About

Aaron Smith is the Gordon Rausser Distinguished Chair and Professor of Agricultural and Resource Economics at the University of California, Berkeley. He joined UC Berkeley in Fall 2024 after spending 23 years as a professor at the University of California, Davis. Originally from New Zealand, he earned his PhD in Economics from the University of California, San Diego. His research addresses economic and policy challenges related to agriculture, energy, and the environment. He has over 50 publications in refereed journals, including the Review of Economics and Statistics, the Journal of Econometrics, the American Journal of Agricultural Economics, and Proceedings of the National Academy of Sciences. His work has been recognized with awards such as the Quality of Communication, Quality of Research Discovery, and Outstanding American Journal of Agricultural Economics Article Awards from the Agricultural and Applied Economics Association, as well as the Quality of Research Discovery Award from the European Association of Agricultural Economists. He is also the cluster lead for socioeconomics and ethics in the AI Institute for the Food System (AIFS) and writes regularly on the Ag Data News blog.

Research topics

• Biology
• Ecology
• Geography
• Environmental resource management
• Zoology
• Botany
• Evolutionary biology

Selected publications

• Was Allen Paul Right? Liquidation Bias in Commodity Futures Markets

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Counting Simplicial Pairs in Hypergraphs

  Studies in computational intelligence · 2025-01-01 · 2 citations

  book-chapter
  PublisherDOI

• Nonstandard representation of the Dirichlet form and application to the comparison theorem

  Mathematische Nachrichten · 2025-03-04

  articleOpen accessSenior authorCorresponding

  Abstract The Dirichlet form is a generalization of the Laplacian, heavily used in the study of many diffusion‐like processes. In this paper, we present a nonstandard representation theorem for the Dirichlet form, showing that the usual Dirichlet form can be well‐approximated by a hyperfinite sum. One of the main motivations for such a result is to provide a tool for directly translating results about Dirichlet forms on finite or countable state spaces to results on more general state spaces, without having to translate the details of the proofs. As an application, we compare the Dirichlet forms of two general Markov processes by applying the transfer of the well‐known comparison theorem for finite Markov processes.

  PublisherOA PDFDOI

• Variance estimation after matching or re-weighting

  ArXiv.org · 2025-06-12

  preprintOpen access

  This paper develops a variance estimation framework for matching estimators that enables valid population inference for treatment effects. We provide theoretical analysis of a variance estimator that addresses key limitations in the existing literature. While Abadie and Imbens (2006) proposed a foundational variance estimator requiring matching for both treatment and control groups, this approach is computationally prohibitive and rarely used in practice. Our method provides a computationally feasible alternative that only requires matching treated units to controls while maintaining theoretical validity for population inference. We make three main contributions. First, we establish consistency and asymptotic normality for our variance estimator, proving its validity for average treatment effect on the treated (ATT) estimation in settings with small treated samples. Second, we develop a generalized theoretical framework with novel regularity conditions that significantly expand the class of matching procedures for which valid inference is available, including radius matching, M-nearest neighbor matching, and propensity score matching. Third, we demonstrate that our approach extends naturally to other causal inference estimators such as stable balancing weighting methods. Through simulation studies across different data generating processes, we show that our estimator maintains proper coverage rates while the state-of-the-art bootstrap method can exhibit substantial undercoverage (dropping from 95% to as low as 61%), particularly in settings with extensive control unit reuse. Our framework provides researchers with both theoretical guarantees and practical tools for conducting valid population inference across a wide range of causal inference applications. An R package implementing our method is available at https://github.com/jche/scmatch2.

  PublisherOA PDFDOI

• The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+$o^2$)

  ArXiv.org · 2025-06-05

  preprintOpen access

  The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of the ABCD model, and its generalization to include outliers (ABCD+$o$), and introduce another variant that allows for overlapping communities, ABCD+$o^2$.

  PublisherOA PDFDOI

• The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities ($$\mathbf {ABCD{+}o}^2$$)

  Lecture notes in computer science · 2025-01-01

  book-chapter
  PublisherDOI

• Counting simplicial pairs in hypergraphs

  Journal of Complex Networks · 2025-06-26 · 1 citations

  articleOpen access

  Abstract We present two ways to measure the simplicial nature of a hypergraph: the simplicial ratio and the simplicial matrix. We show that the simplicial ratio captures the frequency, as well as the rarity, of simplicial interactions in a hypergraph while the simplicial matrix provides more fine-grained details. We then compute the simplicial ratio, as well as the simplicial matrix, for 10 real-world hypergraphs and, from the data collected, hypothesize that simplicial interactions are more and more deliberate as hyperedge size increases. We then present a new Chung-Lu model that includes a parameter controlling (in expectation) the frequency of simplicial interactions. We use this new model, as well as the real-world hypergraphs, to show that multiple stochastic processes exhibit different behaviour when performed on simplicial hypergraphs vs. non-simplicial hypergraphs.

  PublisherDOI

• Partial net returns analysis of Xyway LFR@FMC fungicide application within three water regimes under field conditions

  Scientific Reports · 2025-12-05

  articleOpen accessSenior author

  This analysis assessed the partial net returns of a triazole, at-plant fungicide (i.e., Xyway LFR@FMC) including no fungicide (control) and 1.11 L ha-1 rate under three water scenarios in west Tennessee. In 2022, all water regimes and fungicide treatments had positive average partial net returns compared to rainfed (RF) with no fungicide treatment. However, in 2023 due to beneficial rainfall and low disease pressure during the growing season, low yield differences between treatments resulted in negative partial net returns for all treatments compared to RF with no fungicide treatment. The annualized capital recovery cost of the irrigation equipment was one of the reasons for the negative partial net returns across treatments and particularly water regimes for 2023. An additional factor that influenced the partial net returns analysis was the decline in corn prices between the 2022/23 and the 2023/24 marketing year. Although the Xyway LFR@FMC fungicide application can be profitable for corn production, different environmental factors will determine yield and net return differences each year. Clearly, the investment in irrigation systems has a multiyear return on investment and the application of fungicide is completed before weather and disease pressure is known. As such, long-term weather variability will play an important role in the net returns of Xyway LFR@FMC fungicide application.

  PublisherOA PDFDOI

• Sparse Bayesian multidimensional scaling(s)

  Computational Statistics · 2025-12-24

  articleOpen access

  Abstract Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of objects. Although BMDS has proven useful within, e.g., Bayesian phylogenetic inference, its likelihood and gradient calculations require burdensome $$\mathcal {O}(N^2)$$ floating-point operations, where N is the number of data points. Thus, BMDS becomes impractical as N grows large. We propose and compare two sparse versions of BMDS (sBMDS) that apply log-likelihood and gradient computations to subsets of the observed dissimilarity matrix data. Landmark sBMDS (L-sBMDS) extracts columns, while banded sBMDS (B-sBMDS) extracts diagonals of the data. These sparse variants let one specify a time complexity between $$N^2$$ and N . Under simplified settings, we prove posterior consistency for subsampled distance matrices. Through simulations, we examine the accuracy and computational efficiency across all models using both the Metropolis-Hastings and Hamiltonian Monte Carlo algorithms. We observe approximately 3-fold, 10-fold and 40-fold speedups with negligible loss of accuracy, when applying the sBMDS likelihoods and gradients to 500, 1000 and 5,000 data points with 50 bands (landmarks); these speedups only increase with the size of data considered. Finally, we apply the sBMDS variants to: (1) the phylogeographic modeling of multiple influenza subtypes to better understand how these strains spread through global air transportation networks and (2) the clustering of ArXiv manuscripts based on low-dimensional representations of article abstracts. In the first application, sBMDS contributes to holistic uncertainty quantification within a larger Bayesian hierarchical model. In the second, sBMDS approximates uncertainty quantification for a downstream modeling task.

  PublisherOA PDFDOI

• Perturbation analysis of Markov chain Monte Carlo for graphical models

  Journal of Applied Probability · 2025-01-06

  articleOpen accessSenior authorCorresponding

  Abstract The basic question in perturbation analysis of Markov chains is: how do small changes in the transition kernels of Markov chains translate to chains in their stationary distributions ? Many papers on the subject have shown, roughly, that the change in stationary distribution is small as long as the change in the kernel is much less than some measure of the convergence rate. This result is essentially sharp for generic Markov chains. We show that much larger errors, up to size roughly the square root of the convergence rate, are permissible for many target distributions associated with graphical models. The main motivation for this work comes from computational statistics, where there is often a tradeoff between the per-step error and per-step cost of approximate MCMC algorithms. Our results show that larger perturbations (and thus less-expensive chains) still give results with small error.

  PublisherOA PDFDOI

Recent grants

• Collaborative: Phylogeny, Character Evolution, and Diversification of Extant Ferns

  NSF · $26k · 1997–2002

Frequent coauthors

• Raymond Cranfill

  79 shared

• Tom A. Ranker

  University of Hawaiʻi at Mānoa

  61 shared

• Kathleen M. Pryer

  59 shared

• Christopher H. Haufler

  University of Kansas

  54 shared

• Michael Kessler

  University of Zurich

  53 shared

• P. Hovenkamp

  50 shared

• Sabine Hennequin

  Institut de Systématique, Évolution, Biodiversité

  28 shared

• Harald Schneider

  Xishuangbanna Tropical Botanical Garden

  25 shared

Awards & honors

• Quality of Communication Award from the Agricultural and App…
• Quality of Research Discovery Award from the Agricultural an…
• Outstanding American Journal of Agricultural Economics Artic…
• Quality of Research Discovery Award from the European Associ…