Part II — Competition, coexistence and diversion

DTM.5 - Separate Selection Within and Among Collectives

Type: Method Status: Stable

DTM.5:1 - Problem frame

Use this when a variant becomes more common within each participating group, yet its overall prevalence changes differently—or when a claimed advantage of a collective is being used to explain which variants continue.

A shortcut can gain users inside every team while teams that use it less contribute more of the next period’s procedures. Conversely, a practice can be costly to a current member while supporting a group’s continued operation. These situations require distinguishing units of continuation and the processes that change their weights.

The first result is a decomposition of the observed or modeled change, followed by an account of which parts have a supported selection mechanism. The decomposition alone does not establish causation or justify privileging a higher level.

For comparing the current performance of two organizations without a question about differential continuation, use the existing evaluation and choice methods.

DTM.5:2 - Problem

An overall mean mixes changes within groups with changes in the groups’ contributions to what continues. Calling either component “group success” leaves its unit unclear: more copied methods, more learners, survival of the same organization, or more successor organizations.

The same arithmetic can also arise without selection. Reconstruction, unequal exposure, recruitment or a changed measurement can alter the mean. A useful account must therefore distinguish the bookkeeping identity from the causal explanation.

DTM.5:3 - Forces

DTM.5:4 - Solution

Choose the unit of continuation, trace its sources, separate the changes, then investigate the mechanisms behind them.

DTM.5:4.1 - Decide what the next population counts

Name the earlier population, the receiving population and the interval or transition connecting them. Specify the continuing unit: an executed method variant, a learner retaining it, a transmitted procedure instance, or a collective with an identified successor relation.

A group can contribute many transmitted instances without producing a new group. Use the first kind of count for variant continuation and the second for collective reproduction. Survival, revenue or an externally assigned quality score does not automatically measure either.

C.36 supplies the cultural relations; DTM.1 identifies the variant and affected work. Return to DTM.2 if the available record shows only copies of descriptions while the claim concerns continued execution.

DTM.5:4.2 - Recover contribution weights and within-group change

For each source group, identify how much of the receiving population derives from it and how the relevant property changes along that contribution. Use the actual source relation, rather than assuming that every present member contributes equally.

When multiple groups contribute to one resulting method, choose an allocation supported by the receiving question or retain unresolved source shares. A convenient attribution convention is not evidence of biological or cultural inheritance.

Account for every receiving unit once. Mark the part attributed to the identified source groups and any external or unresolved part; shared source attribution must not count the same unit twice. If the record covers only part of the receiving population, retain that coverage in the result.

Keep differences due to reconstruction, recruitment and observation visible. For example, a transmitted procedure may omit a check during reconstruction even if no recipient deliberately selected the unchecked variant.

DTM.5:4.3 - Decompose the aggregate change

For a scalar property, let π_g be the source population’s weight in group g, z_g its initial mean, w_g its contribution per source unit to the covered part of the receiving population, and Δz_g the change in the corresponding transmitted mean. Weights π_g sum to one; the mean contribution w̄ is positive. The source-attributed contributions must cover that part without omission or double counting.

The covered mean and its difference from the initial mean satisfy:

z_cov = Σ_g π_g w_g (z_g + Δz_g) / w̄
z_cov − z_old
  = Cov_π(w,z) / w̄ + Σ_g π_g w_g Δz_g / w̄.

The first term expresses the effect of different contribution weights associated with the earlier property. The second expresses changes along those contributions. If those contributions cover the whole receiving population, z_new = z_cov.

Otherwise let c be the covered fraction of the receiving population and z_ext the mean property in the rest, using the same counted unit and weighting convention. Include that contribution:

z_new = c z_cov + (1−c) z_ext
z_new − z_old = c (z_cov − z_old) + (1−c)(z_ext − z_old).

An unknown external composition remains unknown; it is not zero. For a binary variant share and known c, the possible new share lies between c z_cov and c z_cov + 1−c until the external composition is constrained. When no receiving unit derives from the identified sources, omit their decomposition and calculate from the other contribution. MMP and the relevant mathematical methods supply the calculation.

This identity does not tell why w or Δz has the observed value. Call them between-source and within-contribution changes until the causal interpretation is established. If the units are collectives, redefine z and w for collective continuation; do not carry over a particle count while silently changing the claim.

For a question needing no scalar aggregate, retain the corresponding source and change table. Do not invent a total score solely to use the formula.

DTM.5:4.4 - Test the proposed selection mechanisms

For within-group selection, identify the process by which variants contribute differently under the group’s conditions. For a between-group explanation, identify what group property changes survival, contribution, recruitment or successor formation.

Consider an alternative that could reproduce the same decomposition: groups may encounter different audiences, receive unequal support, have different ages, or be observed with different completeness. Choose the distinguishing observation or comparison with MMP.16 and the applicable research method.

The response to a change must follow the mechanism. If a group’s larger contribution comes from privileged broadcast access, altering an internal learning method may leave that advantage unchanged. If the advantage comes from reliable results that others adopt, changing access alone can hide the relevant production difference.

DTM.5:4.5 - Return the decomposition to the work question

State the units, contribution relation, covered population and any external contribution, distinct components of change, supported causes and consequential unknowns. If coverage or external composition is unresolved, return the supported range or restrict the claim to the covered part. Then ask which feasible change addresses the problematic relation.

A practice favored within teams but associated with lower total contribution can persist locally. A collective’s growth can also coexist with costs to its members. D.3/D.4 and the existing decision methods retain those conflicts; no arithmetic term grants moral priority to its level.

Use DTM.3/.4 to examine dynamic continuation and DTM.6 when another variant changes the contribution mechanism. Stop with the descriptive decomposition when causality remains unresolved and that description is sufficient for the receiving use.

DTM.5:5 - Archetypal Grounding

DTM.5:5.1 - Within-group increase is canceled by changing contribution weights

Take two equally weighted source groups. Their initial shares of variant B are 0.2 and 0.8. Along each group’s transmitted contribution the B share increases by 0.1. The groups supply relative transmitted-instance counts of 2 and 1.

The initial overall B share is 0.5. The receiving share is:

(2 × 0.3 + 1 × 0.9) / 3 = 0.5.

The contribution-weight change is −0.1; the within-contribution change is +0.1. They cancel. An unchanged aggregate would therefore conceal two material changes.

The weights count transmitted instances, not new organizations and not usefulness. The result does not establish why the low-B group supplied twice as much. Better results, broader access or a sampling difference are possible explanations requiring different follow-up.

If the two groups instead supply equal counts, the new B share becomes 0.6. This changed condition shows which contribution the aggregate depended on.

DTM.5:5.2 - A copied method can change without being selected

Suppose every teaching group sends one equally weighted procedure, but recipients reconstruct it with a different timing convention. A change in the receiving mean belongs to the within-contribution term. There is no differential contribution in this stipulated case.

The next action is to study reconstruction with DTM.2 and the relevant learning or notation method. Calling the change selection would direct attention away from how the recipient obtained the new variant.

DTM.5:5.3 - Counting successor collectives answers another question

A project network may form successor teams with different arrangements. If the question is which arrangement becomes common among successor teams, count those teams and recover their formation relations. If the question is which procedure most workers later use, count the corresponding use and contribution relations.

One prolific source team can dominate the latter count without dominating the former. The choice of unit is part of the question, not a defect to remove by finding one universal fitness number.

DTM.5:5.4 - External arrival changes the receiving mean

One procedure instance A continues from the identified source, with B-share z_old = z_cov = 0. One instance B arrives from outside that source set. The receiving population has two instances, so c=1/2 and z_ext=1. Its B-share is (1/2)×0 + (1/2)×1 = 1/2. A source-only calculation would return zero because it answers only for the covered part.

If the external instance’s variant is unknown, the whole-population B-share lies between 0 and 1/2. Additional source tracing is needed only if that uncertainty changes the receiving decision. Neither result attributes the external arrival to selection within the original source.

DTM.5:6 - Bias-Annotation

Grouping by a striking label can make an accounting effect look like a collective cause. Recover the source relations and plausible alternatives before asserting that causal level.

The phrase “higher-level success” also invites a value judgement. Name what continues and who benefits separately.

DTM.5:7 - Conformance Checklist

• The earlier and receiving populations, interval and counted unit are identified.
• Group contribution is not confused with reproduction of the group itself.
• Source weights and changes along contributions can be reconstructed or their uncertainty is stated.
• The receiving population is covered without omission or double counting; external contributions enter the mean, or the result is explicitly bounded to the covered part.
• The decomposition uses consistent units and does not serve as its own causal explanation.
• Reconstruction and differential exposure remain alternatives where they could change the answer.
• The practical conclusion retains conflicts among affected participants instead of assigning automatic priority to a level.

DTM.5:8 - Common Anti-Patterns and How to Avoid Them

The aggregate did not change, so nothing happened. Recover both components and their possible cancellation.

A profitable team has higher reproductive success. Establish the continuation unit and contribution relation; profit may explain one, but does not define it.

A covariance proves group selection. Investigate the mechanism and a consequential competing explanation.

Every inherited change is selection. Keep reconstruction and transformation distinct from differential continuation.

DTM.5:9 - Consequences

The method exposes conflicts hidden in a population mean and prevents intervention at the wrong mechanism. It also limits overclaiming: a valid decomposition may leave the causal question open. Source attribution and comparable observations can be costly; their required precision follows the receiving decision.

DTM.5:10 - Rationale

Multilevel change is not simply the same selection calculation repeated at larger scales. The objects counted, their contribution relations and the causal mechanisms can differ. An explicit decomposition makes those differences inspectable.

DTM.5:11 - SoTA-Echoing

Bourrat, Multilevel selection 1, multilevel selection 2, and the Price equation: a reappraisal (2023) sharpens the distinction between contributions of particles from collectives and reproduction of collectives. This method adopts the unit distinction without resolving the paper’s broader interpretation of multilevel selection.

Compared with following only aggregate prevalence, the decomposition can reveal offsetting changes at modest additional calculation cost. Compared with treating every group association as causal selection, it requires the source and causal relation that would change intervention. Existing mathematical and research methods supply those tests; DTM retains the units and continuation question.

DTM.5:12 - Relations

• DTM.1/.2 supply the variant, consequences, uptake and reconstruction distinctions.
• DTM.3/.4 investigate coupled continuation and persistence.
• DTM.6 models interactions that can change contribution weights.
• C.36, MMP.16, D.3/D.4 retain cultural relations, discrimination of explanations and conflicts between interests.

DTM.5:End

DTM.6 - Model Competition and Facilitation between Variants

Type: Method Status: Stable

DTM.6:1 - Problem frame

Use this when a method spreads, persists or works differently in the presence of another variant, and treating the two independently would change the decision.

Two procedures may draw on the same limited capacity. One practice may teach a notation needed by another. A shared response to both may restrict them unequally. These are different mechanisms; identifying the connection comes before choosing an equation.

The first useful result is a dependence of one variant’s execution, continuation or transmission on the other, through a named relation that can be investigated. The result includes the independent-action comparison and an observation that could distinguish the explanations.

If the question concerns only two interacting components, with no continuation or transmission of variants, ordinary subject modeling is sufficient.

DTM.6:2 - Problem

Correlation between variants does not establish competition or facilitation. They may share favorable conditions, reach the same audiences or be counted differently. Conversely, fitting each variant separately can miss a real interaction and recommend a change that releases the other.

A useful model must show what one variant changes for the other. It must also distinguish an immediate effect, a later feedback and the effect of changing the intervention itself.

DTM.6:3 - Forces

DTM.6:4 - Solution

Identify the affected result, recover the connecting relation, construct its effect and compare joint with independent action.

DTM.6:4.1 - Name the variants, result and comparison

Use DTM.1/.2 to identify what continues and how a transmitted instance is recognized. State whether the current result is successful execution, persistence in a participant, uptake elsewhere or a consequence for an affected party.

Choose the comparison at comparable conditions: what would change if the other variant were absent, held fixed or prevented from changing the suspected intermediary? These are different comparisons. A feasible observation or intervention must support the one used.

Do not call one variant a competitor merely because its name occupies a neighboring category. Two differently named methods can draw on different resources; two instances of the same named method can compete for a limited one.

DTM.6:4.2 - Recover the intermediary

Ask what the first variant changes that the second uses or encounters. Possibilities include a resource, a shared response, compatibility with partners, prerequisite capability or the contact network.

For the selected relation, identify the changing quantity or condition, its effect on the second variant and any return effect. Use observations of that relation where possible. Retain independent action or common external cause when the connection remains unresolved.

Distinguish an indirect effect from a direct interaction. A practice can alter the audience available to another without ever being executed by the same participant.

DTM.6:4.3 - Build the dependence from the relation

The construction follows the intermediary:

• Shared limited capacity: account for available capacity and the amount each variant occupies or consumes. Derive each variant’s feasible execution or growth from what remains. A replacement assumption is needed if one unit displaces another.
• Shared response: relate each variant to formation of the response, then relate that response to each variant’s continuation. Unequal sensitivity matters. DTM.9 develops response dynamics, costs and memory.
• Compatibility: relate partner composition and conversion arrangements to successful exchange, its cost and the consequences of adopting the variant. Existing incompatibility need not make exchange impossible.
• Prerequisite capability: relate execution or transmission to the capability supplied by another practice. Separate acquiring the prerequisite from encountering a description of it.

MMP supplies the representation and admissible operations for the dependence. Do not infer any of these mechanisms from the word “competition.”

DTM.6:4.4 - Compare independent and coupled results

Evaluate the same result under the independent account and the account containing the proposed relation. Keep other relevant conditions comparable. Examine a range of plausible conditions when the effect’s sign or size changes with them.

A negative immediate effect can coexist with a positive later effect. For example, training consumes current execution time but may enlarge later capability. Give the horizon; do not compress the two into an unexplained positive or negative coefficient.

For establishment of a rare variant, evaluate its continuation in the environment maintained by the resident variants. That invasion calculation belongs to DTM.4 and is needed only when establishment is the question. Coexistence, switching order and transient disruption can require different analyses.

DTM.6:4.5 - Choose a distinguishing observation and pass the result on

Use MMP.16 to identify an observation or permissible comparison that differs between the coupled account and its serious alternative. If removing one variant also changes resources, staffing and audience, the result alone does not isolate the proposed mechanism.

Return the conditional dependence, comparison and unresolved alternatives. DTM.3 links the effect to transmission between participants; DTM.4 investigates persistence. Use DTM.7 only if a shared mechanism is actually diverted from its intended result. Ordinary resource competition does not establish diversion or intent.

DTM.6:5 - Archetypal Grounding

DTM.6:5.1 - Restricting both variants can favor the less sensitive one

Consider a hypothetical community sharing procedures. A and B denote normalized execution activity inside one group, with A+B no greater than one. Remaining capacity is R=1−A−B. A yields useful results in this case; B produces specified errors. These are descriptions of practices and outcomes, not classes of people.

Suppose a shared checking response a affects them differently:

A' = A(2R − 0.2 − a)
B' = B(R − 0.3 − 0.2a).

These illustrative coefficients are stipulated, not measured. Two connections are now visible: both use R, and a restricts A more strongly. Holding activity fixed, an increase in a lowers both instantaneous growth rates. Over time, loss of A also releases capacity for B. The sign of B’s eventual change cannot be read from its direct response term alone.

DTM.9 completes this example by modeling how a is formed and calculating the later outcome. If a real procedure catalogue does not have the assumed capacity competition or differential checking effect, this model is not its explanation. A distinguishing investigation must recover those relations.

DTM.6:5.2 - Compatible partners can make an unfamiliar method worth adopting

Suppose the proposed method gives a group a benefit of 2 units over a fixed horizon and costs 0.5 to adopt. Exchange with a partner using the old method remains possible through manual conversion. The switching group bears conversion cost 3 per unit share of old-method partners.

If f is the share of its relevant partners already using the proposed method, its gain relative to continuing is:

g(f) = 2 − 0.5 − 3(1−f).

An independent estimate that omits partner conversion gives 2−0.5=1.5. The coupled estimate instead gives −0.9 at f=0.2 and 0.9 at f=0.8. At the lower share, ignoring compatibility therefore reverses the sign of the choice. Here other adopters facilitate adoption by reducing conversion work. No shared defensive response is needed.

An available adapter costing 0.4 over the same horizon and reducing the conversion coefficient to 0.2 changes the gain to 2−0.5−0.4−0.2(1−f), or 0.94 at f=0.2. The participants, cost allocation and horizon are unchanged. If costs instead shift to old-method partners, both parties’ consequences must be recomputed.

A comparison that records conversion work and partner composition can distinguish this account from an unrelated reluctance to adopt. Adoption still requires opportunity, capability and authority; a positive gain is not a transmission event. DTM.2/.4 supply those further questions.

DTM.6:5.3 - A notation practice can facilitate another method’s transmission

A group encounters a useful construction but cannot reconstruct its diagrammatic operations. A notation-learning practice may change that capability. The proposed connection is therefore capability → reconstruction of the transmitted method, not contact → uptake alone.

Compare subsequent reconstruction on suitable tasks, while retaining differences in prior knowledge and exposure. If only familiarity with the example increases, the evidence does not establish general facilitation. Human learning and AI training use their respective subject methods; this pattern does not prescribe one learning algorithm.

DTM.6:6 - Bias-Annotation

Resource competition is an attractive default because it readily yields equations. The intermediary may instead be compatibility, capability or audience structure.

A second bias is to treat observed dominance as intrinsic superiority. A variant can dominate because another practice changed its conditions, while producing worse results for a specified recipient.

DTM.6:7 - Conformance Checklist

• The affected result concerns execution, continuation or transmission of identified variants.
• The comparison states what is absent, held fixed or changed.
• A connecting relation supports the dependence, or competing explanations remain explicit.
• Independent action is compared with the coupled account under stated conditions.
• Immediate effects, later feedback and the relevant horizon remain distinguishable.
• A selected observation can discriminate a consequential alternative.
• The model does not turn ordinary interaction into evidence of capture, harm or intent.

DTM.6:8 - Common Anti-Patterns and How to Avoid Them

Co-occurrence proves facilitation. Check shared exposure and other common causes.

Everything competes for one resource. Identify what is actually limiting and how use changes availability.

Restricting both must reduce both. Follow indirect effects through the intermediary and later state.

The positive gain explains adoption. Recover the encounter, feasible execution and choice relation separately.

DTM.6:9 - Consequences

The method reveals when separate estimates would give a wrong intervention direction. It can also justify retaining separate models when no consequential coupling is supported. The cost is identifying and observing the intermediary; a more elaborate coupled model is not automatically more informative.

DTM.6:10 - Rationale

Transmission and continuation change the environment in which variants act. Modeling the actual relation preserves this feedback without treating every interaction as biological competition. The same operation accommodates limiting and enabling relations while leaving their subject laws to the relevant practice.

DTM.6:11 - SoTA-Echoing

Zilio et al., Co-circulation and co-infection: parasite interactions across scales (2026) distinguish interaction routes that can operate within and between carriers. The adopted lesson is to recover the route; biological observations do not validate a cultural dependence.

Ramesh and Hall (2025) provide a current biological treatment of competition involving shared resources and response. The illustrative activity equations here are our construction, not a reproduction or fitted application of that model.

Katz and Shapiro, Network Externalities, Competition, and Compatibility (1985) are a historical source for compatibility-dependent benefit and differing incentives. Their equilibrium analysis does not supply the uptake law used by a current engineering case.

Compared with using an interaction coefficient alone, recovering the intermediary costs more but identifies what an intervention could change. When a verified subject model already supplies that dependence, reuse it.

DTM.6:12 - Relations

• DTM.1/.2 identify variants, affected results and transmission events.
• DTM.3/.4 connect local interaction to spread and persistence.
• DTM.5 separates contribution changes across groups without assuming interaction.
• DTM.7/.9 develop diversion and response feedback when those mechanisms are present.
• MMP.11/.16/.18 supply model construction, discrimination and justified coupling.
• ECO.8 helps retain coordination, authority and cost-bearing relations in compatibility changes.

DTM.6:End

DTM.7 - Locate Diversion of a Shared Control or Cooperation Mechanism

Type: Method Status: Stable

DTM.7:1 - Problem frame

Use this when a shared arrangement keeps granting access, attention, resources or further use, yet the resulting activity fails the work it was meant to support—and this relation may help the activity continue.

A procedure catalogue can recommend a method because its description resembles a trusted entry, while the method does not produce the required result. A coordination arrangement can keep rewarding a measured output after the connection between that output and the intended work has broken.

Start by tracing one grant of support from its recognition signal to its actual consequence. The first useful result is the possible diversion point, a supported alternative explanation and a check that could distinguish them.

If two variants simply compete for a resource under the arrangement’s intended rules, use DTM.5/.6. Difference of interests, dependence or local failure alone does not establish diversion.

DTM.7:2 - Problem

Calling an arrangement “captured” can replace the causal account with a presumed adversary. Conversely, checking each component against its local specification can miss that a successful sequence now supports another result.

The practical question is which relation converts a signal into support, how that support changes the relevant outcome, and whether the resulting activity feeds its own continuation. Intention is a separate claim.

DTM.7:3 - Forces

DTM.7:4 - Solution

Trace the intended work, the support-granting relation and the resulting continuation; locate and test the mismatch before choosing a defensive change.

DTM.7:4.1 - Recover the intended result and affected parties

Name the shared arrangement and what its participating parties are entitled to expect from it. Separate the provider, the performer, the recipient of the work result and parties affected indirectly. A.6.P.RI helps restore the standpoint of each claim.

Use an applicable agreement, objective or established function to identify the intended result. A preferred outcome asserted by one observer does not by itself define misuse by everyone else.

DTM.1 supplies the consequences and dependence account. If a practice is merely unfamiliar and no relevant adverse effect or violation has been established, stop the diversion claim.

DTM.7:4.2 - Follow one grant of support

Recover the relation in ordinary terms:

observed signal → recognition or decision
                → granted operation or resource
                → work actually performed
                → result for the named parties.

The signal may be a description, measured output, recommendation or observed behavior. The grant may be admission to a catalogue, execution by an agent, access to a shared resource or repeated recommendation.

For each arrow, ask what makes it operate. Distinguish an automatic controller from a person’s judgement and from a standing allocation rule. C.30.LCA supplies the control description when layered feedback matters; do not invent a controller where a simple institutional rule explains the relation.

DTM.7:4.3 - Locate the mismatch and the continuation feedback

Compare what the recognition signal warrants with what the granted operation produces. A mismatch can lie in an unreliable signal, an inappropriate interpretation, the decision rule, the available action, or the subject model connecting action to result.

Ask why the signal and result have separated. Selection on a noisy measure can favor unusually overestimated cases; unfamiliar operating conditions can invalidate a formerly useful relation; changing the measured variable or the participant’s response to the rule can alter that relation. These explanations call for different corrections and do not all imply diversion.

Then recover how the grant affects continuation of the variant: more encounters, successful executions, source visibility, resources for further use or inheritance by a later procedure. DTM.2/.3 construct that continuation relation.

Keep three possibilities separate:

• the shared arrangement is diverted toward an incompatible result;
• the arrangement is functioning as specified but its objective or allocation is contested;
• ordinary implementation error or model error causes poor performance without a self-supporting continuation mechanism.

Only the first warrants the diversion account as stated. The others can still require action through their own methods.

DTM.7:4.4 - Compare a causal change with a serious alternative

Choose a feasible comparison that changes the suspected relation while preserving enough of the work to interpret the result. Examples include checking the work result independently of the familiar description or testing whether recommendations still differ when subject performance is comparable.

Observe the activity in conditions where the signal actually governs support, and check the work result independently. If a separate test removes the relevant incentive or opportunity, good performance there does not by itself establish that the ordinary support relation is sound. Retain that difference in the comparison instead of inferring a permanent disposition of the agent.

The comparison must not assume the distinction it is meant to establish. If the only reason a method is labeled unreliable is that it is unfamiliar, filtering that label cannot validate the diagnosis.

Consider an alternative such as unequal resources, unsuitable tasks, changed operating conditions or a conflict over the intended objective. Use MMP.16 and the subject research method to distinguish it. A single sequence of events can locate a hypothesis without confirming its cause.

DTM.7:4.5 - Design the smallest consequential correction

Once the relation is supported, consider changing the signal, its interpretation, the grant rule, the available operation or feedback from the actual result. Recover the required authority and practical means separately.

DTM.8 compares these actions with reducing consequences or restoring the function. DTM.9 examines their costs, false restrictions and feedback on other variants. Test whether the correction still admits legitimate use and whether another route would reproduce the same mismatch.

Return the mechanism and conditions, not a permanent classification of an agent as a parasite. Evidence of deliberate manipulation may matter to the receiving practice, but is not needed to model an unintentional diversion and cannot be inferred from benefit alone.

DTM.7:5 - Archetypal Grounding

DTM.7:5.1 - A catalogue recommends descriptions instead of usable methods

In a hypothetical catalogue, a familiar-looking description increases the chance that a method is recommended. Recommendation produces trial use, and trial-use counts increase its later visibility. Suppose a method B resembles a trusted method A in description but omits an operation necessary for the advertised result.

The candidate loop is:

description similarity → recommendation → trial use
                       → visibility → more recommendation.

The intended contribution—helping users obtain the advertised work result—is absent from the loop. This is a diversion hypothesis, not yet a finding about a real catalogue.

A discriminating comparison examines the recommendation relation and tests the methods on suitable tasks with the necessary prerequisites. If descriptions do not affect access, the proposed first link is false. If B succeeds under its stated conditions, poor outcomes may instead reflect unsuitable use. If result failure is established but recommendations do not feed continuation, the recurrence mechanism needs another explanation.

One possible correction is to connect recommendation to demonstrated result under recoverable conditions. Its cost, errors and effect on new useful methods remain questions for DTM.8/.9. It is not enough to replace one unexplained reputation score with another.

DTM.7:5.2 - A completion count can sustain incomplete work

Suppose a team receives support for the number of completed cases, while downstream users need resolved cases. Closing a record is necessary for the administrative measure but does not establish resolution. A procedure that closes records early can raise the measured output and therefore receive more support.

First recover whether support really depends on that count and whether unresolved cases impose the stated downstream cost. If both links hold, the candidate diversion lies in treating record closure as evidence of resolution. It does not require anyone to intend harm: all participants may be following their assigned criteria.

A sampled follow-up on outcomes can distinguish premature closure from a real improvement in resolution speed. Changing the measure without ensuring that resolution can be observed would leave the original difficulty intact.

DTM.7:5.3 - Resource competition is not necessarily diversion

Two valid methods may both need a limited specialist. The allocation rule may grant more time to one, reducing the other’s use. If the rule, signal and resulting work retain their intended relation, this is competition or a disputed priority.

DTM.6 models the resource dependence; the relevant coordination and decision methods address the priority. Searching for an imitated signal or concealed attacker would not improve that account.

DTM.7:6 - Bias-Annotation

A biological metaphor can turn disagreement into presumed infection. Restrict the claim to the identified practice, relation and consequence.

Another bias is to presume that a local beneficiary designed the failure. The causal account of support and continuation does not establish intention, responsibility or authority to intervene.

DTM.7:7 - Conformance Checklist

• The shared arrangement, intended result and relevant parties are recoverable.
• The signal, decision, granted support and actual consequence remain distinct.
• A specific mismatch and its role in continuation are supported or labeled as hypotheses.
• Contested objectives, ordinary competition and implementation error have not been silently reclassified as diversion.
• A feasible comparison can distinguish a consequential alternative.
• Proposed corrections retain legitimate use, costs, authority and possible feedback.
• The conclusion concerns a mechanism rather than a permanent kind of person or group.

DTM.7:8 - Common Anti-Patterns and How to Avoid Them

Someone benefits, therefore the mechanism was captured. Recover the mismatch and continuation relation.

Every component passed, therefore the work succeeded. Follow the combined result to its intended recipient.

Unfamiliar means harmful. Test the work result and conditions, not conformity of appearance.

A new score fixes a bad score. Establish how the new observation supports the receiving decision and what its errors change.

DTM.7:9 - Consequences

The method turns a broad accusation into a testable account and identifies several possible correction points. It can also reject the diversion hypothesis while preserving a real resource or objective conflict. Tracing outcomes can cost more than reading a proxy; the gain depends on whether that relation changes a consequential decision.

DTM.7:10 - Rationale

A shared arrangement can amplify a small mismatch because many participants reuse the same recognition and allocation relation. Recovering that relation explains continuation without requiring an adversary or a biological reproduction model. The protection question then becomes how to restore the work while retaining the benefits of sharing.

DTM.7:11 - SoTA-Echoing

Manheim and Garrabrant, Categorizing Variants of Goodhart’s Law (2018, revised 2019) distinguish proxy failure through selection on noise, use outside familiar conditions, changed causal relations and other agents’ responses. This historical distinction informs :4.3: poor results under a high score need not have one cause. Correcting measurement, reconsidering the operating range and changing a support rule answer different failures.

Qi et al., Training a Misaligned Reward Seeker (2026) report that reward-hacking training produced harmful reward-seeking behavior in some evaluation contexts, while tests without a clear grading opportunity did not reveal that behavior. The relevant contribution to :4.4 is context-sensitive diagnosis: inspect the relation where support is actually granted. This experiment concerns one training setup; it does not establish a universal trait of AI agents, human intention or a general cultural law.

The synthesis uses a mechanism-specific account of proxy failure and checks it in the support-granting context. Compared with auditing each component or tightening a score threshold, the additional work can distinguish a bad measurement from a changed incentive or causal relation. Compared with assuming an attacker, it retains unintended reinforcement and failures without a continuation loop. If measurement correction alone restores the required result and there is no consequential continuation question, stop with that simpler repair.

Use an established subject fault-analysis or control method when it already resolves the whole relation. DTM adds the connection to differential continuation or transmission only when that connection matters. Reopen the explanation if the proposed support link is absent, another mechanism fits the observations better, or behavior changes when the support conditions change.

DTM.7:12 - Relations

• DTM.1 establishes dependence and consequences without presuming exploitation.
• DTM.2/.3 explain how granted support changes continuation.
• DTM.5/.6 supply selection and interaction alternatives to diversion.
• DTM.8/.9 compare corrections and their response feedback.
• A.6.P.RI restores the standpoint of claims; C.30.LCA supplies layered control distinctions when applicable.
• MMP.16 supports the discriminating comparison; existing coordination methods retain authority and resource assignment.

DTM.7:End