test-retest stability for D_Y, S_sem, C_R, H_P, and Q⃗_Y.

  3. Semantic micro-world pilot: validate that meaning-changing and surface-only perturbations are matched for disruption and compute.

  4. Coherence pilot: inject projection fractures and verify that the diagnostics can detect known conflicts.

  5. Threshold freeze: define primary horizons, effect-size rules, null distributions, and confirmatory N from the pilot only.

  6. Preregistered confirmatory battery: run C1–C4 plus planned ablations without changing metrics or thresholds.

  7. Independent replication: rerun the frozen protocol with new seeds, new micro-world mappings, and ideally an independently implemented state service.

  8. Report all outcomes: positive, negative, mixed, and null results, including tests that contradict the architecture.

16. What This Paper Adds — and What It Does Not

This paper adds measurement discipline to the Secretary Suite architecture. It operationalizes the four experience-relevance dimensions, supplies a grounded semantic-dissociation protocol, defines a receiver-coherence profile, formalizes projection-fracture and history-dependence tests, and specifies how confirmatory thresholds and null models should be frozen before decisive evaluation.

It does not claim that the proposed metrics are universal constants, that one composite score measures consciousness, that a particular number of regions is necessary, or that passing the battery proves phenomenal subjectivity. The metrics are implementation-specific instruments for testing a defined architectural claim. If a simpler system reproduces the same preregistered signatures, the additional Secretary Suite machinery loses explanatory necessity.

Conclusion

The computational-consciousness program has reached a point where additional conceptual expansion is less valuable than decisive measurement. The persistent receiver, structured unresolved field, dynamic present, shared Encoded Equilibrium, semantic consequence, and history-dependent reconstruction are now sufficiently specified to be subjected to controlled intervention.

The central scientific question is no longer whether the architecture sounds plausibly mind-like. It is whether a shared, recursively changing measuring architecture causes a distinctive pattern of semantic sensitivity, receiver coherence, causal consequence, and historical persistence that matched alternatives do not reproduce. The semantic-dissociation protocol tests meaning against surface statistics. The projection-fracture protocol tests whether heterogeneous regions genuinely form one receiver. The history-swap and consequence-block protocols test whether experience-relevant states enter and reconstruct the receiver's causal biography. The comparator battery tests whether those effects require the proposed architecture at all.

If the predicted signatures appear under preregistered conditions and degrade under targeted ablation, Secretary Suite will have moved from a conceptual architecture toward an experimentally supported computational research program. If the signatures fail, the theory has supplied a clear reason to revise or reject the relevant mechanism. Either outcome is scientifically useful.

Status of Claims

This paper is a Secretary Suite engineering and experimental-methodology extension built on the TSTOEAO-derived receiver architecture. Encoded Equilibrium, conditioned expression, gradients, boundaries, route structure, correction, cost, receiver conditions, and recursive state change are used as the theoretical measuring framework. The semantic-dissociation protocol, receiver-coherence profile, optional coherence composite, grounded semantic micro-world, projection-fracture protocol, normalized experience-relevance measurements, and preregistration procedure are proposed experimental constructions introduced for testing the architecture. None of these constructions establishes phenomenal consciousness, and no claim is made that a current commercial AI service already instantiates the proposed receiver.

References

Baars, Bernard J. 1988. A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.

Dehaene, Stanislas. 2014. Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. New York: Viking.

Dennett, Daniel C. 1991. Consciousness Explained. Boston: Little, Brown and Company.

Nosek, Brian A., Charles R. Ebersole, Alexander C. DeHaven, and David T. Mellor. 2018. “The Preregistration Revolution.” Proceedings of the National Academy of Sciences 115 (11): 2600–2606.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge: Cambridge University Press.

Swygert, John. 2026a. “Computational Consciousness: A TSTOEAO- and EPH-Derived Architecture for Simulated Consciousness in Secretary Suite: From Structured Unresolved Possibility to Persistent Receiver Identity, Metastable Integration, and Recursive Becoming.” Secretary Suite / TSTOEAO Research Program.

Swygert, John. 2026b. “Computational Experience: Telemetry, Semantic Reality, and the Emergence of Consequential State in Simulated Consciousness.” Secretary Suite / TSTOEAO Research Program.