{"id":"ec08fee1-8eb6-4eab-8885-23da69151d88","arxiv_id":"2506.17367","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"LLMs assign inconsistent, wording-sensitive money values to waiting, walking, hunger, and pain, sometimes accepting 1 euro for major inconvenience and rejecting free money for no inconvenience.","lead":"Six large language models were asked to put a price on human inconvenience: how much money is enough to wait, walk, go hungry, or accept pain. The answers differ wildly between models and shift with small wording changes, raising doubts about letting AI assistants make these trade-offs for people.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The logistic price thresholds in Tables 1/3 are built on an explicit monotonicity assumption that the paper's own heatmaps violate; this undermines the quantitative claim though the qualitative instability result survives.","rationale":"The reader's weakest_assumption identifies exactly the same methodological fault: the price of inconvenience is defined via a monotone logistic fit while the paper's own results document non-monotonic rejection behavior. I agree that this is the most load-bearing technical concern for the quantitative layer of the paper. My analysis also confirms that the central qualitative finding—that LLMs are fragile and exhibit odd freebie/power-of-ten discontinuities—does not depend on the logistic threshold, because it is visible directly in the heatmaps. The paper even reports a striking single-whitespace prompt sensitivity in footnote 7, which independently supports the fragility claim. I therefore do not move the reader's conditional verdict; rather, the conditional acceptance is appropriate, with the principal condition being that Tables 1 and 3 be re-estimated or qualified for non-monotonic cells. A secondary limitation is the normative phrase 'unreasonably low' without a human baseline, but the objective instability and prompt-fragility evidence already carry the paper's main cautionary conclusion.","tokens_in":10023,"tokens_out":9784,"duration_ms":113750,"concrete_test":"For every fixed-inconvenience cell in Tables 1 and 3, take the raw binary responses at all reward levels, compute the observed acceptance probability per reward level, and run a monotonicity permutation test (e.g., inversions or isotonic-regression residual sum of squares under the null of non-decreasing probabilities). Additionally fit a local logistic smoother or spline and record every reward level at which the smoothed curve crosses 0.5. If a substantial fraction of cells show significant non-monotonicity or multiple 0.5 crossings, the logistic thresholds and rankings must be recomputed with a non-monotonic estimator; if the monotonicity violations are negligible, the concern does not land.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper defines the price of inconvenience as the 50% acceptance threshold of a logistic regression fit and explicitly assumes 'monotonic increase in probabilities' (Section 3, Results). Immediately before defining this quantity, the authors themselves report two classes of non-monotonic behavior in the same data: the freebie dilemma at zero inconvenience and rejection bands at powers-of-ten rewards (Figure 2, Section 3). For a fixed inconvenience quantity crossed by such a rejection band, P(Acceptance) drops as the reward increases through e10/e100/e1000, so the acceptance sequence is not monotone in reward. A monotone logistic curve cannot represent these dips; the estimated 0.5 boundary is a mathematical compromise rather than a well-defined price, and some cells may have multiple crossings or none at all. The bootstrap standard deviations in Tables 1 and 3 quantify sampling variability conditional on the misspecified model, not the error introduced by non-monotonicity. Consequently, the quantitative price comparisons and cross-model rankings in Tables 1 and 3 are not reliable as stated. The qualitative heatmap observations—cross-model variance, prompt fragility, freebie and reward-landmark discontinuities—remain supported independent of the logistic threshold, so this is a conditional-accept concern rather than a fatal one.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript studies how six large language models (GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash, DeepSeek-V3, Llama 3.3-70B, and Mixtral 8x22B) decide binary trade-offs between monetary compensation and four inconveniences: waiting time, walking distance, hunger delay, and pain. The authors collect repeated binary accept/reject decisions over a reward grid, display them as heatmaps, and fit logistic regressions at fixed inconvenience levels to define the 'price of inconvenience' as the 50% acceptance threshold, reporting bootstrapped means and standard deviations in Tables 1 and 3. A robustness study varies the prompt in ten ways: appointment type, gender, language, first-person narration, and chain-of-thought prompting. The central claims are that LLMs exhibit large inter-model variance, fragility to prompt wording, acceptance of very low rewards for major inconveniences, and rejection of free money, and the authors conclude that current LLMs should not be trusted for such decisions.","tokens_in":10228,"tokens_out":4944,"duration_ms":51489,"significance":"If the findings hold, the paper contributes a useful empirical map of LLM behavior in an understudied decision-making domain and provides an open-source framework that others can reuse. The qualitative findings are directly visible in the heatmaps and do not depend on the fitted logistic thresholds; the multi-model design, the four scenarios, and the prompt-variation study are strengths. However, the quantitative 'price of inconvenience' relies on a monotonicity assumption that the paper's own data violate, so the numerical prices and rankings in Tables 1 and 3 are not reliable as currently reported. The code and data release is a significant positive feature that makes the concerns checkable.","major_comments":[{"comment":"The price of inconvenience is defined as the 50% acceptance threshold of a logistic regression fit, and the manuscript explicitly assumes 'monotonic increase in probabilities' (Section 3, Results). The paper's own Figure 2 immediately shows two systematic violations of monotonicity: the freebie dilemma at zero inconvenience and rejection bands at powers-of-ten rewards (e10, e100, e1,000). For a fixed inconvenience quantity crossed by such a rejection band, P(Acceptance) decreases as the reward increases, so a monotone logistic curve cannot represent the data and the fitted 0.5 boundary is not a well-defined price; some cells may have multiple crossings or none at all. The bootstrap standard deviations in Tables 1 and 3 quantify sampling variability conditional on the misspecified model, not the error introduced by non-monotonicity. Because the quantitative rankings and cross-model comparisons in Tables 1 and 3 rely on this quantity, they are not reliable as stated. The qualitative observations from the heatmaps remain supported, but the paper should either use a nonparametric definition of the crossing point, restrict the fitting to monotone regions, or provide an explicit sensitivity analysis that quantifies the impact of non-monotonic cells.","section":"Section 3, 'price of inconvenience' definition and Tables 1 and 3"},{"comment":"Several entries are reported as '>10^3' (e.g., Mixtral in Pain in Table 1, Llama in Chinese in Table 3, Mixtral in Dutch and Chinese in Table 3), yet the aggregate row 'Avg. Value' reports a single number per model in Table 3 and a single average in Table 1. The manuscript does not state whether these censored values enter the average as 1,000, as infinity, are excluded, or are handled by some other rule. Different plausible treatments change the reported averages and model rankings; for example, Mixtral's average in Table 1 is dominated by its censored Pain cell. The authors should disclose the exact imputation or reporting rule, or switch to a censoring-aware summary such as medians or ranks.","section":"Section 3, Tables 1 and 3, censored values"},{"comment":"The abstract and conclusion describe some offers as 'unreasonably low' rewards for 'major inconveniences' (e.g., 1 Euro to wait 10 hours). The descriptive finding that some models accept such offers is well supported, but the normative term 'unreasonably' requires a benchmark that the paper does not provide, such as human valuations, stated user preferences, or a consistency criterion. Without such a benchmark, the paper should either soften the normative language or explicitly frame the benchmark assumption.","section":"Section 3, Figure 2 and Table 1, 'unreasonably low' claims"}],"minor_comments":[{"comment":"The currency symbol is garbled (e.g., 'e1' and 'e1,000'), likely because the Euro sign was lost in LaTeX; these should be rendered consistently as EUR or €.","section":"Abstract and throughout"},{"comment":"There are typos: 'practicioner' in the General Practitioner row of Table 2 and 'chain-of-though' in the Figure 4 caption.","section":"Table 2 and Figure 4 captions"},{"comment":"The text says 'When we ask a follow-up question for an explanation,' but the follow-up prompt is not provided and the resulting responses are not systematically analyzed; including the follow-up wording and at least a brief qualitative summary would make this reproducible.","section":"Section 3, freebie dilemma"},{"comment":"The whitespace example is an anecdote; if it is meant to support the fragility conclusion, it should be accompanied by a systematic test over several whitespace variations or moved to a supplementary analysis.","section":"Footnote 7"},{"comment":"The color-coding legend uses '¡10%' in the caption text; this should be '<10%'.","section":"Table 3"},{"comment":"The figure note says the fit is performed on binary decisions, but with only five runs per reward level the displayed observed probabilities are coarse; the paper should state the number of independent samples per cell and acknowledge the low sample size in the uncertainty discussion.","section":"Section 3, Figure 3"}],"recommendation":"major_revision","confidential_remarks":"The paper's core qualitative contribution is solid and reproducible given the released code and data, but the quantitative framing via logistic thresholds is not defensible as presented because the paper's own heatmaps violate the stated monotonicity assumption. This is a fixable issue: the authors can re-define the price nonparametrically or restrict to monotone regions, and they should also clarify how censored '>10^3' values enter averages. I do not see grounds for rejection, because the headline instability findings are visible in the raw heatmaps and independent of the fitted prices."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know. The qualitative result is real: six LLMs give widely different, prompt-fragile answers when asked to trade money against a user's waiting, walking, hunger, or pain. The heatmaps show big cross-model variance, the freebie dip at zero inconvenience, and sudden rejection bands at powers of ten. That is enough to make the paper's central concern — don't yet trust LLMs with these decisions — credible. The second thing is that the paper's quantitative centerpiece, the 'price of inconvenience' in Tables 1 and 3, does not survive contact with its own data. The price is defined as the 50% point of a logistic fit \"assuming monotonic increase in probabilities,\" and the heatmaps in Figure 2 show the assumption is false: acceptance drops as reward increases through e10/e100/e1000, and the freebie dilemma is a non-monotone dip at X=0. The fitted boundary is a mathematical compromise, not a well-defined price; the bootstrap standard deviations only capture sampling noise conditional on the misspecified model. The stress-test note is right.\n\nWhat the paper does well: the prompt variation suite (first-person, gender, appointment type, four languages, chain-of-thought) is a genuinely useful probe, and the qualitative observation that valuations are unstable is robust. Extending Keeling's pain/pleasure protocol to everyday financial trade-offs is a legitimate move, and the paper ships code and data.\n\nSoft spots beyond the monotonicity issue: five repeats per cell is thin for probability estimates, cells marked '>10^3' mean the logit never crosses 0.5, and the phrase 'unreasonably low' lacks a human baseline — perhaps many humans would also take e1 for ten hours of waiting. No commit hash and API dependence make the artifact somewhat fuzzy.\n\nWho it's for: people working on AI agent safety, alignment, or human-computer interaction. It deserves a serious referee, but the quantitative tables should be reworked — either report a non-parametric description of the decision boundaries or restrict the claims to the qualitative patterns. I would not cite the price tables; I would cite the paper for the heatmap findings.","headline":"Qualitative fragility results are solid; the quantitative 'price of inconvenience' is undermined by the paper's own non-monotonic heatmaps.","tokens_in":10790,"tokens_out":2640,"would_cite":true,"duration_ms":26486,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"LLMs assign inconsistent, often absurdly low monetary prices to user inconvenience, and small prompt changes shift those prices.","keywords":["price of inconvenience","LLM decision-making","agentic AI","prompt sensitivity","freebie dilemma","economic rationality","trade-off valuation","logistic threshold"],"falsifier":"Recompute the thresholds from the released data without the monotonicity assumption—for instance by locating the first reward above 50 percent acceptance rather than the logistic crossing—and check whether the reported rankings and language effects survive; if most thresholds move substantially, the paper's quantitative comparisons do not measure a stable price of inconvenience.","tokens_in":9810,"feed_emoji":"💶","tokens_out":4781,"duration_ms":46854,"temperature":0.7,"pith_summary":"Large language models are being pitched as personal assistants that can decide on our behalf, which means they must put a price on our discomfort. This paper tries to measure that price by asking six LLMs whether they would accept a cash reward for extra waiting, walking, hunger, and pain, and then fitting a threshold at which acceptance crosses 50 percent. The result is a set of valuations that are often extremely cheap (about one euro for a ten-hour wait), sometimes strangely cautious (rejecting 1,000 euros for no inconvenience at all), and highly sensitive to trivial changes in wording, such as switching from third to first person or from English to another language. If these results hold, current LLMs do not have a stable or trustworthy valuation of human inconvenience, which matters as they move into roles where they negotiate time, money, and comfort on a user's behalf.","feed_headline":"LLMs put a €1 price on a 10-hour wait","feed_subtitle":"Six assistants show unstable, prompt-sensitive values for walking, waiting, hunger, and pain.","key_machinery":"The central object is the 'price of inconvenience': the monetary compensation at which an LLM assistant accepts a proposed trade-off with probability 0.5, obtained by fitting a logistic regression to the model's binary accept/reject answers at a given quantity of discomfort across rewards from 0.10 to 1,000 euros. The fit assumes a monotonic increase in acceptance probability with reward, and the paper uses the fitted threshold plus bootstrap uncertainty to rank models and scenarios. The same machinery, with ten prompt variations, is used to measure fragility: a stable price should move little under changes like first-person narration, chain-of-thought instruction, a specified gender, or a different language, but the paper finds that these changes routinely shift the threshold, sometimes by orders of magnitude.","core_discovery":"For each inconvenience scenario (waiting, walking, hunger, pain) and each model, the paper defines the 'price of inconvenience' as the reward at which the model accepts the trade-off with 50 percent probability, estimated by fitting a logistic curve to repeated yes/no answers across a logarithmic reward grid. Across six current models and four scenarios, the paper reports large cross-model spreads—for example, around one euro versus over a hundred euros to accept the same 50-percent pain stimulus—and large within-model swings under prompt variation, including a tenfold or larger change when the prompt is translated into French, Dutch, or Chinese. The authors also document two recurring irregularities: a 'freebie dilemma' in which models reject or undervalue a strictly better offer that imposes no inconvenience, and a tendency to reject rewards at round landmarks of 10, 100, and 1,000 euros. Their central assertion is that these irregularities are common and serious enough that current LLMs cannot be fully trusted to make cash-versus-comfort decisions on behalf of users.","pith_inferences":["The language effect could be confounded with cost-of-living or cultural priors the models attach to a language; a direct test would hold the user's country constant while varying only the language of the prompt.","The freebie dilemma and powers-of-ten rejections suggest the models are applying heuristic suspicion rather than a continuous valuation; this predicts that prices will be more stable after fine-tuning on binary-choice data without such round-number rewards.","If the instability generalizes to other discomfort classes not tested here, such as fatigue, embarrassment, or social inconvenience, the practical risk for agentic assistants is wider than the four scenarios in this paper.","The paper's threshold comparisons could be made directly testable with human participants: elicit human prices for the same scenarios and see whether any LLM's valuation falls inside the human range, which the current study does not do."],"forward_implications":["If LLMs undervalue major inconvenience, an automated assistant left to negotiate on a user's behalf may routinely accept painful or costly delays for trivial compensation.","Prompt sensitivity means two users asking nearly the same question could be steered to very different decisions, opening a route for adversarial or accidental manipulation of a personal assistant's choices.","The documented rejection of free money at zero inconvenience implies that LLMs are not merely optimizing expected value; any deployment that assumes rational choice will mispredict their behavior.","Chain-of-thought prompting reduces the freebie dilemma and powers-of-ten rejections in the paper's experiments, so reasoning prompts may be a partial mitigation, at the cost of noisier decisions.","The price-of-inconvenience metric offers a concrete way to audit assistants before release by comparing models on the asked price for a fixed discomfort."],"supporting_citations":[{"why":"Supplies the price-of-inconvenience definition and the pain-perception setup that this paper extends to monetary trade-offs.","marker":"[14]"},{"why":"Establishes the benchmark of LLM economic rationality against which the paper's irregular trade-off behavior is contrasted.","marker":"[8]"},{"why":"Documents LLM-human preference gaps, such as higher discount rates, motivating concern about how LLMs value delayed or uncomfortable outcomes.","marker":"[9]"},{"why":"Provides the human freebie-dilemma literature used to interpret LLM rejection of zero-inconvenience offers.","marker":"[21]"},{"why":"Supports the freebie-dilemma interpretation by showing that people infer phantom costs from free or extremely cheap offers.","marker":"[22]"},{"why":"Provides the chain-of-thought prompting method used in the robustness experiments.","marker":"[28]"},{"why":"Shows that LLM responses depend on language and dialect, contextualizing the large price shifts observed when prompts are translated.","marker":"[23]"}],"fun_headline_variants":["LLMs price a 10-hour wait at €1","LLMs accept €1 for 10 hours of waiting","Prompt tweaks flip LLM prices for discomfort","LLMs reject free money, undervalue pain","Cash vs comfort: LLMs fail as agents"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that a single threshold can be read off a monotonic acceptance curve, but the models' own responses show non-monotonic dips at zero inconvenience and at powers-of-ten rewards, so the fitted 50-percent point is not guaranteed to be a well-defined price.","fun_headline_variants_meta":{"raw":{"variants":["LLMs price a 10-hour wait at €1","LLMs accept €1 for 10 hours of waiting","Prompt tweaks flip LLM prices for discomfort","LLMs reject free money, undervalue pain","Cash vs comfort: LLMs fail as agents"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000469,"raw_usage":{"total_tokens":2367,"prompt_tokens":1009,"completion_tokens":1358,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":625,"completion_tokens_details":{"reasoning_tokens":1282}},"tokens_in":625,"tokens_out":1358,"duration_ms":10783,"temperature":1.0,"reasoning_tokens":1282,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T19:12:55.317219+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Recompute the thresholds from the released data without the monotonicity assumption—for instance by locating the first reward above 50 percent acceptance rather than the logistic crossing—and check whether the reported rankings and language effects survive; if most thresholds move substantially, the paper's quantitative comparisons do not measure a stable price of inconvenience.","supporting_citations":[{"cited_title":"X., Shan, Y ., Zhong, S","cited_arxiv_id":null,"evidence_quote":"Establishes the benchmark of LLM economic rationality against which the paper's irregular trade-off behavior is contrasted."},{"cited_title":"Frontiers: can large language models capture human preferences?Market- ing Science43(4) (2024) 709–722","cited_arxiv_id":null,"evidence_quote":"Documents LLM-human preference gaps, such as higher discount rates, motivating concern about how LLMs value delayed or uncomfortable outcomes."},{"cited_title":"A., Folkes, V","cited_arxiv_id":null,"evidence_quote":"Provides the human freebie-dilemma literature used to interpret LLM rejection of zero-inconvenience offers."},{"cited_title":"J., Mofradidoost, R., Gray, K","cited_arxiv_id":null,"evidence_quote":"Supports the freebie-dilemma interpretation by showing that people infer phantom costs from free or extremely cheap offers."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the chain-of-thought prompting method used in the robustness experiments."}],"review_version":2}