Papers

Non-governmental organizations often compete for funding while valuing similar social outcomes. Fundraising may expand total giving to a cause or redirect a given funding pool toward the focal organization. This paper studies how cross-organizational purpose alignment changes the composition of such competition. The analysis separates two effects of fundraising: marginal lift in aggregate funding and marginal shift of funding between organizations. Incomplete alignment creates a wedge between an NGO's marginal mission return and the aggregate mission return that is proportional to shift minus lift. When NGOs choose among fundraising technologies, greater purpose alignment shifts effort toward the relatively lift-intensive technology. Relative to the aggregate mission benchmark, incompletely aligned NGOs choose portfolios that are too shift-intensive. A uniform restriction on fundraising may change total effort but cannot generally correct this composition distortion; correcting the portfolio requires changing relative incentives across technologies. A simple microfoundation interprets cause promotion as lift and organization promotion as shift. Productivity differences preserve reallocation when it raises mission output, while organization-specific benefits restore rivalry even under complete alignment. The model therefore predicts how a common purpose changes the form of competition even when its effect on the level of competition is ambiguous.

An organization may bind future decision makers through a commitment over its own lifespan. This paper studies that choice for philanthropic foundations. A foundation may commit to terminate, commit to institutional continuity, or leave termination to future discretion. Discretion preserves state-contingent stopping. Perpetuity instead raises investment in foundation-specific organizational capital whose return is partly lost upon termination. A terminal commitment limits excessive continuation when future governing bodies obtain private benefits from organizational survival. A canonical model yields nonempty regions in which each regime is optimal, while a finite-horizon extension endogenizes the terminal date. Perpetuity is optimal when organizational capital is sufficiently productive relative to the cost of continuing in bad states; discretion is optimal when stopping flexibility is valuable and governance is aligned; a finite lifespan is optimal when survival incentives create sufficiently costly entrenchment. Organizational lifespan is therefore a distinct commitment margin.

When a subset of alternatives is allowed to be the collective choice, one approach is to study the social choice correspondence defined on agents' preferences over alternatives. This paper considers a different approach. We argue that agents should have preferences directly over outcomes. We consider the social choice problem on a closed real interval, and assume a single-peaked domain over intervals. Our focus is on the peak-only social choice function, referred to as the voting scheme. The leftright scheme, which generalizes the minmax scheme, is the only strategy-proof voting scheme. Adding anonymity, it reduces to the generalized median scheme. For an odd number of agents, the median scheme is the only voting scheme satisfying efficiency, anonymity, and strategy-proofness.

This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from information across categories and improves its predictive performance as the dimensionality of the choice environment increases, whereas the mixed logit model does not exhibit the same pattern. MCMC delivers the highest predictive accuracy but is computationally intensive. VI achieves slightly lower predictive performance while substantially reducing runtime. In the empirical application, VI also outperforms the mixed logit benchmark. These findings highlight a trade-off between accuracy and computational feasibility in multi-category demand estimation.

Classical growth theory places physical productivity at the center of economic growth. In modern service-based economies, however, growth increasingly means creating higher-valued products rather than increasing output quantities. This paper develops a theory of value growth based on a socially and institutionally sustained value scale. The framework unifies value creation, finance, and distribution and offers an account of major patterns in US economic history. The proposed framework is intended as a new approach to macroeconomic analysis and policy making.

Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and disparities in access to high-performing programs. At scale, however, recommenders in capacity-constrained settings can be self-defeating: if they steer too many users toward the same items, then even users who were originally predicted to have a high chance of matching to an item may not, due to increased competition. In this paper, we formalize this phenomenon of recommendation-induced congestion; motivated by the NYC high school match, we show that naive recommendations can cause sharp decreases in program acceptance rates, most affecting applicants with the fewest nearby options. Next, we propose and theoretically analyze a congestion-aware, bilevel optimize-and-simulate approach to allocate recommendations and improve match outcomes safely, in equilibrium. Finally, we deploy this approach in the 2025-26 admissions cycle of the NYC high school match, aiming to reduce disparities by highlighting personalized lists of nearby, high-performing programs where an applicant has a high predicted offer likelihood. In a randomized controlled trial, we find that 16.4% of treatment applicants ranked a recommended program, versus 10.5% of control applicants who ranked a program they would have been recommended (57% relative increase; $p$=0.011); 5.6% of treatment applicants matched to such a program, versus 3.3% of control applicants (71% relative increase; $p$=0.071); further, no treatment applicant was rejected from a recommended program. Our findings suggest that recommenders should be analyzed and designed as market-shaping interventions.

Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Drawing on a database of approximately 535 million employment records across 58 countries, we reconstruct the inter-firm network of AI talent flows over 2010–2022. We find that AI talent inflows are becoming increasingly concentrated among a small set of leading firms, with stronger preferential attachment than in general labor mobility. Yet the network core remains contestable: central positions in AI networks change hands far more often than in non-AI networks. Network position also carries information that goes beyond workforce size. This network position is economically consequential: AI network centrality is associated with higher enterprise value, after accounting for workforce scale, assets, and fixed effects. Event-study estimates further indicate that sharp improvements in network position are followed by higher enterprise value relative to matched firms with similar pre-event trajectories. Together, these findings suggest that corporate AI advantage depends not only on how much talent firms accumulate, but also on where they sit in its circulation.

North-star metrics such as customer lifetime value are often too slow and noisy to decide a short A/B test. Teams therefore rely on a proxy metric, commonly chosen by how closely its effects tracked the north star's across past experiments. Validating that choice, or any method for making it, is hard: the only benchmark is the noisy north star, and the number of available past experiments is limited. In addition, proxy and north-star effects are estimated on the same customers, so their sampling errors are correlated. Recent work at major experimentation platforms removes this shared error as contamination, improving estimates of the true-effect covariance. Choosing a proxy, however, is a ranking problem, and a better estimate need not give a better ranking. We measure agreement free of shared error by estimating the two effects on disjoint random halves of each experiment's customers. In an archive of 262 experiments and 69 candidate proxies, the shared error ranks the candidates in a similar order to this agreement (Spearman correlation 0.65): it carries information about proxy quality. The more of it a correction removes, the worse the ranking because removal discards part of the signal but leaves the main sources of ranking noise, the noisy north star and the limited number of experiments, untouched. Archive-calibrated simulations, in which the correct ranking is known, confirm this even when every correction receives the true sampling covariance. Held-out real experiments, evaluated on disjoint customer halves so that shared error cannot bias the comparison, closely reproduce the predicted ordering (Spearman correlation 0.93). Correction can still pay off with more experiments, but the number needed rises steeply with the north star's noise. We map this crossover and give platform teams three inexpensive checks for deciding from their own archive whether and how strongly to correct.

We study object reallocation problems with single-peaked preferences. On the unrestricted domain of strict preferences, top trading cycles (TTC) is the unique rule that satisfies efficiency, individual rationality, and strategy-proofness. On the single-peaked preference domain, the crawler introduced by Bade (2019) satisfies the same properties. We show that the Nash equilibrium allocations of the crawler's direct revelation game form a complete lattice under weak Pareto dominance. The truthful allocation is the greatest element of this lattice, while the initial endowment allocation is its least element.

This study examines whether universities shape the regional innovation effects of public R&D expenditure and whether this role varies across regional development trajectories. We analyse a panel of 1166 EU NUTS 3 regions over the period 2007-2020, combining data on European public R&D funding, patenting performance, university presence and regional development traps. Toa ddress the endogeneity of public R&D expenditure, we employ a control function aproach. The results show that public r&D expendirue positively affects patenting performance in both university and non-university, but the difference between them is not statistically significant. University presence therefore does not sistematically amplify the returns to public R&D. However, its moderating role becomes more pronounced in regions exposed to development trap risk and in regions experiencing persistent stagnation, while evidence is weaker for trap intensity. These fundings suggest that universities act as contingent rather than unviersal catalyst of public R&D. Their contribution appears particularly relevant in vulnerable and persistently stagnant regional trajectories, but less able to offset sever structural constraints. The study contributes to debates on the geography of public R&D and regional development traps by showing that the effectiveness of research funding depends not only on institutional presence, bu also on the development rajectory in which that institutional capacity is embedded.

The middle-income trap is usually identified by comparing a country's income to a threshold. This reveals that convergence has stalled but not where the blockage lies, and it leaves unexplained why the trap is still debated in countries that have already passed the threshold. This study treats the trap as a configuration problem, measuring development along three dimensions - future, outside, and inside potential - with indices built from standardised indicators. The indices are calculated for 34 OECD members in 2000, 2010, and 2020, so that changes in configuration become visible, and the resulting country clusters are interpreted through factor analysis. Two traps and two transition strategies emerge. The first strategy attracts foreign investment and technology, but can itself become a trap: domestic value added stays limited and rising wages erode export competitiveness. The second adds a developmental state focused on the quality of education, building the human capital that raises productivity and innovation. All six CEE members of the OECD had adopted the first strategy by 2010; by 2020 only Estonia had completed the second. Configuration levels and changes track relative income convergence.

We study reward design for delegated representatives who acquire costly information and vote on a binary governance proposal. Effort improves both private information and delegated voting weight. We show that no payment rule can make sincere voting strictly optimal at every positive effort profile under a non-neutral prior, while fixed budgets limit accuracy in large symmetric committees. We then compare proportional sharing with a conditional rule that rewards only members of a sufficiently large winning coalition. Unlike proportional sharing, which supports sincere voting only through indifference, the conditional rule makes sincere voting strictly optimal at symmetric positive effort profiles. It supports sincere equilibria with the same effort as proportional sharing and, under suitable reward-cost conditions, with strictly higher effort and decision accuracy. Insincere equilibria may nevertheless coexist.

Joint-event forecasts combine an estimated dependence model with current marginal forecasts. Retaining historical transforms at the marginal fits available when each outcome arrived creates overlapping estimation errors. We derive their joint influence with the terminal estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton vine. An intercept identity and the stable VAR filter reduce the historical correction to harmonically weighted innovation moments. Terminal slope uncertainty remains in the forecast gradient. A feasible covariance estimator yields asymptotically valid repeated-sample intervals for fixed one-sided event probabilities at the realized forecast state. A same-family terminal refit provides the efficiency comparison. Retaining dated transforms adds a positive semidefinite limiting covariance term, whose event-level cost depends on dependence sensitivity and forecast-state leverage. Monte Carlo comparisons show a small accuracy cost of retaining the transforms and a larger inferential role for current marginal estimation. Allowing Clayton dependence improves downside forecasts relative to Gaussian dependence under each transform rule. A fourteen-variable U.S. real-time forecasting application and simulations with twenty and fifty variables illustrate feasible joint-event estimation and analytic inference for fixed-depth vines.

We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment effect (GATE), even when the edge weights used to construct the exposure are misspecified. We formalize the assumptions needed both in terms of the linearity of potential outcomes and the sparsity of the graph, and prove the asymptotic normality of the Wald estimator. The estimator is straightforward to implement, requiring no assignment simulations over the graph. Our Monte Carlo study demonstrates the strong performance of our approach in the context of a social platform.

Mitigation pathways require fossil power plants to retire well before the end of their technical lives, yet retirement decisions are typically derived from cost-minimizing system models rather than from the decision facing the plant's owner. This study investigates the carbon price threshold at which closure becomes more advantageous for an operator, as well as the ongoing costs associated with continued emissions. A finite-horizon Markov decision process is solved plant by plant for the European fossil fleet of 850 units, with a carbon price that moves stochastically and plants that age deterministically. The price process is constructed so that widening its range leaves the expected price unchanged, isolating the effect of uncertainty from the effect of price level. Closure thresholds fall below recent EU allowance prices for all fuels considered, and the sensitive range lies well beneath them, so further price increases are unlikely to be the binding instrument for retiring this fleet. Because fuel costs rather than emissions determine how much margin a carbon price must erode, the least carbon-intensive capacity retires first. Uncertainty raises the thresholds by about a tenth at an unchanged expected price: a credibly stable carbon price retires the same capacity as a volatile price roughly a tenth higher. Policy credibility therefore substitutes for price level.

We study monopoly regulation when capacity is contractible but subsequent output is not. A firm privately observes its production cost before installing costly capacity and then choosing output, while the regulator cannot use transfers. We provide sufficient conditions under which a single capacity floor implements an optimal mechanism with full participation. The floor raises output by changing investment while leaving utilization to the firm after installation costs are sunk. Under additional conditions, the allocation has three regions: laissez-faire, full utilization of the floor, and underutilization. High-cost types knowingly install capacity they will not fully use, yet produce more than under laissez-faire. This physical waste is a necessary evil of optimal regulation that expands service. We also show that underutilization can persist in optimal regulation with capacity-contingent subsidies or a common entry fee.

Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen them. We argue that the direction depends on the judgement burden, the expertise a learner must supply to screen AI output before learning from it, and that expert verification before release moves this burden from students to an accountable tutor. We test the argument in a two-cohort difference-in-differences design in which one half of a compulsory firstyear university economics course received AI-generated podcasts, FAQs and quiz-based study guides, produced with a source-grounded model and checked by a named graduate teaching assistant (170 students; 340 examination marks). Access was associated with a 2.34-mark advantage on a 50-mark component. The share of marks below the upper-second classification boundary fell by 24.7 percentage points relative to the counterfactual, effects were significant at every threshold from 23 to 31 marks and at none above, and roughly three-quarters of the average originated in the bottom quintile. The threshold estimate is robust to removing the lowest-scoring students from the pre-intervention cohort; the average effect is not. Interviews and feedback from 36 students indicate that the verification label gave students a reason to engage with AI-generated material without ending their scrutiny of it. Evaluations of AI learning resources that report only mean effects cannot detect whether the students the resources are meant to help are the ones who gain.

Compared to self-employment or performance-based pay, fixed wages reduce earnings uncertainty but are prone to market distortions through moral hazard and adverse selection. Using a model of wage contracts under asymmetric information, I show that these distortions map to marginal treatment response functions in a marginal treatment effects (MTE) framework. I apply this framework to a field experiment in which data-entry workers choose between a randomized fixed wage and a piece rate. I find evidence of both moral hazard and adverse selection: Fixed wages reduce worker productivity by an estimated 8.74 percent relative to the mean. Meanwhile, a 10 percent increase in the wage offer attracts a marginal worker whose productivity is higher by 1.90 percent of the mean. Using semi-parametric MTE estimation, I calculate the welfare loss associated with asymmetric information and the marginal values of public funds (MVPFs) for policies aimed at recovering this loss. I find that a tax on piece rates can mitigate adverse selection into fixed wages, generating tax revenue at social costs as low as $0.88 per dollar.

Unions are important economic and political actors at the local level, but the primary source on US union membership, the Current Population Survey (CPS), is unreliable below the state level. Researchers have therefore turned to union-reported membership in federal "LM" filings, geolocating unions and aggregating their reported membership to estimate union density for counties, commuting zones, and congressional districts. We ask whether this practice is sound. We develop and defend multilevel regression with poststratification (MrP) on the CPS as a benchmark, then validate LM-based estimates against it from the state level down. LM, CPS, and MrP are highly correlated at the state level, but this correlation is roughly halved at the congressional-district, commuting-zone, and county levels. The breakdown is driven in part by reporting "lumpiness," which produces improbable values, including union densities exceeding 100%. Missing public sector union members explains only a small part of this divergence. These issues are substantively consequential: in a standard commuting-zone "China shock" regression, swapping density measures changes the import-exposure coefficient enough to alter its magnitude, significance, and sign. We conclude that LM records remain a rich resource for studying unions as organizations, but aggregating LM-reported membership is not a reliable way to measure union density at fine geographic resolution.

This paper investigates the non-emptiness of the fuzzy core and introduces the notion of the TU-fuzzy core for pure exchange economies with discontinuous utility functions. We propose two stability conditions: local fuzzy-coalition stability and local TU-fuzzy-coalition stability. We also introduce an independent-$\pi$-balanced family of subsets of the agent set to construct two payoff-dependent NTU games. These constructions serve as bridges linking the fuzzy core, the TU-fuzzy core, and NTU games. Under the proposed stability conditions, we establish the non-emptiness of the fuzzy core for economies with quasi-concave utility functions and the non-emptiness of the TU-fuzzy core for economies with concave utility functions.

Population prompts are widely used to generate synthetic survey responses, but they combine information supplied at inference with associations already encoded during pretraining. We introduce an alternative construction that maps declared aggregate preference anchors into group-indexed choice policies. For each population, the signs of six Global Preferences Survey (GPS) coordinates deterministically label a shared bank of paired synthetic responses, and Direct Preference Optimization fits a parameter-efficient adapter to those comparisons. We evaluate the adapters on candidate World Values Survey (WVS) items using prompts that omit country names and distinguish four questions: recovery of the imposed labels, transfer of the anchor signal to new text, coherence between the GPS anchors and human WVS responses, and agreement between adapter and human scores. The adapters recover the imposed pairwise labels. On a purposively selected sixteen-country development panel, adapter trust scores completely separate the two GPS-sign groups and have a rank correlation of (0.74) with continuous GPS trust scores. Human-GPS and adapter-human associations remain unresolved on the same panel, and results for the other preference dimensions are heterogeneous. These findings show that an anchored policy can retain a declared aggregate signal without thereby reproducing human response patterns. The contribution is therefore both an inspectable construction and an evaluation framework that separates anchor transfer from human criterion agreement.

We consider a model of collective choice where for each menu there is an agent who is accountable for the choice made. Specifically, we assume that two agents (individuals or selves) allocate choice tasks over different menus using an ownership rule: the owner of the decision over a menu picks what maximizes his own preference and is accountable for it. We characterize three distinct ownership rules under different (nested) choice environments.

Trade-sign errors can change measured trading costs even when classification accuracy is high. We validate the side of Polymarket's public trade prints against the taker leg of each print's on-chain settlement. On twelve selected days between April and August 2026, spanning both exchange generations, 92.1% to 100.0% of prints match a settled taker leg, with exact side and token agreement on all 24.5 million matched pairs. Mint-and-merge settlement makes that taker leg essential: pooling maker and taker legs changes the measured buy share. Signing the full cached tape before settlement selection yields 16.6 million prints signed by every rule; equal-day balanced accuracy is 0.942 for Lee-Ready, 0.768 for the tick test and 0.670 for retrospective bulk volume classification. On 16.4 million identical eligible fills, Lee-Ready raises effective spread by 0.429 cents per share under equal-fill weights; its realised-spread difference is -0.209 cents. Under share-volume weights, taker five-minute midpoint impact is 0.689 cents, while tick and bulk classifications give -0.374 and -0.119 cents. That aggregate sign reversal disappears when tied print rows are excluded. Distortion depends on error-weighted signed outcomes, sample selection and weighting. Receipt-time ordering and unobserved future-quote age limit these delivered-quote accounting quantities; they do not identify causal impact or private information. Supporting venue and collector analyses provide descriptive diagnostics.

Administrative boundary reforms in China alter the assignment of local authority and growth incentives without changing physical geography. We study two reforms with opposite institutional content—county-to-district (centralization: a county absorbed into a municipality as an urban district) and county-to-city (empowerment: a county upgraded to a county-level city)—using a firm-year pollution panel (1998–2013) and a border-pair research design that compares firms on opposite sides of the same county border. Centralizing reform lowers local NOx intensity by 14.8% through cleaner production, without shrinking output or employment; empowering reform raises COD, dust, and wastewater levels by 87–95% through dirty growth. The local abatement is not net abatement: NOx rises in same-prefecture neighbor counties (+0.77, p<0.01), reallocation follows administrative boundaries rather than geographic distance, is confirmed in satellite ambient data, and is not driven by wind or water transport. The reallocation operates through incumbent neighbor firms becoming dirtier, not through firm relocation or new entry. A back-of-the-envelope welfare calculation yields a statistically insignificant welfare gain from the only significant abatement (NOx, \textyen 47 million/yr, CI crossing zero) and a damage of -\textyen 2.3 billion/yr from county-to-city reform. These findings show that boundary reforms reshape the spatial configuration of pollution across administrative boundaries rather than reducing it.

When does automation become an economically self-sustaining process? We define automation breakaway as the condition under which saving generated by physical capital and artificial labour is sufficient to finance continued automation. Artificial labour is labour-like in production but capital-like in accumulation, creating an income base that can support further investment. We characterise breakaway and its dynamic viability, showing that productivity and factor-income composition provide distinct financing channels. Artificial-labour accumulation strengthens the saving base supporting breakaway, while its effect on labour income changes sign with substitutability with human labour. Near full automation, aggregate labour income can contract through fewer human-filled jobs while average labour income in surviving jobs remains positive or rises. Optimal reliance on worker finance can begin to decline while it still provides a positive current net benefit.

A single auction can have both a shill-proof equilibrium and a non-shill-proof equilibrium in the model of Komo, Kominers, and Roughgarden. We give an explicit single item example with three bidder positions, publicly observed actions, positive winning prices, and a strictly regular i.i.d.\ value distribution. Real bidders have strict incentives in both equilibria. An initial public action conveys information about whether the final position is real. The resulting belief changes the middle bidder's response and reverses the first shill's strict preference between two actions. We verify both equilibria and compute their expected revenues.

Quadratic-form test statistics are widely used in econometrics, and their performance depends on accurate variance estimation. Conventional plug-in estimators are consistent under the null hypothesis, but under alternatives the drift in the residuals inflates them and the test loses power. We develop a general framework for variance estimation in such statistics, replacing one of the two squared-residual factors by an auxiliary linear combination of the residuals (“cross-fitting”) chosen to annihilate the drift. We characterize the conditional bias of each estimator exactly. The drift enters the plug-in estimator squared, multiplied by quantities bounded away from zero, so its bias is positive whenever the drift is non-degenerate. It reaches the cross-fit estimator only through the part that survives the cross-fitting, and then only through off-diagonal entries of a residual-maker matrix. From this calculation we obtain conditions under which the cross-fit estimator remains consistent under alternatives while the plug-in estimator does not. At a common critical value the cross-fit test therefore rejects whenever the plug-in test does, and against distant alternatives the plug-in statistic converges to a finite limit, small when few observations carry the departure: its power can tend to zero where the cross-fit test's tends to one. We verify the conditions under primitive assumptions in nonparametric specification testing, overidentification testing, and testing many linear restrictions, and illustrate the procedure on the Oregon Health Insurance Experiment.

Personality is associated with economic outcomes, but whether these relationships are causal remains unclear. We exploit within-family variation in genetic propensity toward the Big Five generated by random genetic transmission from parents to offspring. Combining polygenic indexes from the Lifelines cohort study with 19 years of administrative tax records in the Netherlands, we find that greater genetic propensity toward emotional stability increases earnings and household wealth, while genetic propensity toward conscientiousness increases household wealth. The other three traits show no consistent pattern across outcomes and specifications. The effect of genetic propensity toward emotional stability on earnings remains positive as we progressively account for genetic propensity toward educational attainment, cognitive performance, and depression. These findings strengthen the evidence that personality causally affects earnings and wealth.

Two coefficients enter nearly every quantitative argument about monetary policy: the Phillips-curve slope and the policy rule's response to inflation. For seventy years, each generation that estimated them identified the coefficient conditional on assumptions it maintained, and its successor relaxed one of them, naming the bias this exposed. This survey reconstructs the two programs as a single identification problem. It distinguishes five senses in which a coefficient has been called identified, reads each generation of the curve and of the rule in that key, and shows that the programs fail together because each borrows its identifying condition from the other. The same sequence ran on the latent inflation target and on the optimal rule. The survey closes on what the credible designs license when they leave a range rather than a point: bounds, signs and orderings, and decisions designed for a range.

Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond – a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter's physical action avoids with a pre-selected add-on an authorised assistant can cancel online. An analytical benchmark and a behaviourally calibrated rule market supply ex-ante predictions, and paired branches with frozen versus adaptive sellers separate adoption effects from market feedback. Across thirty simulated markets, executing assistants cut adopters' spending by 13.7 USD per renter-day when sellers are frozen; adaptation claws back about a third, leaving 8.7, with the gains arriving both as lower bills and as rentals completed at all. Sellers raise headline rates while cutting fees, and the calibrated forecast of the burden on unassisted consumers (+3.6) does not transfer: their mean spending change is +0.4, confidence interval -0.6 to +1.3. Seller-model swaps and a within-market transfer of fee-setting to the pricing tool show that fee conduct, and with it the division of the gains, is decided on the seller side. Assistants, we conclude, should be evaluated at market level – completion, total spending, and non-users included – and the comparison layers locate exactly where a calibrated behavioural forecast fails in a language-model market.