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AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable

Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh, Enkelejda Kasneci

arXiv:2606.11456v1cs.CLcs.AIcs.CY

TL;DR

Research on AI-assisted science faces competing concerns about reduced methodological diversity and prompt-steered motivated conclusions. The paper compares coding agents with human analysts and tests prompt interventions at the design and verdict layers. Agents match or exceed human methodological diversity, while a confirmatory prompt sharply changes Claude Code’s verdicts without substantially changing its coefficients.

  • Problem

    Methodological diversity may support discovery but also provide room for motivated inference, while it remains unclear whether AI agents narrow that diversity or make it steerable.

    Source: §1 · p. 2

  • Method

    The study evaluates coding-agent analyses against human teams and uses prompt manipulations to examine methodological pathways, estimates, and verdicts.

    Source: Discussion · p. 12

  • Results

    A confirmatory prompt shifted Claude Code’s verdicts from 2/20 runs supporting the hypothesis to 18/20, while its coefficient distribution remained essentially unchanged.

    Source: Discussion · p. 12

  • Takeaways & Limitations

    Evaluations of AI-assisted research should assess verdicts as well as coefficient estimates because prompt interventions can steer conclusions without materially changing estimates.

    Source: Discussion · p. 13

    The verdict-layer dissociation is clearest for Claude Code under the Confirmatory prompt, and whether it generalizes across agents, prompts, and domains remains open.

    Source: Discussion · p. 13

Abstract

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The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may amplify the analytic flexibility through which researchers reach motivated conclusions. We argue these worries target two empirically separable layers: a design layer of methodological choices, and a verdict layer in which a decision rule maps estimates to a substantive claim. We test both by running 20 independent executions of Claude Code and Codex on a prominent immigration and social-policy against a many-analysts human baseline. At the design layer, Codex matches human methodological diversity and Claude Code produces nearly three times as many specifications; both agents' effect estimates remain broadly aligned with the human consensus, and no agent model exactly matches any human model. A prompt-induced anti-immigration researcher prior reorganizes each agent's methodological decisions but, unlike for biased human analysts in the same data, does not shift aggregate estimates or final verdicts; nor do agents reroute along the methodological axes humans use to bias their estimates. At the verdict layer, an explicit confirmatory prompt flips Claude Code's verdicts from 10% to 90% support while leaving its coefficient distribution essentially unchanged, operating through rule omission rather than rule softening. AI agents can rival or exceed human methodological diversity at the design layer while remaining vulnerable at the verdict layer. In our setting, the locus of AI bias is not estimation but interpretation.

Significance

LLM agents raise two distinct concerns: they may narrow methodological diversity or amplify motivated interpretation. In this study, agents match or exceed human methodological diversity, while a confirmatory prompt changes one agent’s verdicts without materially changing its coefficient distribution.

Significance

  • The authors distinguish concerns about methodological diversity from concerns about motivated conclusions by separating design and verdict layers.Design concerns methodological choices; verdict concerns mapping estimates to substantive claims.Source: Significance · p. 1
  • A confirmatory prompt flipped one agent’s verdicts from 10% to 90% support while leaving its coefficient distribution essentially unchanged.The findings distinguish potential bias in interpretation from bias in estimation.Source: Significance · p. 1
  • The agents rivaled or exceeded human methodological diversity while producing effect estimates close to the human consensus.The paper frames this as evidence against collapse toward a single analytic strategy in this setting.Source: Significance · p. 1

1 Introduction

Methodological pluralism can support discovery but also create opportunities for motivated inference. The paper separates methodological choices from the rules that turn estimates into substantive verdicts, then tests how two coding agents respond to neutral and biased prompts against a human benchmark.

1 Introduction

  • The human benchmark shows why methodological flexibility is double-edged: 73 teams reached estimates from strongly negative to strongly positive, and researcher priors were associated with specifications and conclusions.The same degrees of freedom can support collective discovery or motivated inference within a single analysis.Source: §1 · p. 2
  • LLM agents raise paired concerns: reduced creative diversity may homogenize analyses, while sycophancy and specification-hacking may make their methods steerable by prompts.Observational social science provides a test case because constructs such as socioeconomic status and partisanship admit competing operationalizations.Source: §1 · p. 2
  • The design layer covers methodological choices, whereas the verdict layer maps estimates to substantive claims and narrates the decision rule’s output.The layers can vary independently; the cited human study found supportive test-result shares explained only about a third of deviance in narrated conclusions.Source: §1 · p. 2
  • Twenty runs of two coding agents were compared with 73 human teams testing whether immigration reduces support for social policy.The agents produced broadly human-aligned effect distributions and conclusions, while a confirmatory prompt shifted Claude Code’s verdicts without materially changing coefficients.Source: §1 · p. 2
  • A prompt-induced researcher prior reshuffled both agents’ methodological pathways without shifting aggregate estimates or final verdicts.The agents also did not shift along the methodological choices through which anti-immigration human researchers shifted estimates in the same data.Source: §1 · pp. 2–3

2 Results

The results examine how agents’ methodological spaces and estimates relate to human results, and how prompts affect estimates and verdicts. They also trace how CC and CX respond differently in their verdict-rule structures to a confirmatory prompt.

2.1 Comparing AI Agents and Human Researchers in Methodological Diversity

  • 2.95× more specifications per run were produced by Claude Code than Codex, while the sampled human teams’ per-team volume resembled Codex’s.Claude Code delivered 1,058 valid AME estimates and Codex 359; humans averaged 15.9 specifications per team. Humans averaged 1.8× their planned count, Claude Code 2.6×, and Codex approximately 1.0×; the specification curves show these groups and counts.Source: §2.1 · p. 3, Figure 1 · p. 4

2.2 Comparing AI Agents and Human Researchers in Estimate Similarity

  • Agent AME distributions generally resembled humans’ on four focal outcomes, but both agents diverged on the composite scale.Claude Code also differed on housing and health; Codex’s only other tested divergence was the composite. The composite gap was consistent with agents constructing it from a more uniform item subset than human teams.Source: §2.2 · p. 5
  • Exact reproduction of Brady and Finnigan’s reported estimates was generally rare without the original code, although qualitative conclusions were more reproducible.With code, both agents reached 100% exact matches; agreement on significance and sign exceeded 91% across model-aware conditions. Remaining mismatches primarily reflected undocumented sample-construction choices and a typographical inconsistency in Table 5.Source: §2.2 · p. 5

2.3 Profiling method choices across humans, Codex, and Claude Code

  • Claude Code and Codex differed in estimator and sample choices: Claude Code used both OLS and logit more often, while Codex favored OLS and both agents narrowed country samples relative to humans.Claude Code’s primary specification jointly entered stock and flow, unlike Codex and the sampled human teams; agents also named several planned methods, including alternative-source comparisons, that human plans did not name.Source: §2.3 · pp. 6–7
  • Across 174 substantive decisions, no agent model exactly matched a human model; median Jaccard similarity was 0.38 for humans–CC and 0.45 for humans–CX, with CX closer to humans.Within-group median Jaccard was 0.70 for CX versus ∼0.45 for both humans and CC. Decisions explained 22% of Codex within-run AME variance, while 60% of Claude Code’s verdict deviance was decision-explained.Source: §2.3 · pp. 7–8

2.4 Examining Prompt-Induced Bias

  • Under anti-immigration prompting, agents’ AMEs barely moved and their methodological shifts did not reproduce the human ideological-bias channel.Both agents’ mean AMEs moved from −0.008 to −0.007 SD-units, while human means shifted from −0.001 to −0.013. Claude Code’s country sample widened while its model narrowed; Codex deepened specifications on roughly the same country panel. Verdict changes were modest and within sampling noise.Source: §2.4 · pp. 8–10, Figure 3A

2.5 Examining Specification Hacking

  • Claude Code’s support verdicts rose from 2/20 (10%) to 18/20 (90%) under confirmatory prompting, while Codex’s rose from 3/20 to 5/20 and AME shifts were small for both.The share of AMEs with negative confidence intervals excluding zero moved from 18.7% to 26.0% for Claude Code and 23.4% to 24.7% for Codex; verdict confidence intervals did not overlap for Claude Code but did for Codex.Source: §2.5 · p. 10
  • r = −0.06: CC and CX showed no meaningful cross-agent correlation in their Confirmatory-induced shifts across 193 modeling decisions, and several large shifts ran in opposite directions.For example, CC’s two-way fixed-effects prevalence fell from 66.6% to 45.3%, while CX’s rose from 36.2% to 55.9%.Source: §2.5 · p. 10, Figure 4 · p. 11
  • 12/20 CC Confirmatory plans omitted an explicit verdict criterion, and at least 10 of those runs reached a support verdict, versus 2/9 rule-omitted Default runs.Active rule-softening accounts for just 3 CC Confirmatory runs.Source: §2.5 · p. 10
  • 5/20: CX used disjunctive “stock or flow” support rules under Confirmatory, up from 1/20 in Default, without lowering its significance threshold to p < 0.10.Explicit decision-rule sections increased from 0/20 CX Default plans to 3/20 Confirmatory plans.Source: §2.5 · pp. 10, 12
  • 10% →90%: CC support verdicts rose under the Confirmatory prompt, while AME-level shares overlapped across conditions and CX’s positive-significance share fell from 15.8% to 5.0%.The authors do not interpret CC’s verdict-level compliance as intentional manipulation; they note that AME-only evaluations would miss it.Source: §2.5 · pp. 10–11, Figure 4 · p. 11

Discussion

The findings separate methodological design from substantive verdicts: agents’ diverse analyses can remain broadly aligned with human outcomes, while confirmatory prompting shifts Claude Code’s conclusions without materially changing its coefficient distribution.

Discussion

  • No agent model exceeded Jaccard 0.75 with any sampled human model, despite Claude Code producing nearly three times as many specifications as Codex and the sampled human teams.Claude Code narrowed some dimensions of variation while expanding others, including estimator breadth, robustness analysis, and pre-analysis formalization.Source: Discussion · p. 12
  • 2/20 to 18/20 Claude Code runs supported the hypothesis under a confirmatory prompt, while its coefficient distribution remained essentially unchanged.Plans with an explicit decision-rule section fell from 11/20 to 8/20; among runs omitting a rule, 10/12 supported the hypothesis, versus 2/9 under Default.Source: Discussion · pp. 12–13
  • The two agents changed different methodological components under the same researcher prior, with Default-to-Biased deltas across 193 decisions essentially uncorrelated (r = −0.03).Claude Code changed country-sample construction, whereas Codex changed estimator and control-set choices.Source: Discussion · p. 13
  • The verdict-layer dissociation is clearest for Claude Code under the Confirmatory prompt, while Codex’s estimates and verdicts moved little and broader generalization remains open.The human comparison is asymmetric because agent verdicts came from full conclusion files under prompt manipulation, while human verdicts were structured codes without an equivalent intervention.Source: Discussion · p. 13
  • The study is limited to one observational social-science problem, two agents, and short-horizon workflows; its coding framework may also miss tacit judgment.The authors caution that confirmatory-prompt shifts do not establish intentional deception or strategic manipulation.Source: Discussion · pp. 13–14
  • The authors argue that AI-assisted science should govern methodological diversity by keeping the layer that translates evidence into claims transparent, auditable, and epistemically productive.They suggest agents’ broad exploration may enable comparison of analytical strategies at scales previously impractical for human-led research alone.Source: Discussion · p. 14

3 Methods

The methods compare agents with human analysts across methodological choices, estimates, conclusions, and reproduction accuracy. They use independent agent runs, controlled information and prompt conditions, and code each analytic choice as a binary decision.

3.1 Operational details supplied beyond the paper

  • The task bundle supplied variable names and category mappings, aligned 1996 country codes to ISO-3166, and provided one-year-lagged immigration measures.These details were absent from the paper and its online supplement and were supplied to standardize data construction across three opaque conditions.Source: §3.1 · p. 14

3.2 Evaluation Metrics

  • The evaluation measured methodological coverage and mix, agreement in AMEs and verdicts, between- and within-team variation, and reproduction accuracy across 72 published coefficients.Reproduction scoring included exact significance, odds-ratio and z-score matches, their conjunction, and a relaxed sign-and-significance criterion.Source: §3.2 · pp. 14–15

3.3 Experimental Setup

  • Twenty independent runs per agent tested the immigration hypothesis under Default, Bias, and Confirmatory prompts; reproduction used five information conditions and ten runs per agent per condition.Expansion runs produced plans, scripts, conclusions, and marginal-effects tables; reproduction conditions ranged from a research question alone to full access.Source: §3.3 · p. 15

3.4 Recovering per-decision codes for the AI agents.

  • Each of 193 catalogued analytic choices was coded as a binary decision per agent model, using output files and identical extraction patterns; human proportions came from cri.csv.Country inclusion was coded from executable code rather than design documents.Source: §3.4 · p. 15

3.5 Anonymization of Replication Materials.

  • Research assistants anonymized replication materials and removed residual identifiers found by Claude Code, updating script references to preserve execution consistency.The process aimed to prevent performance from relying on memorized training data.Source: §3.5 · p. 16

4 Related Work

Prior work studies agent reproduction more than upstream research design, while accounts of AI homogenization and prompt sensitivity raise unresolved questions about end-to-end scientific workflows. The expansion prompt specifies a staged analysis task for testing the immigration and social-policy hypothesis.

4.1 AI Agents for Social Science Research

  • Existing agent benchmarks largely emphasize reproducing or replicating analyses, leaving upstream methodological choices in autonomous research workflows comparatively understudied.CORE-Bench, REPRO-BENCH, and ReplicatorBench address computational reproduction, social-science reproduction, and replication workflows, respectively, with stated scope or scale limitations.Source: §4.1 · p. 16

4.2 AI Homogenization

  • Prior work reports reduced collective diversity in AI-assisted creative tasks and warns that shared models may correlate researchers’ analytical choices, a prediction not directly tested in end-to-end empirical workflows.The cited literature includes diversity findings in story writing, collaborative writing, and ideation.Source: §4.2 · pp. 16–17

4.3 LLM Sycophancy and Specification Hacking

  • Sycophancy and specification hacking motivate concern that agents’ research decisions may respond to stated researcher priors or requests for supportive evidence rather than the data alone.The cited work describes outputs aligning with user preferences and models exploiting ambiguity in objectives.Source: §4.3 · p. 17

A.1 Expansion Prompt

  • The expansion prompt asks agents to design and execute a test using ISSP data, specify a pre-analysis plan before analysis, then run it and report a substantive conclusion.The prompt names six required policy outcomes and permits separate, index, or latent-scale analyses.Source: §A.1 · pp. 20–22

B.1 Claude Code

The Claude Code project configuration permits common development operations while blocking web access.

B.1 Claude Code

  • The project settings allow file editing and script execution but block web access, including search, fetch, curl, and wget.Source: §B.1 · p. 24

B.2 Codex

The Codex sandbox confines work to the workspace and disables web search, while allowing network access for package installation.

B.2 Codex

  • The reproducibility profile confines execution to the workspace, disables web search, and allows network access for package installation.Source: §B.2 · pp. 24–25

C.1 Outcome-level distributional fidelity

Across policy outcomes, agent AME distributions track humans on most outcomes but diverge on several, including the composite scale.

C.1 Outcome-level distributional fidelity

  • Claude Code’s AME distributions are significantly compressed relative to humans on housing, health, and the composite scale; Codex differs on old age and the composite scale.The reported Kolmogorov–Smirnov distances are 0.21, 0.35, and 0.38 for Claude Code, and 0.31 and 0.35 for Codex, respectively.Source: Figure 6 · p. 26
  • On jobs, unemployment, and income difference, both agents are statistically indistinguishable from humans.The comparison uses within-cell quantile curves of AMEs from 73 human teams and 20 runs of each agent.Source: Figure 6 · p. 26
  • The figure ranks AME estimates from most negative to most positive and reports agent–human Kolmogorov–Smirnov distances, with asterisks marking P < 0.05.The y-axis is compressed, with breaks at |AME| = 0.05 and 0.50.Source: Figure 6 · p. 26

C.2 Reproduction of Brady & Finnigan (2014)

The agents reproduce Brady and Finnigan’s reported results under varying access to the original study materials. Exact numerical matches depend strongly on access to the original analysis code, while qualitative conclusions are recovered more often.

C.2 Reproduction of Brady & Finnigan (2014)

  • 100% exact matches on every metric occur for both agents when the original code is supplied, under Model + Results + Code and Full Access.Below code access, joint exact matches remain below 1% for Claude Code in all three partial-information conditions and for Codex with methods alone.Source: §C.2 · p. 27, Figure 7 · p. 28
  • 92.5%/97.8% of cells have the correct significance marker and effect sign for Claude Code/Codex once the regression specification is provided.From the methods section alone, accuracy is 68.6%/77.8%, respectively.Source: §C.2 · pp. 27–28, Figure 7 · p. 28
  • 58.1% vs. 17.2% is Codex’s vs. Claude Code’s exact odds-ratio match under Model, the largest partial-information gap between the agents.The paper suggests inconsistent recovery of the original estimation routine and rounding convention may explain run-to-run differences.Source: §C.2 · p. 27, Figure 7 · p. 28

C.3 Explaining the Variability

The paper relates researchers’ analytic decisions to AME variation and subjective conclusions using mixed-effects models and multinomial logit. The same decision set explains different parts of the output for Codex and Claude Code.

C.3 Explaining the Variability

  • 26 of 174 substantive decisions form a consensus core used by at least half of all three groups’ models.Human and agent decision spaces diverge asymmetrically: 32 decisions occur in human models but in none of either agent’s models, while four decisions are common in both agents but rare among humans.Source: §C.3 · pp. 28–29
  • The analysis models standardized AMEs using 15 m07adj decisions and models team conclusions using four predictors; human variance estimates use the full 71-team cohort.The 20-team human subsample yields an unstable between-team estimate of approximately 81%.Source: §C.3 · p. 29, Figure 8 · p. 30
  • 22% of Codex’s within-run AME variance is explained by 15 decisions, compared with 1% for Claude Code and 2% of human within-team variance.The same decisions explain 16% of Codex total AME variance, while 4% of human total AME variance is explained.Source: §C.3 · p. 29, Figure 8 · p. 30
  • 60% of Claude Code’s conclusion deviance is explained by decisions, versus 26% for Codex and 12% for humans.For Claude Code, those decisions explain only 0.2% of total AME variance and 1% within-run.Source: §C.3 · p. 29, Figure 8 · p. 30

C.4 Model Specification Coding and Distribution

The decision-frequency table extends Breznau et al.’s coding scheme to human teams and AI-agent runs, with agent flags produced by automated parsing and treated as approximations.

C.4 Model Specification Coding and Distribution

  • The extended table reports percentages of models in which each decision variable is present for humans, Codex, and Claude Code.Human entries use 20 randomly sampled teams; each agent group comprises 20 runs.Source: Table 1 · p. 31
  • Agent decision flags are automated approximations derived by parsing marginal_effects.csv, replication_code.py, and conclusion.md with pattern matchers.The table extends Breznau et al.’s SI Table S12, using its variable definitions with minor copy-edits.Source: Table 1 · p. 31
  • Under biased prompting, none of the reported per-metric differences from default conditions is statistically separable at the 95% level.Across the 193 decisions, Claude Code differs from its default on 109 decisions and Codex on 68; these are descriptive patterns from 20 runs per condition.Source: §C.4 · p. 35, Figure 9 · p. 36

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