Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
Authors: Cyrus Cousins
Vijay Keswani
Vincent Conitzer
Hoda Heidari
Jana Schaich Borg
Walter Sinnott-Armstrong
Venue: ICLR 2026 Poster (2026)
Keywords: Human-Centric AI
Moral Preference Elicitation
Axiomatic Analysis
Interpretable Machine Learning
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We propose a new way to align AI with human decision-making by modeling the cognitive processes behind choices, with an axiomatic approach: features are processed with learned rules, then aggregated with a fixed rule e.g., Bradley-Terry.

Abstract: Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, to which AI models are aligned. However, standard elicitation methods often fail to capture the true cognitive processes behind human decision making, such as the use of heuristics or simplifying structured thought patterns. To address this limitation, we take an axiomatic approach to learning cognitively faithful decision processes from pairwise comparisons. Building on the vast literature characterizing cognitive processes that contribute to human decision-making and pairwise comparisons, we derive a class of models in which individual features are first processed with learned rules, then aggregated via a fixed rule, such as the Bradley-Terry rule, to produce a decision. This structured processing of information ensures that such models are realistic and feasible candidates to represent underlying human decision-making processes. We demonstrate the efficacy of this modeling approach by learning interpretable models of human decision making in a kidney allocation task, and show that our proposed models match or surpass the accuracy of prior models of human pairwise decision-making.

Published as a conference paper at ICLR 2026
TOWARDS COGNITIVELY- DECISION- MAKING MODELS TO IMPROVE AI ALIGNMENT
Cyrus Cousins" Duke University
Vijay Keswani ITT Delhi
Vincent Conitzer CMU
Hoda Heidari CMU
Jana Schaich Borg Duke University
Walter Sinnott- Sinnott-Armstrong Duke University
ABSTRACT
Recent Al trends seek to align Al models to learned human-centric objectives, such personal preferences, utility, societal values. Using standard preference elici- tation methods, researchers and practitioners build models of human decisions and judgments, to which AI models aligned. However, standard elicitation methods often fail to capture the cognitive processes behind human decision making, such as heuristics or simplifying structured thought patterns. To address this failure, we take axiomatic approach to learning cognitively faithful decision processes from pairwise comparisons. Building on the literature analyzing cognitive processes that shape human decision-making, we derive model class in which features are first processed with learned rules, then aggregated via a fixed rule, such as the Bradley-Terry rule, to produce decision. This structured processing of informa- tion ensures that such models are realistic and feasible candidates to represent underlying human modeling approach by learning interpretable models of human decision making in kidney allocation task, and show that our proposed models match surpass the accuracy of prior models of human pairwise decision makiin...
making processes. We demonstrate the efficacy of this
INTRODUCTION
Computational models of human cognition allow us quantify and study the factors that influence our decisions, and when accurate, they help explain commonalities human decision processes across different tasks (Gigerenzer et al., 2000; Crockett, 2016). This may also be why computational models to predict human behavior have found applications in personalized Al tools various settings, e.g., healthcare, autonomous vehicles, and resource allocation, to increase users' confidence that AI will be aligned with their preferences (Ji et al., 2023; Kim et al., 2018; Noothigattu et al., 2019).
Despite promising use cases, current AI models of human prcision-making typically do not attempt to faithfully replicate cognitive processes. Modern AI tools built using preference elicitation (Lee et al., 2019; Awad et al., 2018; Johnston et al., 2023; Freedman et al., 2020; Rafailov al., 2023) and rein- forcement learning (Ng et al., 2000; Christiano et al., 2017: Kaufmann et al., 2023) implicitly assume that a reward/ohjective model based on a prespecified hypothesis class can accurately predict human decisions, but such tools are agnostic about how faithful these models are to cognitive processes. However, recent works highlight the lack of suitable hypothesis classes to capture human choices or their values (Stray et al., 2021; Boerstler et al., 2024; Leike et al., 2018). Unsuitable modeling classes introduce the possibility of inappropriate optimization formulations and reward hacking (Pan et al., 2022; Hubinger et al., 2019). and can lead arbitrarily erroneous inferential models learned from human responses (Zhuang & Hadfield-Menell. 2020: Pan et al., 2022). Additionally, such misaligned models are limited in their explanatory power, undermining any intrinsic trust placed in the Al's decisions (Jacovi et al., 2021). These problems are further exacerbated when Al is used in high-stakes domains, including moral domains such as healtheare and sentencing Kim et al., 2018), where stakcholders often expect an AI to justify its decision in a similar manner
et al., 2021:
*Correspondence ori origing nal www.iiincccuucouu..ooco cyruscous ns@ com

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and to the same extent as humans (Lima et al., 2021). Modeling done in prior moral decision- decision-making contexts either favors simple model classes - e.g., linear models and decision trees (Lee et al., 2019; Noothigattu et al., 2018; Freedman et al., 2020), whose fit for human decision-making is highly context-specific (Ganzach, 2001; Zorman et al., 1997). or employs uninterpretable classes e.g., neural networks or random forests (Wiedeman et al., 2020; Ueshima & Takikawa, 2024) whose decision processes cannot be validated due to their uninterpretable designs. A qualitative study by Keswani et al. (2025) note this process misalignment as crucial issue around AI-trust in morally-salient tasks like kidney transplant allocation, with one study participant expressing concern: "they don't 't think like a human... what they might think should be ranked as priority, I may not".
Such human concerns highlight that, for certain real- wolld applications. building trustworthy Al tools requires accurately simulating human decision processes. However, human decision processes are often not captured by standard hypothesis classes. For example, consider humans' use of decision heuristics, defined by Gigerenzer & Gaissmaier (2011) as a strategy "that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods." " Keswani et al. (2025) document several heuristics people use when deciding which patient should get available kidney. Their study participants often used form of hiatus heuristic, employing thresholds to isolate relevant patient feature information; e.g., some treated "number of dependents" binary (none any), disregarding the actual quantity. Similarly, rather than contrast patients holistically, some participants used simple aggregation methods, such as the tallying heuristic. which simply counts the number of features favoring each patient to make their choices. While the role of such heuristics in human decision processes is well established (Gigerenzer & Gaissmaier, 2011: I; Shah & Oppenheimer, 2008; Gigerenzer et al., 2000), models learned from human decisions using standard hypothesis classes often don't It capture or explain the heuristics people use to make decisions. For example, if a decision- maker uses the threshold based heuristic mentioned above, linear models would fail to capture it, while neural networks or multivariate monotonic regressors may learn the heuristic, but would fail to explain its role in the decision process, instead representing it some aoproximately-equuvaaa
but hopelessly opaque process. To explain simulate human decision processes accurately, we need methods to learn and represent these heuristics (and other decision-making nuances). To that end, this paper explores hypothesis classes that more accurately capture the cognitive processes people use to make their decisions in pairwise comparison settings.
This work presents learning framework for computational models of pairwise human decision- making, which is motivated by prior work on human cognition. Classical theoretical axiomatic models of choice impose strong structural assumptions on preferences, e-g., strong transitivity concepts and independence of irrelevant alternatives (von Neumann & Morgenstern, 1944; Luce et al., 1959). However, decades of empirical work demonstrate that these assumptions are systematically violated in human decision- decision-making, particularly in comparative and comtext-dependent settings (Tversky, 1969; Tversky& Kahneman, 1974). We thus propose a set of substantially weaker axioms (section 3) that constrain how comparative judgments are made, and from these axioms, we derive a class of feasible cognitively faithful decision- decision-making models. This allows us to retain interpretability and theoretical grounding without contradicting empirically observed heuristic decision processes. Indeed, because our axioms are weaker than classical alternatives, they do not fully specify a decision making process, but rather, they constrain the feasible space of decision making. The approach is attributes pragmatic: Our axioms are somewhat prescriptive, as they represent normatively-desirat of decision making, but they also have descriptive element, as they relax existing classical axioms that were themselves attempts to describe human behavior. Similarly, the violation of our axioms is not necessarily critical failure of human reasoning, but as relatively simple claims, the axioms can be understood more easily than complex models, and they can be studied qualitatively and assessed for their replicability in Al systems that align to human decisions.
Given any pairwise comparison between two options I1, Z2 E Rd, the decision maker''ss 's preference characterized by two-stage process (section 3.1). The first stage captures the editing rules that the decision- the information presented in feature j E [d]] Each feature j may have its own decision rule, and these rules work towards transforming x. (9))) in a manner that reflects the feature's contribution to the decision-maker's 's final choice (e.g., whether the decision- thresholds, ignores, linearly transforms, or otherwise processes the feature). We allow this transformation to be contextual in nature, whereby the value of one or more features can influence the transformation used for another feature. Such conditional transformations capture feature interactions in our hypothesis
employs over individual features ()) (0))
x22 to ppocces/simpliit//rraaee
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class, increasing the models' expressiveness while ensuring that we still learn feature-level decision rules. The second stage captures the decision rule that aggregates the processed features to choose the preferred option. This aggregation rule can be simple the tallying heuristic (Gigerenzer et al., 2000) or as complicated as Bradley-Terry (1952) aggregation for probabilistic preferences. Beyond the motivations from prior works in cognitive science and preference modeling, we show that our axiomatic basis yields the two-stage model class (section 3.2). Moreover, more stringent assumptions yield special cases of interest, such as logistic regression, probit regression, and monotonic decision rules. Finally, we assess the empirical efficacy of our proposed framework over synthetic and real- world datasets in the kidney allocation domain (section 4), domain where users have said cognitive process alignment is particularly important. We find that our method can learn models that explain the decision rules that people use for kidney allocation decisions, while ensuring that the learned model has similar or better predictive accuracy than other modeling approaches.
2 RELATED WORK
Methods for learning preferences from comparative choices have been proposed in many contexts, such as for recommender systems (Kalloori et al., 2018; Guo & Sanner, 2010; Chen & Pu, 2004), language model personalization (Rafailov et al., 2023; Jiang et al., 2024; Ziegler et al., 2019; Ouyang et al., 2022). and explanatory frameworks in psychology (Lichtenstein & Slovic, 2006; Ben-Akiva et al., 2019; Charness et al., 2013). Recently, preference learning and elicitation of stakeholder preferences has flourished in the context of human-centric Al and Al alignment (Jiang et al., 2024; et al., 2023; Capel Brereton, 2023; Feffer et al., 2023; Kim et al., 2018; Johnston et al., 2023; Sinnott-Armstrong et al., 2021: Liscio et al., 2024; Lee al., 2019). The usability of learned models in real settings relies on both their predictive accuracy and their alignment to humans' decision-making (Mukherjee et al., 2024; Keswani et al., 2025; Capel & Brereton, 2023; Lima et al., 2021). However, modeling strategies in most prior Al alignment work are agnostic about whether they actually capture human Decision-making motivated by the decision rules people report for pairwise comparisons. Like this work, Noothigattu al. (2020) study the class of binary decision rules arising from pairwise comparisons. However, their axioms concern MLE estimation over data, and thus are distribution sensitive, whereas our axioms dictate laws as to how probabilistic preferences must behave over their domain. They also do not characterize model classes arising from their axioms, but rather study whether known classes satisfy them, making their work more taxonomically descriptive than prescriptive. Ge et al. (2024) continue this line of inquiry, again with axioms related to estimation from datasets.
processes. Our work fills this gap by proposing modeling classes explicitly
There have been other efforts to computationally model human decision-makine Bourgin et al. (2019) propose models bounded by theoretical properties of human decision-maker that are fine-tuned with real-world data, but they aim for accurate predictions, rather than cognitive fidelity. Peterson et al. (2021) use neural networks to implement and test cognitive models of participant choices for pairs of gambles. This analysis provides information on the fit of various cognitive models, but does not provide mechanism to learn the pecision-making process from an individual's 's data. Plonsky et al. (2017) and Payne et al. (1988) use psychological theories to select feature transformations or simulate prespecified heuristics obtain gains in predictive power by curating the learning tasks to be more cognitive aligned with human decision processes. However, the applicability of these methods is limited when feature transformations are unknown a priori and vary across individuals. Our work also uses relevant works in psychology to identify appropriate decision rule assumptions, but we learn feature transformations from human decisions. Other works use neural networks alongside theory-driven cognitive models to simulate human decision-making (Fintz et al., 2022; Lin et al., 2022), but such models of learned decision processes are largely uninterpretable. Several studies also illustrate that simple heuristic-based models predict human responses well in classification (Holte, 1993; Şimşek & Buckmann, 2015; Brighton, 2006; Czerlinski et al., 1999; Dawes, 1979), supporting our goal of learning heuristics from decisions. Yet, the heterogeneity of heuristics used by different people makes it difficult to find a single model to accurately simulate the responses for an entire population. Our work addresses this issue by learning individual-level decision rules from data.
3 A MODEL OF COGNITIVELY
RECISION-MAKING
In our setting, a decision-maker is presented with a pairwise comparison (æ), X2) between two options from the domain X C Rdd, i.e. each with d descriptive features. For each E [d], let X; C RR denote
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the input space for feature i, such that X' = X2 x....X Xa. The decision-maker's maker's response function H :xx'x x X + [o, 1] denotes the probability of choosing the first option 21 for any given pairwise comparison (01, To learn H from observed data, suppose we have dataset S containing the decision-maker's responses to N pairwise comparisons of the kind (I1, T2, r), where r E {0, 1). is the binary decision, with r 1 if the decision-maker deterministically chose x1, and 0 otherwise.
Given dataset S, we aim to learn a model A that accurately simulates the decision-maken ' response function. Taking a general learning approach this problem, given hypothesis class of models H. we can search for a model from H that minimizes the predictive loss over S. A common approach to learning H is to start with standard hypothesis classes, such as classes of linear functions, decision trees, or neural networks. However, as discussed earlier, the assumptions of these classes are likely not faithful to the cognitive processes people truly use to reach their own decisions (Keswani et al., 2025). As such, when using these classes, the resulting model can be difficult to validate and may lead to erroneous predictions in cases where the model class cannot capture the computation required to reach the "correct" decision. We aim to identify a hypothesis class that yields the most accurate model while faithfully representing humans' actual decision-making processes. Such models have the added benefit of being interpretable for human users. To come up with cognitively faithful computational models, we first discuss the computational properties commonly observed in human decision-making processes, and then use these properties construct an appropriate hypothesis class.
3.1
MARIING-MAKING MODELS
Prior works on cognitive models for human decision-making point a reliance on decision rules and heuristics in comparative settings (Gigerenzer et al., 2000; Gigerenzer & Gaissmaier, 2011; Brandstätter et al., 2006; Kahneman & Tversky, 2013; Kahneman & Frederick, 2005). Based on this literature, we construct hypothesis classes that consist of hierarchical decision rules. Our proposed strategy models decision-making for pairwise comparison of options (x1, as a two-step process. In the first step, the decision maker processes each feature in I1 and 22 to edit or transform the information presented by this feature. In the second step, the decision- maker combines the edited information from all features to select the dominant option. Our hypothesis class will consist of functions that reflect this hierarchical process.
Editing Rules The decision rules or heuristic functions used in the first step are referred to editing rules. Editing rules operate at the level of individual features of each element in the given pair and operationalize how a decision-make the editing rule for feature i, with X² the output domain post- postt-diting. Examples of editing include feature simplification (e.g., zeroing out features considered irrelevant), transformation (e.g.. log transformation for features with "diminishing returns" or scaling), or leaving the feature unchanged.
processes the given feature value. Let h X, + X² denote
While these rules often structurally simple, reflecting their use reduce cognitive load (Gigerenzer & Gaissmaier, 20,1), there can be significant heterogeneity in the editing used in different contexts. For example, prior works note feature interactions, where the editing rule used for the ith feature ar(() can depend on the values of other features of x (Keswani al., 2025). To account for this, will consider the choice of editing rule conditional on the decision context features, denoted by the values of features in a set w C [d]. On one extreme, w 0, implying that each feature is edited independently (i.e., no feature interactions), as w grows, model complexity increases as conditional interactions involve more features, and the other extreme, w = [d], implies that the choice of editing function for each feature in x can depend on the values of all other features. To account for this context W. for any option E X. we will denote the contextual editing rule operating over feature i 3. by hmm X + X/. where W, w \ (i).
Remark 3.1 (Examples of Editing Rules). The use of editing rules is well-supported by literature in behavioral economics and social psychology. Montgomery (1983) describes the phase of "separating relevant information from less relevant information which can be discarded," and Kahneman & Tversky (2013) argue that this allows for decision-making based only the most essential information. In some settings, editing rules represent feature importance assignment (Payne, 1976; Gigerenzer et al., 2000). In other cases, editing reflects feature transformations to isolate information relevant to the task (Ajzen, 1996; Shah & Oppenheimer. 2008), e.g., thresholding to discretize fiscetiizeeeaturee features, or hng-transformation to model diminishing returns (Tversky, 1972; Kubanek, 2017).
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Complementarity
Weak Transitivity
Codomain Span
Noninteractive Compositionality
22
21
X
21
333
((p.2)
p
N
Key:
Assumed Prediction
Consequent Prediction
Feature
Modificatio
Figure 1: Visualization of the axioms of definition 3.3. Solid arrows represent assumed choice probabilities. dashed arrows represent choice probabilities implied by an axiom, and in noninteructive compositionality dotted arrows represent feature modifications. Arrows may be labeled with choice probabilities.
Dominance Testing In the second step of the decision makiin process, the edited features are compared across the two options (x1, 222) and then combined to reach the final decision on which option is the dominant one. We will refer to this second step as the dominance testing rule. Let hau X' x X" + [0, 1] denote the dominance test function, where X" X x... X X/-
Remark 3.2 (Examples of Dominance Testing rules). Several dominance testing rules have been studied prior literature, including the tallying up heuristic mentioned earlier (Czerlinski et al., 1999; Gigerenzer & Gaissmaier, 201 I) and the prominent feature heuristic (choose the element favored by the most prominent feature for which there is non-zero difference in edited feature value) (Persson et al., 2022; Tversky et al., 1988). Alternately, one could create additive probabilistic functions to capture various dominance testing rules observed in practice, e.g., Braalee-Terry aggregation (1952).
With this setup, we model decision maker's 's response to any pairwise comparison as first applying editing functions h iim
1. over each feature for both options, given context w and wi w \ (i). and
then combining the edited information to reach the final decision using the dominance testing rule haa Hence, our proposed hypothesis class contains such two-stage models, namely
H
{ 21, x2 + hear (vi E [d], x1 + him
(x (0(5))) x/2 2, +
(2r (8)
Han, hom E Hout } hi
The properties that characterize classes Hinner and Houter are discussed in the next section.
3.2
AXIOMATIC CHARACTERIZATION OF DECISION RULES
The above-described two-stage process is motivated by extensive decision-making literature. An additional compelling motivation for this hypothesis class comes from an axiomatic characterization of human decision processes for pairwise comparisons. The axioms below describe simple properties expected to be satisfied in an ideal comparative choice model (independent of its functional form).
Definition 3.3 (Axioms of Binary Choice). The following axioms describe a binary preference function H x2), X²2 + y. Unless otherwise stated, each must hold for all I1, 22, 23 E X.
Complementarity: The order in which two options are presented should not impact the option that is eventually selected. Formally,
require that H (Z), = =1 [ X2).
2. Weak Transitivity (WT): The comparison (x11I35) can be expressed as a continuous function of the probabiliries H (211 222) and ( 2:)), i.e., for some continuous f [0, 1]2 - [0, 1), it holds that ( f(H x22,, H (x2, x3))-
3. Codomain Span: For any p E (0, 1) and æ1 X, there exists 32 E X such that H H x2) p.
4. Noninteractive Compositionaliiy (NC): Suppose some 21 E X, and x'y, x²2, x² E X such that x' and x²L are obtained by changing different features of I1, and x' produced by applying both changes to F1. We then require that H(11, x²"') can be computed from H (x1, x/2) and H (x1, Conditionally Interactive Compositionality (CIC): Given a set of condition features C [d]. suppose some E X, and x/2, x22 xld E X such that 2/2 and x are obtained by changing different features (neither in w) of x,,, and x²2 is produced by applying both changes to IA. We then require that (x1122")) can be computed from H(I,, x/)) and H (x1,, x!)).
These axioms are visualized in fig. 1. Complementarity (1) ensures that the order in which options are presented should not matter, and predicted probabilities sum to 1. Weak transitivity (2) requires that comparisons between (x) 22) and (Iz, 2z) carry all the information about the items Iy T2, 2y to also compare 233), i.e., the first two pairwise comparisons suffice to "complete the triangle" of all
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three pairwise comparisons. Codomain span (3) is technical condition, essentially describing type of continuity, as to specify the decision rule's ' transitivity law for all possible probabilities, we must ensure that all possible probabilities actually arise in comparisons over the domain X. Theorem 3.4 item shows that these properties work together to induce a strong form of transitivity in probabilistic predictions, and the discrete form given item 1 relaxes the codomain span (3) assumption.
Axioms & characterize the extent to which different features interact in the decision process. Noninteractive compositionality encodes form of feature independence, requiring that the impact of changing two different features is compositional, which is less restrictive than assuming linearity, but restricts interactions between features in a similar manner. Essentially, this axiom characterizes a generalized additive model (GAM) (Hastie, 2017), as it dictates how information is synthesizes across features. Conditional interactivity then generalizes this idea, allowing for non-additive conditional feature interactions. We propose this axiom to account for a larger class of models that consider the decision context, where the impact of changing two features additively compositional, except if one of the features is condition feature (part of the context described in section 3.1).
Axioms 1-3 are prescriptive, describing properties that may be considered desirable by many, whereas 4-5 are not assumed to hold universally (potentially varying even within individual), but rather they suffice characterize a class of simple decision- makinn rules when they do hold. This stands in contrast to the strong assumptions made by classical axiomatic models of decision making. Moreover, they are agnostic to the functional form of the decision rule and do not assume linear utility, stable preferences, optimality.
We emphasize that our axioms are strictly weaker than those commonly used classical choice theory. For example, WT does not assume that preferences are globally transitive or derivable from a single latent utility function; it merely requires that a comparison between two options can be constructed from intermediate comparisons. Similarly, NC is weaker than additive utility or independence of irrelevant alternatives: it constrains how feature modifications combine without assuming linearity or ruling out contextual dependence. CIC further relaxes this requirement by explicitly allowing structured feature interactions. As result, our framework accommodates heuristic and context-sensitive decision processes documented in empirical studies, while still enabling formal analysis and learnability guarantees. We now show that the decision-making processes that satisfy these axioms follow the two-stage process outlined in section 3.1.
Theorem 3.4 (Axiomatic Factoring Characterization). We now explore the consequences of our axioms on characterizing the response function H(.--) of decision-maker
Atomic Model Suppose axiom I, and also that X is a countable domain. Then there exists an atomic rule him- X + R and haw IR [0, 1] s.f. (-.-)) )) can be factored
H ( x2) = how (1im((x))
(x2))
(.)- Trannsitivvyy Suppose axioms 1-3 /Complementarity. WT, and Codomain Span) hold. Then there exists symmetric continuous random variable with full support and CDF ((-) s.t.
H (x11 = (a- (1 + a- (H
Moreover, there exists some atomic rule ham X - such that H(.,)) can be factored as H x2) (Nimn (lin((x2))
3. Unconditional Factor Model: Suppose axioms 1-3 and 4 (NC). Then there exist inner functions hi Xx + RRfor [d] such that the atomic rule Nian and decision rule H. factor as
Wian (x) = => (x
H(11, x22) = hou
"iim (x22
i=1
i=1
Conditional Factor Model: Suppose axioms 1-3 and 5 (CIC). Then there exist conditional inner functions h :X4 + R for E [d] and condition features (context) w [d]. w, w [i). s.t.
(211
(2)
1,
(Iz
(5)
Item 1 shows that axiom 1 suffices to reduce binary choices to a function of difference of atomic predictions for continuous transitivity laws f(..)) ). i..., to H(x x2) = A (Nina (21) - (x2))))), thus recovering the two-stage modeling process described in section 3.1. However, there is little

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structure beyond this: Such factoring exists, but it is by no means unique, and it may well be intransitive. Item then imposes weak transitivity through axiom 2, additional continuity structure through axiom 3, and paired with continuity of the transitivity law f(:,),, we may conclude that (+ is a sigmoid function (CDF). Axioms 4-5 are then needed to control feature interactions, and dictate two-stage model structure wherein each feature is processed individually or conditionally.
Intuitively, with o-transitivity, we require that transitive probabilities can be computed via addition within the sigmoid. Therefore, transitivity along chains of probabílities above & grow ever closer to 1 as (-) is applied to a growing sum. In the parlance of generalized linear models, o plays the role of link function. Maadley-Terry, models, such as logistic regressors, employ the logistic sigmoid, but other sigmoids, such as the Gaussian CDF (with probit link function) also see use, e.g., in GLMs (McCullagh & Nelder, 1989).
We now describe strong assumptions that may only be appropriate in specific real-world domains, but nevertheless produce specific properties for the hypothesis class of editing function Humer Unlike our axioms of definition 3.3, these assumptions are highly situational, and they describe behavior that may be considered reasonable some circumstances, but their absence should rarely be viewed as surprising undesirable in and of itself.
Definition 3.5 [Assumptions on Binary Preference Models), We state assumptions on H that are suitable for discrete or continuous domains. These discrete properties must apply to all 2 22 X. 1. a- o-Linearity: There exists some w E R. such that o o H (z), 22) = w (I) - I2).
Monotonicity: If x 3 T2, then (II, T2) S 2
We now combine these stronger assumptions with the results of the axiomatic analysis of theorem 3.4. Recall that our basic axioms imply the factoring
H = (2 +> o (1inn(())) - Nimn (12)) himn E Hina)
Theorem 3.6 (Axiomatic Models). Suppose axioms 144. The following restrict Him or each H((0)
1. Suppose o-linearity. Then (((0) {o + wa/w w R}, thus H {I1, + o(w (x1 x2)) w Rd).
Suppose monotonicity. Then H(0) {h: : - R x Sy = Mx) S h(y))-
3. Multivariate Monotonic Models: If we assumee monotonicity but relax noninteractive composition- R R.s.t I デ - h(z) S h(7))-
ality, then H. = {x1, I2 + (hh(21)
h((x2) la: :=:x Conditional GAM Tree If we relax NC CIC, then himl() may be represented as a hybrid model
of decision tree / GAM model, starting with a tree over Xe where each leaf contains a GAM over
X3 w. Formally, we have H = [ (I(())() (()())))((()))))))))))) [ T + (R + d
From this perspective, derive few valuable insights: Logistic regression with nonnegative weights is abstractly characterized by Bradley- Terry aggregation (o(u) = logistic(u)) and linearity (and implicitly implies noninteractivity and monotonicity). If lincarity is relaxed to monotonicity, we obtain the class of univariate monotonic models, and subsequently when noninteractivity is relaxed, obtain univariate monotonic models with conditional interactions. Similarly, probit regression is characterized by taking (u) (u), i.e., the Gaussian CDF function, and linearity.
Extending Pairwise Comparisons to n- Way Choice We focus on binary preference elicitation because is a well-studied task in the decision-making literature. To extend our binary preference framework to learn probabilistic rankings over items, we use the choice axiom of Luce et al. (1959): Introducing additional items should not change the probability ratio of choosing Without WT, there are (") degrees of freedom (DoF) to complementary binary preferences, and a probabilistic ranking that accords with these binary preferences in general does not exist (e.g., preferences over rocks- rocks-paper-seoe paper--eisssoorsssssss-eessss--- cannot accord with the choice axiom). However, with WT, there are only 1 DoF, as a tree of predictions spanning items can be completed via transitivity.
to choosing x2,
Theorem 3.7 (Proportionality of Choice). Suppose probabilistic decision rule over n 2 3 items that obeys the choice axiom. Then H---)) admits a o-transitive factoring with logistic a()).
Theorem 3.7 uniquely characterizes the Bradley-Teny model, but is only interesting insofar as the model is believable. Alternative models of n-way decision making are also well-studied, such as noisy observation, where utility noise is observed for each option. and the largest observation wins, resulting in a probability distribution over outcomes. In particular, the Bradlcy- -eerry model arises (non-uniquely) from homoskedastic Gumbel noise, but of course the Thurstone-Mo Mosteller model
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Our two-stage model for this participant Plots below show the editing rules (with context [num of dependents])
Chiiz tost batten lub rd
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Button
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Festure: furn Alcohali Drink
Feature: Iaam of Post Crines
Frauur Marr of Depencents
Logistic Classifler basell model
Decision Tree baseline model
CHRPAAL
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samples 152 vallu- [74, 78)
Thue
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samples value [60,
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alco A <= 3.0
life A <om 255
alco A < 3.0 samples 64
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Figure 2: Interpretable models learned for P4 of Study One. For two-stage model and logistic classifier, we plot the contributions of each value of each feature to the final choice i.e., we plot lima()). We also show the decision tree, truncated to the first three layers. Note the differences in model interpretation between these strategics, with our model providing insight into the hcuristics used by P4.
(Thurstone, 1927; Mosteller, 1951) arises from homoskedastic Gaussian noise (hence the probit link function), and from this perspective, o-transitivity can be generalized to n-way decision models whenever the distribution with CDF a decomposes as the difference of two i.i.d. random variables, i.e., is in the difference-fore decomposable (DFD) family, although such decompositions are not always unique (Carnal& Dozzi 1989; Ewerhart & Serena, 2025).
Overall, with reasonable Nomainnsspecion assumptions, we recover other standard modeling classes from our two-stage modeling framework, highlighting the expressive power our modeling class. Importantly, assumptions like linearity and monotonicity are domain-dependent and need to be qualitatively justified for the problem context. Classes, like logistic regressors, that implicitly encode these assumptions may not generate cognitively-fatthii models when the human decision process violates these assumptions. Instead, we based our modeling class on generic axioms of human decision processes, which can be further constrained by domain-specifee assumptions necessary.
4 EMPIRICAL ANALYSIS ON KIDNEY ALLOCATION DATA
We test our modeling strategy to learn computational models of people's 's moral judgments regarding the allocation of kidneys to patients based on their medical attributes (e.g.. transplant outcomes) and non-medical attributes (e.g.. dependents and lifestyles) (Boerstler et al., 2024; Chan et al., 2024). Prior works have noted several limitations of existing modeling approaches in this domain (Keswani al., 2025), making this is a relevant case study to analyze our proposed framework.
Real-world dataset We use the dataset of Boerstler et al. (2024), which spans two studies where participants were presented with several kidney allocation scenarios (15 participants in Study One and 40 in Study Two). In both studies, each kidney allocation scenario comprises a pairwise comparison between two patients (see fig. 4 for an example). Participants were instructed to choose which patient should be given an available kidney. In Study One, the patient features presented are the patient's s number of dependents, projected life years gained from the transplant (LYG), alcoholic drinks per day, and number of crimes committed. In Study Two, the patient features are the patient's 's number of elderly dependents, LYG, years waiting for the transplant, weekly work hours post-transplant, and obesity level. We present further details in appendix B.
Synthetic dataset We also created a synthetic dataset representing multiple simulated decision- makers. This dataset contains pairwise comparisons of hypothetical kidney patients described using four features: number of dependents, LYG, years waiting, and number of crimes committed. The goal is simulate decision makers who explicitly the heuristics observed in prior studies to assess how well our model and other baselines recover these heuristics. The heuristics are taken from Keswani et al. (2025), who provide user-reported qualitative accounts of decision rules people use
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for kidney allocation decisions. Using their observations, we create five simulated decision- makers, DMI-DM5, each using different decision rules over the presented patient features. For instance, DM1 decides between A and B with the following process: (a) assign point to the patient with higher LYG; (b) assign point to each patient with dependents; (c) assign point to each patient on the waiting list for > years; (d) add (0,, 1) noise (i.e., homoskedastic Thhrsston-MMstelll process) to the difference of patient scores, and choose A if difference>> O and B otherwise (DMI's 's process is mathematically described in fig. 3). Other simulated decision-makeer makers (DM2-DM5) also use various heuristics, such as thresholding. diminishing returns, and tallying (described in appendix B). Our dataset contains 1000 pairwise comparisons from each simulated DM.
Methodology and Baselines We compare our model to several learning approaches, namely Bradley- Terry and Drift Diffusion models from cognitive science literature, and common supervised learning approaches, such as logistic and elastic-net classifiers, SVM, GAM with spline terms, decision trees, multi-layer perception (MLP), k-NN, and random forests. Drift Diffusion is the only model we consider that requires reaction times per scenario, thus it is not run for the simulated DMs. We use logistic models and decision tree models as the "interpretable" methods to compare our model's interpretability to. Since moral judgments vary substantially across individuals, all experiments operate over individual- wwwpriddaaapaaiiiiat-----eee- 70-30 train-test split, and report test accuracy over 20 repetitions. For our framework, we minimize the predictive loss to learn the editing functions h (which assign scoreg IR to each feature value), constraining all hin to be monotonic (since all features in our dataset can be seen impact the final choice monotonically) We implement two variants of this framework: (A) with cross-entropy loss and or (z) (1 + E --- and (B) with hinge loss and a as the identity function. The first variant is aligned with the probabilistic framework of theorem 3.4, while the second framework is better suited to assessing our learned model on the "hard classification" metric of 0-1 predictive accuracy. Context is limited to one feature for the real-world datasets, chosen using cross- pross-validation, and 0 for the synthetic dataset. Appendix B provides additional implementation details.
data. For each decision-maker (real or simulated), we use a
Results Across all datasets, we observe that our nognitively-motivteed motivated models achieve high ac- curacy and provide deeper insight into decision- making processes than other baselines.
Average Accuracy (stddev) Study One Study Two Simulated
Model
Cognitive models
.89 (.05)
.88 (.05)
Aggregated Performance. The mean accuracy of each model over all participants is shown in ta- ble 1. Overall, on both real and synthetic data, our framework produces models at least as accu- rafe all baselines. Individual-level performance is presented in appendix B. Additionally, edit- ing functions hinn learned for the decision-makers provide insight into how they process the input features to reach their final decision. We demon- strate this interpretability through case studies of the editing functions learned for the simulated decision-maker DM1 and real-world participant P4 from Study One.
Bradlcy-Terry
.90 (.06)
.78 (.06)
.77 (.06)
Supervised learning models
Logistic Cif
.90 (.06)
.89 (.05)
.85 (.07)
Elastic Net
.89 (.04)
.88 (.05)
(.07)
SVM
.89 (.06)
.89 (.05)
.85 (.07)
GAM
.87 (.09)
.84 (.11)
.88 (.08)
Decision Tree
.83 (.06)
.79 (.06)
(.11)
K-NN
.85 (.06)
.82 (.05)
.79 (.08)
MLP
.89 (.05)
.86 (.06)
.87 (.08)
Random Forest
.86 (.05)
.85 (.04)
.87 (.08)
Our Models
cross-entropy loss .90 (.06)
.90 (.05)
.89 (.10)
hinge loss .90 (.06)
.89 (.06)
.89 (.08)
Participant P4 in Study One. The two-stage model learned for P4 (w/ hinge loss) is illustrated in fig. (top row). The editing function plots show the following nuances of their decision-mate -mainng process for pairwise comparisons of kidney patients: (a) number of dependents and number of alcoholic drinks are the two most important features, as reflected by the large choice contributions of these features; (b) number of past crimes is considered mostly irrelevant by this participant: (c) life decades gained is relevant only when the patient has zero dependents, indicating a conditional interaction between these two features; (d) for number of dependents, a difference of 1 vs dependents is much more significant to the final choice than difference of 2 vs I dependents approximately a threshold decision rule such that any non-zero number of dependents contributes equally; (e) similarly for life decades gained and number of alcoholic drinks, for certain conditions on number of dependents, the participant employs threshold decision rules. Our model is able to learn all of these decision-making nuances with an accuracy of 0.78 (# 0.05), but trained logistic classifier (bottom-left in fig. 2) has lower accuracy of
Table 1:
Performance statisties of all individualized models on kidney allocation datasets.

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Simulated DM process input: patients
Our two-stage model for this DM
Crukke contributton
scontayy (i) 0) 1 > O)) b. sccorBuie tn signix )
c. scorewuar + sign [max(x2""1 6)- maxd (x
scorraxp + scoreare wuryors N(L.))
e choice - AIES elss B
recture Num Degardents
Festunc Line Sears Gared
besture Years Walling
Logistic Classifier model
Decbion Tree Model top three loyers
One Co
25
75
Figure 3: Models learned for the simulated decision-maker DMI. On the top left, we present the mathematical description of the DMI's process. Once again, we present each model's interpretation of the process used by the decision-maker. Observe that our two-stage model most accurately captures the simulated process.
0.76 (+ 0.04) and only uncovered points (a) and (b) above, while trained decision-tree model (bottom-right in fig. 2) has an accuracy of 0.70 (# 0.03) and only uncovered points (a), (c), and (d).
Simulated DMI. Figure 3 (top right) shows how our model recovers the rules used by DMI. E.g.. for the feature number for dependents, deeps captures the increase in choice contribution when this feature has non-zero value, and for the years waiting feature, Muai learns the increased contribution of values exceeding 6 years. Both successfully represent the threshold functions used in DMI. Note that neither of these observations can be easily inferred from logistic or decision tree models (bottom row of fig. 3). Our model is also more accurate (0.76 + 0.02) than logistic (0.74 = 0.02) and decision tree (0.68 + 0.04) models. Appendix B shows similar results for other simulated decision-makers Overall, our models provide a better understanding of the decision- making processes, without sacrificing predictive accuracy.
DISCUSSION, LIMITATIONS, AND FUTURE WORK
This work provides a modeling strategy to learn human decision processes from pairwise comparisons. The primary goal of this strategy is fudelity, such that best-fit models from these classes are more faithful to humans' actual decision-makiie demonstrates that these classes emerge from natural decision -aaiin axioms, and our empirical analysis shows that greater fidelity would lead to greater model accuracy some cases. The advantages of gegnitively-faithii models will depend on the application. In applications like online recommender systems, may be sufficient to predict human behavior accurately, without simulating their actual decision processes. However, cognitively-faithi high-stakes Al domains (e.g., healthcare and sentencing), where the interpretability and alignment of Al's decisions with the users is pivotal in establishing trust (Jacovi et al., 2021). cennitively-fatthiie models would also be preferable domains with no "ground truth" (like kidney allocation), where model validation requires the user to understand and evaluate the model's decision process. Lastly, soauttieel-faithhiie models are essential for any use of AI in moral domains, since stakeholders expect an AI to justify its moral decisions in a similar manner as humans (Lima et al., 2021).
than standard hypothesis spaces. Our theoretical analysis
models would be desirable in other
Our mathematical model is motivated by human behavior, but due to the complexity of the latter, any such effort is inherently limited. The interpretability features of our framework need further validation through real-world user studies. While this work provides a proof-of-concep for a computational framework to learn from human decisions, future user studies can help assess the extent which users understand and trust the learned two-stage models. Our axioms characterize the "ideal" decision- making process of an individual; i.e., the process they would prefer to follow in the absence of any internal/external constraints, or the process that describes their judgments about what "should be done." " However, human decisions are known to deviate from the ideal in many ways, e.g., sometimes exhibiting transitivity and complementarity violations (Tversky et al., 1990), among others. One thus wonders, does precluding such deviations aligns an AI system to the processes humans actually use, or instead to some idealized version thereof? Both have their virtues, but must be aware of the differences. We hope that future work can study this issue, and explore ways to extend our two-stage hypothesis classes to quantify deviations of one's "ideal" judgments from their implemented choices.
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ACKNOWLEDGMENTS
VK, CC, JSB, and WSA are grateful for the financial support from OpenAl and Duke University.
HH acknowledges support from the CMU-NIST Cooperative Research Center AI Measurement Science Engineering (AIMSEC), and the AI Research Institutes Program funded by the National Science Foundation under Al Institute for Societal Decision Making (AI-SDM), Award No. 2229881. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not reflect the views of the funding agencies.
REPRODUCIBILITY STATEMENT
For theoretical results, full proofs of all results provided in Appendix A. To facilitate reproducibil- ity of the empirical results, the primary methodological details of our simulations are described in Section and additional descriptions, including simulated DM construction processes, training procedures, and hyperparameter values, are given in Appendix B. Additionally, source code for these simulations available https: /tt::////tttttttt ctw.hhh..ooomoecooo com wtt../////waaaeeeeaaaa///ooooite Models/
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Reviews:
[{"review_id": "vIk2z0oVVa", "reviewer": "Reviewer_QKXr", "summary": "This paper proposes an axiomatic approach to learning cognitively faithful models of human decision-making from pairwise comparisons. The approach is grounded in the heuristics literature and aims to learn individual-level decision rules that better reflect actual cognitive processes rather than idealized rational preferences. The proposed model uses a two-stage process: features are first transformed through learned rules (feature editing functions), then aggregated via a fixed rule such as Bradley-Terry to produce decisions. This framework can be viewed as extending prospect theory beyond decisions under risk, enabling the capture of non-linear feature transformations. The authors demonstrate their approach on a kidney allocation task, showing that their models achieve comparable or superior accuracy to existing methods while maintaining interpretability.", "questions": "**Q1: Application scope and practical utility**\n- Can the authors provide clearer guidance on when their approach would be preferred over simpler methods (e.g., decision trees) or more theoretically grounded models? What are the specific data regimes or decision contexts where this method offers distinct advantages?\n- Suggestion: Include empirical analysis comparing performance across different dataset sizes and complexity levels to delineate the method's practical niche.\n\n**Q2: Generalization to new features and contexts**\n- How does the model handle scenarios where decision-makers encounter new features not present in the training data? Can the learned feature editing functions transfer or adapt to novel attributes?\n- Suggestion: Conduct experiments demonstrating the model's ability (or inability) to generalize beyond the training feature space, and discuss potential extensions that could improve transferability.\n\n**Q3: Validation of cognitive faithfulness**\n- While the model is motivated by cognitive faithfulness, no direct validation is provided. Can the authors demonstrate that the learned rules actually correspond to participants' self-reported decision strategies or mental processes?\n- Suggestion: Include qualitative validation through participant interviews, think-aloud protocols, or post-hoc surveys asking participants to confirm whether the learned rules reflect their actual reasoning. Alternatively, compare the learned feature editing functions against known psychological phenomena.\n\n**Q4: Handling axiom violations**\n- Given that the foundational axioms (complementarity, weak transitivity, noninteractive compositionality) are known to be violated in human behavior, how do the authors justify building models upon these assumptions? What happens when participant behavior systematically violates these axioms?\n- Suggestion: Empirically test how often the axioms are violated in your data, and discuss either: (a) model extensions that can accommodate violations, or (b) why the violations may be negligible in your specific application domain.\n\n**Q5: Distinguishing preferences from cognitive constraints**\n- How can the model distinguish between genuine preferences and artifacts of bounded rationality or cognitive limitations? Are the learned feature editing functions capturing what people *want* or merely what they *can* process?\n- Suggestion: Design experiments that vary cognitive load or time pressure to test whether the learned rules remain stable, or conduct comparative analysis with decisions made under different resource constraints.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "•\t**Interpretability**: The proposed decision models maintain interpretability similar to conventional decision-making models, making the learned rules accessible and understandable to practitioners.\n\n•\t**Rigorous theoretical foundation**: The paper provides rigorous axiomatic derivation as the mathematical foundation for the modeling approach, grounding the method in established decision theory principles.", "weaknesses": "•\t**Limited application scope**: The modeling approach remains purely descriptive, positioned between decision trees (with added constraints) and linear classifiers (with relaxed constraints). Without sufficient data, more theoretical models may be preferable; with abundant data, decision trees might perform better. This intermediate position may limit the method's practical applicability.\n\n•\t**Generalizability concerns**: Compared to models that infer internal motivations and goals, this data-driven approach may struggle to generalize to new contexts or features not present in training data. This limitation is inherent to heuristic-based approaches and has been a longstanding criticism in the human decision-making literature (e.g., by Gigerenzer's critics).\n\n•\t**Marginal performance gains and lack of validation**: The proposed model shows only marginal improvement over baseline methods (0.78 vs. 0.76 for logistic classifier). More critically, while the authors emphasize cognitive faithfulness, they provide no direct empirical evidence that their learned models better align with participants' self-reported beliefs or decision strategies.\n\n•\t**Questionable axiomatic assumptions**: The \"natural decision-making axioms\" employed (complementarity, weak transitivity, and noninteractive compositionality) were proposed in 1960s decision theories and have been repeatedly shown to be violated in human behavior. As the authors acknowledge in their discussion, humans notoriously violate such decision rules, undermining the cognitive faithfulness claim.\n\n•\t**Limited predictive validity of rule-based models**: Rule-based (heuristics-based) decision-making usually provides substantially worse predictions than value-oriented models in most contexts (see Erev et al., 2010, Journal of Behavioral Decision Making), raising questions about when the proposed approach would be preferred.\n\n•\t**Confounding cognitive limitations with preferences**: Bounded rationality suggests that human decision-making is constrained by limited cognitive resources. The measured feature editing functions may simply reflect these cognitive limitations rather than genuine preferences or decision rules, making it unclear what the model actually captures about decision-makers' underlying values or goals.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uqTKuMzLwS", "reviewer": "Reviewer_E8xC", "summary": "The paper proposes a cognitively-faithful class of decision-making models derived from simple axioms over binary choices. A decision is modeled as a two-stage process: (i) feature-wise “editing rules” that threshold, transform, or ignore attributes (optionally conditioned on a small context set), and (ii) an aggregation rule such as Bradley–Terry or a sigmoid link to produce pairwise choice probabilities. The axioms (complementarity, weak transitivity, codomain span, and compositionality) yield factorization theorems connecting the class to logistic/probit special cases and to conditional GAM-like structures. Empirically, on kidney-allocation decisions with real participants and simulated decision-makers, the learned two-stage models match or surpass standard baselines while exposing interpretable per-feature rules.", "questions": "See Weaknesses", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The work offers a principled axiomatic foundation and shows that these axioms imply a two-stage, feature-edited factorization with a sigmoid link (σ-transitivity), recovering classical models (logistic/probit) under additional assumptions and yielding conditional, GAM-like structures when allowing limited interactions. This yields interpretable inner functions that visualize per-feature heuristics (e.g., thresholds, diminishing returns) on real and simulated subjects, while achieving accuracy on par with or better than logistic, GAM, trees, and other baselines in kidney-allocation tasks.", "weaknesses": "First, the axioms and factorization lean on properties (weak transitivity, compositionality) that can be violated in human judgments; the paper acknowledges that axioms target an “ideal” process, not observed behavior under biases, so model fidelity in settings with known transitivity/complementarity violations remains uncertain without further user studies.\n\nSecond, expressiveness hinges on the chosen editing-rule families and the limited context set ω (one feature for real data). If key interactions require broader context or non-monotone transformations, the learned inner functions may underfit or attribute effects incorrectly; identifiability is also delicate because the factorization need not be unique, which can complicate interpretation claims.\n\nThird, empirical scope is narrow: pairwise kidney-allocation tasks with small participant counts (Study One: 15, Study Two: 40) and a limited feature set. Although the models visualize plausible heuristics, the paper needs real-world validation of interpretability and trust claims; broader domains (non-moral choices, higher-dimensional attributes, n-way decisions beyond pairwise) and robustness under distribution shift are largely untested.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FHmSr0tz3E", "reviewer": "Reviewer_6AEu", "summary": "This paper introduces a framework for *cognitively-faithful modeling of human decision processes* in pairwise comparisons. The goal is to address that current learning and AI alignment methods focus on outcomes of human decisions (such as pairwise preferences or rewards) but do not capture the *cognitive processes* that produce them.\n\nThe authors present an axiomatic approach to human pairwise decision-making based on five principles (complementarity, weak transitivity, codomain span, noninteractive, and conditional compositionality). From these axioms, they develop a *two-stage model*. First, they define a set of *editing rules* that modify or simplify feature information (such as thresholding and tallying). Then, they introduce a *dominance testing rule* that combines the edited features into a probabilistic choice. The resulting model framework unites some classical models as special cases and seeks to connect interpretability with cognitive realism.\n\nEmpirically, the authors evaluate a kidney allocation task using real human data and synthetic data simulating known heuristics. Their models achieve comparable or superior accuracy to baselines (logistic, GAM, MLP, decision trees, etc.) while offering interpretable insight into human heuristics. The paper concludes by discussing implications for AI alignment and emphasizing the importance of faithfulness to human reasoning in high-stakes moral decisions.", "questions": "1. Could the authors provide quantitative evidence that the learned editing rules correspond to reported human heuristics (e.g., through previous studies showing that feature-importance aligns with qualitative interviews)?\n2. Is the approach computationally scalable for large-feature or neural representations (e.g., embedding-based preference learning)?\n3. Could cognitively faithful modeling inform fairness-sensitive decisions, where human heuristics themselves may be biased?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper identifies (ttbomk) an under-explored problem: existing preference-learning frameworks rarely attempt cognitive fidelity, even though interpretability and trust depend on it. The argument is well contextualized in AI alignment and moral decision-making literature.\n\n* The axiomatic formulation is intuitive and well-organized. The derivation from axioms to model families (logistic, monotonic, conditional GAM) is clear. \n\n* The paper builds on the work of Gigerenzer, Kahneman, and others to formalize heuristics in a way that is compatible with ML, representing an ambitious and valuable interdisciplinary effort.\n\n* The kidney allocation domain is well-motivated, connects to prior moral-AI work (e.g., Sinnott-Armstrong et al. or Keswani et al.), and emphasizes the importance of process alignment beyond just accuracy.\n\n* The examples of individual decision-makers (e.g., participant P4, simulated DM1) are insightful. The figures clearly illustrate how the proposed model captures thresholding and conditional heuristics that logistic or tree-based models overlook.\n\n* The authors openly acknowledge limitations and the idealized nature of their axioms, which strengthens the credibility of the paper.", "weaknesses": "*  Although the example is well motivated and exemplifies that the theoretical formulation works, the empirical validation focuses on a single decision dataset with a small sample size ($n=15$ in study I and $n=40$ in study II). It is unclear how the approach scales to richer, higher-dimensional, or continuous domains.\n\n* Although the paper claims \"cognitive faithfulness,\" it mainly assesses accuracy and qualitative interpretability. There is no quantitative measurement of *faithfulness* (such as user validation or alignment with verbal reports).\n\n* The paper's analysis of the literature is comprehensive and strong, but it could incorporate more discussion on computational cognitive modeling (e.g., modern differentiable cognitive architectures [[1](https://www.nature.com/articles/s41598-022-15679-5), [2](https://proceedings.mlr.press/v216/hamalainen23a/hamalainen23a.pdf)]) to better emphasize its contribution and even identify potential synergies.\n\n* Releasing the code would help the reader replicate the learning setup. There are anonymous ways of sharing it, such as web pages or adding it to OR as supplementary material. \n\n* Minor: Table 1 is quite dense and poorly organized (fonts are too small, column alignment is unclear).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Rj9ncQRUOZ", "reviewer": "Reviewer_xeec", "summary": "This paper introduces a principled framework for learning \"cognitively-faithful\" models of human decision-making from pairwise comparisons. The central thesis is that for AI alignment to be successful, particularly in high-stakes domains, the models learning human preferences must not only be predictively accurate but also reflect the underlying cognitive processes. The authors propose a two-stage model architecture: editing stage applies feature-level transformation rules to capture how humans simplify and process information (e.g., applying thresholds, ignoring features), and the dominance testing stage aggregates these processed features to produce a choice. The key contribution is a rigorous axiomatic framework from which this two-stage structure is formally derived. The authors prove that their general framework can be specialized to recover classic models like logistic and probit regression under stronger assumptions. The method is evaluated on real-world and synthetic data from the kidney allocation domain, where it demonstrates competitive accuracy against a range of baselines while providing highly interpretable, participant-level insights into decision heuristics.", "questions": "Primary questions already posed as weaknesses earlier. I was also curious about:\n1. **Quantifying Axiom Violations:** Can you report empirical rates of transitivity or complementarity violations in your human data, and how these affect model accuracy or interpretability?\n2. **Reverse Use for Diagnosis:** Could your framework be used inversely to *detect* when individuals systematically violate the axioms by analyzing poor model fit or structured residuals?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Principled Axiomatic Foundation:** The paper's greatest strength is its axiomatic approach. Rather than proposing an ad-hoc model architecture, the authors derive their two-stage framework from a set of simple axioms of choice. This provides a strong theoretical grounding for the model class and elegantly justifies its structure. It moves the field from simply using models that fit to reasoning about why certain model structures are appropriate.\n\n2.  **Cognitive Plausibility and High Interpretability:** The model architecture directly maps onto well-documented cognitive strategies like heuristic-based decision-making. The empirical results persuasively demonstrate this strength, revealing plausible and nuanced individual-level heuristics.", "weaknesses": "1.  **Empirical Score:** Evaluation is limited to the kidney allocation setting and synthetic benchmarks. The cognitive heuristics and moral trade-offs in this domain are quite specific. The paper's broad claims about providing a general framework for cognitively-faithful modeling would be significantly more convincing if broadly validated in less moral or more perceptual decision domains.\n\n2.  **Unvalidated Interpretability:** The central claim of the paper is \"cognitive fidelity\" and the resulting interpretability. While the learned models and their visualizations are highly plausible, these are ultimately claims about human psychology. Without user studies to confirm that people find these explanations understandable, trustworthy, and more insightful than those from other interpretable models (e.g., partial dependence plots from a GAM), the \"interpretability\" remains a feature of the model rather than a proven benefit for human-AI collaboration.\n\n3.  **Idealized Axioms vs. Bounded Rationality:** The axiomatic framework describes an \"ideal\" decision-maker who is, for instance, perfectly transitive (Weak Transitivity). This is a useful and standard starting point for theoretical modeling. However, a vast literature in behavioral economics is dedicated to studying systematic *violations* of such axioms. A weakness of the current work is that it does not deeply engage with the implications of these violations. The framework is used to model choices that *fit* the axioms, but not to diagnose or characterize interesting deviations from them.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)