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on Discrete Choice Models |
| By: | John C. Whitehead; Paul Hindsley; O. Ashton Morgan |
| Abstract: | This paper tests convergent validity between the travel cost method and contingent valuation method using opt-in panel data on recreation trips to South Florida. Data come from four survey waves administered through the Dynata panel between April 2024 and March 2025, yielding a final sample of 2103 Florida residents who took a recent recreation trip and plan another. Respondents are split into day trippers and overnight trippers. Travel cost models estimate recreation demand with a truncated negative binomial specification, using both average and minimum travel cost to 11 South Florida sites as the price measure. Contingent valuation models use a panel logit specification with two dependent variables, an initial yes/no response to a hypothetical trip cost increase and a version adjusted for respondent certainty. Consumer surplus and willingness to pay estimates, along with Krinsky-Robb confidence intervals, are computed for all eight models. Willingness to pay exceeds consumer surplus in six of eight comparisons, but only one difference is statistically significant. The results suggest that Travel Cost Method and Contingent Valuation Method welfare estimates from opt-in panel data are largely convergent valid. The findings support continued, cautious use of opt-in panel data for recreation valuation. Key Words: travel cost method, contingent valuation method, convergent validity, opt-in panel data. |
| JEL: | Q51 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:apl:wpaper:26-10 |
| By: | Maria Kovalenko; Georgy Lukyanov |
| Abstract: | This paper studies social learning when buying access to previous decisions also determines whose decision enters the record. Individuals first receive private information and then decide whether to pay for access. A positive price selects relatively weak private beliefs. With one founding observation, every buyer copies it, and the record acquires no further information. With a richer initial history, buyers can disagree and the record can improve; nevertheless, a price bounded away from zero prevents complete learning, even with unbounded private beliefs. We then consider a rule under which a buyer makes a preliminary choice before access and a revised choice afterward. A positive probability of implementing the preliminary choice makes it follow the private signal. Retaining both choices restores complete learning under a fixed effective fee or pooled myopic pricing, including with bounded beliefs. The rule reduces willingness to pay and sacrifices some current decision accuracy. A worked example shows how the subsequent improvement in information can outweigh both initial losses. The results show why preserving a judgment made before exposure can change what later users learn from the same selected population. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25071 |
| By: | Matthew J. Baker; Lisa M. George |
| Abstract: | We derive the exact likelihood of the Berry (1994) share-form nested logit and show it contains a Jacobian term that depends on nest size and the nesting parameter. Omitting the term biases within-nest substitution estimates wherever choice sets vary across markets. Commonly-used count instruments for the within-nest share fail exclusion when product counts enter demand directly. The corrected likelihood identifies substitution without them, making crowding estimable. We apply the estimator to Medicare Advantage plan proliferation after 2019 elimination of the ``meaningful difference'' requirement. Estimates indicate plan proliferation raised consumer surplus in most markets, but ignoring crowding overstates the gains. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12987 |
| By: | Gavin Kader |
| Abstract: | The Money Pump Index (MPI) of Echenique et al. (2011) measures the severity of consumer irrationality, but computing the exact mean and median MPI over all revealed preference cycles is NP-hard (Smeulders et al., 2013). Existing solutions rely on heuristic proxies, such as evaluating only shorter cycles or bounding the MPI. By framing revealed preferences as a directed graph, this paper projects choice violations onto fundamental cycle bases, which are minimal sets of linearly independent cycles that span the graph's entire cycle space. This yields computationally tractable estimators for the mean and median MPI that are asymptotically equivalent to the original MPI. Applying this methodology to the scanner dataset analyzed by Echenique et al. (2011) and Smeulders et al., (2013), the proposed estimators compute quickly and with negligible small-sample bias. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.16086 |
| By: | Arkarup Basu Mallik; Mihir Bhattacharya; Anuj Bhowmik |
| Abstract: | We study an attribute-based model of stochastic choice in which attention to each attribute is limited, and characterize two choice rules within it. The Multiplicative Attention Rule (MAR) rewards an alternative only for the attributes on which it ranks first, combining attention to these multiplicatively across attributes; it captures choice deferral driven by the cognitive load of attending to `too many' attributes. The Additive Attention Rule (AAR) instead averages attention across attributes and credits an alternative by its full ordinal rank within the menu. This difference in aggregation is what separates the two rules behaviourally: AAR reproduces the Attraction Effect while holding the relevant attributes fixed across menus, something MAR can do only when the attribute set itself changes, though AAR, in turn, cannot accommodate the Compromise Effect. Both rules are characterized by axioms on observable choice data, and in both cases the underlying attention parameters and attribute-based preferences are uniquely identified from that data. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01333 |
| By: | Katsutoshi Nohara |
| Abstract: | This study applies a partial proportional-odds model to examine whether people’s psychological distance from climate change changed during a period encompassing the unprecedented heat experienced in Japan in 2023 and 2024. It also investigates how psychological distance and environmental knowledge are associated with willingness to pay for climate change mitigation measures. The study draws on two repeated cross-sectional surveys using identical questionnaires administered to different respondents in 2022 and 2025. Propensity scores and overlap weighting were used to improve the comparability of the two survey samples by balancing observed differences in age, gender, and prefecture of residence. After this adjustment, respondents in 2025 were more likely than comparable respondents in 2022 to perceive climate change as psychologically proximate. Specifically, the predicted probability of reporting a high level of psychological proximity was approximately 13 percentage points higher in 2025. Although this difference cannot be attributed exclusively to the record-breaking heat experienced in 2023 and 2024, the findings suggest that the unprecedented heat may have contributed to making climate change appear more immediate and personally relevant. Furthermore, the contingent valuation analysis shows that the positive association between environmental knowledge and willingness to pay depends on psychological distance: greater environmental knowledge is associated with higher willingness to pay, particularly among individuals who perceive climate change as psychologically close. These findings provide important insights into the design and communication of climate policies. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:toh:tupdaa:89 |
| By: | Erik O. Kimbrough (Chapman University); Brennan McDavid (Chapman University); Diba Vazirian (Chapman University) |
| Abstract: | This paper studies delegation to artificial intelligence in a setting where human principals retain the consequences of delegated choices. Participants wrote prompts instructing ChatGPT-4o mini how to choose on their behalf in three canonical economic domains: risky choice, intertemporal choice, and social allocation. We then elicited the compensation participants required to let the AI’s choices count for payment and compared participants’ own choices to choices generated from their prompts. The design produces two central empirical objects: a revealed measure of reluctance to delegate, captured by willingness to accept compensation for AI delegation, and a behavioral measure of alignment, captured by the share of decisions on which the participant and AI made the same choice. We supplement the original experiment with two benchmarks: a human-agent follow-up in which other participants attempted to implement the same prompts, and an ex post robustness exercise using GPT-5.5. The results show substantial reluctance to delegate despite moderate-to-high alignment, and they suggest that misalignment reflects not only model limitations but also the difficulty of communicating delegable preferences through short natural-language prompts. |
| Keywords: | AI, Trust, Experiments, Preferences |
| JEL: | C91 D91 D83 D81 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:chu:wpaper:26-11 |
| By: | Isaac Cheah; Ethan Pancer; Jianping Huang; Aimee Pink; Béatrice Parguel (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique); Fong Keng-Highberger |
| Abstract: | Research examining artificial intelligence's (AI) role in food choice largely asks whether AI helps people make better decisions, treating it as an information source that improves the accuracy or personalization of recommendations. We suggest that this framing captures one point on a broader continuum of how AI impacts food decisions. AI can act, often simultaneously, as an advisor consulted for information, a decision partner that actively shares in constructing the choice, or an ambient influence that shapes what appears desirable and available before deliberate choice begins. As AI becomes more conversational and more embedded across the food environment, this continuum is increasingly populated in less visible regions, yet the field remains concentrated on the advisor role. We focus on the decision-partner role, where AI participates in value construction, goal clarification, attribute weighting, and choice justification, raising questions about agency, responsibility, and delegation. We articulate four mechanisms that describe this participation, namely preference offloading, responsibility reallocation, confidence calibration, and food-decision skill erosion. Because food choice is frequent, often habitual, and commonly involves trade-offs among hedonic and utilitarian goals, it is an ideal domain in which these process-level effects can compound. We draw on evidence from consumer psychology, automation research, and cognitive science, noting where boundary conditions remain untested in food contexts, and close with a research agenda organized around agency, reliance, responsibility, confidence, and cognitive effort. When AI shares in constructing a food choice, the values being optimized may no longer be entirely the consumer's own. |
| Keywords: | Artificial intelligence, Food choice, Decision-making, Consumer psychology, Algorithm reliance |
| Date: | 2027–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05752256 |
| By: | Pavel Kireyev |
| Abstract: | LLMs increasingly act as purchasing agents, which makes the LLM, not the user, the one choosing among the options that satisfy a request; its preferences quietly fix what gets bought and what it costs. Hotel booking is a clean instance: a high-volume choice settled on a few comparable attributes, where the pick reveals those preferences. We introduce PriceBench, a diagnostic benchmark that recovers an LLM's price, quality, and brand preferences from its booking choices with a logit choice model, applied to 28 LLMs from 8 providers on 3, 600 hotel tasks from 179 real New York City properties. We find that capability is associated with how consistently an LLM chooses, not with what it chooses: more capable LLMs hold stronger, more consistent preferences, while weaker ones either lock onto one position, exploitable by whoever controls listing order, or choose almost indifferently. What those preferences favor varies sharply across providers and even within one family: price sensitivity spans more than an order of magnitude, and the price/quality trade-off moves mean booked nightly price from \$247 to \$393 on identical tasks. What an agent buys must therefore be measured per LLM, not inferred, and we release the tasks, code, and all 28 response sets. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.31468 |
| By: | Gonzalez-Navarro, Marco; Hall, Jonathan D.; Wheeler, Harrison; Williams, Rik |
| Abstract: | We study how ride-hailing and public transit interact – an open question with importantimplications for transportation policy and urban form. We develop a model of mode choice that features three key mechanisms: (i) substitution, whereby riders switch from Uber to transit near stations; (ii) last-mile complementarity, whereby Uber is used to access transit; and (iii) a dynamic choice-set effect, whereby outbound mode choice constrains return options, givingUber option value. Using proprietary Uber trip data around 650 rail station openings worldwide, we estimate how ride-hailing responds when public transit improves. We document three patterns consistent with these mechanisms: Uber usage rises sharply within 100 meters of new stations (last-mile complementarity), declines locally among nearby residents and workers (substitution), and falls modestly overall for these users as local reductions are partially offsetby increased Uber usage elsewhere (option value). Estimating the model on these moments, we find that all three channels are quantitatively important, with substitution the largest individualchannel but last-mile complementarity and option value together more than offsetting it, so that Uber and transit are net complements. Policy implications include: (a) a ride-hailing tax induces users to switch to private vehicles rather than transit; (b) a subsidy for last-mile ride-hailing trips is more effective at increasing transit use when it targets the per-kilometercost rather than the fixed cost; and (c) increases in transit use are best induced by a congestion charge that subsidizes transit fares rather than by policies relying on ride-hailing taxes and/or last-mile subsidies. |
| Keywords: | Social and Behavioral Sciences, ride-hailing, subways, public transit |
| Date: | 2026–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:agrebk:qt29v1x3gc |
| By: | Hanping Chen; Zeqi Wu |
| Abstract: | This paper investigates the construction of moment restrictions in dyadic network formation models with unobserved individual heterogeneity under nontransferable utility. Using observed links and covariates from five-node pentads, we construct moment restrictions that do not depend on individual fixed effects. For a broad class of covariate specifications, the construction is minimal in the sense that it uses the least possible number of nodes and dyads. Based on these moment restrictions, we propose the pentad-GMM estimator. We establish asymptotic normality of the pentad-GMM estimator in dense, sparse, and ultra-sparse network regimes, with regime-specific convergence rates and asymptotic variances. These results provide a basis for inference across all three regimes. To make our method computationally efficient, we develop an algorithm that reduces the computational cost of the estimator from na\"ive \(O(N^5)\) to \(O(N^3)\). We apply the proposed method to an academic-discussion network, and find academic homophily and a positive association between link formation and a potential partner's openness to different perspectives. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12545 |
| By: | Davood Wadi; Yu Ma |
| Abstract: | Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab, " an adaptation of information-board process tracing that places product attributes behind costly tool calls, we examine how marketing pricing cues (i.e., just-below pricing and promotional framing) influence AI shopping agents. Across eight commercially deployed LLMs from three providers, we trace pre-choice information acquisition. Under zero cost, pricing cues rarely mislead. Imposing acquisition costs under a vague goal prompt leads LLMs to omit diagnostic attributes required to compute unit price and choose suboptimal choices resembling human heuristics. Relative to a specific goal prompt that mainly preserves diagnostic search and choice optimality, a vague goal prompt under constraints creates a search-mediated vulnerability. This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.28372 |
| By: | Taotao He; Runfa Hu |
| Abstract: | We study the computational complexity of peak-oriented rationalizability, a survey based revealed-preference test introduced by Seror (2026). We provide a polynomial-time algorithm for testing rationalizability and recovering a utility function, establishing that peak-oriented preference elicitation is computationally tractable. In contrast, we show that computing the peak-oriented Houtman-Maks index is NP-hard. These results delineate the precise computational boundaries of peak-oriented revealed-preference analysis. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.30042 |
| By: | Siwei Chen; Pengbo Wang |
| Abstract: | A collective rule must often choose a public outcome on a constrained boundary. We characterize truthful choice with diagonal unanimity on the entire boundary of any compact full-dimensional strictly convex body in finite dimension at least two. On the full domain of linear support preferences, ordinary individual strategy-proofness and diagonal unanimity force the rule to select one fixed participant's preferred point at every profile. The population is any fixed finite nonempty set. No Pareto condition, boundary smoothness, or continuity or measurability of the rule is assumed. If interior outcomes are allowed instead, two or more participants can use positive fixed weighted averages of preferred points for truthful non-dictatorial compromise on the same type domain. The proof derives regularity from the two agents' incentive inequalities, transports a common support-measure bound across reports, and makes every convexified attainable menu a Minkowski summand of the feasible body. Boundary generation then forces fixed control. The result extends the spherical public-good location question and identifies the role of boundary-valued choice in eliminating truthful compromise. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.26302 |
| By: | Arkarup Basu Mallik; Mihir Bhattacharya; Ojasvi Khare |
| Abstract: | We study strategy-proof social choice where agents have single-dipped preferences and outcomes may be sets of alternatives. While point-valued strategy-proof rules select an endpoint, we ask which outcomes survive when sets are compared through responsive extensions. Under the standard responsive extension, preferences over fixed-length intervals remain single-dipped, so the endpoint restriction persists and anonymity yields a single-threshold rule. Under responsive dominance, the incomplete order shared by all responsive extensions, an interior interval can survive because the two extreme intervals need not be comparable. For arbitrary sets, requiring every chosen alternative to be most preferred by some agent yields voting-by-committees rules, with one committee per endpoint; anonymous rules have a two-threshold quota form. The characterization holds on the entire endpoint-peaked domain, and every such rule is group-strategy-proof. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.14059 |
| By: | Andrzej Tokajuk; Jaros{\l}aw A. Chudziak |
| Abstract: | Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjustment. We address this gap through a comparison of seven losses and five models across major cryptocurrencies. Validation-based alignment adjusts the forecast level before the raw and aligned forecasts are evaluated using statistical scores and one-day Value-at-Risk. Before alignment, marginal score variation is greater across losses. After alignment, model choice becomes the larger source of variation in the full five-model comparison, while cross-loss differences in VaR breach rates narrow substantially. Our contribution is a comprehensive evaluation of loss and model choice that shows why losses can appear so influential in raw comparisons and how this interpretation changes when forecast level and downstream risk are considered explicitly. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27024 |