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A Communication-First Account of Explanation

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper proposes that explanatory goodness is the expected-utility gain a pragmatic listener gets from a message, and shows that classic explanatory virtues follow.

desk verdict A serious and readable attempt to derive explanatory virtues from RSA-style pragmatics; the normality result is solid, but the central goodness measure has a formal glitch and the 'emergence' story leans on unconstrained decision problems. read the letter →

arxiv 2505.03732 v2 pith:5WVHCPG4 submitted 2025-05-06 cs.MA

Jacqueline Harding , Tobias Gerstenberg , Thomas Icard This is my paper · ORCID
classification cs.MA
keywords causalexplanationconversationalpragmaticsexpectedutilitymanipulationgameselectionnormalityproportionalitypragmaticspeaker-listenerreasoning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Causal explanations are good, the paper claims, when they help the listener decide and act. The proposed measure of explanatory goodness is the increase in expected utility a listener obtains from a message, compared with acting on what he already believed: a message is good exactly when it moves the listener from worse actions to better ones. From this single quantity, together with a default 'manipulation game' in which the listener picks a variable to intervene on, the paper derives the classic explanatory virtues: attention to the listener's interests and knowledge, invariance across background conditions, minimality and simplicity, proportionality between cause and effect, and normal-vs-abnormal causal selection. Actual causation still anchors the account, but only as the literal content of 'FACT because X=x'; everything else about explanation is treated as communication. If the account succeeds, philosophical questions about explanation and psychological findings about causal judgment are two sides of one expected-utility calculation.

What carries the argument

The load-bearing machinery is a pragmatic speaker-listener hierarchy combined with a reward-sensitive decision problem. The literal listener updates on the semantic content that $X=x$ is an actual cause of FACT; the pragmatic speaker chooses an utterance to maximize the expected reward this literal listener will obtain, minus a message cost; the pragmatic listener interprets the message as evidence about what the speaker knew and chose. The key auxiliary object is the manipulation game: a decision problem in which the listener's action is choosing an endogenous variable to intervene on, and the reward is the probability, over background contexts, that intervening on that variable switches the truth of FACT. This game converts the abstract idea that explanations support what-if-things-had-been-different questions into a concrete payoff, and it is what makes normality and causal-structure effects derivable. The central identity defining the account is the goodness equation, which evaluates explanations as the difference between expected utility after and before the message.

What would settle it

Run a matched causal-selection study with two causal structures and a listener decision problem whose payoff matrix is sensitive to one cause but not the other, as in the roof-replacement design. The model predicts the speaker will cite the cause the decision problem is sensitive to even when both causes are equally likely and equally informative; if human speakers instead pick the statistically abnormal cause regardless of the decision problem, the central claim fails.

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Extended reading notes

Core claim

The paper's central claim is that explanatory goodness is a utility increase. Fix a listener with a prior over causal situations, a decision problem with actions $A$ and rewards $R$, and a message $m$ of the form 'FACT because $X=x$'. After hearing $m$, the listener updates to a posterior that reflects both the literal truth that $X=x$ is an actual cause of FACT and the pragmatic fact that the speaker chose $m$. Writing $\pi_L(a\mid m)$ for the listener's action policy after the message and $\pi_{\mathrm{Prior}}(a\mid m)$ for the policy before any message, $$\mathrm{Goodness}(m,M,u)=\sum_{a\in A}\pi_L(a\mid m)\,R(a,M,u)-\sum_{a\in A}\pi_{\mathrm{Prior}}(a\mid m)\,R(a,M,u).$$ A message is good exactly when it lets the listener act better than he would have acted. The paper argues that this one quantity, together with a manipulation game—a default decision problem in which the listener chooses an endogenous variable to intervene on so as to change the truth value of FACT—makes the recognized explanatory virtues emerge. Minimality is not assumed but results from message costs; sensitivity to what the listener already knows follows from the speaker's aim to be useful; proportionality follows from the listener's decision problem being sensitive to the level of the cited variable; and the empirical pattern that abnormal causes are cited in conjunctive structures while normal causes are cited in disjunctive structures is derived from the manipulation game's payoff matrix.

Load-bearing premise

The load-bearing premise is that every 'why?' question can be paired with a specified decision problem for the listener, or with the manipulation game as a default, and that each message has a known cost; choose different goals or costs and the derived virtues change.

Editorial extensions

If this is right

  • Explanations become graded and listener-relative: the same sentence can be good for one listener and bad for another, depending only on that listener's decision problem and prior.
  • Minimality is a cost-driven preference, not a definitional rule: when a longer message removes more relevant uncertainty than its extra cost, the longer explanation is the better one.
  • Proportionality and level selection follow from decision problems: speakers cite 'red' rather than 'scarlet' when the listener's actions are sensitive to the coarser variable.
  • The model predicts and explains the causal-selection pattern: abnormal causes in conjunctive structures, normal causes in disjunctive structures, and listeners' ability to infer structure and normality from the speaker's choice.
  • Known causes can still explain: citing a cause the listener already knows can be informative, because the speaker's choice of that cause reveals which causal structure is actual, as in the late-meeting example.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If goodness is defined by expected-utility gain, then measuring explanation quality in practice requires fixing the listener's decision problem; the paper offers the manipulation game as a default, but any empirical test must decide when that default applies.
  • The framework suggests a quantitative bridge to causal-selection experiments: the manipulation game's reward weighting matches existing $\Delta P$ and counterfactual-effect-size measures, so model parameters could be fit to data on causal judgments.
  • One testable extension is to make the speaker's message cost an explicit function of listener processing difficulty, such as reading time or misinterpretation rate, turning the simplicity discussion into a measurable prediction.
  • For scientific explanation, the account would need to treat a scientific community as a single listener with aggregated knowledge and aims; the formalism gives a route to do this, but the paper leaves that aggregation unspecified.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper develops a formal, rational-speech-act account of causal explanation. A literal listener updates a prior over a set of causal model/context pairs upon hearing a message "FACT because X=x"; a pragmatic speaker selects a message to maximize the listener's expected reward minus a message cost; and a pragmatic listener interprets the speaker's choice using a higher-order model. Explanatory goodness is defined in Eq. (8) as the difference between the listener's expected utility with and without the message. The central claim is that classic explanatory virtues—sensitivity to downstream interests, background knowledge, invariance, minimality/simplicity, proportionality, and normality-based causal selection—emerge from this communication-first core rather than being stipulated. The paper supports this claim with worked examples (roof replacement, milk theft, late meeting, pigeon pecking) and contrasts its account with Halpern and Pearl's treatment of explanation.

Significance. If the framework can be adequately constrained, it would offer a valuable bridge between RSA-style formal pragmatics, interventionist accounts of causal explanation, and the empirical literature on causal selection. The paper is clear about its formal machinery, the contrast with Halpern and Pearl is carefully argued, and the normality-based results in §4.3 are genuine derivations from a single, well-defined manipulation game (Def. 3) rather than from bespoke payoff tables. That said, the paper's central claim that the virtues 'emerge' is not currently established: the examples demonstrate that the framework can reproduce these virtues under favorable choices of decision problem and cost, not that they follow from the communication-first core itself. The framework is therefore best read, at this stage, as a promising formal template with illustrative case studies.

major comments (3)
  1. [§3.4, Eq. (8)] The central measure is mis-specified. Goodness(m, M, u) = Σ_a π_L(a|m)·R(a, M, u) − Σ_a π_Prior(a|m)·R(a, M, u) does not express the listener's expected utility as the prose claims. The first term evaluates rewards at the actual context (M, u) rather than averaging R(a, ·) over the posterior P_L(·|m); the expectation inside the softmax defining π_L (Eq. 3) is not the same as an expectation of final reward. The baseline term π_Prior(a|m) is also problematic: it conditions the prior action distribution on the message, even though it is supposed to represent the listener's behavior without any explanation; the intended object appears to be an unconditional prior policy π_Prior(a). Because Eq. (8) defines the account's central quantity, this ambiguity must be corrected before the examples can be interpreted quantitatively.
  2. [§4.1–§4.5, Tables 1–2; Eq. (6)] The 'emergence' claim is underdetermined by the free choice of the listener's decision problem (A, R) and of Cost(m). Each virtue is illustrated with a different bespoke payoff matrix or cost function: Table 1 for roof replacement, the confrontation payoff in the milk-theft example, the target-purchasing payoff in the pigeon example, and only the normality effects in §4.3 are derived from the manipulation game. Since R and Cost are unconstrained, the same machinery can prefer opposite causes in the same causal structure under alternative reward specifications (as the paper itself shows in §4.1.3 with flipped payoffs). Without a principled procedure for fixing (A, R) and Cost from the explanatory context—or a restriction of the general claim to the manipulation-game default—the paper has not shown that the virtues emerge; it has shown that they can be represented.
  3. [§4.4–§4.5] The derivations of minimality, simplicity, and proportionality rest on inequalities for Cost(m) and on payoff matrices that are chosen to be sensitive to exactly the distinction at issue. In §4.4 the paper acknowledges that the precise payoffs are arbitrary, and in §4.5 the preference for 'red' over 'scarlet' follows from a payoff matrix with a separate action for each causal structure. The paper's disclaimer in Section 4 that it is 'not to offer a full-fledged defence' is honest, but the gap is load-bearing: the selling point of the framework is that virtues emerge rather than being stipulated, and the current examples do not rule out equally natural specifications that would reverse the predicted preferences.
minor comments (6)
  1. [§4.4, Example 4] The second listed utterance is repeated as 'M = 1 because C = 1' ('Dana took the milk'); it should be 'M = 1 because D = 1'.
  2. [§4.5, Example 7] In the text preceding the example, 'the penguin has been trained to peck at a target' should presumably read 'the pigeon'.
  3. [§3.1, after Eq. (2)] The text says the normalizing constant Z is a sum over 'all possible messages m'; it should be a sum over worlds (M, u), since Eq. (2) normalizes a distribution over K.
  4. [§3.4, Eq. (8)] The quantity π_Prior(a|m) is never formally defined. The paper should either define it explicitly or replace it with an unconditional prior policy π_Prior(a).
  5. [§3.3] The weighted decision-problem decomposition R(a, M, u) = Σ_i w_i R_i(a_i, M, u) is mentioned informally, but the action space (as a product) and the treatment of the weights are never specified; a formal definition would help.
  6. [§4.4] The redundancy measure US(m, M, u) − max_{m' ≠ m} US(m, M, u) is called a quantification of redundancy, but it ignores message costs; the text should state clearly that it is a cost-free measure.

Circularity Check

2 steps flagged · score 4.0 of 10

Partial circularity: the downstream-interest and invariance virtues are built into Eq. (8) and Definition 3, while the normality results retain independent content.

  1. self definitional [Section 3.4, Eq. (8); Section 4.1]
    ""Goodness(m, M, u) = ∑_{a∈A} π_L(a | m) · R(a, M, u) − ∑_{a∈A} π_Prior(a | m) · R(a, M, u) (8) ... This means that m is a good explanation to the extent that it helps the listener achieve his goals. ... In giving explanations, speakers are sensitive to listeners’ downstream interests. ... On our picture, this is unsurprising.""

    Explanatory goodness is defined in Eq. (8) as the increase in the listener's expected reward in the listener's decision problem (A,R). The 'downstream interests' virtue then merely restates the definition: a good explanation is one that helps the listener achieve his goals. Nothing here is derived from the communication dynamics; any account that defined goodness as expected utility gain would automatically have this feature. The paper's own phrase 'this is unsurprising' confirms that the virtue is stipulated rather than emergent.

  2. self definitional [Section 3.3.1, Definition 3; Section 4.3]
    ""R(X, M, u) = ∑_{u′∈Val(U)} P(u′) · Manipulates(X, FACT | M, u′) ... the agent wins a point just in case he successfully manipulates FACT in that situation. ... Our model concretises Woodward’s claim that invariant causal relationships are more useful for listeners, via the notion of a manipulation game (Definition 3).""

    The manipulation-game reward is defined as the probability-weighted count of contexts in which intervening on X changes FACT. Thus 'invariant causal relationships are more useful' and 'explanations identify good points of intervention' are not independent predictions; they are the content of the reward function. The model stipulates that the listener's goal is manipulation across background conditions, and then reports that manipulation success is valued. This is a definitional artifact of Definition 3, not an emergent consequence of conversational pragmatics.

full rationale

The two steps above are the only places where a claimed 'emergence' reduces to a definitional choice. The remaining derivations have independent content: the background-knowledge result follows from the Bayesian update and the speaker-utility trade-off; the normality and causal-selection predictions come from a symmetric manipulation game together with an exogenous prior inequality P(UR) < P(UD); and the listener-inference results use the pragmatic listener's posterior from Eq. (7), which is not already contained in the goodness definition. The paper's self-citations (Icard et al. 2017; Gerstenberg and Icard 2020; Kirfel et al. 2022, 2024) serve as empirical benchmarks rather than as a load-bearing uniqueness theorem, so they do not add circularity. The main non-circular weakness is underdetermination: the decision problem (A,R) and Cost(m) are chosen freely in each illustrative example, and Eq. (8)'s baseline uses πPrior(a|m) although the text says the listener has not received m. These are formal-adequacy concerns, not circularity. Score 4 reflects that the normality results are genuinely derived, while two of the listed 'virtues' are built into the definitions of Goodness and of the manipulation-game reward.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The framework is honest about carrying a fair amount of modeling machinery. The most consequential additions are the cost function and the manipulation game; the former directly produces minimality, the latter produces normality effects. The model does not fit data, so these are modeling assumptions rather than fitted parameters, but they are free enough that the empirical content is not yet sharp.

free parameters (5)
  • Rationality parameters beta_L and beta_S = set to infinity for most analyses
    Temperature parameters in Equations 3, 5, and 6 that control how close agents are to exact utility maximization; results are presented for the maximizing limit, with no robustness check.
  • Message cost function Cost(m) = specified by inequalities only (e.g., Cost(C=1,D=1) > Cost(C=1))
    Assigned by hand in Section 4.4 to produce minimality or non-minimality preferences; its form is not independently constrained.
  • Listener priors and P(U) distributions = uniform or P(UR) < P(UD) in examples
    Chosen per example; the normality predictions in Section 4.3 depend on the inequality P(UR) < P(UD).
  • Example reward functions R(a,M,u) = payoff matrices in Tables 1-4
    The authors note that the precise payoffs are arbitrary; the qualitative results follow from which columns differ, but the scoring depends on these choices.
  • Decision problem weights w_i = 1/2 each in Section 4.1.3
    Chosen for simplicity to combine two decision problems; other weights change which explanation is best.
assumptions (5)
  • standard math Structural causal models with interventions as mechanism replacement (Definition 1).
    Used throughout as the formal representation of causal knowledge; imported from Pearl, Spirtes et al.
  • domain assumption An egalitarian account of actual causation, in which overdetermining events all count as causes.
    Section 2.3 states the account is compatible with any such analysis and requires that A, B, C all count as causes in overdetermination.
  • domain assumption The semantics of 'because': 'FACT because X=x' is true iff X=x is an actual cause of FACT (Equation 1).
    This is the key substantive assumption that tethers explanation to actual causation.
  • domain assumption Rational speech act hierarchy and cooperative speaker maximizing listener's expected utility.
    Equations 3-7 import the RSA framework; it is assumed that speakers select utterances to improve listener decisions.
  • ad hoc to paper The manipulation game is a reasonable proxy for the listener's interests when the specific decision problem is unknown.
    Definition 3 introduces this game; the normality predictions rely on it, and its use is motivated by interventionist intuitions rather than derived.

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Cite this review

Pith. "Pith review of A Communication-First Account of Explanation." pith.science (2026). https://pith.science/paper/5WVHCPG4

@misc{pith2026250503732,
  author       = {Pith},
  title        = {Pith review of: A Communication-First Account of Explanation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5WVHCPG4}},
  note         = {Machine review of arXiv:2505.03732}
}
read the original abstract

This paper develops a formal account of causal explanation, grounded in a theory of conversational pragmatics, and inspired by the interventionist idea that explanation is about asking and answering what-if-things-had-been-different questions. We illustrate the fruitfulness of the account, relative to previous accounts, by showing that widely recognised explanatory virtues emerge naturally, as do subtle empirical patterns concerning the impact of norms on causal judgments. This shows the value of a communication-first approach to explanation: getting clear on explanation's communicative dimension is an important prerequisite for philosophical work on explanation. The result is a simple but powerful framework for incorporating insights from the cognitive sciences into philosophical work on explanation, which will be useful for philosophers or cognitive scientists interested in explanation.

Figures

Figures reproduced from arXiv: 2505.03732 by the authors.

Figure 1
Figure 1. , where arrows represent functional relationships. (To fully specify the model M(A∧B)∨C , we would also need to define a probability distribution P(UA, UB, UC), which in the simplest cases would factor as a product P(UA) · P(UB) · P(UC).) UA UC UB A C B E fE(a, b, c) = max(c, min(a, b)) fA fC fB [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Bob’s epistemic state K in Example 1. Red nodes indicate that the variable is false (has value 0), and green true. The explanandum (known to have value 1) is in yellow. HP are aware of this issue (Halpern and Pearl, 2005b, p.902). They suggest that A = 1 be viewed as a partial explanation of E = 1. Although this response might seem plausible when the cause named is part of a larger conjunction of causes (as in uA,B … view at source ↗
Figure 3
Figure 3. Bob’s epistemic state K in Example 3. is only a partial explanation of the fire’s starting. But this is clearly a worse explanation than R = 1 alone in world MD. This highlights the counterintuitiveness of EX2; it doesn’t allow for cases in which the listener has uncertainty not only about the context, but also about the structure itself.11 As such, it cannot accommodate a key function of explanation identified in S… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Bob’s epistemic state in Example 4 What this example suggests is that there will be situations in which EX3 is too stringent a requirement on explanation. Indeed, it seems like pragmatic theories of explanation are well-placed to accommodate these kinds of situations. …
Figure 5
Figure 5. Figure 5: Bob’s epistemic state K in Example 5. Suppose the actual world is given by MT . Then intuitively the best explanation is “because you were late” (T = 1). Importantly, the point is not merely that this is the only explanation available to Alice. Rather, providing this e…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Empathy in Explanation

    cs.HC 2025-06 conditional novelty 5.0 of 10

    People choose explanations partly to spare listeners' regret, and a regret-aware computational model captures this behavior.

Reference graph

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.