COUPLED CONFIRMATION BIAS – A DEVELOPMENT

In this article, I expand on the previously presented hypothesis about the existence of conjugate confirmation bias (CCB). This is solely an attempt to explain observations I have made in my professional practice. I make no claims to truth—rather, I invite discussion on whether this new phenomenon can be explained in this way. In any case, my hypothesis clearly requires testing. I provide the conditions for its falsification in this and the previous texts.

Levels of Escalation (L₁, L₂, …)

To understand the mechanism of feedback loops in conflicts where parties consult their interpretations with AI systems, it is not enough to describe the conflict as a sequence of different interpretations of the same behaviors. The key mechanism lies elsewhere: interpretation influences behavior, and the changed behavior becomes the subject of a new interpretation made by the other side.

The conflict between A and B (players) begins at level L₁ — the baseline level of interpreting the actions and intentions of the other party.

However, it is crucial to note that L₁, L₂, L₃ are not merely successive executions of the same move. Each subsequent level includes a new or intensified behavior A(n), generated (and subjectively considered necessary) by A’s Decision DA(n) under the influence of A’s Interpretation IA(n‑1) of the opponent’s previous move B(n‑1).

This means that the interpretation of the move from level L(n‑1) is the beginning of sequence L(n).

At this level L(n), move A(n) will be subjected to interpretation IB(n) by player B, who will make decision DB(n) on how to respond. Its effect will be move B(n+1), which brings the entire conflict to a new escalation level L(n+1).

Escalation is therefore not solely a cognitive process — it is a cognitive‑behavioral process.

Importantly, this model refers to a simple “exchange” of moves and does not cover situations in which the players:

• may perform multiple moves simultaneously or in short sequences before the other side recognizes and interprets them,
• or situations in which information about moves A(n) reaches B with such delay that B is effectively responding to A(n‑2) or even earlier moves. Interestingly, a significant asymmetry may arise in this respect (one of the parties may make more or faster movements, or they will have a faster noticeable or real effect).

The Basic Escalation Loop

A performs move A₁.

B consults A₁ with AI. AI typically does not create new meanings. Its dominant function is to stabilize and reinforce the user’s intuitions.

These intuitions, however, are not neutral.

Humans almost always begin with the fundamental attribution error (FBA) — the tendency to explain others’ behaviors through internal traits, intentions, and motives, while underestimating situational factors. This is especially easy when one must justify one’s stance to superiors, behavior reviewers, or stakeholders.

Attributing negative traits to the other side is energetically cheap and easy. Moreover, it automatically allows one to attribute opposite traits to oneself. In this way, it is easy to move from a dispute to a conflict of values and to “cement” one’s position. It is easy to corner oneself and lose room for maneuver. De‑escalation leading to settlement may then be perceived as a betrayal of those “values.”

FBA does not operate only at the beginning of a conflict. It is applied every time a new behavior of the other side appears. The result is tunnel interpretation. But there is a risk that it affects both sides.

Under conditions of AI consultation, it is additionally reinforced through the Coupled Confirmation Bias (CCB).

B increasingly believes that A’s action had negative or hostile intentions.

B responds with move B₁. From B’s perspective, this is a defensive reaction to a perceived threat.

A interprets B₁ through the same mechanism. A reactivates FBA, again reinforced by AI. The attribution filter does not reset; it accumulates.

A consults B₁ with AI and concludes that a response is necessary.

A performs move A₂.

And here comes the key transition:

A₂ is not merely interpreted at a higher level — it is executed from a higher level of subjective defensiveness. Interpretation changes behavior, and behavior changes the conflict.

This moment marks the transition from L₁ to L₂ — not as a change in interpretation, but as a real behavioral escalation. It involves readiness to inflict and receive stronger blows and losses.

Alternative Paths After A₁: Conflict as a Multi‑Branch Structure

Move A₁ does not have to automatically lead to escalation in the manner described above. The extended model assumes several alternative trajectories.

1. Pause by B — P₁

After A₁, B may not respond immediately. We denote this as P₁ (pause).

A pause is not a neutral state. Waiting is not an empty, zero, or negligible posture. On the contrary — the lack of a move is information that A interprets as AI₁.

After P₁, several outcomes are possible:

AR₁ (A Resignation)

The pause is interpreted as a signal to withdraw → de‑escalation.

ARSM₁ (A Repeats Same Move)

A repeats A₁, testing the reaction.

ASAM₁ (A Searches an Alternative Move on the Same Level)

A searches for another action at the same level L₁.

AE₁ (A Escalates)

The pause is interpreted as avoidance, manipulation, or passive aggression → unilateral escalation to L₂.

FBA makes AE₁ more likely, especially when the interpretation of the pause is consulted with AI.

It is very important that escalation in such a situation may be treated by A as a means to force B to engage in talks or return to them. This is logical if A cannot impose its will directly at the current escalation stage, and B refuses negotiations entirely or simulates them.

However, the escalation move (escalation for the sake of de‑escalation) by A may be perceived by B as a real threat. B may respond by:

• initiating talks,
• declaring that it will match the stakes — responding symmetrically to escalation,
• or pre‑empting AE₁ with its own escalation move BE₁.

It is crucial to distinguish that this move is not an ordinary response to A₁. It is chronologically so (unless ARSM₁ or especially ASAM₁ occurred), but not sequentially. The decision to pre‑empt escalation is made based on FBA and within the CCB process.

There’s also a risk that, through interaction with the language model, B will become convinced that reaching an understanding with A is impossible. He will remain in a state of pause, accepting A’s subsequent moves. I propose that AI may have a real impact on strengthening his initial cognitive biases toward A, which could make it more difficult for him to decide to de-escalate. Instead, it will reinforce the need to wait out A’s moves and then—over time—as the situation worsens—to make an escalating move. This may be an intentional escalation for the sake of de-escalation.

2. Lateral Response B₁ and Entry into Ping‑Pong (PP₁)

B may also respond with move B₁ at the same escalation level, leading to a ping‑pong sequence (PP₁).

Classical conflict theories assume that such symmetrical exchange:

• stabilizes the situation, or
• eventually leads to de‑escalation through exhaustion of resources, attention, and determination.

However, this assumption relies on a silent condition: the absence of systematic reinforcement of interpretations and determination.

Ping‑Pong as the Main Space Where CCB Manifests

In this model, I propose a different thesis:

Under conditions of continuous AI consultation, ping‑pong ceases to be a stabilizing mechanism and becomes the main carrier of escalation.

Why?

Each subsequent exchange in ping‑pong provides new behavioral data. Each of these behaviors is:

• interpreted through the lens of FBA,
• reinforced by AI,
• incorporated into an increasingly coherent narrative about the other side’s intentions.

Instead of leading to conflict fatigue, ping‑pong:

• hardens the parties’ convictions,
• increases certainty that previous measures are ineffective,
• strengthens the belief that “raising the stakes” is necessary.

As a result:

• the probability of escalation increases with the number of ping‑pong exchanges,
• the dynamics of escalation depend on the intensity of the exchanged “courtesies.”

It is in ping‑pong that the coupled confirmation bias manifests most fully: two AI‑stabilized narratives collide, generating escalation without ill will, without aggressive intent, and often without the parties’ awareness.

Security Dilemma Without Ill Intent

At this stage, the conflict begins to resemble the classical security dilemma:

• each side acts defensively,
• each perceives the other as increasingly aggressive,
• neither consciously seeks escalation,
• yet escalation occurs.

AI acts here as an accelerator of interpretive certainty, reducing ambiguity and reinforcing narrative coherence on both sides.

Theoretical Context and Novelty of the Model

This model develops earlier research on human–AI cognitive loops (e.g., M. Glickman, T. Sharot, B. Wang), which focused mainly on a single user.

In this approach, the key element is the collision of two mutually reinforcing interpretive loops in interaction.

This mechanism is potentially even more unstable in triadic and multi‑party systems, where the escalation threshold is lower and narrative synchronization is more difficult.

9. Possibilities for Falsifying the Model

Any theory aspiring to the status of a scientific framework must be falsifiable. The model of coupled interpretive loops (CCB) meets this requirement because it generates specific, testable predictions that can be empirically confirmed or refuted.

9.1. AI’s Influence on Strengthening the Fundamental Attribution Error

The model would be falsified if:

• AI consultations did not increase the tendency to attribute negative intentions,
• AI weakened, rather than strengthened, FBA,
• AI users didn’t show smaller empathy and samller tolerance for ambiguity than the control group.

9.2. AI’s Influence on Escalatory Behavior

The model would be refuted if:

• AI‑consulting users did not show greater propensity for escalation,
• AI consultations did not influence the choice of moves A₂/B₂,
• escalation levels in the AI group and the control group were identical.

9.3. Ping‑Pong Logic

The model would be falsified if:

• the number of ping‑pong exchanges did not correlate with escalation,
• ping‑pong under AI conditions led to greater de‑escalation than in the control group,
• AI consultations did not influence the interpretation of subsequent moves.

9.4. Epistemic Asymmetry

The model would be refuted if:

• AI users and human‑advisor users showed identical escalation patterns,
• epistemic asymmetry had no effect on conflict dynamics.

10. Author’s Statement

This text was prepared with the assistance of language models. Their help was not generative in nature, but testing, editorial, and supplementary.

Conclusions

• The fundamental attribution error operates at every stage of conflict.
• AI reinforces FBA through CCB, giving interpretations a veneer of objectivity.
• AI‑consulted conflicts have a multi‑branch, not linear, structure.
• Pause, lateral response, and ping‑pong are critical decision states.
• Ping‑pong under AI may reverse the classical logic of de‑escalation.
• Escalation may be a function of the number and intensity of exchanges, not merely ill intent.
• The model is falsifiable — and this makes it a theory, not a dogma.

If you are interested in my hypothesis, you can read this article: https://pmc.ncbi.nlm.nih.gov/articles/PMC11860214/? and this one: https://dl.acm.org/doi/10.1145/3664190.3672520

Here is my main text about CCB:

COUPLED CONFIRMATION BIAS – MY CONCEPT

You can read also this one:

HOW WILL AI IMPACT DISPUTE DYNAMICS IN 2026?