We show that a rational agent with true and refinable knowledge of events cannot know if she knows everything or not. This epistemic limitation is not resolved by introspection about tautologies or by learning about new events.

Antitrust authorities frequently rely on structural divestitures to address competitive concerns raised by mergers. Using census-level establishment data and proprietary transaction records from the U.S. grocery sector, we provide systematic evidence on the long-run effects of such remedies. Divested stores experience an average 31 percent decline in employment over five years, driven by elevated exit rates and persistent contraction among surviving establishments. Sales similarly decline. Transaction-level evidence indicates that divested assets are systematically weaker and are often transferred to lower-capability buyers. These findings suggest that structural remedies may be less effective when the implementation of divestitures allows merging parties substantial discretion over the assets and buyers involved.

We extend the Fokker-Planck framework of Froseth (2026, arXiv:2603.05283) to populations of investors with heterogeneous, persistent return-generating ability. When the drift coefficient in the Langevin equation for log-wealth varies across investors, the proportional wealth tax remains a uniform drift shift but ceases to be neutral in the economic sense: its real incidence differs across ability types, and the stationary wealth distribution changes shape. We derive the extended Fokker-Planck equation on the joint space of log-wealth and ability, characterise the conditions under which the drift-shift symmetry breaks, and identify the consequences for asset prices and portfolio allocations. The analysis connects the neutrality results of Froseth (2026, arXiv:2603.05264) and the Fokker-Planck dynamics of Froseth (2026, arXiv:2603.05283) to the heterogeneous-returns literature, notably the "use-it-or-lose-it" mechanism of Guvenen, Kambourov, Kuruscu, Ocampo-Diaz and Chen (2023).

Post-growth has emerged as an umbrella term for various sustainability visions that advocate the pursuit of environmental sustainability, social equity, and human wellbeing, while questioning the continued pursuit of economic growth. Although there are increasing calls to include post-growth scenarios in high-level assessments, a coherent framework with what is required to model post-growth adequately remains absent. This article addresses this gap by: (1) identifying the minimum requirements for post-growth models, and (2) establishing a set of model elements for representing specific policy themes. Drawing on a survey of modellers and on relevant post-growth literature, we develop a framework of minimum requirements for post-growth modelling that integrates three spheres: biophysical, economic, and social, and links them to post-growth goals. Within the biophysical sphere, we argue that embeddedness requires the inclusion of resource use and pollution, environmental limits, and feedback mechanisms from the environment onto society. Within the economic sphere, models should disaggregate households, incorporate limits to technological change and decoupling, include different types of government interventions, and calculate GDP or output endogenously. Within the social sphere, models should represent time use, material and non-material need satisfiers, and the affordability of essential goods and services. Specific policies and transformation scenarios require additional features, such as sectoral disaggregation or representation of the financial system. Our framework guides the development of models that can simulate both post-growth and pro-growth policies and scenarios, an urgently needed tool for informing policymakers and stakeholders about the full range of options for pursuing sustainability, equity, and wellbeing.

A policymaker often wants to steer a decision-maker toward one of two actions, but lacks reliable knowledge of how the decision-maker perceives uncertainty or evaluates risk. We formalize a notion of robust paternalism: a modification a' of a desired action a is robustly more attractive than a relative to b if, for every belief over states and every increasing concave utility function, whenever the decision-maker prefers a to b, she also prefers a' to b. We characterize all such modifications directly in terms of state-dependent payoffs and discuss applications to political competition, bilateral trade, insurance, and information acquisition.

We study the design of an auction for an income-generating asset such as an intellectual property license. Each bidder has a signal about his future income from acquiring the asset. After the asset is allocated, the winner's income from the asset is realized privately. The principal can audit the winner, at a cost, and then charge a payment contingent on the winner's realized income. We solve for an auction that maximizes the principal's revenue, net of auditing costs. The winning bidder is charged linear royalties up to a cap, beyond which there is no auditing. A higher bidder pays more in cash upfront and faces a lower royalty cap.

An updating rule specifies how an agent reacts to information. An updating rule is Blackwell monotone if more information is always better for an agent in a decision problem and strictly Blackwell monotone if, in addition, there is always a decision problem in which more information is strictly better for an agent. Bayes' law is strictly Blackwell monotone, and I show that within a broad class of updating rules--those that distort the Bayesian posteriors in a signal-independent manner--it is the only strictly Blackwell-monotone updating rule. If an agent's decisions are evaluated non-paternalistically (according to her beliefs), the Blackwell-monotone updating rules are affine distortions of the Bayesian posteriors.

We study what changes to an agent's decision problem increase her value for information. We prove that information becomes more valuable if and only if the agent's reduced-form payoff in her belief becomes more convex. When the transformation corresponds to the addition of an action, the requisite increase in convexity occurs if and only if a simple geometric condition holds, which extends in a natural way to the addition of multiple actions. We apply these findings to two scenarios: a monopolistic screening problem in which the good is information and delegation with information acquisition.

Increases in national concentration have been a salient feature of industry dynamics in the U.S. and have contributed to concerns about increasing market power. Yet, local trends may be more informative about market power, particularly in the retail sector where consumers have traditionally shopped at nearby stores. We find that local concentration has increased almost in parallel with national concentration using novel Census data on product-level revenue for all U.S. retail stores between 1992 and 2012. The increases in concentration are broad based, affecting most markets, products, and retail industries. We show that the expansion of multi-market firms into new markets explains most of the increase in national retail concentration, with consolidation via increases in local market shares increasing in importance between 1997 and 2007, and single-market firms playing a negligible role. Finally, we find that increases in local concentration can explain one-quarter to one-third of the observed rise in retail gross margins.

We leverage a quasi natural experiment from India on introduction of free bus schemes for women across five states to study it's impact on women's workforce participation. We use two rounds of the representative Time Use Survey and a triple difference estimation strategy, complemented by an event study framework to identify the causal relationship of interest. Findings reveal that the bus scheme was successful in improving women's paid work participation and duration of employment. We confirm that these results are not merely a continuation of prior trends. The scheme's effects are concentrated among early adopters like Punjab and Tamil Nadu, two states with historically different levels of women's workforce participation. We also find disproportionately higher effects for women residing in more patriarchal districts with higher mobility restrictions. We argue that the scheme works through easing of non-financial binding constraints, which lowers the barriers to women's mobility and workforce participation.

This paper studies the 2021 U.S. inflation forecasting failure. I show that the failure was primarily driven by sample composition rather than functional-form misspecification: estimation samples dominated by the Great Moderation underweight supply-shock regimes, and expectations anchored to that regime were slow to recognize the shift. Three historically informed adjustments, an intercept correction, a similarity re-estimation on 1970s data, and a kernel-weighted estimator, substantially close the forecast gap, and the gains extend to eight additional U.S. price indices. Household survey respondents over 60, whose lifetime includes the 1970s, reported higher inflation expectations from early 2021, consistent with experience-based learning; younger cohorts remained anchored to the prevailing regime. A controlled experiment with large language models conditioned on ``experienced'' and ``young'' professional personas confirms that experiential priors generate significant forecast differences under a common training leakage assumption. Across all three exercises, the source of the prior mattered more than the sophistication of the model.

This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics, accommodating nonlinearities, network interactions, and cross-sectional dependence in the multivariate case. Existence and uniqueness of a stationary solution is established via a coupling argument tailored to the discontinuities inherent in binary data. A key theoretical result, further supported by our empirical illustration on S&P 100 data, shows that, under a rare-events scaling, aggregates of such binary processes converge to a Poisson autoregression, providing a micro-foundation for this widely used count model. Maximum likelihood estimation is proposed and illustrated empirically.

Consumers discover their preferences through experience, yet the sequence and composition of those experiences are often designed by firms, digital platforms, or policymakers. We introduce a ``data-design'' framework for preference discovery, in which the structure of consumption data shapes learning. Bundling generates correlated exposure across goods, so utility surprises propagate through the co-consumption network. When estimation errors are known, bias-targeted design can shut down learning and amplify misperceptions. Conversely, robust design uses only the geometry of past co-consumption: popularity-biased bundles slow learning, while correlation-breaking bundles accelerate preference discovery. The framework thus explains how dominant platforms can sustain biased demand through exposure design, and why effective regulation may need to intervene on the structure of exposure itself rather than only on prices or market shares.

Electricity markets are changing, driven by large-scale renewable integration and rising demand from electrification and digitalisation. This raises fundamental questions about how electricity prices form as the relationships among key price determinants evolve. Here we apply causal discovery to characterise these dynamics across major supply- and demand-side drivers of wholesale electricity prices in Texas, where rapid renewable growth intersects with surging demand. We show that wind generation has become the dominant causal driver of day-ahead electricity prices with effects more than 3 times larger than those of natural gas prices, overturning the view of the Texas market as gas-price-driven. Wind reduces prices locally but redistributes congestion costs across regions in seasonally varying patterns. Natural gas prices remain causally relevant, though their influence is modest and the dominant gas benchmark changes over time. Electricity demand also shows region- and period-specific causal effects. These findings highlight the need for causal models that capture time-varying relationships across both supply and demand to guide system planners and market participants navigating the ongoing transition.

In centralized assignment problems, agents may have preferences over joint rather than individual assignments, such as couples in residency matching or siblings in school choice and daycare. Standard preference estimation methods typically ignore such complementarities. This paper develops an empirical framework that explicitly incorporates them. Using data from daycare assignment in a municipality in Japan, we estimate a model in which families incur both additional commuting distance and a fixed non-distance disutility when siblings are assigned to different facilities. We find that split assignment generates a large disutility, equivalent to more than twice the average commuting distance. We then simulate counterfactual assignment policies that vary the strength of sibling priority and evaluate welfare. The sibling priority reform that we designed and that was implemented in 2024 increases welfare by 6.4% while reducing inequality in assignment rates across sibling groups; models that ignore sibling complementarities substantially understate these gains. At the same time, we uncover a clear efficiency-equity tradeoff: along the frontier, increasing mean welfare by 100 meters is associated with an increase in inequality of about 1.7 percentage points, and the welfare-maximizing policy reverses much of the reform's reduction in inequality, largely through the displacement of households without siblings.

A proportional wealth tax - a levy on the stock of wealth - preserves portfolio neutrality by acting as a uniform drift shift in the Fokker-Planck equation for wealth dynamics. We extend this result to the full system of ownership taxes (eierkostnader) that a shareholder faces: a corporate tax on gross profits, a capital income tax on the risk-free return, a dividend and capital gains tax on the excess return, and a wealth tax on net assets. Each tax modifies the drift of the wealth process in a distinct way - multiplicative rescaling, constant shift, or regime-dependent compression - while leaving the diffusion coefficient unchanged. We show that the combined system preserves portfolio neutrality under three conditions: (i) the capital income tax rate equals the corporate tax rate, (ii) the shielding rate equals the risk-free rate, and (iii) the wealth tax assessment is uniform across assets. When these conditions hold, the after-tax excess return is a uniform rescaling of the pre-tax excess return by the factor $(1-τ_c)(1-τ_d)$, and the drift-shift symmetry of the wealth-tax-only case generalises to a drift-shift-and-rescale symmetry. We classify the distortions that arise when each condition fails and show that flow-tax distortions and stock-tax distortions are additively separable: they do not interact. The shielding deduction - a feature of several real-world tax systems, including the Norwegian aksjonaermodellen - emerges as the mechanism that restores the symmetry between equity and debt taxation within this framework. Calibrated to the Norwegian dual income tax, conditions (i) and (ii) hold by institutional design; the only binding distortion is non-uniform wealth tax assessment, which generates portfolio tilts roughly 300 times larger than any residual flow-tax channel.

Signaling is wasteful. But how wasteful? We study the fraction of surplus dissipated in a separating equilibrium. For isoelastic environments, this waste ratio has a simple formula: $β/(β+σ)$, where $β$ is the benefit elasticity (reward to higher perception) and $σ$ is the elasticity of higher types' relative cost advantage. The ratio is constant across types and is independent of other parameters, including convexity of cost in the signal. We show that the directional effects of $β$ and $σ$ on waste extend to non-isoelastic environments.

This paper considers quantile-welfare evaluation of social welfare as an alternative to utilitarian evaluation. Manski (1988) originally proposed and studied maximization of quantile utility as a model of individual decision making under uncertainty, juxtaposing it with maximization of expected utility. That paper's primary motivation was to exploit the fact that maximization of quantile utility requires only an ordinal formalization of utility, not a cardinal one. This paper transfers these ideas from analysis of individual decision making to analysis of social planning. We begin by summarizing basic theoretical properties of quantile welfare in general terms. We then turn attention to health cost-effectiveness analysis and consider measurement and econometric issues arising in that context. We propose a procedure to nonparametrically bound the quantile welfare of health states using data from binary-choice time-tradeoff (TTO) experiments of the type regularly performed by health economists.

A robust thermodynamic relation between inflation corrected monetary valuation and energy emerges from existing work. This is based on the energy used, the aggregate efficiency of all production processes ($Λ(t)$) in terms of Joules per dollar of gross world product, and the gross world product: $\frac{E_A(t) \text{[J]}}{Λ(t) \text{[J/\$]}} = Y(t) [\$]$ where: J = Joules, \$= currency. This directs us to the production system and all of its processes in addition to alternatives to carbon energy. The original relation appeared in 'Are there basic physical constraints on future anthropogenic emissions of carbon dioxide?' (Garrett 2011). There a foundation assumption was made that a variable $λ$ representing energy per dollar would disprove the presented model. However, because $λ$ has dimension [$\frac{E}{\$ \; GWP}$], it represents the aggregate efficiency of all global production, and cannot be a constant in an economic model. Thus, aggregate production efficiency is: $Λ(t) \equiv \sum {λ_i(t) \cdot \frac{P_i}{GWP}}$. The claimed 50 year constant relation of $W$ ($\sum_{i = n}^{t} Y_i \text{ [\$]}$) to the energy $E$ of the final year is incorrect -- the relation is not flat, nor should this be expected. The graph of $W$ from 1970 back in time is shown to have an historic minimum in 1970 driven by growth in energy consumption and increasing efficiency of energy use that is unlikely to be repeated. With improvements, a robust thermodynamic model is obtained that has general application to the relationship between money and energy and may be useable for evaluating the health of currencies and economies.

Generative AI is transforming higher education, yet systematic evidence on student adoption, usage patterns, and perceived learning impacts remains scarce. Using survey data from a selective U.S. college, we document rapid generative-AI adoption, reaching over 80 percent within two years of ChatGPT's release. Adoption varies sharply across disciplines, demographics, and achievement levels. Students use AI both to augment their learning -- by obtaining explanations and feedback -- and to automate coursework by generating final outputs, with augmentation more common than automation. Students generally perceive AI as benefiting their learning, and these beliefs are strongly correlated with adoption. Institutional policies shape usage but have uneven effects, in part because awareness and compliance vary across student groups. These findings suggest that effective AI policies must distinguish between uses that enhance learning and those that substitute for it.

We extend Kuhn's Theorem to games of the extensive form with unawareness. We prove that if a game of the extensive form with unawareness has perfect recall, then for each mixed strategy there is an equivalent behavior strategy. We show that the converse does not hold under unawareness without restricting the evolution of the player's awareness to constant awareness along paths of play. Both directions of Kuhn's Theorem for games of the extensive form with unawareness require a condition complementary to perfect recall that rules out falsely believing in some events when the player is unaware of the actual past events.

The breaching of planetary boundaries and the potentially catastrophic consequences of climate change are leading researchers to question the endless pursuit of economic growth. Several macroeconomic modelling studies have now examined whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable, with mixed results. However, stability has not previously been explored at the microeconomic level, where it is important to know the consequences of zero-growth on e.g., distribution of firm sizes, market instability and risk of individual firm bankruptcy. Here we address this by developing an agent-based model incorporating Minskyan financial dynamics, the Post-Growth DYNamic Agent-based MINskyan (PG-DYNAMIN) model, and carrying out simultaneous macro- and microeconomic analyses. Accounting for the fact that growing capitalist economies are unstable and produce crises, we compare the relative stability of growth and zero-growth scenarios. This is achieved by tweaking an exogenous productivity parameter. We find zero-growth scenarios are viable yet exhibit distinct dynamics from growth scenarios. Under zero-growth, GDP was less volatile, there was reduced systemic risk in the credit network, lower unemployment rates, a higher wages share of GDP for workers, lower corporate debt to GDP ratio, and a reduction in market instability. Additionally, there was a higher rate of inflation, lower profit share of GDP for firms, increased market concentration, more economic crises with higher severity, and increased default probabilities for firms during periods of crises.

Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such as the United States and the United Kingdom, far more land is allocated to golf courses than to renewable energy facilities. Areas equivalent to those currently used for golf could support the installation of up to 842 GW of solar and 659 GW of wind capacity in the top ten countries with the most golf courses. In many of these countries, this potential exceeds both current installed capacity and medium-term projections. These findings underscore the untapped potential of rethinking land use priorities to accelerate the transition to renewable energy.

We analyze how firms should design wage contracts when workers collaborate in teams and effort costs depend on colleagues through a peer network. Performance-based compensation generates incentives that cascade through the organization, which firms target to boost profits. We analyze optimal incentive design if firms can -- and can't -- fully discriminate across workers, and when the production technology is separable or complementary across divisions. When workers' effort is substitutable, the most central workers -- those who influence most colleagues directly and indirectly -- receive the steepest incentives only when output risk is sufficiently large; otherwise firms prioritize workers who are closer to those they influence. When production technology exhibits complementarity across teams, stronger incentives are assigned to workers who influence colleagues in small teams that receive little influence from others. We derive a sufficient network statistic that measures the profit loss when firms must compensate workers of varying centrality equally. Finally, we apply our findings to organizational design questions, such as optimal firm structure and workforce investments.

We identify the distributional and quantile marginal treatment effect functions when the outcome is right-censored. Our method requires a conditionally exogenous instrument and random censoring. We propose asymptotically consistent semi-parametric estimators and valid inferential procedures for the target functions. To illustrate, we evaluate the effect of alternative sentences (fines and community service vs. no punishment) on recidivism in Brazil. Our results highlight substantial treatment effect heterogeneity: we find that people whom most judges would punish take longer to recidivate, while people who would be punished only by strict judges recidivate at an earlier date than if they were not punished.

Doubly robust (DR) estimators guard against model misspecification but remain sensitive to weak covariate overlap. We show that trimming propensity scores reduces variance but eliminates double robustness. We introduce DR estimators that retain double robustness after trimming through bias correction, preserving the original causal targets across unconfoundedness, instrumental variables, and difference-in-differences designs. In four applications, the proposed estimator yields more precise estimates: ruling out large mortality effects of Medicaid expansion, detecting workforce growth from mental health reform, recovering the Black--White test score gap without strong functional form restrictions, and recovering a positive 401(k) savings effect consistent with the prior literature.

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 \$_{2021}/kWh to over 1.00 \$_{2021}/kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 \$_{2021}/kWh to 0.29 \$_{2021}/kWh, presumably due to cost reductions in renewable energy and storage technologies. Furthermore, we identify and discuss seven key reasons why LCOEs are frequently overestimated or underestimated in literature, and how this can be prevented in the future. Our overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve.

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.

Real-world problems are becoming highly complex and, therefore, have to be solved with combinatorial optimisation (CO) techniques. Motivated by the strong increase of publications on CO, 8,393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organisations and authors as well as their collaborations, the most relevant CO problems, solution methods and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.